From 22f2ac378013592d667f173d3121cb3a5386e12c Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 12:25:21 +0000
Subject: [PATCH 01/70] fix(registry): dispatch rms_norm to the CUDA backend on
 CUDA

`test_registry_dispatches_rms_norm` asserts that the registry resolves
`rms_norm` to `RMSNormCudaOp` whenever CUDA and the compiled kernels are
both present, but `OpBackend` had no CUDA member for this operator and the
CUDA priority list contained only `PYTORCH_NATIVE_RMS_NORM`, so the assert
could never hold. The test therefore fails on any CUDA machine that builds
the native extension, and only passes when `_HAS_CUDA_RMSNORM` is false --
which is why an unbuilt CI has not caught it.

`RMSNormCudaOp` is already a first-class backend elsewhere: it is the
`"cuda"` candidate in `gtest/operator_specs.py` and is used directly by
`attention_preprocess.py`. Only the registry was missing it.

Add `OpBackend.CUDA_RMS_NORM` and put it ahead of the PyTorch reference in
the CUDA priority list. Because `_load_backend` only catches import errors
and this module imports cleanly without `_C`, a CUDA-first list would
otherwise hand out an op that raises at call time on an unbuilt install; so
`RMSNormCudaOp.__init__` now validates the extension and its three symbols,
matching `_require_cuda_activation` in the activation ops. The registry
already treats a backend whose construction raises as unavailable, so the
list degrades to `NativeRMSNormOp` as before.

Only the `cuda` priority map changes; rocm/musa/cpu/npu are untouched.

Verified on 2x B200 (sm_100, driver 580.126.20, torch 2.13.0+cu130) with the
extension rebuilt from source:

  pytest tests/test_rms_norm.py -q
  pytest tests/ rl_engine/tests/ -q -p no:randomly \
      --ignore=tests/test_rocm_aiter_api_contract.py

tests/test_rms_norm.py passes, including test_registry_dispatches_rms_norm, which
fails on the merge-base. The full suite gains no failure. The ignored file fails
to import on the merge-base as well.

Absolute suite counts are reported in the PR description against a named base
commit, not here: they shift whenever a sibling test is added, so a count frozen
in a commit message goes stale the moment the branch is rebased.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 rl_engine/kernels/ops/cuda/norm/rmsnorm.py | 27 +++++++++++++++++
 rl_engine/kernels/registry.py              |  6 +++-
 tests/test_rms_norm.py                     | 35 ++++++++++++++++++++++
 3 files changed, 67 insertions(+), 1 deletion(-)

diff --git a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
index 773325c5d..f7af2a8bc 100644
--- a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
+++ b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
@@ -5,6 +5,25 @@
 from rl_engine.kernels.ops.vjp_fp32 import reduce_rows_fp32, rmsnorm_dweight_rows_fp32
 
 
+def _require_cuda_symbols(what: str, *names: str) -> None:
+    """Raise when the compiled kernels backing ``what`` are missing.
+
+    The registry treats a backend whose construction raises as unavailable and
+    falls through to the next candidate, so calling this from ``__init__`` is
+    what lets a CUDA-first priority list degrade to the PyTorch reference on a
+    build without the extension. Mirrors ``_require_cuda_activation`` in the
+    activation ops.
+    """
+    if not _EXT_AVAILABLE or _C is None:
+        raise RuntimeError(f"{what} requires the compiled rl_engine._C extension.")
+    missing = [name for name in names if not hasattr(_C, name)]
+    if missing:
+        raise RuntimeError(
+            f"{what} symbols ({', '.join(missing)}) are not compiled into _C. "
+            "Rebuild the extension with csrc/cuda/rmsnorm.cu."
+        )
+
+
 class RMSNormCuda(torch.autograd.Function):
     """
     PyTorch autograd wrapper for CUDA RMSNorm.
@@ -99,6 +118,14 @@ class RMSNormCudaOp:
 
     backward_impl = "cuda_rmsnorm_dx_declared_fp32_rowfold_dw"
 
+    def __init__(self) -> None:
+        _require_cuda_symbols(
+            "CUDA RMSNorm",
+            "rmsnorm_forward",
+            "rmsnorm_backward_dx",
+            "rmsnorm_backward_dw",
+        )
+
     def __call__(self, x, weight, *, eps=1e-6):
         return self.forward(x, weight, eps=eps)
 
diff --git a/rl_engine/kernels/registry.py b/rl_engine/kernels/registry.py
index 9eeb1c42b..3b935499a 100644
--- a/rl_engine/kernels/registry.py
+++ b/rl_engine/kernels/registry.py
@@ -154,6 +154,7 @@ class OpBackend(Enum, metaclass=_KernelEnumMeta):
     )
 
     # RMSNorm(pre-norm / QK-Norm) - pure Pytorch reference(ws1 ground-truth)
+    CUDA_RMS_NORM = "rl_engine.kernels.ops.cuda.norm.rmsnorm.RMSNormCudaOp"
     TRITON_RMS_NORM = "rl_engine.kernels.ops.triton.rmsnorm_triton.RMSNormTritonOp"
     PYTORCH_NATIVE_RMS_NORM = "rl_engine.kernels.ops.pytorch.norm.rms_norm.NativeRMSNormOp"
 
@@ -589,7 +590,10 @@ def __init__(self):
                     OpBackend.TRITON_BATCH_INVARIANT_LOGP,
                     OpBackend.PYTORCH_BATCH_INVARIANT_LOGP,
                 ],
-                "rms_norm": [OpBackend.PYTORCH_NATIVE_RMS_NORM],
+                "rms_norm": [
+                    OpBackend.CUDA_RMS_NORM,
+                    OpBackend.PYTORCH_NATIVE_RMS_NORM,
+                ],
                 "lm_head": [OpBackend.PYTORCH_NATIVE_LM_HEAD],
                 "embedding": [OpBackend.PYTORCH_NATIVE_EMBEDDING],
                 "silu": [
diff --git a/tests/test_rms_norm.py b/tests/test_rms_norm.py
index e8dcaa0a2..cfc12ab68 100644
--- a/tests/test_rms_norm.py
+++ b/tests/test_rms_norm.py
@@ -247,6 +247,41 @@ def test_backward_batch_invariance_slice():
     assert torch.equal(x_slice.grad, grad_x_full_sliced)
 
 
+# 9b. The CUDA backend must report itself unavailable by failing construction,
+# which is the seam the registry uses to fall back (see _get_or_create_backend).
+def test_cuda_op_construction_fails_without_extension(monkeypatch):
+    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
+
+    monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", False)
+    monkeypatch.setattr(cuda_rmsnorm, "_C", None)
+    with pytest.raises(RuntimeError, match="requires the compiled rl_engine._C extension"):
+        cuda_rmsnorm.RMSNormCudaOp()
+
+
+def test_cuda_op_construction_fails_when_symbols_missing(monkeypatch):
+    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
+
+    class _WithoutRMSNorm:  # a built extension that lacks the rmsnorm symbols
+        pass
+
+    monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", True)
+    monkeypatch.setattr(cuda_rmsnorm, "_C", _WithoutRMSNorm())
+    with pytest.raises(RuntimeError, match="are not compiled into _C"):
+        cuda_rmsnorm.RMSNormCudaOp()
+
+
+@requires_cuda
+def test_registry_falls_back_to_native_without_extension(monkeypatch):
+    """A CUDA-first priority list must still resolve on a build without _C."""
+    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.kernels.registry import KernelRegistry
+
+    monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", False)
+    monkeypatch.setattr(cuda_rmsnorm, "_C", None)
+    # A fresh registry, so the cached instance from other tests is not reused.
+    assert isinstance(KernelRegistry().get_op("rms_norm"), NativeRMSNormOp)
+
+
 # 10. Registry dispatch resolves to the hardware op when available
 def test_registry_dispatches_rms_norm():
     from rl_engine.kernels.registry import kernel_registry

From ba9b89e34408671b66a4e10a9e9ced570373358e Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 18:06:44 +0000
Subject: [PATCH 02/70] fix(norm): validate only used CUDA symbols and exercise
 explicit fallback

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 rl_engine/kernels/ops/cuda/norm/rmsnorm.py |  1 -
 tests/test_rms_norm.py                     | 34 ++++++++++++++++++++--
 2 files changed, 32 insertions(+), 3 deletions(-)

diff --git a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
index f7af2a8bc..21be8dfb8 100644
--- a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
+++ b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
@@ -123,7 +123,6 @@ def __init__(self) -> None:
             "CUDA RMSNorm",
             "rmsnorm_forward",
             "rmsnorm_backward_dx",
-            "rmsnorm_backward_dw",
         )
 
     def __call__(self, x, weight, *, eps=1e-6):
diff --git a/tests/test_rms_norm.py b/tests/test_rms_norm.py
index cfc12ab68..0b2a6178f 100644
--- a/tests/test_rms_norm.py
+++ b/tests/test_rms_norm.py
@@ -270,7 +270,6 @@ class _WithoutRMSNorm:  # a built extension that lacks the rmsnorm symbols
         cuda_rmsnorm.RMSNormCudaOp()
 
 
-@requires_cuda
 def test_registry_falls_back_to_native_without_extension(monkeypatch):
     """A CUDA-first priority list must still resolve on a build without _C."""
     from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
@@ -279,7 +278,38 @@ def test_registry_falls_back_to_native_without_extension(monkeypatch):
     monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", False)
     monkeypatch.setattr(cuda_rmsnorm, "_C", None)
     # A fresh registry, so the cached instance from other tests is not reused.
-    assert isinstance(KernelRegistry().get_op("rms_norm"), NativeRMSNormOp)
+    assert isinstance(KernelRegistry().get_op("rms_norm", device="cuda"), NativeRMSNormOp)
+
+
+@pytest.mark.parametrize("missing", ["rmsnorm_forward", "rmsnorm_backward_dx"])
+def test_registry_falls_back_when_required_symbol_is_missing(monkeypatch, missing):
+    from types import SimpleNamespace
+
+    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.kernels.registry import KernelRegistry
+
+    symbols = {name: object() for name in ("rmsnorm_forward", "rmsnorm_backward_dx")}
+    del symbols[missing]
+    monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", True)
+    monkeypatch.setattr(cuda_rmsnorm, "_C", SimpleNamespace(**symbols))
+    assert isinstance(KernelRegistry().get_op("rms_norm", device="cuda"), NativeRMSNormOp)
+
+
+def test_registry_cuda_requires_only_used_symbols_and_cpu_stays_native(monkeypatch):
+    from types import SimpleNamespace
+
+    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.kernels.registry import KernelRegistry
+
+    monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", True)
+    monkeypatch.setattr(
+        cuda_rmsnorm,
+        "_C",
+        SimpleNamespace(rmsnorm_forward=object(), rmsnorm_backward_dx=object()),
+    )
+    registry = KernelRegistry()
+    assert isinstance(registry.get_op("rms_norm", device="cuda"), RMSNormCudaOp)
+    assert isinstance(registry.get_op("rms_norm", device="cpu"), NativeRMSNormOp)
 
 
 # 10. Registry dispatch resolves to the hardware op when available

From 1dd6dbb698c35b7e666cda77a8df5b32fb46ca80 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 4 Oct 2026 01:48:41 +0000
Subject: [PATCH 03/70] test(rmsnorm): gate the dispatch test on the symbols
 the CUDA op requires

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 tests/test_rms_norm.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/test_rms_norm.py b/tests/test_rms_norm.py
index 0b2a6178f..2785ec678 100644
--- a/tests/test_rms_norm.py
+++ b/tests/test_rms_norm.py
@@ -17,9 +17,10 @@
 try:
     from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
 
+    # The same two symbols RMSNormCudaOp.__init__ requires; a build that has them
+    # dispatches to the CUDA op, so the dispatch test must agree with that guard.
     _HAS_CUDA_RMSNORM = _EXT_AVAILABLE and all(
-        hasattr(_C, name)
-        for name in ("rmsnorm_forward", "rmsnorm_backward_dx", "rmsnorm_backward_dw")
+        hasattr(_C, name) for name in ("rmsnorm_forward", "rmsnorm_backward_dx")
     )
 except ImportError:  # pragma: no cover - import can fail when the extension is not built.
     _HAS_CUDA_RMSNORM = False

From 6a4f07818c75b14af84c7a8b7ed45aaa99a69659 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 4 Oct 2026 12:51:23 +0000
Subject: [PATCH 04/70] fix(rmsnorm): guard the CUDA launchers on the input's
 device

The forward, backward-dx and both backward-dw launchers took the current
CUDA stream without switching to the input's device, so a tensor on cuda:1
while cuda:0 is current launched on the wrong GPU. Add a device guard on
the input's device in each launcher. Use at::cuda::OptionalCUDAGuard with
the headers included unconditionally, as activation.cu does: those
launchers compile in the ROCm build too, where the file's existing
c10::cuda::CUDAGuard stays inside the !USE_ROCM block. A two-device test
checks that the op runs on the input's device and matches the
single-device result.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 csrc/cuda/rmsnorm.cu   |  8 ++++++--
 tests/test_rms_norm.py | 27 +++++++++++++++++++++++++++
 2 files changed, 33 insertions(+), 2 deletions(-)

diff --git a/csrc/cuda/rmsnorm.cu b/csrc/cuda/rmsnorm.cu
index 1adaca5bd..b32bc5af4 100644
--- a/csrc/cuda/rmsnorm.cu
+++ b/csrc/cuda/rmsnorm.cu
@@ -1,9 +1,7 @@
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
-#if !defined(USE_ROCM)
 #include <c10/cuda/CUDAGuard.h>
 #include <c10/cuda/CUDAException.h>
-#endif
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cuda_bf16.h>
@@ -251,6 +249,9 @@ void rmsnorm_forward_cuda(
     torch::Tensor rstd,
     double eps
 ) {
+    // Launch on x's device: the current CUDA stream belongs to the current
+    // device, which need not be x's.
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
     int T = x.size(0);
     int H = x.size(1);
     int threads = choose_threads(H);
@@ -283,6 +284,7 @@ void rmsnorm_backward_dx_cuda(
     torch::Tensor rstd,
     torch::Tensor dx
 ) {
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
     int T = x.size(0);
     int H = x.size(1);
     int threads = choose_threads(H);
@@ -315,6 +317,7 @@ void rmsnorm_backward_partial_dw_cuda(
     torch::Tensor mask,
     torch::Tensor partial_dw
 ) {
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
     int T = x.size(0);
     int H = x.size(1);
 
@@ -342,6 +345,7 @@ void rmsnorm_backward_reduce_dw_cuda(
     torch::Tensor partial_dw,
     torch::Tensor dw
 ) {
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(partial_dw));
     int chunks = partial_dw.size(0);
     int H = partial_dw.size(1);
 
diff --git a/tests/test_rms_norm.py b/tests/test_rms_norm.py
index 2785ec678..b17a3f302 100644
--- a/tests/test_rms_norm.py
+++ b/tests/test_rms_norm.py
@@ -379,6 +379,33 @@ def test_cuda_triton_rms_norm_matches_native_forward_and_backward(impl, dtype, r
     )
 
 
+@requires_cuda_rmsnorm
+@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="requires at least two CUDA devices")
+def test_cuda_rms_norm_runs_on_the_input_device_not_the_current_one():
+    # The launchers take the current CUDA stream, which belongs to the current
+    # device; they must switch to x's device first or a cuda:1 input while
+    # cuda:0 is current launches on the wrong GPU.
+    torch.manual_seed(0)
+    x_cpu = torch.randn(8, 768, dtype=torch.float32)
+    w_cpu = torch.randn(768, dtype=torch.float32)
+    dy_cpu = torch.randn(8, 768, dtype=torch.float32)
+
+    def run(device):
+        x = x_cpu.to(device=device, dtype=torch.bfloat16).requires_grad_(True)
+        w = w_cpu.to(device=device, dtype=torch.bfloat16).requires_grad_(True)
+        y = rmsnorm_cuda(x, w, eps=_EPS)
+        y.backward(dy_cpu.to(device=device, dtype=torch.bfloat16))
+        torch.cuda.synchronize(device)
+        return y.detach(), x.grad.detach(), w.grad.detach()
+
+    with torch.cuda.device(0):
+        expected = run("cuda:0")
+        actual = run("cuda:1")
+    for got, want in zip(actual, expected, strict=True):
+        assert got.device == torch.device("cuda:1")
+        assert torch.equal(got.cpu(), want.cpu())
+
+
 @requires_cuda
 @pytest.mark.parametrize("impl", ["triton", "cuda"])
 def test_cuda_triton_rms_norm_deterministic_repeat(impl):

From 6741d16505b6c40c8abab3c91d887af8d3f76baf Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 14:03:12 +0000
Subject: [PATCH 05/70] feat(ws1): Qwen3-Next RMSNorm references and
 zero-centred CUDA offset

Advances RFC #428 C1 on the CUDA track. Claim level: L0 repeatable and L1
batch-invariant. L2 is NOT claimed -- see "On exactness against vLLM" below.

Qwen3-Next's decoder and final norms store a zero-centred weight and compute
`x * rstd * (1 + w)`, with the `1 +` applied in fp32 after the upcast. Folding
it into a bf16 weight beforehand rounds the offset away, so it has to reach the
kernel as a parameter rather than being pre-applied by the caller.

  NativeRMSNormOp            gains `weight_offset` (default 0.0)
  Qwen3NextRMSNormOp         subclasses it, overriding only weight_offset = 1.0
  Qwen3NextRMSNormGatedOp    GDN gated norm: plain w, weight multiply in fp32
  Qwen3NextRMSNormGatedHFOp  the transformers convention, kept as a witness

The offset is applied under `if cls.weight_offset:` rather than unconditionally,
because `0.0 + w` rewrites -0.0 to +0.0. torch.equal does not notice that, but a
bitwise comparison does, and the plain path must stay bit-for-bit what it was.
A test pins it at the bit level.

The gated pair exists because transformers and vLLM disagree on where the gated
norm's weight multiply happens, and the gap is not a ULP: on bf16 /
head_v_dim=128 they differ in 35% of elements with max|diff| = 6.25e-2.
Isolating the cast order alone reproduces the gap (5.3e-2), so the cast order
dominates rather than the reduction order. The two conventions share their
validation and normalization and differ only in a `_scale_by_weight` hook.

CUDA: `weight_offset` added to the forward and dx kernels, defaulting to 0.0 so
every existing caller and binding is unaffected. The dw kernel is untouched:
d/dw (offset + w) == d/dw w.

  weight_offset=1.0 vs an explicit fp32 (1 + w) weight   bitwise equal
  weight_offset=1.0 vs a bf16-folded (1 + w) weight      differs, as required
  default offset vs the previous kernel                  bitwise equal

The first line is the correctness argument: the in-kernel offset is the same
arithmetic as the fp32 reference, not an approximation. The second is a
regression guard -- if it ever passes, the offset has stopped being fp32.

On exactness against vLLM
-------------------------
Measured over 40 seeds (bf16, head_v_dim=128, 512 rows):

  ours vs forward_native            6/40 seeds differ, worst 1.56e-2
  ours vs forward_cuda             18/40 seeds differ, worst 3.91e-3
  forward_native vs forward_cuda   21/40 seeds differ, worst 1.56e-2

vLLM's own two paths are not bitwise equal to each other, so "bitwise equal to
vLLM" is undefined until a single provider is named. In fp32 the two paths
differ on ~36% of elements, every one by an fp32 ULP -- the tree shapes differ,
the semantics do not. What this reproduces is the convention (fp32 weight
multiply, single trailing cast); the residual is the reduction tree.

The reduction stays the repo's fixed 32-wide chunked sum, which is what buys
L1. It was introduced for NPU but is needed on CUDA too: over 20 seeds at
H=2048 in bf16 -- Qwen3-Next's own hidden_size and dtype -- a plain mean(-1)
broke slice invariance on 1 of 20 while the chunked reduction broke on 0 of 20.
Matching stock vLLM bitwise would mean adopting a reduction that is not itself
batch-invariant, i.e. trading L1 for L2.

tests/check_qwen3_next_norm_providers.py pins the dispatch facts and bounds the
gap, asserting magnitudes rather than equality so a vLLM bump that changes the
provider fails loudly. It is named `check_` rather than `test_`, following
tests/distributed/check_*.py: it imports real vLLM, and
tests/test_framework_operator_integrations.py asserts vllm is absent from
sys.modules, an invariant any collected test importing vLLM would break for the
whole session.

Also: `rl_engine/_C.pyi` updated for the new `weight_offset` argument (CI runs
mypy against it), `tests/test_qwen3_next_norm.py` added to the CI test list in
.github/workflows/ci.yml, and an operator page added per
docs/operators/README.md ("the documentation page is part of the operator
contract").

Verified on 2x B200 (sm_100, driver 580.126.20, torch 2.13.0+cu130, triton
3.7.1, vllm 0.30.0, transformers 5.17.0):

  pytest tests/test_qwen3_next_norm.py -q
  pytest tests/check_qwen3_next_norm_providers.py -q
  pytest tests/ rl_engine/tests/ -q -p no:randomly \
      --ignore=tests/test_rocm_aiter_api_contract.py

Both new files pass and the full suite gains no failure. The ignored file fails
to import on the merge-base as well.

Absolute suite counts are reported in the PR description against a named base
commit, not here: they shift whenever a sibling test is added, so a count frozen
in a commit message goes stale the moment the branch is rebased.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/ci.yml                      |   2 +-
 csrc/cuda/rmsnorm.cu                          |  29 +-
 csrc/ops.cpp                                  |  24 +-
 docs/.nav.yml                                 |   1 +
 docs/operators/README.md                      |   1 +
 docs/operators/qwen3-next-rms-norm.md         | 103 ++++
 rl_engine/_C.pyi                              |   2 +
 rl_engine/kernels/ops/cuda/norm/rmsnorm.py    |  37 +-
 .../ops/pytorch/norm/qwen3_next_rms_norm.py   | 198 +++++++
 .../kernels/ops/pytorch/norm/rms_norm.py      |  30 +-
 tests/check_qwen3_next_norm_providers.py      | 232 ++++++++
 tests/test_qwen3_next_norm.py                 | 502 ++++++++++++++++++
 12 files changed, 1127 insertions(+), 34 deletions(-)
 create mode 100644 docs/operators/qwen3-next-rms-norm.md
 create mode 100644 rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
 create mode 100644 tests/check_qwen3_next_norm_providers.py
 create mode 100644 tests/test_qwen3_next_norm.py

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 75627621e..5f8c900b3 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -67,7 +67,7 @@ jobs:
         run: |
           python -m pytest rl_engine/tests/test_dispatch.py -v
           PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 python -m pytest tests/test_attention_correctness.py -q -rs
-          python -m pytest tests/test_forward_invariance.py tests/test_tolerance_contract.py tests/test_ws1_workload.py tests/test_gradient_invariance.py tests/test_elementwise_inventory.py tests/test_four_judgment_matrix.py tests/test_op_checks.py tests/test_operator_inputs.py tests/test_profiler.py tests/test_kv_consistency.py tests/test_ws1_qwen3_dense.py tests/test_ws1_chain_integration.py -q
+          python -m pytest tests/test_forward_invariance.py tests/test_tolerance_contract.py tests/test_ws1_workload.py tests/test_gradient_invariance.py tests/test_elementwise_inventory.py tests/test_four_judgment_matrix.py tests/test_op_checks.py tests/test_operator_inputs.py tests/test_profiler.py tests/test_kv_consistency.py tests/test_ws1_qwen3_dense.py tests/test_ws1_chain_integration.py tests/test_qwen3_next_norm.py -q
 
       - name: Run Cross-Configuration Contract Tests (CPU-safe)
         run: |
diff --git a/csrc/cuda/rmsnorm.cu b/csrc/cuda/rmsnorm.cu
index b32bc5af4..fc6c425d6 100644
--- a/csrc/cuda/rmsnorm.cu
+++ b/csrc/cuda/rmsnorm.cu
@@ -108,7 +108,8 @@ __global__ void rmsnorm_fwd_kernel(
     float* __restrict__ rstd,
     int T,
     int H,
-    float eps
+    float eps,
+    float weight_offset
 ) {
     int row = blockIdx.x;
     int tid = threadIdx.x;
@@ -135,10 +136,13 @@ __global__ void rmsnorm_fwd_kernel(
 
     __syncthreads();
 
-    // Write y = x * rstd * weight.
+    // Write y = x * rstd * (weight_offset + weight). The offset is added in
+    // fp32 after the upcast: a zero-centred weight (Qwen3-Next, Gemma) must not
+    // have its "+1" folded into the low-precision weight beforehand, which would
+    // round the offset and break the bitwise contract.
     for (int col = tid; col < H; col += blockDim.x) {
         float xv = load_as_float<scalar_t>(x_row + col);
-        float wv = load_as_float<weight_t>(weight + col);
+        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
         float out = xv * row_rstd * wv;
         store_from_float<scalar_t>(y_row + col, out);
     }
@@ -153,7 +157,8 @@ __global__ void rmsnorm_bwd_dx_kernel(
     const float* __restrict__ rstd,
     scalar_t* __restrict__ dx,
     int T,
-    int H
+    int H,
+    float weight_offset
 ) {
     int row = blockIdx.x;
     int tid = threadIdx.x;
@@ -167,7 +172,7 @@ __global__ void rmsnorm_bwd_dx_kernel(
     for (int col = tid; col < H; col += blockDim.x) {
         float dyv = load_as_float<scalar_t>(dy_row + col);
         float xv = load_as_float<scalar_t>(x_row + col);
-        float wv = load_as_float<weight_t>(weight + col);
+        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
         local_dot += dyv * wv * xv;
     }
 
@@ -179,7 +184,7 @@ __global__ void rmsnorm_bwd_dx_kernel(
     for (int col = tid; col < H; col += blockDim.x) {
         float dyv = load_as_float<scalar_t>(dy_row + col);
         float xv = load_as_float<scalar_t>(x_row + col);
-        float wv = load_as_float<weight_t>(weight + col);
+        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
 
         float out = r * dyv * wv - xv * coeff;
         store_from_float<scalar_t>(dx_row + col, out);
@@ -247,7 +252,8 @@ void rmsnorm_forward_cuda(
     torch::Tensor weight,
     torch::Tensor y,
     torch::Tensor rstd,
-    double eps
+    double eps,
+    double weight_offset
 ) {
     // Launch on x's device: the current CUDA stream belongs to the current
     // device, which need not be x's.
@@ -270,7 +276,8 @@ void rmsnorm_forward_cuda(
                 rstd.data_ptr<float>(),
                 T,
                 H,
-                static_cast<float>(eps)
+                static_cast<float>(eps),
+                static_cast<float>(weight_offset)
             );
         });
     });
@@ -282,7 +289,8 @@ void rmsnorm_backward_dx_cuda(
     torch::Tensor x,
     torch::Tensor weight,
     torch::Tensor rstd,
-    torch::Tensor dx
+    torch::Tensor dx,
+    double weight_offset
 ) {
     const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
     int T = x.size(0);
@@ -303,7 +311,8 @@ void rmsnorm_backward_dx_cuda(
                 rstd.data_ptr<float>(),
                 dx.data_ptr<x_t>(),
                 T,
-                H
+                H,
+                static_cast<float>(weight_offset)
             );
         });
     });
diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index 11fda1159..4e786e5b3 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -260,14 +260,16 @@ void rmsnorm_forward_cuda(
   torch::Tensor weight,
   torch::Tensor y,
   torch::Tensor rstd,
-  double eps);
+  double eps,
+  double weight_offset);
 
 void rmsnorm_backward_dx_cuda(
   torch::Tensor dy,
   torch::Tensor x,
   torch::Tensor weight,
   torch::Tensor rstd,
-  torch::Tensor dx);
+  torch::Tensor dx,
+  double weight_offset);
 
 void rmsnorm_backward_partial_dw_cuda(
   torch::Tensor dy,
@@ -296,7 +298,8 @@ static void rmsnorm_check_input(const torch::Tensor& x, const char* name) {
 std::vector<torch::Tensor> rmsnorm_forward(
   torch::Tensor x,
   torch::Tensor weight,
-  double eps)
+  double eps,
+  double weight_offset)
 {
   rmsnorm_check_input(x, "x");
   rmsnorm_check_input(weight, "weight");
@@ -309,7 +312,7 @@ std::vector<torch::Tensor> rmsnorm_forward(
   auto y = torch::empty_like(x);
   auto rstd = torch::empty({T}, x.options().dtype(torch::kFloat32));
 
-  rmsnorm_forward_cuda(x, weight, y, rstd, eps);
+  rmsnorm_forward_cuda(x, weight, y, rstd, eps, weight_offset);
 
   return {y, rstd};
 }
@@ -318,7 +321,8 @@ torch::Tensor rmsnorm_backward_dx(
   torch::Tensor dy,
   torch::Tensor x,
   torch::Tensor weight,
-  torch::Tensor rstd)
+  torch::Tensor rstd,
+  double weight_offset)
 {
   rmsnorm_check_input(dy, "dy");
   rmsnorm_check_input(x, "x");
@@ -333,7 +337,7 @@ torch::Tensor rmsnorm_backward_dx(
 
   auto dx = torch::empty_like(x);
 
-  rmsnorm_backward_dx_cuda(dy, x, weight, rstd, dx);
+  rmsnorm_backward_dx_cuda(dy, x, weight, rstd, dx, weight_offset);
 
   return dx;
 }
@@ -712,8 +716,12 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         &det_gemm_db_transposed,
         "Batch-invariant deterministic GEMM backward in canonical [N,K] layout");
     // registry RMSNorm
-    m.def("rmsnorm_forward", &rmsnorm_forward, "Batch-invariant RMSNorm forward CUDA");
-    m.def("rmsnorm_backward_dx", &rmsnorm_backward_dx, "Batch-invariant RMSNorm backward dx CUDA");
+    m.def("rmsnorm_forward", &rmsnorm_forward, "Batch-invariant RMSNorm forward CUDA",
+          py::arg("x"), py::arg("weight"), py::arg("eps"),
+          py::arg("weight_offset") = 0.0);
+    m.def("rmsnorm_backward_dx", &rmsnorm_backward_dx, "Batch-invariant RMSNorm backward dx CUDA",
+          py::arg("dy"), py::arg("x"), py::arg("weight"), py::arg("rstd"),
+          py::arg("weight_offset") = 0.0);
     m.def("rmsnorm_backward_dw", &rmsnorm_backward_dw, "Deterministic RMSNorm backward dweight CUDA");
 #if !defined(USE_ROCM)
     m.def(
diff --git a/docs/.nav.yml b/docs/.nav.yml
index 7d71230a3..9133710de 100644
--- a/docs/.nav.yml
+++ b/docs/.nav.yml
@@ -29,6 +29,7 @@ nav:
       - operators/sampling.md
       - operators/det-gemm.md
       - operators/embedding.md
+      - operators/qwen3-next-rms-norm.md
   - Developer Guide:
     - contributing/README.md
     - General:
diff --git a/docs/operators/README.md b/docs/operators/README.md
index 00f4cbb45..0d955ed18 100644
--- a/docs/operators/README.md
+++ b/docs/operators/README.md
@@ -32,4 +32,5 @@ Every operator page should include:
 - [Matmul](matmul.md)
 - [Sampling](sampling.md)
 - [Token Embedding](embedding.md)
+- [Qwen3-Next RMSNorm (zero-centred)](qwen3-next-rms-norm.md)
 - [Operator Doc Template](../contributing/operator-doc-template.md)
diff --git a/docs/operators/qwen3-next-rms-norm.md b/docs/operators/qwen3-next-rms-norm.md
new file mode 100644
index 000000000..292139bea
--- /dev/null
+++ b/docs/operators/qwen3-next-rms-norm.md
@@ -0,0 +1,103 @@
+# Qwen3-Next RMSNorm (zero-centred)
+
+## Summary
+
+Qwen3-Next's decoder and final norms store a **zero-centred** weight and compute
+`x * rstd * (1 + w)` rather than `x * rstd * w`. The `1 +` is applied in fp32,
+after the upcast — folding it into a low-precision weight beforehand rounds the
+offset away and silently breaks any bitwise claim.
+
+This operator exists for RFC #428 C1 (Embedding / RMSNorm / residual / final norm
+exactness) on the Qwen3-Next rollout-vs-replay path. The Gated DeltaNet block uses
+a *different* weight convention and a different cast order; it is a separate
+operator.
+
+Upstream references: `transformers` `Qwen3NextRMSNorm`, and vLLM's `GemmaRMSNorm`,
+which `vllm/model_executor/models/qwen3_next.py` aliases as `Qwen3NextRMSNorm`.
+
+## Entry Point
+
+```python
+from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import Qwen3NextRMSNormOp
+from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp, rmsnorm_cuda
+
+y = Qwen3NextRMSNormOp().forward(x, weight, eps=1e-6)       # reference
+y = Qwen3NextRMSNormCudaOp().forward(x, weight, eps=1e-6)   # CUDA
+
+# The offset is a kernel parameter, not a pre-pass:
+y = rmsnorm_cuda(x, weight, eps=1e-6, weight_offset=1.0)
+```
+
+## Backends
+
+| Backend | Wrapper | Native symbol | Status |
+| --- | --- | --- | --- |
+| CUDA | `Qwen3NextRMSNormCudaOp` | `rl_engine._C.rmsnorm_forward` (`weight_offset=1.0`) | Supported |
+| ROCm | — | — | Not implemented |
+| PyTorch fallback | `Qwen3NextRMSNormOp` | — | Supported (WS1 gold) |
+
+`Qwen3NextRMSNormOp` subclasses `NativeRMSNormOp` and overrides only
+`weight_offset`; the base applies it in fp32.
+
+## Tensor Contract
+
+| Argument | Shape | Dtype | Requirements |
+| --- | --- | --- | --- |
+| `x` | `[..., H]` | fp32 / bf16 / fp16 | CUDA path requires contiguous |
+| `weight` | `[H]` | matches `x` | zero-centred (upstream inits to zeros) |
+| `eps` | scalar | float | inside the sqrt; `1e-6` for Qwen3-Next |
+
+## Dispatch Behavior
+
+Registered as the `qwen3_next_rms_norm` gtest operator. On CUDA the registry
+prefers `Qwen3NextRMSNormCudaOp`; every other platform resolves to the PyTorch
+reference. The CUDA op validates the compiled symbols in `__init__`, so on a build
+without the extension construction raises and the registry falls through to the
+reference rather than handing out an op that fails at call time.
+
+## Accuracy
+
+Claim level: **L0 repeatable, L1 batch-invariant**. L2 is not claimed.
+
+The reduction is the repo's fixed 32-wide chunked sum
+(`shape_invariant_rstd`), which is what makes a row's result independent of the
+batch layout. It deliberately differs from upstream's `mean(-1)`: measured on B200,
+over 20 seeds at `H=2048` in bf16, a plain `mean(-1)` broke slice invariance on 1
+of 20 while the chunked reduction broke on 0 of 20.
+
+The cost is that the decoder norm is **not** bitwise equal to stock vLLM — 7
+elements of 1048576 differ, `max|diff| = 1.56e-2` in bf16 at `H=2048`. That gap is
+inherent: matching stock vLLM bitwise would mean adopting a reduction that is not
+itself batch-invariant, i.e. trading L1 for L2.
+
+The in-kernel offset is exact, not an approximation: `weight_offset=1.0` is bitwise
+equal to passing an explicit fp32 `1 + w` weight, and differs from a bf16-folded
+`1 + w`, both asserted.
+
+Tolerances come from `tolerance_contract.json` (`reduction` × dtype); no private
+thresholds.
+
+## Performance Notes
+
+The CUDA path reuses the existing `rmsnorm_fwd_kernel` reduction
+(`block_reduce_sum` over `choose_threads(H)`), so the offset costs one fp32 add per
+element and no extra memory traffic.
+
+```bash
+python scripts/check_operator.py --op qwen3_next_rms_norm --candidate cuda \
+    --device cuda --dtype bf16 --check-grad
+```
+
+## Tests
+
+```bash
+python -m pytest tests/test_qwen3_next_norm.py -v
+```
+
+## Known Limitations
+
+- CUDA only; no ROCm, Ascend or Triton backend.
+- Not bitwise against stock vLLM (see Accuracy); an L2 claim needs the strict
+  provider on both sides, per RFC #428 §1 item 1.
+- Measured on sm_100 (B200). Per RFC #428 §2.2 no claim carries across
+  H100/H200/B100/B200.
diff --git a/rl_engine/_C.pyi b/rl_engine/_C.pyi
index fb3c4379c..b911feaf7 100644
--- a/rl_engine/_C.pyi
+++ b/rl_engine/_C.pyi
@@ -260,12 +260,14 @@ def rmsnorm_forward(
     x: torch.Tensor,
     weight: torch.Tensor,
     eps: float,
+    weight_offset: float = ...,
 ) -> list[torch.Tensor]: ...
 def rmsnorm_backward_dx(
     dy: torch.Tensor,
     x: torch.Tensor,
     weight: torch.Tensor,
     rstd: torch.Tensor,
+    weight_offset: float = ...,
 ) -> torch.Tensor: ...
 def rmsnorm_backward_dw(
     dy: torch.Tensor,
diff --git a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
index 21be8dfb8..3b72e497d 100644
--- a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
+++ b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
@@ -30,16 +30,18 @@ class RMSNormCuda(torch.autograd.Function):
     """
 
     @staticmethod
-    def forward(ctx, x, weight, mask=None, eps=1e-6):
+    def forward(ctx, x, weight, mask=None, eps=1e-6, weight_offset=0.0):
         """
         Forward:
-          y = x * rsqrt(mean(x^2) + eps) * weight
+          y = x * rsqrt(mean(x^2) + eps) * (weight_offset + weight)
 
         Input:
           x:      [T, H], fp16/bf16/fp32 CUDA tensor
           weight: [H],    fp16/bf16/fp32 CUDA tensor
           mask:   [T],    bool CUDA tensor
           eps:    float
+          weight_offset: float, added to weight in fp32 inside the kernel.
+                  1.0 selects the zero-centred (1 + w) convention.
 
         Output:
           y: [T, H]
@@ -64,10 +66,11 @@ def forward(ctx, x, weight, mask=None, eps=1e-6):
             assert mask.dim() == 1, "mask must be [T]"
             assert mask.shape[0] == x.shape[0], "mask length mismatch"
 
-        y, rstd = _C.rmsnorm_forward(x, weight, float(eps))
+        y, rstd = _C.rmsnorm_forward(x, weight, float(eps), float(weight_offset))
 
         ctx.save_for_backward(x, weight, rstd, mask)
         ctx.eps = eps
+        ctx.weight_offset = float(weight_offset)
 
         return y
 
@@ -81,7 +84,7 @@ def backward(ctx, grad_out):
         x, weight, rstd, mask = ctx.saved_tensors
         dy = grad_out.contiguous()
 
-        dx = _C.rmsnorm_backward_dx(dy, x, weight, rstd)
+        dx = _C.rmsnorm_backward_dx(dy, x, weight, rstd, ctx.weight_offset)
 
         # The shape-independent FP32 left fold preserves the C2 Batch/Chunk
         # reduction order while the CUDA reducer executes it in one launch.
@@ -101,16 +104,18 @@ def backward(ctx, grad_out):
             family="cuda",
         )
 
-        return dx, dw, None, None
+        # dw is unchanged by the offset: d/dw (offset + w) == d/dw w.
+        return dx, dw, None, None, None
 
 
-def rmsnorm_cuda(x, weight, eps=1e-6, mask=None):
+def rmsnorm_cuda(x, weight, eps=1e-6, mask=None, weight_offset=0.0):
     """
     use:
         y = rmsnorm_cuda(x, weight)
         y = rmsnorm_cuda(x, weight, mask=mask)
+        y = rmsnorm_cuda(x, weight, weight_offset=1.0)   # zero-centred weight
     """
-    return RMSNormCuda.apply(x, weight, mask, eps)
+    return RMSNormCuda.apply(x, weight, mask, eps, weight_offset)
 
 
 class RMSNormCudaOp:
@@ -124,6 +129,9 @@ def __init__(self) -> None:
             "rmsnorm_forward",
             "rmsnorm_backward_dx",
         )
+    #: Added to the weight in fp32 inside the kernel. Subclasses override it;
+    #: 0.0 is the plain convention.
+    weight_offset = 0.0
 
     def __call__(self, x, weight, *, eps=1e-6):
         return self.forward(x, weight, eps=eps)
@@ -131,7 +139,9 @@ def __call__(self, x, weight, *, eps=1e-6):
     def forward(self, x, weight, *, eps=1e-6):
         hidden = x.shape[-1]
         x_2d = x.contiguous().view(-1, hidden)
-        y_2d = rmsnorm_cuda(x_2d, weight.contiguous(), eps=eps)
+        y_2d = rmsnorm_cuda(
+            x_2d, weight.contiguous(), eps=eps, weight_offset=self.weight_offset
+        )
         return y_2d.view_as(x)
 
     def parameter_vjp_contributions_fp32(self, *, x, weight, grad_output, eps=1e-6):
@@ -140,3 +150,14 @@ def parameter_vjp_contributions_fp32(self, *, x, weight, grad_output, eps=1e-6):
         rstd = torch.rsqrt(x32.square().mean(dim=-1) + float(eps))
         rows = grad_output.float() * x32 * rstd.unsqueeze(-1)
         return {"weight": rows}
+
+
+class Qwen3NextRMSNormCudaOp(RMSNormCudaOp):
+    """Zero-centred CUDA RMSNorm: ``y = x * rstd * (1 + weight)``.
+
+    The decoder and final norms of Qwen3-Next (and Gemma) store a zero-centred
+    weight. The ``+1`` is applied inside the kernel after the fp32 upcast, so it
+    is never rounded through the low-precision weight dtype.
+    """
+
+    weight_offset = 1.0
diff --git a/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py b/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
new file mode 100644
index 000000000..5261245d0
--- /dev/null
+++ b/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
@@ -0,0 +1,198 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Qwen3-Next RMSNorm references (WS1 ground truth for RFC #428 C1).
+
+Qwen3-Next ships two RMSNorm conventions that differ both in how the weight is
+applied and in where the dtype casts sit. They are kept as separate operators
+because the cast order is part of the contract, not a flag:
+
+``Qwen3NextRMSNorm`` (decoder / final norm)
+    normalize in fp32, scale by ``(1 + weight)`` in fp32, cast once at the end.
+    The stored weight is zero-centred, so the ``1 +`` offset MUST be applied
+    after the fp32 upcast -- folding it into a bf16 weight first rounds the
+    offset and silently breaks the bitwise claim.
+
+``Qwen3NextRMSNormGated`` (inside the Gated DeltaNet block)
+    normalize in fp32, scale by a plain (non zero-centred) weight, then gate by
+    ``silu(gate)``. Where the weight multiply happens is NOT agreed upstream --
+    see below -- so it is an explicit part of the operator identity here.
+
+Divergence at the gated-norm boundary (measured)
+-------------------------------------------------
+``transformers`` and vLLM do not agree on the gated variant:
+
+* vLLM (``RMSNormGated``, both ``forward_native`` and the FLA Triton
+  ``forward_cuda``) keeps the normalized value in fp32 for the weight multiply.
+* ``transformers`` (``Qwen3NextRMSNormGated``) casts the normalized value back
+  to the input dtype *before* the weight multiply.
+
+On B200 / bf16 / ``head_v_dim=128`` these differ in 35% of elements with
+``max|diff| = 6.25e-2``; swapping only the cast order reproduces the gap
+(``5.3e-2``), so the cast order -- not the reduction order -- dominates. Because
+RFC #428 claims L2 exactness against **vLLM rollout**, the fp32 multiply is the
+strict default; the transformers convention is kept as a named witness so the
+divergence stays testable instead of being silently picked.
+
+What "agrees with vLLM" means here, precisely
+----------------------------------------------
+Only the *convention* is reproduced, not the bits. vLLM's own two paths are not
+bitwise equal to each other: over 40 seeds (bf16, ``head_v_dim=128``, 512 rows),
+``forward_native`` and ``forward_cuda`` disagreed on 21, worst
+``max|diff| = 1.56e-2``. Against this operator the figures were 6/40 and 18/40.
+So "bitwise equal to vLLM" is undefined until a single provider is named, and
+this operator does not claim it -- see the reduction-order note below.
+
+Both reuse :func:`shape_invariant_rstd` so the reduction order -- and hence the
+result -- never depends on the batch layout (RFC #428 section 6, item 2). The
+reduction order therefore deliberately differs from upstream's ``mean(-1)``;
+what is reproduced exactly is the weight convention and the cast order.
+
+Why the chunked reduction is kept on CUDA too
+---------------------------------------------
+:func:`shape_invariant_rstd` was introduced for NPU, where ``mean``/``sum`` pick
+shape-dependent kernels. Measured on B200 (sm_100) it is needed on CUDA as well:
+over 20 seeds at ``H=2048`` in bf16 -- Qwen3-Next's own ``hidden_size`` and dtype
+-- a plain ``mean(-1)`` broke slice invariance (``rstd(x[3:5]) != rstd(x)[3:5]``)
+on 1 of 20, while the chunked reduction broke on 0 of 20. Failures were also seen
+at ``H=5120`` in fp32.
+
+The cost is that the decoder norm is NOT bitwise equal to stock vLLM: 7 elements
+of 1048576 differ (``max|diff| = 1.56e-2``, bf16, H=2048). That gap is inherent --
+matching stock vLLM bitwise would mean adopting a reduction that is itself not
+batch-invariant, i.e. trading L1 for L2. These operators therefore claim L0 and L1
+only; an L2 claim needs the strict provider on both sides, per RFC #428 section 1,
+item 1.
+"""
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+
+from rl_engine.kernels.ops.pytorch.norm.rms_norm import (
+    NativeRMSNormOp,
+    check_norm_weight,
+    shape_invariant_rstd,
+)
+
+__all__ = [
+    "Qwen3NextRMSNormOp",
+    "Qwen3NextRMSNormGatedOp",
+    "Qwen3NextRMSNormGatedHFOp",
+]
+
+
+class Qwen3NextRMSNormOp(NativeRMSNormOp):
+    """Zero-centred RMSNorm: ``out = x * rstd * (1 + weight)``.
+
+    Only the weight convention differs from :class:`NativeRMSNormOp`, so that is
+    all this overrides. The base applies the offset in fp32, after the upcast,
+    which is what ``transformers`` and vLLM both do -- folding ``1 +`` into a
+    bf16 weight beforehand would round the offset away.
+    """
+
+    weight_offset = 1.0
+
+
+class Qwen3NextRMSNormGatedOp:
+    """Gated RMSNorm used by the Gated DeltaNet block (vLLM/strict convention).
+
+    ``out = (x * rstd * weight) * silu(gate)``, with every multiply in fp32 and
+    a single cast on the way out. This is the convention vLLM's ``RMSNormGated``
+    uses with ``norm_before_gate=True``, which is what the RFC #428 L2 claim is
+    measured against.
+
+    Not a subclass of the plain op: it takes an extra tensor and its epilogue
+    differs, so it is not a drop-in substitute for one.
+
+    The weight is plain, NOT zero-centred -- upstream initializes it to ones.
+    """
+
+    def __call__(
+        self,
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        gate: torch.Tensor,
+        *,
+        eps: float = 1e-6,
+    ) -> torch.Tensor:
+        return self.forward(x, weight, gate, eps=eps)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        gate: torch.Tensor,
+        *,
+        eps: float = 1e-6,
+    ) -> torch.Tensor:
+        return self._rms_norm_gated(x, weight, gate, eps=eps, output_dtype=x.dtype)
+
+    def forward_fp32(
+        self,
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        gate: torch.Tensor,
+        *,
+        eps: float = 1e-6,
+    ) -> torch.Tensor:
+        """Ground-truth: fp32 in, fp32 out."""
+        return self._rms_norm_gated(x, weight, gate, eps=eps, output_dtype=torch.float32)
+
+    # ------------------------------------------------------------------ #
+    # Shared by both conventions; only `_scale_by_weight` differs.
+    # ------------------------------------------------------------------ #
+    @staticmethod
+    def _normalized(
+        x: torch.Tensor, weight: torch.Tensor, gate: torch.Tensor, *, eps: float
+    ) -> torch.Tensor:
+        check_norm_weight(x, weight)
+        if gate.shape != x.shape:
+            raise ValueError(
+                f"gate must match x, got tuple(gate.shape)={tuple(gate.shape)} "
+                f"vs tuple(x.shape)={tuple(x.shape)}"
+            )
+        x_f = x.float()
+        rstd = shape_invariant_rstd(x_f, float(eps)).unsqueeze(-1)
+        return x_f * rstd
+
+    @staticmethod
+    def _scale_by_weight(
+        normed: torch.Tensor, weight: torch.Tensor, input_dtype: torch.dtype
+    ) -> torch.Tensor:
+        """vLLM: the weight multiply stays in fp32."""
+        del input_dtype
+        return normed * weight.float()
+
+    @classmethod
+    def _rms_norm_gated(
+        cls,
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        gate: torch.Tensor,
+        *,
+        eps: float,
+        output_dtype: torch.dtype,
+    ) -> torch.Tensor:
+        normed = cls._normalized(x, weight, gate, eps=eps)
+        scaled = cls._scale_by_weight(normed, weight, x.dtype)
+        # The gate activation is evaluated in fp32 and promotes the product.
+        gated = scaled * F.silu(gate.float())
+        return gated.to(output_dtype)
+
+
+class Qwen3NextRMSNormGatedHFOp(Qwen3NextRMSNormGatedOp):
+    """``transformers`` gated convention: weight multiply in the input dtype.
+
+    Kept so the HF-vs-vLLM divergence documented in the module docstring stays
+    covered by a test rather than discovered in a drift report. Do NOT use this
+    for an L2 claim against vLLM rollout.
+    """
+
+    @staticmethod
+    def _scale_by_weight(
+        normed: torch.Tensor, weight: torch.Tensor, input_dtype: torch.dtype
+    ) -> torch.Tensor:
+        """transformers: round-trip through the input dtype first."""
+        return weight * normed.to(input_dtype)
diff --git a/rl_engine/kernels/ops/pytorch/norm/rms_norm.py b/rl_engine/kernels/ops/pytorch/norm/rms_norm.py
index 135556c3c..66d4723fa 100644
--- a/rl_engine/kernels/ops/pytorch/norm/rms_norm.py
+++ b/rl_engine/kernels/ops/pytorch/norm/rms_norm.py
@@ -86,6 +86,15 @@ def strict_add_rms_norm(
     return _strict_add_rms_norm(x, residual, weight, eps)
 
 
+def check_norm_weight(x: torch.Tensor, weight: torch.Tensor) -> None:
+    """Shared shape guard for the RMSNorm family (plain, zero-centred, gated)."""
+    if weight.dim() != 1 or weight.shape[0] != x.shape[-1]:
+        raise ValueError(
+            f"weight must be 1-D of size x.shape[-1]={x.shape[-1]}, "
+            f"got tuple(weight.shape)={tuple(weight.shape)}"
+        )
+
+
 def shape_invariant_rstd(x_f: torch.Tensor, eps: float) -> torch.Tensor:
     """Shape-invariant per-row rstd (the shared RMSNorm statistic).
 
@@ -113,6 +122,10 @@ class NativeRMSNormOp:
     out = x * rsqrt(mean(x^2, dim=-1) + eps) * weight
     """
 
+    #: Added to the weight in fp32 before it scales the normalized value.
+    #: Subclasses set 1.0 for the zero-centred ``(1 + w)`` convention.
+    weight_offset = 0.0
+
     def __init__(self) -> None:
         pass
 
@@ -151,21 +164,24 @@ def forward_fp32(
     # ------------------------------------------------------------------ #
     # Helpers
     # ------------------------------------------------------------------ #
-    @staticmethod
+    @classmethod
     def _rms_norm(
+        cls,
         x: torch.Tensor,
         weight: torch.Tensor,
         *,
         eps: float,
         output_dtype: torch.dtype,
     ) -> torch.Tensor:
-        if weight.dim() != 1 or weight.shape[0] != x.shape[-1]:
-            raise ValueError(
-                f"weight must be 1-D of size x.shape[-1]={x.shape[-1]}, "
-                f"got tuple(weight.shape)={tuple(weight.shape)}"
-            )
+        check_norm_weight(x, weight)
         x_f = x.float()
         rstd = shape_invariant_rstd(x_f, float(eps)).unsqueeze(-1)
         normed = x_f * rstd
-        out = normed * weight.float()
+        scale = weight.float()
+        # Guarded rather than unconditional: `0.0 + w` rewrites -0.0 to +0.0,
+        # which torch.equal does not notice but a bitwise comparison does. The
+        # plain path must stay bit-for-bit what it was.
+        if cls.weight_offset:
+            scale = cls.weight_offset + scale
+        out = normed * scale
         return out.to(output_dtype)
diff --git a/tests/check_qwen3_next_norm_providers.py b/tests/check_qwen3_next_norm_providers.py
new file mode 100644
index 000000000..062052cfa
--- /dev/null
+++ b/tests/check_qwen3_next_norm_providers.py
@@ -0,0 +1,232 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Which vLLM gated-RMSNorm path is the strict provider, and how far apart are they.
+
+Named ``check_`` rather than ``test_`` on purpose, following
+``tests/distributed/check_*.py``: this module imports real vLLM, and
+``tests/test_framework_operator_integrations.py`` asserts that ``vllm`` is absent
+from ``sys.modules`` -- an invariant any collected test importing vLLM would break
+for the whole session. Run it explicitly:
+
+    pytest tests/check_qwen3_next_norm_providers.py -v
+
+RFC #428 section 2.1 defines L2 as "bitwise identical to vLLM rollout". For
+Qwen3-Next's GDN gated norm that phrase is not well defined until a single
+provider is named, because vLLM ships several and they do not agree bitwise with
+each other.
+
+This module pins two things so a vLLM upgrade cannot move them silently:
+
+1. **Dispatch facts** -- which provider actually runs, asserted on the env
+   defaults and the registered ops rather than inferred from reading one branch.
+2. **The size of the gap** -- a seed sweep that asserts an upper bound on the
+   disagreement and on the mismatch rate. It deliberately does NOT assert
+   equality; the point is to keep the number honest, not to pretend it is zero.
+
+Measured on 2x B200 (sm_100, torch 2.13.0+cu130, vllm 0.30.0), bf16,
+``head_v_dim=128``, 512 rows, 40 seeds:
+
+===============================  ==================  ================
+comparison                       seeds not bitwise   worst max|diff|
+===============================  ==================  ================
+ours vs ``forward_native``       6 / 40              1.56e-2
+ours vs ``forward_cuda``         18 / 40             3.91e-3
+``forward_native`` vs ``cuda``   21 / 40             1.56e-2
+===============================  ==================  ================
+"""
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import Qwen3NextRMSNormGatedOp
+
+# vLLM is imported inside the checks, never at module scope, so that merely
+# collecting this file does not pull it into sys.modules.
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required")
+
+# Qwen3-Next-80B-A3B-Instruct: linear_value_head_dim / rms_norm_eps.
+_HEAD_V_DIM = 128
+_EPS = 1e-6
+_ROWS = 512
+_SEEDS = 40
+
+# The gated norm is constructed by vLLM's GDN block with exactly these settings;
+# see vllm/model_executor/layers/mamba/gdn/qwen_gdn_linear_attn.py.
+_NORM_BEFORE_GATE = True
+_GROUP_SIZE = None
+_ACTIVATION = "silu"
+
+
+@pytest.fixture(scope="module")
+def vllm_config_ctx():
+    """`RMSNormGated` is a CustomOp and refuses to build outside a config context."""
+    pytest.importorskip("vllm", reason="vLLM is required to identify the provider")
+    from vllm.config import VllmConfig, set_current_vllm_config
+
+    with set_current_vllm_config(VllmConfig()):
+        yield
+
+
+def _make_norm():
+    from vllm.model_executor.layers.layernorm import RMSNormGated
+
+    return RMSNormGated(
+        _HEAD_V_DIM,
+        eps=_EPS,
+        group_size=_GROUP_SIZE,
+        norm_before_gate=_NORM_BEFORE_GATE,
+        activation=_ACTIVATION,
+    )
+
+
+def _inputs(seed: int, dtype: torch.dtype, rows: int = _ROWS):
+    g = torch.Generator(device="cuda").manual_seed(seed)
+    x = torch.randn(rows, _HEAD_V_DIM, device="cuda", dtype=dtype, generator=g)
+    gate = torch.randn(rows, _HEAD_V_DIM, device="cuda", dtype=dtype, generator=g)
+    weight = torch.randn(_HEAD_V_DIM, device="cuda", dtype=dtype, generator=g)
+    return x, gate, weight
+
+
+def _disagreement(a: torch.Tensor, b: torch.Tensor) -> tuple[float, float]:
+    """(worst absolute difference, fraction of elements that differ bitwise)."""
+    bits_a = a.float().view(torch.int32)
+    bits_b = b.float().view(torch.int32)
+    mismatch = int((bits_a != bits_b).sum())
+    worst = (a.float() - b.float()).abs().max().item()
+    return worst, mismatch / a.numel()
+
+
+# --------------------------------------------------------------------------- #
+# 1. Dispatch facts -- which provider actually runs
+# --------------------------------------------------------------------------- #
+def test_custom_op_has_distinct_native_and_cuda_paths(vllm_config_ctx):
+    """`forward_native` is a reference; `forward_cuda` is what CustomOp dispatches."""
+    norm = _make_norm()
+    assert type(norm).forward_cuda is not type(norm).forward_native
+
+
+def test_gdn_decode_provider_env_defaults_are_recorded():
+    """Pin the env defaults that decide which GDN decode kernel runs.
+
+    These are what make ``fused_recurrent_gated_delta_rule_packed_decode`` (not
+    ``fused_sigmoid_gating_delta_rule_update``) the rollout decode path. If a
+    vLLM bump flips either default, the provider identity behind any L2 claim
+    changes, so this must fail loudly rather than drift.
+    """
+    pytest.importorskip("vllm", reason="vLLM is required to identify the provider")
+    import vllm.envs as envs
+
+    observed = {
+        "VLLM_GDN_DECODE_KERNEL": envs.VLLM_GDN_DECODE_KERNEL.strip().lower(),
+        "VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE": bool(
+            envs.VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE
+        ),
+    }
+    assert observed == {
+        "VLLM_GDN_DECODE_KERNEL": "cuda",
+        "VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE": True,
+    }, (
+        f"GDN decode provider defaults changed: {observed}. Re-derive which kernel "
+        "the rollout decode path takes before relying on any exactness claim."
+    )
+
+
+# --------------------------------------------------------------------------- #
+# 2. The gap, bounded -- never asserted to be zero
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize(
+    "dtype, max_abs, max_mismatch_rate",
+    [
+        # bf16 rounding absorbs most of the reduction-tree difference, so few
+        # elements move -- but each that does moves by a whole bf16 ULP.
+        (torch.bfloat16, 2e-2, 0.05),
+        # fp32 has nothing to absorb it: ~36% of elements differ, every one of
+        # them by an fp32 ULP. Bounding the rate here would be measuring the
+        # wrong thing; the magnitude is what says the two agree semantically.
+        (torch.float32, 1e-5, 1.0),
+    ],
+)
+def test_vllm_paths_disagree_only_within_bounds(vllm_config_ctx, dtype, max_abs, max_mismatch_rate):
+    """vLLM's own two paths differ; bound how much.
+
+    A growing magnitude means the reduction trees have diverged semantically,
+    which would invalidate treating either as the reference. A high mismatch
+    *rate* at ULP magnitude does not -- it just means the tree shapes differ.
+    """
+    norm = _make_norm().to("cuda", dtype)
+    worst_abs, worst_rate, differing = 0.0, 0.0, 0
+    for seed in range(_SEEDS):
+        x, gate, weight = _inputs(seed, dtype)
+        norm.weight.data = weight.clone()
+        native = norm.forward_native(x, gate)
+        cuda = norm.forward_cuda(x, gate)
+        abs_d, rate = _disagreement(native, cuda)
+        worst_abs, worst_rate = max(worst_abs, abs_d), max(worst_rate, rate)
+        differing += int(rate > 0.0)
+
+    assert worst_abs <= max_abs, (
+        f"vLLM forward_native vs forward_cuda worst |diff| {worst_abs:.3e} exceeds "
+        f"{max_abs:.3e} over {_SEEDS} seeds ({differing} seeds differ)"
+    )
+    assert worst_rate <= max_mismatch_rate
+
+
+@pytest.mark.parametrize("path", ["forward_native", "forward_cuda"])
+def test_ours_tracks_each_vllm_path_within_bounds(vllm_config_ctx, path):
+    """Our strict op follows vLLM's convention; bound the residual.
+
+    Not an equality assertion. We reproduce the fp32 weight multiply and the
+    single trailing cast, but our reduction is the repo's fixed 32-wide chunked
+    sum rather than whatever tree the provider uses, so a few elements straddle
+    a rounding boundary.
+    """
+    dtype = torch.bfloat16
+    ours = Qwen3NextRMSNormGatedOp()
+    norm = _make_norm().to("cuda", dtype)
+
+    worst_abs, worst_rate = 0.0, 0.0
+    for seed in range(_SEEDS):
+        x, gate, weight = _inputs(seed, dtype)
+        norm.weight.data = weight.clone()
+        reference = getattr(norm, path)(x, gate)
+        abs_d, rate = _disagreement(ours.forward(x, weight, gate), reference)
+        worst_abs, worst_rate = max(worst_abs, abs_d), max(worst_rate, rate)
+
+    assert worst_abs <= 2e-2, f"worst |diff| vs {path} was {worst_abs:.3e}"
+    assert worst_rate <= 0.05, f"mismatch rate vs {path} was {worst_rate:.3%}"
+
+
+# --------------------------------------------------------------------------- #
+# 3. Batch invariance -- the property we DO claim
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize("path", ["forward_native", "forward_cuda"])
+def test_vllm_gated_norm_is_batch_invariant(vllm_config_ctx, path):
+    """If this ever fails, vLLM stops being a coherent L2 target.
+
+    Kept as a guard rather than a claim about our code: a provider whose output
+    depends on unrelated rows cannot anchor a bitwise contract.
+    """
+    dtype = torch.bfloat16
+    norm = _make_norm().to("cuda", dtype)
+    for seed in range(8):
+        x, gate, weight = _inputs(seed, dtype)
+        norm.weight.data = weight.clone()
+        full = getattr(norm, path)(x, gate)
+        for n in (1, 2, 8, 16, 32, 48, 64, 256):
+            sliced = getattr(norm, path)(x[:n], gate[:n])
+            assert torch.equal(sliced, full[:n]), f"{path} seed={seed} n={n}"
+
+
+def test_our_gated_op_is_batch_invariant():
+    """The L1 claim for this operator, bitwise."""
+    dtype = torch.bfloat16
+    ours = Qwen3NextRMSNormGatedOp()
+    for seed in range(8):
+        x, gate, weight = _inputs(seed, dtype)
+        full = ours.forward(x, weight, gate)
+        for n in (1, 2, 8, 16, 32, 48, 64, 256):
+            assert torch.equal(ours.forward(x[:n], weight, gate[:n]), full[:n])
diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
new file mode 100644
index 000000000..c66bf2ba7
--- /dev/null
+++ b/tests/test_qwen3_next_norm.py
@@ -0,0 +1,502 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""WS1 C1 (RFC #428): Qwen3-Next RMSNorm conventions and batch invariance.
+
+Claim levels exercised here (RFC #428 section 2.1):
+  * L0 repeatable    -- identical inputs reproduce bitwise identical outputs.
+  * L1 batch-invariant -- a row is unaffected by unrelated rows, padding or order.
+
+L2 (train-rollout exact against vLLM) is NOT claimed by this file; it needs the
+rollout engine on the other side.
+
+The reduction order here is the repo's fixed 32-wide chunked reduction, which
+differs from upstream's ``mean(-1)``. What is reproduced exactly is the weight
+convention and the cast order, so comparisons against the upstream formula are
+tolerance based while the invariance assertions are bitwise.
+"""
+
+from __future__ import annotations
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import (
+    Qwen3NextRMSNormGatedHFOp,
+    Qwen3NextRMSNormGatedOp,
+    Qwen3NextRMSNormOp,
+)
+
+# Qwen3-Next-80B-A3B-Instruct config.json
+_HIDDEN = 2048  # hidden_size
+_HEAD_V_DIM = 128  # linear_value_head_dim -- the gated norm width
+_EPS = 1e-6  # rms_norm_eps
+
+
+def _rand(shape, seed):
+    g = torch.Generator().manual_seed(seed)
+    return torch.randn(*shape, generator=g, dtype=torch.float32)
+
+
+# --------------------------------------------------------------------------- #
+# Upstream formulas, transcribed from transformers/models/qwen3_next.
+# These pin the exact semantics; they are deliberately verbatim.
+# --------------------------------------------------------------------------- #
+def _hf_rms_norm(x, weight, eps=_EPS):
+    output = x.float() * torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + eps)
+    output = output * (1.0 + weight.float())
+    return output.type_as(x)
+
+
+def _vllm_rms_norm_gated(x, weight, gate, eps=_EPS):
+    """vLLM ``RMSNormGated.forward_static`` with ``norm_before_gate=True``."""
+    orig_dtype = x.dtype
+    x, weight, z = x.float(), weight.float(), gate.float()
+    variance = x.pow(2).mean(dim=-1, keepdim=True)
+    out = (x * torch.rsqrt(variance + eps)) * weight
+    out = out * F.silu(z)
+    return out.to(orig_dtype)
+
+
+def _hf_rms_norm_gated(x, weight, gate, eps=_EPS):
+    input_dtype = x.dtype
+    h = x.to(torch.float32)
+    variance = h.pow(2).mean(-1, keepdim=True)
+    h = h * torch.rsqrt(variance + eps)
+    h = weight * h.to(input_dtype)
+    h = h * F.silu(gate.to(torch.float32))
+    return h.to(input_dtype)
+
+
+# --------------------------------------------------------------------------- #
+# 1. The zero-centred (1 + w) convention
+# --------------------------------------------------------------------------- #
+def test_zero_weight_is_identity_scaling():
+    """weight == 0 must leave the normalized value untouched, bitwise.
+
+    This is what separates the zero-centred convention from the plain one: a
+    plain RMSNorm returns all zeros for a zero weight, this one returns the
+    bare normalized value.
+    """
+    from rl_engine.kernels.ops.pytorch.norm.rms_norm import (
+        NativeRMSNormOp,
+        shape_invariant_rstd,
+    )
+
+    x = _rand((4, _HIDDEN), seed=0)
+    zeros = torch.zeros(_HIDDEN)
+
+    x_f = x.float()
+    bare = x_f * shape_invariant_rstd(x_f, _EPS).unsqueeze(-1)
+    assert torch.equal(Qwen3NextRMSNormOp().forward_fp32(x, zeros, eps=_EPS), bare)
+
+    # ... and the plain convention really does differ here.
+    assert torch.equal(
+        NativeRMSNormOp().forward_fp32(x, zeros, eps=_EPS), torch.zeros_like(bare)
+    )
+
+
+def test_weight_offset_is_one():
+    """w and w+1 scaling relationship: out(w) == norm * (1 + w)."""
+    op = Qwen3NextRMSNormOp()
+    x = _rand((2, _HEAD_V_DIM), seed=1)
+    unit = op.forward_fp32(x, torch.zeros(_HEAD_V_DIM))  # scale = 1
+    doubled = op.forward_fp32(x, torch.ones(_HEAD_V_DIM))  # scale = 2
+    torch.testing.assert_close(doubled, 2.0 * unit, atol=1e-6, rtol=1e-6)
+
+
+def test_offset_applied_in_fp32_not_folded_into_bf16():
+    """The 1 + w offset must not be pre-rounded through bf16.
+
+    A weight one bf16 ULP below zero stays distinguishable from exactly zero
+    once the offset is added in fp32; folding (1 + w) into bf16 first would
+    collapse both to 1.0 and lose the difference.
+    """
+    op = Qwen3NextRMSNormOp()
+    x = _rand((2, _HEAD_V_DIM), seed=2)
+    tiny = torch.full((_HEAD_V_DIM,), -(2**-9), dtype=torch.bfloat16)
+    folded = (1.0 + tiny.float()).bfloat16()  # the wrong way
+    assert not torch.equal(
+        op.forward_fp32(x, tiny),
+        op.forward_fp32(x, folded - 1.0),
+    )
+
+
+# --------------------------------------------------------------------------- #
+# 2. L1 -- batch invariance, bitwise
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize("hidden", [_HIDDEN, _HEAD_V_DIM])
+def test_batch_invariance_slice(hidden):
+    op = Qwen3NextRMSNormOp()
+    w, x = _rand((hidden,), seed=3), _rand((8, 32, hidden), seed=4)
+    full = op.forward_fp32(x, w)
+    assert torch.equal(op.forward_fp32(x[:1], w), full[:1])
+    assert torch.equal(op.forward_fp32(x[3:5], w), full[3:5])
+
+
+@pytest.mark.parametrize("batch", [1, 2, 8, 16, 32, 48, 64])
+def test_batch_invariance_across_concurrency(batch):
+    """RFC #428 section 10: batch size / concurrency axis 1..64."""
+    op = Qwen3NextRMSNormOp()
+    w = _rand((_HIDDEN,), seed=5)
+    target = _rand((1, _HIDDEN), seed=6)
+    others = _rand((batch - 1, _HIDDEN), seed=7) if batch > 1 else None
+    alone = op.forward_fp32(target, w)
+    for position in ("first", "last"):
+        if others is None:
+            batched, index = target, 0
+        elif position == "first":
+            batched, index = torch.cat([target, others]), 0
+        else:
+            batched, index = torch.cat([others, target]), batch - 1
+        assert torch.equal(op.forward_fp32(batched, w)[index : index + 1], alone)
+
+
+def test_batch_invariance_with_padding():
+    op = Qwen3NextRMSNormOp()
+    w = _rand((_HIDDEN,), seed=8)
+    x = _rand((4, _HIDDEN), seed=9)
+    padded = torch.cat([x, _rand((6, _HIDDEN), seed=10)], dim=0)
+    assert torch.equal(op.forward_fp32(padded, w)[:4], op.forward_fp32(x, w))
+
+
+def test_gated_batch_invariance_slice():
+    op = Qwen3NextRMSNormGatedOp()
+    w = _rand((_HEAD_V_DIM,), seed=11)
+    x = _rand((8, 32, _HEAD_V_DIM), seed=12)
+    gate = _rand((8, 32, _HEAD_V_DIM), seed=13)
+    full = op.forward_fp32(x, w, gate)
+    assert torch.equal(op.forward_fp32(x[:1], w, gate[:1]), full[:1])
+    assert torch.equal(op.forward_fp32(x[3:5], w, gate[3:5]), full[3:5])
+
+
+# --------------------------------------------------------------------------- #
+# 2b. The fixed-order reduction is what buys invariance -- on CUDA too
+# --------------------------------------------------------------------------- #
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required")
+@pytest.mark.parametrize("hidden", [_HIDDEN, _HEAD_V_DIM])
+def test_chunked_reduction_is_slice_invariant_on_device(hidden):
+    """The rstd statistic must be slice-invariant on the accelerator, every seed.
+
+    Measured counterpoint on B200/bf16/H=2048: a plain ``mean(-1)`` broke this on
+    1 of 20 seeds. We assert only the property we rely on -- asserting that torch's
+    ``mean`` is broken would be a brittle test of someone else's kernel.
+    """
+    from rl_engine.kernels.ops.pytorch.norm.rms_norm import shape_invariant_rstd
+
+    for seed in range(20):
+        g = torch.Generator(device="cuda").manual_seed(seed)
+        x = torch.randn(64, hidden, device="cuda", dtype=torch.bfloat16, generator=g)
+        full = shape_invariant_rstd(x.float(), _EPS)
+        assert torch.equal(shape_invariant_rstd(x[3:5].float(), _EPS), full[3:5]), seed
+        assert torch.equal(shape_invariant_rstd(x[:1].float(), _EPS), full[:1]), seed
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required")
+def test_op_batch_invariance_on_device():
+    """L1 for the op itself, on the accelerator, in the model's dtype."""
+    op = Qwen3NextRMSNormOp()
+    g = torch.Generator(device="cuda").manual_seed(0)
+    x = torch.randn(64, _HIDDEN, device="cuda", dtype=torch.bfloat16, generator=g)
+    w = torch.randn(_HIDDEN, device="cuda", dtype=torch.bfloat16, generator=g)
+    full = op.forward(x, w)
+    for n in (1, 2, 8, 16, 32, 48, 64):
+        assert torch.equal(op.forward(x[:n], w), full[:n]), n
+
+
+# --------------------------------------------------------------------------- #
+# 3. L0 -- repeatable
+# --------------------------------------------------------------------------- #
+def test_deterministic_repeat():
+    op = Qwen3NextRMSNormOp()
+    x, w = _rand((64, _HIDDEN), seed=14), _rand((_HIDDEN,), seed=15)
+    first = op.forward_fp32(x, w)
+    for _ in range(10):
+        assert torch.equal(op.forward_fp32(x, w), first)
+
+
+# --------------------------------------------------------------------------- #
+# 4. Agreement with the upstream formula (tolerance, not bitwise: the reduction
+#    order differs by design)
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize("hidden", [_HIDDEN, _HEAD_V_DIM])
+def test_matches_upstream_formula_fp32(hidden):
+    op = Qwen3NextRMSNormOp()
+    x, w = _rand((4, 16, hidden), seed=16), _rand((hidden,), seed=17)
+    torch.testing.assert_close(op.forward_fp32(x, w), _hf_rms_norm(x, w), atol=1e-6, rtol=1e-6)
+
+
+def test_gated_strict_matches_vllm_formula_fp32():
+    """The strict gated op follows vLLM, which is what the L2 claim targets."""
+    op = Qwen3NextRMSNormGatedOp()
+    x = _rand((4, 16, _HEAD_V_DIM), seed=18)
+    w = _rand((_HEAD_V_DIM,), seed=19)
+    gate = _rand((4, 16, _HEAD_V_DIM), seed=20)
+    torch.testing.assert_close(
+        op.forward_fp32(x, w, gate),
+        _vllm_rms_norm_gated(x, w, gate),
+        atol=1e-6,
+        rtol=1e-6,
+    )
+
+
+def test_gated_hf_witness_matches_hf_formula_fp32():
+    op = Qwen3NextRMSNormGatedHFOp()
+    x = _rand((4, 16, _HEAD_V_DIM), seed=18)
+    w = _rand((_HEAD_V_DIM,), seed=19)
+    gate = _rand((4, 16, _HEAD_V_DIM), seed=20)
+    torch.testing.assert_close(
+        op.forward_fp32(x, w, gate),
+        _hf_rms_norm_gated(x, w, gate),
+        atol=1e-6,
+        rtol=1e-6,
+    )
+
+
+def test_gated_conventions_diverge_in_low_precision():
+    """Pin the HF-vs-vLLM gated divergence (RFC #428 first-divergence boundary).
+
+    With fp32 inputs the two conventions coincide, because the HF round-trip is
+    an identity. In bf16 they do not, and the gap is far larger than a ULP: this
+    is why the convention is part of the operator identity and not a detail.
+    """
+    strict, witness = Qwen3NextRMSNormGatedOp(), Qwen3NextRMSNormGatedHFOp()
+    x = _rand((64, _HEAD_V_DIM), seed=33).bfloat16()
+    w = _rand((_HEAD_V_DIM,), seed=34).bfloat16()
+    gate = _rand((64, _HEAD_V_DIM), seed=35).bfloat16()
+
+    a, b = strict.forward(x, w, gate), witness.forward(x, w, gate)
+    assert not torch.equal(a, b), "the two gated conventions must be distinguishable"
+    assert (a.float() - b.float()).abs().max() > 1e-3
+
+    # ... and in fp32 the round-trip vanishes, so they agree bitwise.
+    xf, wf, gf = x.float(), w.float(), gate.float()
+    assert torch.equal(strict.forward(xf, wf, gf), witness.forward(xf, wf, gf))
+
+
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
+def test_matches_upstream_formula_low_precision(dtype):
+    op = Qwen3NextRMSNormOp()
+    x = _rand((4, 16, _HIDDEN), seed=21).to(dtype)
+    w = _rand((_HIDDEN,), seed=22).to(dtype)
+    got, ref = op.forward(x, w), _hf_rms_norm(x, w)
+    assert got.dtype == ref.dtype == dtype
+    torch.testing.assert_close(got.float(), ref.float(), atol=2e-2, rtol=1.6e-2)
+
+
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
+def test_gated_hf_witness_low_precision(dtype):
+    """Covers the mid-computation cast back to the input dtype."""
+    op = Qwen3NextRMSNormGatedHFOp()
+    x = _rand((4, 16, _HEAD_V_DIM), seed=23).to(dtype)
+    w = _rand((_HEAD_V_DIM,), seed=24).to(dtype)
+    gate = _rand((4, 16, _HEAD_V_DIM), seed=25).to(dtype)
+    got, ref = op.forward(x, w, gate), _hf_rms_norm_gated(x, w, gate)
+    assert got.dtype == ref.dtype == dtype
+    torch.testing.assert_close(got.float(), ref.float(), atol=2e-2, rtol=1.6e-2)
+
+
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
+def test_gated_strict_low_precision(dtype):
+    op = Qwen3NextRMSNormGatedOp()
+    x = _rand((4, 16, _HEAD_V_DIM), seed=23).to(dtype)
+    w = _rand((_HEAD_V_DIM,), seed=24).to(dtype)
+    gate = _rand((4, 16, _HEAD_V_DIM), seed=25).to(dtype)
+    got, ref = op.forward(x, w, gate), _vllm_rms_norm_gated(x, w, gate)
+    assert got.dtype == ref.dtype == dtype
+    torch.testing.assert_close(got.float(), ref.float(), atol=2e-2, rtol=1.6e-2)
+
+
+# --------------------------------------------------------------------------- #
+# 5. dtype paths and guards
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16, torch.float16])
+def test_dtype_paths(dtype):
+    op = Qwen3NextRMSNormOp()
+    x = _rand((2, 16, _HIDDEN), seed=26).to(dtype)
+    w = _rand((_HIDDEN,), seed=27).to(dtype)
+    assert op.forward(x, w).dtype == dtype
+    assert op.forward_fp32(x, w).dtype == torch.float32
+
+
+def test_eps_inside_sqrt():
+    op = Qwen3NextRMSNormOp()
+    out = op.forward_fp32(torch.zeros(1, _HIDDEN), torch.zeros(_HIDDEN))
+    assert torch.isfinite(out).all()
+    assert torch.equal(out, torch.zeros(1, _HIDDEN))
+
+
+def test_bad_weight_shape_raises():
+    op = Qwen3NextRMSNormOp()
+    with pytest.raises(ValueError, match="weight must be 1-D"):
+        op.forward_fp32(_rand((2, _HIDDEN), seed=28), _rand((_HIDDEN - 1,), seed=29))
+
+
+def test_gated_shape_mismatch_raises():
+    op = Qwen3NextRMSNormGatedOp()
+    x, w = _rand((2, _HEAD_V_DIM), seed=30), _rand((_HEAD_V_DIM,), seed=31)
+    with pytest.raises(ValueError, match="gate must match x"):
+        op.forward_fp32(x, w, _rand((3, _HEAD_V_DIM), seed=32))
+
+
+# --------------------------------------------------------------------------- #
+# 6. CUDA kernel: the offset is applied in fp32 inside the kernel
+# --------------------------------------------------------------------------- #
+_CUDA_RMSNORM = False
+if torch.cuda.is_available():  # pragma: no branch - probe only
+    try:
+        from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
+
+        _CUDA_RMSNORM = _EXT_AVAILABLE and hasattr(_C, "rmsnorm_forward")
+    except ImportError:  # pragma: no cover
+        _CUDA_RMSNORM = False
+
+requires_cuda_rmsnorm = pytest.mark.skipif(
+    not _CUDA_RMSNORM, reason="CUDA RMSNorm extension is not available"
+)
+
+
+@requires_cuda_rmsnorm
+def test_cuda_offset_equals_explicit_fp32_weight():
+    """``weight_offset=1.0`` must equal passing an fp32 ``1 + w``, bitwise.
+
+    This is the correctness proof for doing the offset inside the kernel: it is
+    the same arithmetic as the fp32 reference, not an approximation of it.
+    """
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_cuda
+
+    torch.manual_seed(0)
+    x = torch.randn(512, _HIDDEN, device="cuda", dtype=torch.bfloat16)
+    w = torch.randn(_HIDDEN, device="cuda", dtype=torch.bfloat16)
+
+    offset = rmsnorm_cuda(x, w, eps=_EPS, weight_offset=1.0)
+    explicit = rmsnorm_cuda(x, (1.0 + w.float()), eps=_EPS)
+    assert torch.equal(offset, explicit)
+
+
+@requires_cuda_rmsnorm
+def test_cuda_offset_is_not_folded_through_bfloat16():
+    """Pre-rounding ``1 + w`` to bf16 must give a different answer.
+
+    If this ever becomes equal, the offset has stopped being applied in fp32 and
+    the zero-centred contract is silently broken.
+    """
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_cuda
+
+    torch.manual_seed(0)
+    x = torch.randn(512, _HIDDEN, device="cuda", dtype=torch.bfloat16)
+    w = torch.randn(_HIDDEN, device="cuda", dtype=torch.bfloat16)
+
+    offset = rmsnorm_cuda(x, w, eps=_EPS, weight_offset=1.0)
+    folded = rmsnorm_cuda(x, (1.0 + w.float()).bfloat16(), eps=_EPS)
+    assert not torch.equal(offset, folded)
+
+
+@requires_cuda_rmsnorm
+def test_cuda_default_offset_preserves_plain_convention():
+    """An unset offset must leave the existing kernel behaviour untouched."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import RMSNormCudaOp, rmsnorm_cuda
+
+    torch.manual_seed(0)
+    x = torch.randn(256, _HEAD_V_DIM, device="cuda", dtype=torch.bfloat16)
+    w = torch.randn(_HEAD_V_DIM, device="cuda", dtype=torch.bfloat16)
+    assert torch.equal(RMSNormCudaOp().forward(x, w, eps=_EPS), rmsnorm_cuda(x, w, eps=_EPS))
+
+
+@requires_cuda_rmsnorm
+@pytest.mark.parametrize("batch", [1, 2, 8, 16, 32, 48, 64, 512])
+def test_cuda_zero_centred_batch_invariance(batch):
+    """L1 for the zero-centred CUDA op across the RFC #428 concurrency axis."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp
+
+    op = Qwen3NextRMSNormCudaOp()
+    torch.manual_seed(0)
+    x = torch.randn(512, _HIDDEN, device="cuda", dtype=torch.bfloat16)
+    w = torch.randn(_HIDDEN, device="cuda", dtype=torch.bfloat16)
+    assert torch.equal(op.forward(x[:batch], w, eps=_EPS), op.forward(x, w, eps=_EPS)[:batch])
+
+
+@requires_cuda_rmsnorm
+def test_cuda_zero_centred_within_tolerance_of_golden():
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp
+
+    torch.manual_seed(0)
+    x = torch.randn(256, _HIDDEN, device="cuda", dtype=torch.bfloat16)
+    w = torch.randn(_HIDDEN, device="cuda", dtype=torch.bfloat16)
+    got = Qwen3NextRMSNormCudaOp().forward(x, w, eps=_EPS)
+    ref = Qwen3NextRMSNormOp().forward_fp32(x, w, eps=_EPS)
+    torch.testing.assert_close(got.float(), ref, atol=2e-2, rtol=1.6e-2)
+
+
+@requires_cuda_rmsnorm
+def test_cuda_zero_centred_backward_is_offset_aware():
+    """dx must see (1 + w); dw is offset-independent since d/dw (1+w) == d/dw w."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_cuda
+
+    torch.manual_seed(0)
+    x = torch.randn(128, _HEAD_V_DIM, device="cuda", dtype=torch.float32)
+    w = torch.randn(_HEAD_V_DIM, device="cuda", dtype=torch.float32)
+    dy = torch.randn(128, _HEAD_V_DIM, device="cuda", dtype=torch.float32)
+
+    grads = {}
+    for name, off in (("plain", 0.0), ("zero_centred", 1.0)):
+        xg = x.clone().requires_grad_(True)
+        wg = w.clone().requires_grad_(True)
+        rmsnorm_cuda(xg, wg, eps=_EPS, weight_offset=off).backward(dy.clone())
+        grads[name] = (xg.grad.clone(), wg.grad.clone())
+
+    assert torch.isfinite(grads["zero_centred"][0]).all()
+    assert not torch.equal(grads["plain"][0], grads["zero_centred"][0])  # dx differs
+    assert torch.equal(grads["plain"][1], grads["zero_centred"][1])  # dw does not
+
+
+# --------------------------------------------------------------------------- #
+# 8. `__call__` is the documented entry point and must agree with `forward`
+# --------------------------------------------------------------------------- #
+def test_call_matches_forward():
+    x, w = _rand((4, _HIDDEN), seed=40), _rand((_HIDDEN,), seed=41)
+    op = Qwen3NextRMSNormOp()
+    assert torch.equal(op(x, w, eps=_EPS), op.forward(x, w, eps=_EPS))
+
+
+@pytest.mark.parametrize("cls", [Qwen3NextRMSNormGatedOp, Qwen3NextRMSNormGatedHFOp])
+def test_gated_call_matches_forward(cls):
+    x = _rand((4, _HEAD_V_DIM), seed=42)
+    w = _rand((_HEAD_V_DIM,), seed=43)
+    gate = _rand((4, _HEAD_V_DIM), seed=44)
+    op = cls()
+    assert torch.equal(op(x, w, gate, eps=_EPS), op.forward(x, w, gate, eps=_EPS))
+
+
+def test_zero_centred_op_inherits_the_plain_reference():
+    """The only difference from the plain op is the weight convention.
+
+    Pins the inheritance: if the subclass ever grows its own `_rms_norm`, this
+    stops being true and the two references can drift apart silently.
+    """
+    from rl_engine.kernels.ops.pytorch.norm.rms_norm import NativeRMSNormOp
+
+    assert issubclass(Qwen3NextRMSNormOp, NativeRMSNormOp)
+    assert Qwen3NextRMSNormOp.weight_offset == 1.0
+    assert NativeRMSNormOp.weight_offset == 0.0
+    assert Qwen3NextRMSNormOp._rms_norm.__func__ is NativeRMSNormOp._rms_norm.__func__
+
+
+def test_plain_reference_is_unchanged_by_the_offset_plumbing():
+    """Adding `weight_offset` to the base must not perturb the plain path.
+
+    `0.0 + w` rewrites -0.0 to +0.0, which torch.equal does not notice, so this
+    compares the raw bits.
+    """
+    from rl_engine.kernels.ops.pytorch.norm.rms_norm import NativeRMSNormOp
+
+    x = _rand((2, _HEAD_V_DIM), seed=45)
+    w = torch.zeros(_HEAD_V_DIM)
+    w[0] = -0.0
+    got = NativeRMSNormOp().forward_fp32(x, w, eps=_EPS)
+    x_f = x.float()
+    from rl_engine.kernels.ops.pytorch.norm.rms_norm import shape_invariant_rstd
+
+    expected = (x_f * shape_invariant_rstd(x_f, _EPS).unsqueeze(-1)) * w.float()
+    assert torch.equal(got.view(torch.int32), expected.view(torch.int32))

From 5f5654a99268ba7e7bfc0aef646bf46e1c7d379d Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 18:15:09 +0000
Subject: [PATCH 06/70] fix(norm): preserve signed zero and use CUDA statistics
 for parameter VJPs

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 csrc/cuda/rmsnorm.cu                       |  9 ++--
 rl_engine/kernels/ops/cuda/norm/rmsnorm.py | 17 +++++---
 tests/test_qwen3_next_norm.py              | 51 +++++++++++++++++++---
 3 files changed, 60 insertions(+), 17 deletions(-)

diff --git a/csrc/cuda/rmsnorm.cu b/csrc/cuda/rmsnorm.cu
index fc6c425d6..5b570c557 100644
--- a/csrc/cuda/rmsnorm.cu
+++ b/csrc/cuda/rmsnorm.cu
@@ -142,7 +142,8 @@ __global__ void rmsnorm_fwd_kernel(
     // round the offset and break the bitwise contract.
     for (int col = tid; col < H; col += blockDim.x) {
         float xv = load_as_float<scalar_t>(x_row + col);
-        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
+        float wv = load_as_float<weight_t>(weight + col);
+        if (weight_offset != 0.0f) wv += weight_offset;
         float out = xv * row_rstd * wv;
         store_from_float<scalar_t>(y_row + col, out);
     }
@@ -172,7 +173,8 @@ __global__ void rmsnorm_bwd_dx_kernel(
     for (int col = tid; col < H; col += blockDim.x) {
         float dyv = load_as_float<scalar_t>(dy_row + col);
         float xv = load_as_float<scalar_t>(x_row + col);
-        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
+        float wv = load_as_float<weight_t>(weight + col);
+        if (weight_offset != 0.0f) wv += weight_offset;
         local_dot += dyv * wv * xv;
     }
 
@@ -184,7 +186,8 @@ __global__ void rmsnorm_bwd_dx_kernel(
     for (int col = tid; col < H; col += blockDim.x) {
         float dyv = load_as_float<scalar_t>(dy_row + col);
         float xv = load_as_float<scalar_t>(x_row + col);
-        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
+        float wv = load_as_float<weight_t>(weight + col);
+        if (weight_offset != 0.0f) wv += weight_offset;
 
         float out = r * dyv * wv - xv * coeff;
         store_from_float<scalar_t>(dx_row + col, out);
diff --git a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
index 3b72e497d..7d5a7a754 100644
--- a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
+++ b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
@@ -129,6 +129,7 @@ def __init__(self) -> None:
             "rmsnorm_forward",
             "rmsnorm_backward_dx",
         )
+
     #: Added to the weight in fp32 inside the kernel. Subclasses override it;
     #: 0.0 is the plain convention.
     weight_offset = 0.0
@@ -139,16 +140,18 @@ def __call__(self, x, weight, *, eps=1e-6):
     def forward(self, x, weight, *, eps=1e-6):
         hidden = x.shape[-1]
         x_2d = x.contiguous().view(-1, hidden)
-        y_2d = rmsnorm_cuda(
-            x_2d, weight.contiguous(), eps=eps, weight_offset=self.weight_offset
-        )
+        y_2d = rmsnorm_cuda(x_2d, weight.contiguous(), eps=eps, weight_offset=self.weight_offset)
         return y_2d.view_as(x)
 
     def parameter_vjp_contributions_fp32(self, *, x, weight, grad_output, eps=1e-6):
-        del weight
-        x32 = x.float()
-        rstd = torch.rsqrt(x32.square().mean(dim=-1) + float(eps))
-        rows = grad_output.float() * x32 * rstd.unsqueeze(-1)
+        hidden = x.shape[-1]
+        _, rstd = _C.rmsnorm_forward(
+            x.contiguous().reshape(-1, hidden),
+            weight.contiguous(),
+            float(eps),
+            float(self.weight_offset),
+        )
+        rows = rmsnorm_dweight_rows_fp32(x, grad_output, rstd=rstd.reshape(x.shape[:-1]))
         return {"weight": rows}
 
 
diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
index c66bf2ba7..9fdf02e73 100644
--- a/tests/test_qwen3_next_norm.py
+++ b/tests/test_qwen3_next_norm.py
@@ -79,10 +79,7 @@ def test_zero_weight_is_identity_scaling():
     plain RMSNorm returns all zeros for a zero weight, this one returns the
     bare normalized value.
     """
-    from rl_engine.kernels.ops.pytorch.norm.rms_norm import (
-        NativeRMSNormOp,
-        shape_invariant_rstd,
-    )
+    from rl_engine.kernels.ops.pytorch.norm.rms_norm import NativeRMSNormOp, shape_invariant_rstd
 
     x = _rand((4, _HIDDEN), seed=0)
     zeros = torch.zeros(_HIDDEN)
@@ -92,9 +89,7 @@ def test_zero_weight_is_identity_scaling():
     assert torch.equal(Qwen3NextRMSNormOp().forward_fp32(x, zeros, eps=_EPS), bare)
 
     # ... and the plain convention really does differ here.
-    assert torch.equal(
-        NativeRMSNormOp().forward_fp32(x, zeros, eps=_EPS), torch.zeros_like(bare)
-    )
+    assert torch.equal(NativeRMSNormOp().forward_fp32(x, zeros, eps=_EPS), torch.zeros_like(bare))
 
 
 def test_weight_offset_is_one():
@@ -500,3 +495,45 @@ def test_plain_reference_is_unchanged_by_the_offset_plumbing():
 
     expected = (x_f * shape_invariant_rstd(x_f, _EPS).unsqueeze(-1)) * w.float()
     assert torch.equal(got.view(torch.int32), expected.view(torch.int32))
+
+
+@requires_cuda_rmsnorm
+@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
+def test_cuda_plain_signed_zero_is_preserved(dtype):
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_cuda
+
+    x = torch.ones(2, 128, device="cuda", dtype=dtype)
+    x[1].neg_()
+    weight = torch.full((128,), -0.0, device="cuda", dtype=dtype)
+    actual = rmsnorm_cuda(x, weight)
+    expected = x * weight
+    bits = torch.int32 if dtype == torch.float32 else torch.int16
+    assert torch.equal(actual.view(bits), expected.view(bits))
+
+
+@requires_cuda_rmsnorm
+@pytest.mark.parametrize("offset", [0.0, 1.0])
+@pytest.mark.parametrize("shape", [(512, 128), (2, 3, 2048)])
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
+def test_cuda_parameter_contributions_reproduce_backward(offset, shape, dtype):
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp, RMSNormCudaOp
+    from rl_engine.kernels.ops.vjp_fp32 import reduce_rows_fp32
+
+    torch.manual_seed(128)
+    x = torch.randn(shape, device="cuda", dtype=dtype)
+    weight = torch.randn(shape[-1], device="cuda", dtype=dtype, requires_grad=True)
+    upstream = torch.randn_like(x)
+    op = RMSNormCudaOp() if offset == 0.0 else Qwen3NextRMSNormCudaOp()
+    op(x, weight).backward(upstream)
+    rows = op.parameter_vjp_contributions_fp32(x=x, weight=weight, grad_output=upstream)["weight"]
+    folded = reduce_rows_fp32(rows.reshape(-1, shape[-1])).to(dtype)
+    assert torch.equal(weight.grad, folded)
+
+
+def test_zero_centred_cuda_constructor_rejects_missing_extension(monkeypatch):
+    from rl_engine.kernels.ops.cuda.norm import rmsnorm
+
+    monkeypatch.setattr(rmsnorm, "_EXT_AVAILABLE", False)
+    monkeypatch.setattr(rmsnorm, "_C", None)
+    with pytest.raises(RuntimeError, match="requires the compiled"):
+        rmsnorm.Qwen3NextRMSNormCudaOp()

From b11b957ef6eba3b1ae7ceab68dd342ff04a8b243 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 14:34:21 +0000
Subject: [PATCH 07/70] feat(cuda): gated RMSNorm kernel for the Qwen3-Next GDN
 block

Adds the CUDA kernel behind `Qwen3NextRMSNormGatedOp`, and registers both it and
the zero-centred decoder norm from the previous commit.

  y = x * rstd * (weight_offset + weight) * act(gate)

Every multiply is fp32 with one cast at the store, matching vLLM's RMSNormGated
with norm_before_gate=True and group_size=None -- the only configuration the GDN
block constructs. Other configurations are rejected rather than approximated.
`expf` is used rather than the `__expf` intrinsic: the fast intrinsic trades
accuracy for speed and would move the result away from the fp32 reference.

Forward reuses the existing block_reduce_sum / choose_threads(H), so for a fixed
H the reduction tree is independent of the row count -- that is the L1 guarantee
-- and `rstd` comes out bitwise identical to the ungated kernel for the same x,
which is asserted.

Backward is assembled from deterministic pieces:
  dx      new kernel, the ungated dx with (w + offset) -> (w + offset) * act(z)
  dweight reuses rmsnorm_dweight_rows_fp32 + the ascending-row fp32 left fold
  dgate   row-local and reduction-free, fp32 in the wrapper

Both backwards route through one `_fold_dweight_rows` helper so the file keeps a
single left-fold entrypoint, which tests/test_vjp_fp32.py pins, and one
`_require_cuda_symbols` helper so the module has a single availability contract.

The gated op is deliberately NOT a subclass of RMSNormCudaOp: it takes an extra
required tensor, so it cannot stand in for one. Same reasoning as on the PyTorch
side, and stated in its docstring so the question is not reopened.

Registration: `rms_norm_gated` and `qwen3_next_rms_norm` in OP_SPECS (both with a
cuda-sm90 candidate, as every other reduction spec carries), operator_inputs
builders, OpBackend members and priority maps on all five platforms,
test_dispatch assertions, and the WS1 registered-ops set in
tests/test_ws1_gtest_gpu.py. Operator pages added per docs/operators/README.md,
which states the page is part of the operator contract.

Claim level: L0 repeatable and L1 batch-invariant. NOT L2 -- see
tests/check_qwen3_next_norm_providers.py, which pins the dispatch facts and
bounds the gap against vLLM rather than asserting equality.

Verified on 2x B200 (sm_100, driver 580.126.20, torch 2.13.0+cu130, vllm 0.30.0):

  pytest tests/test_qwen3_next_norm.py -q
  pytest tests/check_qwen3_next_norm_providers.py -q
  python scripts/check_operator.py --op rms_norm_gated --candidate cuda \
      --device cuda --dtype bf16 --check-grad        -> pass_rate=1.0000
  python scripts/check_operator.py --op qwen3_next_rms_norm --candidate cuda \
      --device cuda --dtype bf16 --check-grad        -> pass_rate=1.0000
  pytest tests/ rl_engine/tests/ -q -p no:randomly \
      --ignore=tests/test_rocm_aiter_api_contract.py

Both new files pass, both operators report pass_rate=1.0000 on the CUDA
candidate, and the full suite gains no failure. The ignored file fails to import
on the merge-base as well.

Absolute suite counts are reported in the PR description against a named base
commit, not here: they shift whenever a sibling test is added, so a count frozen
in a commit message goes stale the moment the branch is rebased.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 csrc/cuda/rmsnorm.cu                        | 197 ++++++++++++++++++
 csrc/ops.cpp                                |  96 +++++++++
 docs/.nav.yml                               |   1 +
 docs/operators/README.md                    |   1 +
 docs/operators/qwen3-next-rms-norm-gated.md | 114 +++++++++++
 docs/operators/qwen3-next-rms-norm.md       |   4 +-
 rl_engine/kernels/gtest/operator_inputs.py  |  23 +++
 rl_engine/kernels/gtest/operator_specs.py   |  30 +++
 rl_engine/kernels/ops/cuda/norm/rmsnorm.py  | 207 +++++++++++++++++--
 rl_engine/kernels/registry.py               |  32 +++
 rl_engine/tests/test_dispatch.py            |  36 ++++
 tests/test_qwen3_next_norm.py               | 215 ++++++++++++++++++++
 tests/test_ws1_gtest_gpu.py                 |   2 +
 13 files changed, 940 insertions(+), 18 deletions(-)
 create mode 100644 docs/operators/qwen3-next-rms-norm-gated.md

diff --git a/csrc/cuda/rmsnorm.cu b/csrc/cuda/rmsnorm.cu
index 0c09b3a4a..c80945300 100644
--- a/csrc/cuda/rmsnorm.cu
+++ b/csrc/cuda/rmsnorm.cu
@@ -198,6 +198,130 @@ __global__ void rmsnorm_bwd_dx_kernel(
 }
 
 
+// ---------------------------------------------------------------------------
+// Gated RMSNorm (Qwen3-Next GDN block).
+//
+//   y = x * rstd * (weight_offset + weight) * act(gate)
+//
+// The gate activation and the weight multiply are both evaluated in fp32 and
+// there is exactly one cast, at the store. This mirrors vLLM's RMSNormGated
+// with norm_before_gate=True, which is how the GDN block constructs it.
+//
+// ACT selects the gate activation: 0 = silu/swish, 1 = sigmoid. expf (not the
+// __expf intrinsic) is used deliberately -- the fast intrinsic trades accuracy
+// for speed and would put the result further from the fp32 reference.
+// ---------------------------------------------------------------------------
+
+template <int ACT>
+__device__ __forceinline__ float gate_activation(float z) {
+    const float sigma = 1.0f / (1.0f + expf(-z));
+    return (ACT == 0) ? z * sigma : sigma;
+}
+
+template <int ACT>
+__device__ __forceinline__ float gate_activation_grad(float z) {
+    const float sigma = 1.0f / (1.0f + expf(-z));
+    // d/dz [z * sigma] = sigma * (1 + z * (1 - sigma));  d/dz [sigma] = sigma * (1 - sigma)
+    return (ACT == 0) ? sigma * (1.0f + z * (1.0f - sigma)) : sigma * (1.0f - sigma);
+}
+
+
+template <typename scalar_t, typename weight_t, int ACT>
+__global__ void rmsnorm_gated_fwd_kernel(
+    const scalar_t* __restrict__ x,
+    const weight_t* __restrict__ weight,
+    const scalar_t* __restrict__ gate,
+    scalar_t* __restrict__ y,
+    float* __restrict__ rstd,
+    int T,
+    int H,
+    float eps,
+    float weight_offset
+) {
+    int row = blockIdx.x;
+    int tid = threadIdx.x;
+
+    const scalar_t* x_row = x + row * H;
+    const scalar_t* gate_row = gate + row * H;
+    scalar_t* y_row = y + row * H;
+
+    float local_sum = 0.0f;
+
+    // The statistic is over x only; the gate never enters the reduction, so
+    // rstd here is bit-identical to the ungated kernel's for the same x.
+    for (int col = tid; col < H; col += blockDim.x) {
+        float xv = load_as_float<scalar_t>(x_row + col);
+        local_sum += xv * xv;
+    }
+
+    float sum = block_reduce_sum(local_sum);
+
+    float row_rstd = rsqrtf(sum / static_cast<float>(H) + eps);
+
+    if (tid == 0) {
+        rstd[row] = row_rstd;
+    }
+
+    __syncthreads();
+
+    for (int col = tid; col < H; col += blockDim.x) {
+        float xv = load_as_float<scalar_t>(x_row + col);
+        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
+        float zv = load_as_float<scalar_t>(gate_row + col);
+        float out = xv * row_rstd * wv * gate_activation<ACT>(zv);
+        store_from_float<scalar_t>(y_row + col, out);
+    }
+}
+
+
+template <typename scalar_t, typename weight_t, int ACT>
+__global__ void rmsnorm_gated_bwd_dx_kernel(
+    const scalar_t* __restrict__ dy,
+    const scalar_t* __restrict__ x,
+    const weight_t* __restrict__ weight,
+    const scalar_t* __restrict__ gate,
+    const float* __restrict__ rstd,
+    scalar_t* __restrict__ dx,
+    int T,
+    int H,
+    float weight_offset
+) {
+    int row = blockIdx.x;
+    int tid = threadIdx.x;
+
+    const scalar_t* dy_row = dy + row * H;
+    const scalar_t* x_row = x + row * H;
+    const scalar_t* gate_row = gate + row * H;
+    scalar_t* dx_row = dx + row * H;
+
+    float local_dot = 0.0f;
+
+    // Identical to the ungated dx, with the per-column scale (w + offset)
+    // replaced by (w + offset) * act(gate): the gate is a constant wrt x.
+    for (int col = tid; col < H; col += blockDim.x) {
+        float dyv = load_as_float<scalar_t>(dy_row + col);
+        float xv = load_as_float<scalar_t>(x_row + col);
+        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
+        float zv = load_as_float<scalar_t>(gate_row + col);
+        local_dot += dyv * (wv * gate_activation<ACT>(zv)) * xv;
+    }
+
+    float dot = block_reduce_sum(local_dot);
+
+    float r = rstd[row];
+    float coeff = dot * r * r * r / static_cast<float>(H);
+
+    for (int col = tid; col < H; col += blockDim.x) {
+        float dyv = load_as_float<scalar_t>(dy_row + col);
+        float xv = load_as_float<scalar_t>(x_row + col);
+        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
+        float zv = load_as_float<scalar_t>(gate_row + col);
+
+        float out = r * dyv * (wv * gate_activation<ACT>(zv)) - xv * coeff;
+        store_from_float<scalar_t>(dx_row + col, out);
+    }
+}
+
 template <typename scalar_t>
 __global__ void rmsnorm_partial_dw_kernel(
     const scalar_t* __restrict__ dy,
@@ -325,6 +449,79 @@ void rmsnorm_backward_dx_cuda(
 }
 
 
+void rmsnorm_gated_forward_cuda(
+    torch::Tensor x,
+    torch::Tensor weight,
+    torch::Tensor gate,
+    torch::Tensor y,
+    torch::Tensor rstd,
+    double eps,
+    double weight_offset,
+    int64_t activation
+) {
+    int T = x.size(0);
+    int H = x.size(1);
+    int threads = choose_threads(H);
+    size_t smem = threads * sizeof(float);
+
+    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+    AT_DISPATCH_FLOATING_TYPES_AND2(at::kHalf, at::kBFloat16, x.scalar_type(), "rmsnorm_gated_forward_cuda", [&] {
+        using x_t = scalar_t;
+        AT_DISPATCH_FLOATING_TYPES_AND2(at::kHalf, at::kBFloat16, weight.scalar_type(), "rmsnorm_gated_forward_weight_cuda", [&] {
+            using w_t = scalar_t;
+            if (activation == 0) {
+                rmsnorm_gated_fwd_kernel<x_t, w_t, 0><<<T, threads, smem, stream>>>(
+                    x.data_ptr<x_t>(), weight.data_ptr<w_t>(), gate.data_ptr<x_t>(),
+                    y.data_ptr<x_t>(), rstd.data_ptr<float>(),
+                    T, H, static_cast<float>(eps), static_cast<float>(weight_offset));
+            } else {
+                rmsnorm_gated_fwd_kernel<x_t, w_t, 1><<<T, threads, smem, stream>>>(
+                    x.data_ptr<x_t>(), weight.data_ptr<w_t>(), gate.data_ptr<x_t>(),
+                    y.data_ptr<x_t>(), rstd.data_ptr<float>(),
+                    T, H, static_cast<float>(eps), static_cast<float>(weight_offset));
+            }
+        });
+    });
+}
+
+
+void rmsnorm_gated_backward_dx_cuda(
+    torch::Tensor dy,
+    torch::Tensor x,
+    torch::Tensor weight,
+    torch::Tensor gate,
+    torch::Tensor rstd,
+    torch::Tensor dx,
+    double weight_offset,
+    int64_t activation
+) {
+    int T = x.size(0);
+    int H = x.size(1);
+    int threads = choose_threads(H);
+    size_t smem = threads * sizeof(float);
+
+    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+    AT_DISPATCH_FLOATING_TYPES_AND2(at::kHalf, at::kBFloat16, x.scalar_type(), "rmsnorm_gated_backward_dx_cuda", [&] {
+        using x_t = scalar_t;
+        AT_DISPATCH_FLOATING_TYPES_AND2(at::kHalf, at::kBFloat16, weight.scalar_type(), "rmsnorm_gated_backward_dx_weight_cuda", [&] {
+            using w_t = scalar_t;
+            if (activation == 0) {
+                rmsnorm_gated_bwd_dx_kernel<x_t, w_t, 0><<<T, threads, smem, stream>>>(
+                    dy.data_ptr<x_t>(), x.data_ptr<x_t>(), weight.data_ptr<w_t>(),
+                    gate.data_ptr<x_t>(), rstd.data_ptr<float>(), dx.data_ptr<x_t>(),
+                    T, H, static_cast<float>(weight_offset));
+            } else {
+                rmsnorm_gated_bwd_dx_kernel<x_t, w_t, 1><<<T, threads, smem, stream>>>(
+                    dy.data_ptr<x_t>(), x.data_ptr<x_t>(), weight.data_ptr<w_t>(),
+                    gate.data_ptr<x_t>(), rstd.data_ptr<float>(), dx.data_ptr<x_t>(),
+                    T, H, static_cast<float>(weight_offset));
+            }
+        });
+    });
+}
+
 void rmsnorm_backward_partial_dw_cuda(
     torch::Tensor dy,
     torch::Tensor x,
diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index 4e786e5b3..b37ab102c 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -271,6 +271,26 @@ void rmsnorm_backward_dx_cuda(
   torch::Tensor dx,
   double weight_offset);
 
+void rmsnorm_gated_forward_cuda(
+  torch::Tensor x,
+  torch::Tensor weight,
+  torch::Tensor gate,
+  torch::Tensor y,
+  torch::Tensor rstd,
+  double eps,
+  double weight_offset,
+  int64_t activation);
+
+void rmsnorm_gated_backward_dx_cuda(
+  torch::Tensor dy,
+  torch::Tensor x,
+  torch::Tensor weight,
+  torch::Tensor gate,
+  torch::Tensor rstd,
+  torch::Tensor dx,
+  double weight_offset,
+  int64_t activation);
+
 void rmsnorm_backward_partial_dw_cuda(
   torch::Tensor dy,
   torch::Tensor x,
@@ -342,6 +362,73 @@ torch::Tensor rmsnorm_backward_dx(
   return dx;
 }
 
+static void rmsnorm_gated_check(
+  const torch::Tensor& x,
+  const torch::Tensor& weight,
+  const torch::Tensor& gate,
+  int64_t activation)
+{
+  rmsnorm_check_input(x, "x");
+  rmsnorm_check_input(weight, "weight");
+  rmsnorm_check_input(gate, "gate");
+
+  TORCH_CHECK(x.dim() == 2, "x must be 2D [T, H]");
+  TORCH_CHECK(weight.dim() == 1, "weight must be 1D [H]");
+  TORCH_CHECK(x.size(1) == weight.size(0), "x.size(1) must equal weight.size(0)");
+  TORCH_CHECK(gate.sizes() == x.sizes(), "gate must have the same shape as x");
+  TORCH_CHECK(
+    gate.scalar_type() == x.scalar_type(),
+    "gate must have the same dtype as x");
+  // 0 = silu/swish, 1 = sigmoid. Anything else fails closed rather than
+  // silently computing a different activation (RFC #428 section 6, item 7).
+  TORCH_CHECK(
+    activation == 0 || activation == 1,
+    "activation must be 0 (silu) or 1 (sigmoid), got ", activation);
+}
+
+std::vector<torch::Tensor> rmsnorm_gated_forward(
+  torch::Tensor x,
+  torch::Tensor weight,
+  torch::Tensor gate,
+  double eps,
+  double weight_offset,
+  int64_t activation)
+{
+  rmsnorm_gated_check(x, weight, gate, activation);
+
+  auto T = x.size(0);
+  auto y = torch::empty_like(x);
+  auto rstd = torch::empty({T}, x.options().dtype(torch::kFloat32));
+
+  rmsnorm_gated_forward_cuda(x, weight, gate, y, rstd, eps, weight_offset, activation);
+
+  return {y, rstd};
+}
+
+torch::Tensor rmsnorm_gated_backward_dx(
+  torch::Tensor dy,
+  torch::Tensor x,
+  torch::Tensor weight,
+  torch::Tensor gate,
+  torch::Tensor rstd,
+  double weight_offset,
+  int64_t activation)
+{
+  rmsnorm_gated_check(x, weight, gate, activation);
+  rmsnorm_check_input(dy, "dy");
+  rmsnorm_check_input(rstd, "rstd");
+
+  TORCH_CHECK(dy.sizes() == x.sizes(), "dy must have the same shape as x");
+  TORCH_CHECK(rstd.dim() == 1 && rstd.size(0) == x.size(0), "rstd must be [T]");
+
+  auto dx = torch::empty_like(x);
+
+  rmsnorm_gated_backward_dx_cuda(
+    dy, x, weight, gate, rstd, dx, weight_offset, activation);
+
+  return dx;
+}
+
 torch::Tensor rmsnorm_backward_dw(
   torch::Tensor dy,
   torch::Tensor x,
@@ -723,6 +810,15 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
           py::arg("dy"), py::arg("x"), py::arg("weight"), py::arg("rstd"),
           py::arg("weight_offset") = 0.0);
     m.def("rmsnorm_backward_dw", &rmsnorm_backward_dw, "Deterministic RMSNorm backward dweight CUDA");
+    m.def("rmsnorm_gated_forward", &rmsnorm_gated_forward,
+          "Batch-invariant gated RMSNorm forward CUDA",
+          py::arg("x"), py::arg("weight"), py::arg("gate"), py::arg("eps"),
+          py::arg("weight_offset") = 0.0, py::arg("activation") = 0);
+    m.def("rmsnorm_gated_backward_dx", &rmsnorm_gated_backward_dx,
+          "Batch-invariant gated RMSNorm backward dx CUDA",
+          py::arg("dy"), py::arg("x"), py::arg("weight"), py::arg("gate"),
+          py::arg("rstd"), py::arg("weight_offset") = 0.0,
+          py::arg("activation") = 0);
 #if !defined(USE_ROCM)
     m.def(
         "reduce_rows_fp32_left_fold",
diff --git a/docs/.nav.yml b/docs/.nav.yml
index 9133710de..88dea32e5 100644
--- a/docs/.nav.yml
+++ b/docs/.nav.yml
@@ -30,6 +30,7 @@ nav:
       - operators/det-gemm.md
       - operators/embedding.md
       - operators/qwen3-next-rms-norm.md
+      - operators/qwen3-next-rms-norm-gated.md
   - Developer Guide:
     - contributing/README.md
     - General:
diff --git a/docs/operators/README.md b/docs/operators/README.md
index 0d955ed18..0b93b3d90 100644
--- a/docs/operators/README.md
+++ b/docs/operators/README.md
@@ -33,4 +33,5 @@ Every operator page should include:
 - [Sampling](sampling.md)
 - [Token Embedding](embedding.md)
 - [Qwen3-Next RMSNorm (zero-centred)](qwen3-next-rms-norm.md)
+- [Qwen3-Next Gated RMSNorm](qwen3-next-rms-norm-gated.md)
 - [Operator Doc Template](../contributing/operator-doc-template.md)
diff --git a/docs/operators/qwen3-next-rms-norm-gated.md b/docs/operators/qwen3-next-rms-norm-gated.md
new file mode 100644
index 000000000..1bc531ea6
--- /dev/null
+++ b/docs/operators/qwen3-next-rms-norm-gated.md
@@ -0,0 +1,114 @@
+# Qwen3-Next Gated RMSNorm
+
+## Summary
+
+The norm inside Qwen3-Next's Gated DeltaNet block:
+
+```
+out = x * rstd * weight * silu(gate)
+```
+
+It is a different operator from the decoder norm, not a variant of it: the weight is
+plain rather than zero-centred (upstream initializes it to ones), it takes a second
+input tensor, and — critically — `transformers` and vLLM disagree about where the
+weight multiply happens. See [Qwen3-Next RMSNorm](qwen3-next-rms-norm.md) for the
+decoder-norm convention.
+
+Added for RFC #428 C1 on the Qwen3-Next rollout-vs-replay path.
+
+## Entry Point
+
+```python
+from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import (
+    Qwen3NextRMSNormGatedOp,      # strict / vLLM convention
+    Qwen3NextRMSNormGatedHFOp,    # transformers convention, kept as a witness
+)
+from rl_engine.kernels.ops.cuda.norm.rmsnorm import (
+    Qwen3NextRMSNormGatedCudaOp,
+    rmsnorm_gated_cuda,
+)
+
+y = Qwen3NextRMSNormGatedCudaOp().forward(x, weight, gate, eps=1e-6)
+y = rmsnorm_gated_cuda(x, weight, gate, eps=1e-6, activation="sigmoid")
+```
+
+## Backends
+
+| Backend | Wrapper | Native symbol | Status |
+| --- | --- | --- | --- |
+| CUDA | `Qwen3NextRMSNormGatedCudaOp` | `rl_engine._C.rmsnorm_gated_forward` | Supported |
+| ROCm | — | — | Not implemented |
+| PyTorch fallback | `Qwen3NextRMSNormGatedOp` | — | Supported (WS1 gold) |
+
+The CUDA op is deliberately **not** a subclass of `RMSNormCudaOp`: it takes an extra
+required tensor, so it cannot stand in for one.
+
+## Tensor Contract
+
+| Argument | Shape | Dtype | Requirements |
+| --- | --- | --- | --- |
+| `x` | `[..., H]` | fp32 / bf16 / fp16 | contiguous on the CUDA path |
+| `weight` | `[H]` | matches `x` | plain, NOT zero-centred |
+| `gate` | same as `x` | same as `x` | shape and dtype are enforced |
+| `eps` | scalar | float | `1e-6` for Qwen3-Next |
+| `activation` | — | `"silu"`/`"swish"`/`"sigmoid"` | anything else is rejected |
+
+Only `norm_before_gate=True` and `group_size=None` are implemented — the single
+configuration vLLM's GDN block constructs. Other configurations fail closed rather
+than being approximated (RFC #428 §6 item 7).
+
+## Dispatch Behavior
+
+Registered as `rms_norm_gated`. CUDA prefers the kernel; every other platform
+resolves to the PyTorch reference. `__init__` validates the compiled symbols, so on a
+build without the extension the registry falls through instead of returning an op
+that raises at call time.
+
+## Accuracy
+
+Claim level: **L0 repeatable, L1 batch-invariant**. L2 is not claimed.
+
+`transformers` casts the normalized value back to the input dtype *before* the weight
+multiply; vLLM keeps it in fp32. On B200 / bf16 / `head_v_dim=128` the two differ in
+35% of elements with `max|diff| = 6.25e-2`, and isolating the cast order alone
+reproduces the gap — so the cast order dominates, not the reduction order. Because
+RFC #428 measures L2 against vLLM rollout, the fp32 multiply is the strict default;
+`Qwen3NextRMSNormGatedHFOp` keeps the other convention as a tested witness.
+
+"Bitwise equal to vLLM" is undefined until a provider is named: vLLM's own
+`forward_native` and `forward_cuda` disagreed on 21 of 40 seeds (worst 1.56e-2 in
+bf16; ~36% of elements at fp32 ULP in fp32). What this reproduces is the convention;
+the residual is the reduction tree.
+
+`rstd` is bitwise identical to the ungated kernel's for the same `x` — asserted, so a
+gate leaking into the statistic would fail.
+
+Backward is assembled from deterministic pieces: `dx` from a row-local kernel,
+`dweight` from fp32 row contributions reduced by the ascending-row left fold, and
+`dgate` elementwise in fp32 with no reduction.
+
+## Performance Notes
+
+Reuses the existing `block_reduce_sum` / `choose_threads(H)` reduction, so the gate
+costs one extra load and one fp32 multiply per element.
+
+```bash
+python scripts/check_operator.py --op rms_norm_gated --candidate cuda \
+    --device cuda --dtype bf16 --check-grad
+```
+
+## Tests
+
+```bash
+python -m pytest tests/test_qwen3_next_norm.py -v
+# imports real vLLM, so it is not collected by default:
+python -m pytest tests/check_qwen3_next_norm_providers.py -v
+```
+
+## Known Limitations
+
+- CUDA only; no ROCm, Ascend or Triton backend.
+- `norm_before_gate=False` and grouped norms are not implemented.
+- Not bitwise against either vLLM path (see Accuracy).
+- Measured on sm_100 (B200); RFC #428 §2.2 forbids carrying the claim across
+  H100/H200/B100/B200.
diff --git a/docs/operators/qwen3-next-rms-norm.md b/docs/operators/qwen3-next-rms-norm.md
index 81fdde9b3..e5e9a47c0 100644
--- a/docs/operators/qwen3-next-rms-norm.md
+++ b/docs/operators/qwen3-next-rms-norm.md
@@ -9,8 +9,8 @@ offset away and silently breaks any bitwise claim.
 
 This operator exists for RFC #428 C1 (Embedding / RMSNorm / residual / final norm
 exactness) on the Qwen3-Next rollout-vs-replay path. The Gated DeltaNet block uses
-a *different* weight convention and a different cast order; it is a separate
-operator.
+a *different* weight convention and a different cast order; see
+[Gated RMSNorm](qwen3-next-rms-norm-gated.md).
 
 Upstream references: `transformers` `Qwen3NextRMSNorm`, and vLLM's `GemmaRMSNorm`,
 which `vllm/model_executor/models/qwen3_next.py` aliases as `Qwen3NextRMSNorm`.
diff --git a/rl_engine/kernels/gtest/operator_inputs.py b/rl_engine/kernels/gtest/operator_inputs.py
index ca3b7c120..1cd03d31f 100644
--- a/rl_engine/kernels/gtest/operator_inputs.py
+++ b/rl_engine/kernels/gtest/operator_inputs.py
@@ -26,6 +26,8 @@ def make_operator_inputs(
 ) -> dict[str, Any]:
     builders = {
         "rms_norm": _make_rms_norm_inputs,
+        "rms_norm_gated": _make_rms_norm_gated_inputs,
+        "qwen3_next_rms_norm": _make_rms_norm_inputs,
         "qk_norm": _make_qk_norm_inputs,
         "pack": _make_pack_inputs,
         "matmul": _make_matmul_inputs,
@@ -54,6 +56,8 @@ def operator_shape_name(op_name: str, args: argparse.Namespace) -> str:
     vocab = _arg_int(args, "vocab", DEFAULT_VOCAB)
     names = {
         "rms_norm": f"{batch}x{seq}x{_normalized_dim(args)}",
+        "rms_norm_gated": f"{batch}x{seq}x{_arg_int(args, 'head_dim', DEFAULT_HEAD_DIM)}",
+        "qwen3_next_rms_norm": f"{batch}x{seq}x{_normalized_dim(args)}",
         "qk_norm": f"{batch}x{seq}x{_arg_int(args, 'n_heads', DEFAULT_N_HEADS)}x"
         f"{_arg_int(args, 'head_dim', DEFAULT_HEAD_DIM)}",
         "pack": f"{batch}x{seq}x{_normalized_dim(args)}",
@@ -91,6 +95,25 @@ def _make_rms_norm_inputs(
     }
 
 
+def _make_rms_norm_gated_inputs(
+    args: argparse.Namespace, dtype: torch.dtype, device: torch.device
+) -> dict[str, Any]:
+    """Gated RMSNorm inputs at the GDN block's width.
+
+    The gated norm normalizes over ``linear_value_head_dim`` (128 for
+    Qwen3-Next), not ``hidden_size``, so it follows ``head_dim`` rather than
+    ``normalized_dim``.
+    """
+    batch, seq = _batch_seq(args)
+    head_dim = _arg_int(args, "head_dim", DEFAULT_HEAD_DIM)
+    return {
+        "x": _floating_tensor((batch, seq, head_dim), args, dtype, device, offset=0),
+        "weight": _floating_tensor((head_dim,), args, dtype, device, offset=1),
+        "gate": _floating_tensor((batch, seq, head_dim), args, dtype, device, offset=2),
+        "eps": _arg_float(args, "eps", DEFAULT_RMS_EPS),
+    }
+
+
 def _make_qk_norm_inputs(
     args: argparse.Namespace, dtype: torch.dtype, device: torch.device
 ) -> dict[str, Any]:
diff --git a/rl_engine/kernels/gtest/operator_specs.py b/rl_engine/kernels/gtest/operator_specs.py
index 4b86c2b7e..cccbc23b2 100644
--- a/rl_engine/kernels/gtest/operator_specs.py
+++ b/rl_engine/kernels/gtest/operator_specs.py
@@ -46,6 +46,36 @@ def _load_object(path: str) -> Any:
         },
         grad_input_names=("x", "weight"),
     ),
+    "rms_norm_gated": OperatorSpec(
+        name="rms_norm_gated",
+        op_class="reduction",
+        gold_path=(
+            "rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm.Qwen3NextRMSNormGatedOp"
+        ),
+        gold_method="forward_fp32",
+        candidate_paths={
+            "pytorch": (
+                "rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm.Qwen3NextRMSNormGatedOp"
+            ),
+            "cuda": ("rl_engine.kernels.ops.cuda.norm.rmsnorm.Qwen3NextRMSNormGatedCudaOp"),
+            "cuda-sm90": ("rl_engine.kernels.ops.cuda.norm.rmsnorm.Qwen3NextRMSNormGatedCudaOp"),
+        },
+        grad_input_names=("x", "weight", "gate"),
+    ),
+    "qwen3_next_rms_norm": OperatorSpec(
+        name="qwen3_next_rms_norm",
+        op_class="reduction",
+        gold_path=("rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm.Qwen3NextRMSNormOp"),
+        gold_method="forward_fp32",
+        candidate_paths={
+            "pytorch": (
+                "rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm.Qwen3NextRMSNormOp"
+            ),
+            "cuda": "rl_engine.kernels.ops.cuda.norm.rmsnorm.Qwen3NextRMSNormCudaOp",
+            "cuda-sm90": ("rl_engine.kernels.ops.cuda.norm.rmsnorm.Qwen3NextRMSNormCudaOp"),
+        },
+        grad_input_names=("x", "weight"),
+    ),
     "qk_norm": OperatorSpec(
         name="qk_norm",
         op_class="reduction",
diff --git a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
index af9a31358..fc1aa9554 100644
--- a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
+++ b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
@@ -5,23 +5,14 @@
 from rl_engine.kernels.ops.vjp_fp32 import reduce_rows_fp32, rmsnorm_dweight_rows_fp32
 
 
-def _require_cuda_symbols(what: str, *names: str) -> None:
-    """Raise when the compiled kernels backing ``what`` are missing.
+def _fold_dweight_rows(rows: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+    """The single left-fold entrypoint for this backend's dweight reductions.
 
-    The registry treats a backend whose construction raises as unavailable and
-    falls through to the next candidate, so calling this from ``__init__`` is
-    what lets a CUDA-first priority list degrade to the PyTorch reference on a
-    build without the extension. Mirrors ``_require_cuda_activation`` in the
-    activation ops.
+    Both the plain and the gated backward route through here so the file keeps
+    one auditable reduction path; the ascending-row fp32 fold is what makes
+    dweight independent of the batch layout.
     """
-    if not _EXT_AVAILABLE or _C is None:
-        raise RuntimeError(f"{what} requires the compiled rl_engine._C extension.")
-    missing = [name for name in names if not hasattr(_C, name)]
-    if missing:
-        raise RuntimeError(
-            f"{what} symbols ({', '.join(missing)}) are not compiled into _C. "
-            "Rebuild the extension with csrc/cuda/rmsnorm.cu."
-        )
+    return reduce_rows_fp32(rows).to(dtype)
 
 
 class RMSNormCuda(torch.autograd.Function):
@@ -92,7 +83,7 @@ def backward(ctx, grad_out):
         # Multiplication is part of the pre-existing mask contract, including
         # IEEE propagation for non-finite inactive contributions.
         rows = rows * mask.to(dtype=rows.dtype).unsqueeze(-1)
-        dw = reduce_rows_fp32(rows).to(weight.dtype)
+        dw = _fold_dweight_rows(rows, weight.dtype)
         record_backward(
             "rms_norm",
             kernel_id=(
@@ -166,3 +157,187 @@ class Qwen3NextRMSNormCudaOp(RMSNormCudaOp):
     """
 
     weight_offset = 1.0
+
+
+# --------------------------------------------------------------------------- #
+# Gated RMSNorm (Qwen3-Next GDN block)
+# --------------------------------------------------------------------------- #
+
+def _require_cuda_symbols(what: str, *names: str) -> None:
+    """Raise when the compiled kernels backing ``what`` are missing.
+
+    The registry treats a backend whose construction raises as unavailable and
+    falls through, so calling this from ``__init__`` is what lets a CUDA-first
+    priority list degrade to the PyTorch reference on a build without the
+    extension. Mirrors ``_require_cuda_activation`` in the activation ops.
+    """
+    if not _EXT_AVAILABLE or _C is None:
+        raise RuntimeError(f"{what} requires the compiled rl_engine._C extension.")
+    missing = [name for name in names if not hasattr(_C, name)]
+    if missing:
+        raise RuntimeError(
+            f"{what} symbols ({', '.join(missing)}) are not compiled into _C. "
+            "Rebuild the extension with csrc/cuda/rmsnorm.cu."
+        )
+
+
+#: Gate activations understood by the CUDA kernel, in binding order.
+_GATE_ACTIVATIONS = {"silu": 0, "swish": 0, "sigmoid": 1}
+
+
+def _gate_activation_fp32(gate: torch.Tensor, activation: int) -> torch.Tensor:
+    """act(gate) in fp32, matching the kernel's ``gate_activation``."""
+    gate32 = gate.float()
+    return torch.nn.functional.silu(gate32) if activation == 0 else torch.sigmoid(gate32)
+
+
+def _gate_activation_grad_fp32(gate: torch.Tensor, activation: int) -> torch.Tensor:
+    """d act(gate) / d gate in fp32, matching ``gate_activation_grad``."""
+    gate32 = gate.float()
+    sigma = torch.sigmoid(gate32)
+    if activation == 0:
+        return sigma * (1.0 + gate32 * (1.0 - sigma))
+    return sigma * (1.0 - sigma)
+
+
+class RMSNormGatedCuda(torch.autograd.Function):
+    """Autograd wrapper for the gated CUDA RMSNorm.
+
+    Forward is the fused kernel. Backward is assembled from deterministic
+    pieces: ``dx`` from a row-local CUDA kernel, ``dweight`` from fp32 row
+    contributions reduced by the ascending-row left fold, and ``dgate`` purely
+    elementwise in fp32 (no reduction, so batch invariance is trivial).
+    """
+
+    @staticmethod
+    def forward(ctx, x, weight, gate, eps=1e-6, weight_offset=0.0, activation=0):
+        """
+        Forward:
+          y = x * rsqrt(mean(x^2) + eps) * (weight_offset + weight) * act(gate)
+
+        Input:
+          x, gate: [T, H], fp16/bf16/fp32 CUDA tensors of matching dtype
+          weight:  [H]
+          activation: 0 = silu/swish, 1 = sigmoid
+        """
+        assert x.is_cuda and weight.is_cuda and gate.is_cuda, "inputs must be CUDA tensors"
+        assert x.is_contiguous() and weight.is_contiguous() and gate.is_contiguous()
+        assert x.dim() == 2, "x must be [T, H]"
+        assert weight.dim() == 1, "weight must be [H]"
+        assert gate.shape == x.shape, "gate must match x"
+        assert _EXT_AVAILABLE and hasattr(_C, "rmsnorm_gated_forward"), (
+            "Gated RMSNorm CUDA extension is unavailable. Rebuild with csrc/cuda/rmsnorm.cu."
+        )
+
+        y, rstd = _C.rmsnorm_gated_forward(
+            x, weight, gate, float(eps), float(weight_offset), int(activation)
+        )
+
+        ctx.save_for_backward(x, weight, gate, rstd)
+        ctx.eps = eps
+        ctx.weight_offset = float(weight_offset)
+        ctx.activation = int(activation)
+
+        return y
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        x, weight, gate, rstd = ctx.saved_tensors
+        dy = grad_out.contiguous()
+        act = ctx.activation
+
+        dx = _C.rmsnorm_gated_backward_dx(
+            dy, x, weight, gate, rstd, ctx.weight_offset, act
+        )
+
+        # dweight: the gate is a per-element constant here, so the ungated row
+        # contributions apply once dy carries act(gate).
+        gate_act = _gate_activation_fp32(gate, act)
+        rows = rmsnorm_dweight_rows_fp32(x, dy.float() * gate_act, rstd=rstd)
+        dw = _fold_dweight_rows(rows, weight.dtype)
+
+        # dgate: row-local and reduction-free.
+        normed = x.float() * rstd.unsqueeze(-1)
+        scale = weight.float() + ctx.weight_offset
+        dgate = (dy.float() * normed * scale * _gate_activation_grad_fp32(gate, act)).to(
+            gate.dtype
+        )
+
+        record_backward(
+            "rms_norm_gated",
+            kernel_id=(
+                "rl_engine._C.rmsnorm_gated_backward_dx"
+                "+rl_engine.kernels.ops.vjp_fp32.rmsnorm_dweight_rows_fp32"
+                "+rl_engine.kernels.ops.vjp_fp32.reduce_rows_fp32"
+            ),
+            impl="cuda_rmsnorm_gated_dx_declared_fp32_rowfold_dw",
+            family="cuda",
+        )
+
+        return dx, dw, dgate, None, None, None
+
+
+def rmsnorm_gated_cuda(x, weight, gate, eps=1e-6, weight_offset=0.0, activation="silu"):
+    """
+    use:
+        y = rmsnorm_gated_cuda(x, weight, gate)
+        y = rmsnorm_gated_cuda(x, weight, gate, activation="sigmoid")
+    """
+    if activation not in _GATE_ACTIVATIONS:
+        raise ValueError(
+            f"activation must be one of {sorted(_GATE_ACTIVATIONS)}, got {activation!r}"
+        )
+    return RMSNormGatedCuda.apply(
+        x, weight, gate, eps, weight_offset, _GATE_ACTIVATIONS[activation]
+    )
+
+
+class Qwen3NextRMSNormGatedCudaOp:
+    """CUDA gated RMSNorm for the Qwen3-Next GDN block.
+
+    Deliberately not a subclass of :class:`RMSNormCudaOp`: it takes an extra
+    required tensor, so it cannot stand in for one.
+
+    ``out = x * rstd * weight * silu(gate)``, every multiply in fp32 with a
+    single cast at the store. The weight is plain, not zero-centred, matching
+    vLLM's ``RMSNormGated`` with ``norm_before_gate=True`` and ``group_size=None``
+    -- which is exactly how the GDN block constructs it. Other configurations
+    are rejected rather than approximated.
+    """
+
+    backward_impl = "cuda_rmsnorm_gated_dx_declared_fp32_rowfold_dw"
+
+    #: The gated weight is plain; kept as an attribute so the surface matches
+    #: the ungated op and a zero-centred variant stays one subclass away.
+    weight_offset = 0.0
+    activation = "silu"
+
+    def __init__(self) -> None:
+        _require_cuda_symbols(
+            "Gated CUDA RMSNorm", "rmsnorm_gated_forward", "rmsnorm_gated_backward_dx"
+        )
+
+    def __call__(self, x, weight, gate, *, eps=1e-6):
+        return self.forward(x, weight, gate, eps=eps)
+
+    def forward(self, x, weight, gate, *, eps=1e-6):
+        hidden = x.shape[-1]
+        x_2d = x.contiguous().view(-1, hidden)
+        gate_2d = gate.contiguous().view(-1, hidden)
+        y_2d = rmsnorm_gated_cuda(
+            x_2d,
+            weight.contiguous(),
+            gate_2d,
+            eps=eps,
+            weight_offset=self.weight_offset,
+            activation=self.activation,
+        )
+        return y_2d.view_as(x)
+
+    def parameter_vjp_contributions_fp32(self, *, x, weight, gate, grad_output, eps=1e-6):
+        del weight
+        x32 = x.float()
+        rstd = torch.rsqrt(x32.square().mean(dim=-1) + float(eps))
+        act = _GATE_ACTIVATIONS[self.activation]
+        rows = grad_output.float() * _gate_activation_fp32(gate, act) * x32 * rstd.unsqueeze(-1)
+        return {"weight": rows}
diff --git a/rl_engine/kernels/registry.py b/rl_engine/kernels/registry.py
index 3b935499a..f3aa51bb1 100644
--- a/rl_engine/kernels/registry.py
+++ b/rl_engine/kernels/registry.py
@@ -158,6 +158,18 @@ class OpBackend(Enum, metaclass=_KernelEnumMeta):
     TRITON_RMS_NORM = "rl_engine.kernels.ops.triton.rmsnorm_triton.RMSNormTritonOp"
     PYTORCH_NATIVE_RMS_NORM = "rl_engine.kernels.ops.pytorch.norm.rms_norm.NativeRMSNormOp"
 
+    # Zero-centred RMSNorm (Qwen3-Next decoder / final norm)
+    CUDA_QWEN3_NEXT_RMS_NORM = "rl_engine.kernels.ops.cuda.norm.rmsnorm.Qwen3NextRMSNormCudaOp"
+    PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM = (
+        "rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm.Qwen3NextRMSNormOp"
+    )
+
+    # Gated RMSNorm (Qwen3-Next Gated DeltaNet block)
+    CUDA_RMS_NORM_GATED = "rl_engine.kernels.ops.cuda.norm.rmsnorm.Qwen3NextRMSNormGatedCudaOp"
+    PYTORCH_NATIVE_RMS_NORM_GATED = (
+        "rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm.Qwen3NextRMSNormGatedOp"
+    )
+
     # Generic fallback
     TRITON_GENERIC = "rl_engine.kernels.ops.triton.generic.TritonOp"
     PYTORCH_ATTN = "rl_engine.kernels.ops.pytorch.attention.NativeAttentionOp"
@@ -594,6 +606,14 @@ def __init__(self):
                     OpBackend.CUDA_RMS_NORM,
                     OpBackend.PYTORCH_NATIVE_RMS_NORM,
                 ],
+                "rms_norm_gated": [
+                    OpBackend.CUDA_RMS_NORM_GATED,
+                    OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED,
+                ],
+                "qwen3_next_rms_norm": [
+                    OpBackend.CUDA_QWEN3_NEXT_RMS_NORM,
+                    OpBackend.PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM,
+                ],
                 "lm_head": [OpBackend.PYTORCH_NATIVE_LM_HEAD],
                 "embedding": [OpBackend.PYTORCH_NATIVE_EMBEDDING],
                 "silu": [
@@ -649,6 +669,8 @@ def __init__(self):
                 ],
                 "matmul": [OpBackend.PYTORCH_NATIVE_MATMUL],
                 "rms_norm": [OpBackend.PYTORCH_NATIVE_RMS_NORM],
+                "rms_norm_gated": [OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED],
+                "qwen3_next_rms_norm": [OpBackend.PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM],
                 "lm_head": [OpBackend.PYTORCH_NATIVE_LM_HEAD],
                 "embedding": [OpBackend.PYTORCH_NATIVE_EMBEDDING],
                 "silu": [OpBackend.TRITON_SILU, OpBackend.PYTORCH_NATIVE_SILU],
@@ -690,6 +712,8 @@ def __init__(self):
                     OpBackend.TRITON_RMS_NORM,
                     OpBackend.PYTORCH_NATIVE_RMS_NORM,
                 ],
+                "rms_norm_gated": [OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED],
+                "qwen3_next_rms_norm": [OpBackend.PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM],
                 "lm_head": [OpBackend.PYTORCH_NATIVE_LM_HEAD],
                 "embedding": [
                     OpBackend.TRITON_EMBEDDING,
@@ -718,6 +742,8 @@ def __init__(self):
                 "batch_invariant_logp": [OpBackend.PYTORCH_BATCH_INVARIANT_LOGP],
                 "matmul": [OpBackend.PYTORCH_NATIVE_MATMUL],
                 "rms_norm": [OpBackend.PYTORCH_NATIVE_RMS_NORM],
+                "rms_norm_gated": [OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED],
+                "qwen3_next_rms_norm": [OpBackend.PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM],
                 "lm_head": [OpBackend.PYTORCH_NATIVE_LM_HEAD],
                 "embedding": [OpBackend.PYTORCH_NATIVE_EMBEDDING],
                 "silu": [OpBackend.PYTORCH_NATIVE_SILU],
@@ -758,6 +784,12 @@ def __init__(self):
             OpBackend.ASCEND_RMS_NORM,
             OpBackend.PYTORCH_NATIVE_RMS_NORM,
         ]
+        self._priority_map["npu"]["rms_norm_gated"] = [
+            OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED,
+        ]
+        self._priority_map["npu"]["qwen3_next_rms_norm"] = [
+            OpBackend.PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM,
+        ]
         self._priority_map["npu"]["embedding"] = [
             OpBackend.ASCEND_EMBEDDING,
             OpBackend.PYTORCH_NATIVE_EMBEDDING,
diff --git a/rl_engine/tests/test_dispatch.py b/rl_engine/tests/test_dispatch.py
index d095619ee..ed873c2dd 100644
--- a/rl_engine/tests/test_dispatch.py
+++ b/rl_engine/tests/test_dispatch.py
@@ -4,6 +4,7 @@
 import sys
 from types import ModuleType
 
+import pytest
 import torch
 
 import rl_engine.platforms.device as device_module
@@ -256,3 +257,38 @@ def test_executor_flow():
         print("\n All infrastructure tests passed!")
     except Exception as e:
         print(f"\n Test failed with error: {e}")
+
+
+def test_gated_rms_norm_priority_is_cuda_first_with_pytorch_fallback():
+    """The gated norm has a CUDA kernel; every other platform falls back.
+
+    No Triton, ROCm or Ascend gated kernel exists yet, so those platforms must
+    resolve to the PyTorch reference rather than to nothing -- an operator
+    missing from a priority map falls through to ``OpBackend.PYTORCH_NATIVE``,
+    which is the logprob op, not a norm.
+    """
+    registry = KernelRegistry()
+
+    assert registry._priority_map["cuda"]["rms_norm_gated"] == [
+        OpBackend.CUDA_RMS_NORM_GATED,
+        OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED,
+    ]
+    for platform in ("rocm", "musa", "cpu", "npu"):
+        assert registry._priority_map[platform]["rms_norm_gated"] == [
+            OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED
+        ], platform
+
+
+def test_gated_rms_norm_cuda_backend_reports_absence_by_failing_construction(monkeypatch):
+    """Without the compiled symbols the CUDA backend must be skipped, not returned."""
+    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import Qwen3NextRMSNormGatedOp
+
+    monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", False)
+    monkeypatch.setattr(cuda_rmsnorm, "_C", None)
+    with pytest.raises(RuntimeError, match="requires the compiled rl_engine._C extension"):
+        cuda_rmsnorm.Qwen3NextRMSNormGatedCudaOp()
+
+    # ... and the registry must therefore hand out the reference, not raise.
+    resolved = KernelRegistry().get_op("rms_norm_gated")
+    assert isinstance(resolved, Qwen3NextRMSNormGatedOp)
diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
index a62d8410f..c1f7e9d73 100644
--- a/tests/test_qwen3_next_norm.py
+++ b/tests/test_qwen3_next_norm.py
@@ -458,6 +458,221 @@ def test_cuda_zero_centred_backward_is_offset_aware():
     assert torch.equal(grads["plain"][1], grads["zero_centred"][1])  # dw does not
 
 
+# --------------------------------------------------------------------------- #
+# 7. Gated RMSNorm CUDA kernel (the GDN block's norm)
+# --------------------------------------------------------------------------- #
+_HAS_CUDA_GATED = False
+if torch.cuda.is_available():  # pragma: no branch - probe only
+    try:
+        from rl_engine.kernels.ops.base import _C as _C_probe
+        from rl_engine.kernels.ops.base import _EXT_AVAILABLE as _EXT_probe
+
+        _HAS_CUDA_GATED = _EXT_probe and hasattr(_C_probe, "rmsnorm_gated_forward")
+    except ImportError:  # pragma: no cover
+        _HAS_CUDA_GATED = False
+
+requires_cuda_gated = pytest.mark.skipif(
+    not _HAS_CUDA_GATED, reason="gated RMSNorm CUDA extension is not available"
+)
+
+# tolerance_contract.json, judgments/forward_accuracy/by_op_class/reduction/bfloat16
+_BF16_ATOL, _BF16_RTOL = 2e-2, 1.6e-2
+
+
+def _gated_cuda_inputs(seed=0, rows=512, hidden=_HEAD_V_DIM, dtype=torch.bfloat16):
+    g = torch.Generator(device="cuda").manual_seed(seed)
+    x = torch.randn(rows, hidden, device="cuda", dtype=dtype, generator=g)
+    gate = torch.randn(rows, hidden, device="cuda", dtype=dtype, generator=g)
+    weight = torch.randn(hidden, device="cuda", dtype=dtype, generator=g)
+    return x, weight, gate
+
+
+@requires_cuda_gated
+def test_cuda_gated_matches_golden_within_contract():
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp
+
+    x, w, gate = _gated_cuda_inputs()
+    got = Qwen3NextRMSNormGatedCudaOp().forward(x, w, gate, eps=_EPS)
+    ref = Qwen3NextRMSNormGatedOp().forward_fp32(x, w, gate, eps=_EPS)
+    torch.testing.assert_close(got.float(), ref, atol=_BF16_ATOL, rtol=_BF16_RTOL)
+
+
+@requires_cuda_gated
+def test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel():
+    """The gate must not perturb the normalization statistic.
+
+    Same x, same rstd, bitwise -- otherwise the gate has leaked into the
+    reduction and the two kernels no longer share a contract.
+    """
+    from rl_engine.kernels.ops.base import _C
+
+    x, w, gate = _gated_cuda_inputs()
+    _, rstd_gated = _C.rmsnorm_gated_forward(x, w, gate, _EPS, 0.0, 0)
+    _, rstd_plain = _C.rmsnorm_forward(x, w, _EPS, 0.0)
+    assert torch.equal(rstd_gated, rstd_plain)
+
+
+@requires_cuda_gated
+@pytest.mark.parametrize("batch", [1, 2, 8, 16, 32, 48, 64, 512])
+def test_cuda_gated_batch_invariance(batch):
+    """L1 across the RFC #428 concurrency axis, bitwise."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp
+
+    op = Qwen3NextRMSNormGatedCudaOp()
+    x, w, gate = _gated_cuda_inputs()
+    full = op.forward(x, w, gate, eps=_EPS)
+    assert torch.equal(op.forward(x[:batch], w, gate[:batch], eps=_EPS), full[:batch])
+
+
+@requires_cuda_gated
+def test_cuda_gated_zero_gate_zeroes_output():
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp
+
+    x, w, _ = _gated_cuda_inputs()
+    out = Qwen3NextRMSNormGatedCudaOp().forward(x, w, torch.zeros_like(x), eps=_EPS)
+    assert torch.equal(out, torch.zeros_like(out))
+
+
+@requires_cuda_gated
+def test_cuda_gated_unit_weight_is_plain_norm_times_silu():
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import (
+        Qwen3NextRMSNormGatedCudaOp,
+        rmsnorm_cuda,
+    )
+
+    x, _, gate = _gated_cuda_inputs()
+    ones = torch.ones(_HEAD_V_DIM, device="cuda", dtype=x.dtype)
+    got = Qwen3NextRMSNormGatedCudaOp().forward(x, ones, gate, eps=_EPS).float()
+    ref = rmsnorm_cuda(x, ones, eps=_EPS).float() * F.silu(gate.float())
+    torch.testing.assert_close(got, ref, atol=_BF16_ATOL, rtol=_BF16_RTOL)
+
+
+@requires_cuda_gated
+def test_cuda_gated_sigmoid_activation():
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda, rmsnorm_cuda
+
+    x, w, gate = _gated_cuda_inputs()
+    got = rmsnorm_gated_cuda(x, w, gate, eps=_EPS, activation="sigmoid").float()
+    ref = rmsnorm_cuda(x, w, eps=_EPS).float() * torch.sigmoid(gate.float())
+    torch.testing.assert_close(got, ref, atol=_BF16_ATOL, rtol=_BF16_RTOL)
+
+
+@requires_cuda_gated
+def test_cuda_gated_backward_matches_autograd_golden():
+    """dx, dweight and dgate against the fp32 reference's autograd."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda
+
+    x, w, gate = _gated_cuda_inputs()
+    x, w, gate = x.float(), w.float(), gate.float()
+    dy = torch.randn_like(x)
+
+    got = [t.clone().requires_grad_(True) for t in (x, w, gate)]
+    rmsnorm_gated_cuda(*got, eps=_EPS).backward(dy)
+    ref = [t.clone().requires_grad_(True) for t in (x, w, gate)]
+    Qwen3NextRMSNormGatedOp().forward_fp32(*ref, eps=_EPS).backward(dy)
+
+    for name, a, b in zip(("dx", "dweight", "dgate"), got, ref):
+        torch.testing.assert_close(a.grad, b.grad, atol=1e-4, rtol=1e-4, msg=name)
+
+
+@requires_cuda_gated
+@pytest.mark.parametrize("batch", [1, 8, 64, 512])
+def test_cuda_gated_dweight_is_batch_invariant(batch):
+    """A row's contribution to dweight must not depend on the batch it sits in.
+
+    The ascending-row fp32 left fold makes dweight over the first n rows exactly
+    the fold of those n row-contributions -- so the n-row run must reproduce the
+    512-row run's prefix, bitwise.
+    """
+    from rl_engine.kernels.ops.base import _C
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda
+    from rl_engine.kernels.ops.vjp_fp32 import reduce_rows_fp32
+
+    x, w, gate = _gated_cuda_inputs()
+    x, w, gate = x.float(), w.float(), gate.float()
+    dy = torch.randn_like(x)
+
+    wg = w.clone().requires_grad_(True)
+    rmsnorm_gated_cuda(x[:batch], wg, gate[:batch], eps=_EPS).backward(dy[:batch])
+
+    # rstd from the full-batch forward: the row statistic is batch-invariant, so
+    # its prefix is what the n-row run must have seen. Recomputing it with
+    # mean(-1) instead would compare against a different reduction.
+    _, rstd_full = _C.rmsnorm_gated_forward(x, w, gate, _EPS, 0.0, 0)
+    rows = dy * F.silu(gate) * x * rstd_full.unsqueeze(-1)
+    assert torch.equal(wg.grad, reduce_rows_fp32(rows[:batch]))
+
+
+@requires_cuda_gated
+def test_cuda_gated_weight_offset_is_applied_in_fp32():
+    """The gated kernel carries the same offset contract as the plain one."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda
+
+    x, w, gate = _gated_cuda_inputs()
+    offset = rmsnorm_gated_cuda(x, w, gate, eps=_EPS, weight_offset=1.0)
+    explicit = rmsnorm_gated_cuda(x, (1.0 + w.float()), gate, eps=_EPS)
+    assert torch.equal(offset, explicit)
+    folded = rmsnorm_gated_cuda(x, (1.0 + w.float()).bfloat16(), gate, eps=_EPS)
+    assert not torch.equal(offset, folded)
+
+
+@requires_cuda_gated
+def test_cuda_gated_parameter_vjp_contributions_match_the_fold():
+    """The harness hook must return the same rows the backward folds."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp
+
+    x, w, gate = _gated_cuda_inputs(rows=64)
+    x, w, gate = x.float(), w.float(), gate.float()
+    dy = torch.randn_like(x)
+
+    rows = Qwen3NextRMSNormGatedCudaOp().parameter_vjp_contributions_fp32(
+        x=x, weight=w, gate=gate, grad_output=dy, eps=_EPS
+    )["weight"]
+    rstd = torch.rsqrt(x.square().mean(dim=-1) + _EPS)
+    expected = dy * F.silu(gate) * x * rstd.unsqueeze(-1)
+    torch.testing.assert_close(rows, expected, atol=1e-6, rtol=1e-6)
+
+
+@requires_cuda_gated
+def test_cuda_gated_sigmoid_backward_uses_the_sigmoid_derivative():
+    """The activation gradient has two branches; only silu was covered."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda
+
+    x, w, gate = _gated_cuda_inputs(rows=64)
+    x, w, gate = x.float(), w.float(), gate.float()
+    dy = torch.randn_like(x)
+
+    got = [t.clone().requires_grad_(True) for t in (x, w, gate)]
+    rmsnorm_gated_cuda(*got, eps=_EPS, activation="sigmoid").backward(dy)
+
+    ref = [t.clone().requires_grad_(True) for t in (x, w, gate)]
+    rstd = torch.rsqrt(ref[0].square().mean(dim=-1) + _EPS)
+    out = (ref[0] * rstd.unsqueeze(-1) * ref[1]) * torch.sigmoid(ref[2])
+    out.backward(dy)
+    for name, a, b in zip(("dx", "dweight", "dgate"), got, ref):
+        torch.testing.assert_close(a.grad, b.grad, atol=1e-4, rtol=1e-4, msg=name)
+
+
+@requires_cuda_gated
+def test_cuda_gated_rejects_bad_activation():
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda
+
+    x, w, gate = _gated_cuda_inputs(rows=8)
+    with pytest.raises(ValueError, match="activation must be one of"):
+        rmsnorm_gated_cuda(x, w, gate, eps=_EPS, activation="gelu")
+
+
+@requires_cuda_gated
+def test_cuda_gated_rejects_mismatched_gate():
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda
+
+    x, w, gate = _gated_cuda_inputs(rows=8)
+    with pytest.raises((AssertionError, RuntimeError)):
+        rmsnorm_gated_cuda(x, w, gate[:4], eps=_EPS)
+    with pytest.raises((AssertionError, RuntimeError)):
+        rmsnorm_gated_cuda(x, w, gate.float(), eps=_EPS)
+
+
 # --------------------------------------------------------------------------- #
 # 8. `__call__` is the documented entry point and must agree with `forward`
 # --------------------------------------------------------------------------- #
diff --git a/tests/test_ws1_gtest_gpu.py b/tests/test_ws1_gtest_gpu.py
index 2f4ec8648..84b6daccb 100644
--- a/tests/test_ws1_gtest_gpu.py
+++ b/tests/test_ws1_gtest_gpu.py
@@ -36,6 +36,8 @@ def test_all_ws1_single_ops_are_registered():
     names = set(operator_names())
     assert {
         "rms_norm",
+        "rms_norm_gated",
+        "qwen3_next_rms_norm",
         "qk_norm",
         "det_gemm",
         "attention",

From d03c8523a6e39bb616deead401d13ff9b53b4740 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 21:36:52 +0000
Subject: [PATCH 08/70] fix(norm): contract tolerances and corrected claims for
 the Qwen3-Next norms

Tests: the low-precision and CUDA-vs-golden comparisons hard-coded
atol=2e-2/rtol=1.6e-2, which is the contract's elementwise bf16 row. They now
resolve forward_accuracy for op_class="reduction" from tolerance_contract.json.
That loosens bf16 (5e-2/2e-2) and tightens fp16 (1e-3/1e-3, previously 2e-2);
the fp16 cases still pass. The vLLM provider-gap bounds in
tests/check_qwen3_next_norm_providers.py are labelled as gap bounds, not
contract thresholds.

Docs and docstrings: correct statements this branch had committed.
- The operator page no longer says the op is registered or prints a
  check_operator command; the gtest spec and registry entry arrive with the
  gated-norm PR.
- Withdrawn: "7 of 1048576 differ" (single seed, no script), the 35% / 5.3e-2
  cast-order isolation (an fp32 round-trip is a no-op), "needed on CUDA as
  well", and "matching vLLM means trading L1 for L2" (one unreproduced
  observation). Replaced with the scoped claim levels and the probe results.
- The provider check no longer claims to establish which path vLLM
  dispatches, or a per-element fp32 ULP bound.
- The module docstring is cut to the contract and links to the operator page.

Comments: why the kernel's offset add is guarded (signed zero, with the
pinning test), and why parameter_vjp_contributions_fp32 passes the offset.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 csrc/cuda/rmsnorm.cu                          |  3 +
 docs/operators/qwen3-next-rms-norm.md         | 60 +++++++++------
 rl_engine/kernels/ops/cuda/norm/rmsnorm.py    |  2 +
 .../ops/pytorch/norm/qwen3_next_rms_norm.py   | 76 +++++--------------
 tests/check_qwen3_next_norm_providers.py      | 43 ++++++-----
 tests/test_qwen3_next_norm.py                 | 22 ++++--
 6 files changed, 102 insertions(+), 104 deletions(-)

diff --git a/csrc/cuda/rmsnorm.cu b/csrc/cuda/rmsnorm.cu
index 5b570c557..0c09b3a4a 100644
--- a/csrc/cuda/rmsnorm.cu
+++ b/csrc/cuda/rmsnorm.cu
@@ -143,6 +143,9 @@ __global__ void rmsnorm_fwd_kernel(
     for (int col = tid; col < H; col += blockDim.x) {
         float xv = load_as_float<scalar_t>(x_row + col);
         float wv = load_as_float<weight_t>(weight + col);
+        // Guarded: `-0.0f + 0.0f` is +0.0f, so an unconditional add would flip the
+        // sign bit of -0.0 weights on the plain path. Pinned by
+        // tests/test_qwen3_next_norm.py::test_cuda_plain_signed_zero_is_preserved.
         if (weight_offset != 0.0f) wv += weight_offset;
         float out = xv * row_rstd * wv;
         store_from_float<scalar_t>(y_row + col, out);
diff --git a/docs/operators/qwen3-next-rms-norm.md b/docs/operators/qwen3-next-rms-norm.md
index 292139bea..81fdde9b3 100644
--- a/docs/operators/qwen3-next-rms-norm.md
+++ b/docs/operators/qwen3-next-rms-norm.md
@@ -49,32 +49,47 @@ y = rmsnorm_cuda(x, weight, eps=1e-6, weight_offset=1.0)
 
 ## Dispatch Behavior
 
-Registered as the `qwen3_next_rms_norm` gtest operator. On CUDA the registry
-prefers `Qwen3NextRMSNormCudaOp`; every other platform resolves to the PyTorch
-reference. The CUDA op validates the compiled symbols in `__init__`, so on a build
-without the extension construction raises and the registry falls through to the
-reference rather than handing out an op that fails at call time.
+Not registered on this branch: there is no `qwen3_next_rms_norm` gtest spec and no
+registry entry yet. Both arrive with the gated-norm PR. Until then, construct the
+ops directly as in "Entry Point". The CUDA op validates the compiled symbols in
+`__init__`, so on a build without the extension construction raises instead of
+handing out an op that fails at call time.
 
 ## Accuracy
 
-Claim level: **L0 repeatable, L1 batch-invariant**. L2 is not claimed.
+Claim levels:
 
-The reduction is the repo's fixed 32-wide chunked sum
-(`shape_invariant_rstd`), which is what makes a row's result independent of the
-batch layout. It deliberately differs from upstream's `mean(-1)`: measured on B200,
-over 20 seeds at `H=2048` in bf16, a plain `mean(-1)` broke slice invariance on 1
-of 20 while the chunked reduction broke on 0 of 20.
+- **L1** (prefix-slice and concurrency) for `Qwen3NextRMSNormCudaOp`; padding,
+  packing and order are not tested for the CUDA op.
+- **L1** (slice, concurrency and padding) for `Qwen3NextRMSNormOp`.
+- **L0** for `Qwen3NextRMSNormOp` only (CPU, fp32); no test repeats the CUDA op.
+- L2 is not claimed.
 
-The cost is that the decoder norm is **not** bitwise equal to stock vLLM — 7
-elements of 1048576 differ, `max|diff| = 1.56e-2` in bf16 at `H=2048`. That gap is
-inherent: matching stock vLLM bitwise would mean adopting a reduction that is not
-itself batch-invariant, i.e. trading L1 for L2.
+The PyTorch reference uses the repo's fixed 32-wide chunked sum
+(`shape_invariant_rstd`, introduced in `8ed1693` as a device-agnostic reference).
+On B200, over 20 seeds at `H=2048` in bf16, a plain `mean(-1)` broke slice
+invariance on 1 of 20 seeds (slices `x[3:5]` and `x[:1]` of 64 rows; which one
+failed was not recorded), and the chunked reduction broke on none. That is a recorded
+observation, not an assertion.
+
+The CUDA kernel has its own fixed-order reduction (per-thread strided partial sums,
+then `block_reduce_sum`). It is not bitwise equal to the PyTorch reference.
+
+Neither is bitwise equal to vLLM. In one-off probes (not committed checks), the
+reference differed from every vLLM path tried (eager, inductor-compiled, and eager
+under `VLLM_BATCH_INVARIANT=1`) on 36–39 of 40 seeds, `max|diff| <= 1.56e-2`
+(bf16, `H=2048`, 512 rows). The difference is attributed to reduction order, but that
+has not been isolated. Only the no-residual call was compared; vLLM's
+`fused_add_rms_norm` path (every decoder norm except layer 0's input norm) has no
+reference here.
 
 The in-kernel offset is exact, not an approximation: `weight_offset=1.0` is bitwise
 equal to passing an explicit fp32 `1 + w` weight, and differs from a bf16-folded
 `1 + w`, both asserted.
 
-Tolerances come from `tolerance_contract.json` (`reduction` × dtype); no private
+Accuracy tests resolve their tolerances from `tolerance_contract.json`
+(`forward_accuracy`, `reduction` × dtype). The bounds in
+`tests/check_qwen3_next_norm_providers.py` are provider-gap bounds, not contract
 thresholds.
 
 ## Performance Notes
@@ -83,11 +98,6 @@ The CUDA path reuses the existing `rmsnorm_fwd_kernel` reduction
 (`block_reduce_sum` over `choose_threads(H)`), so the offset costs one fp32 add per
 element and no extra memory traffic.
 
-```bash
-python scripts/check_operator.py --op qwen3_next_rms_norm --candidate cuda \
-    --device cuda --dtype bf16 --check-grad
-```
-
 ## Tests
 
 ```bash
@@ -97,7 +107,11 @@ python -m pytest tests/test_qwen3_next_norm.py -v
 ## Known Limitations
 
 - CUDA only; no ROCm, Ascend or Triton backend.
-- Not bitwise against stock vLLM (see Accuracy); an L2 claim needs the strict
-  provider on both sides, per RFC #428 §1 item 1.
+- Not registered as a gtest operator or in the registry on this branch (see
+  "Dispatch Behavior").
+- Not bitwise against vLLM (see Accuracy). An L2 claim needs a single source of
+  truth for the forward on both sides, per RFC #428 §0 item 1.
+- The gated pair (`Qwen3NextRMSNormGatedOp`, `Qwen3NextRMSNormGatedHFOp`) is
+  documented with its CUDA kernel in the gated-norm PR, not on this page.
 - Measured on sm_100 (B200). Per RFC #428 §2.2 no claim carries across
   H100/H200/B100/B200.
diff --git a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
index 7d5a7a754..af9a31358 100644
--- a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
+++ b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
@@ -145,6 +145,8 @@ def forward(self, x, weight, *, eps=1e-6):
 
     def parameter_vjp_contributions_fp32(self, *, x, weight, grad_output, eps=1e-6):
         hidden = x.shape[-1]
+        # Only `rstd` is used, and it does not depend on the offset; the offset is
+        # passed so this is the same call the forward makes, not because it matters.
         _, rstd = _C.rmsnorm_forward(
             x.contiguous().reshape(-1, hidden),
             weight.contiguous(),
diff --git a/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py b/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
index 5261245d0..2b3c410a9 100644
--- a/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
+++ b/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
@@ -3,66 +3,26 @@
 
 """Qwen3-Next RMSNorm references (WS1 ground truth for RFC #428 C1).
 
-Qwen3-Next ships two RMSNorm conventions that differ both in how the weight is
-applied and in where the dtype casts sit. They are kept as separate operators
-because the cast order is part of the contract, not a flag:
+Qwen3-Next ships two RMSNorm conventions. They differ in how the weight is applied
+and in where the dtype casts sit, so they are separate operators rather than a flag:
 
 ``Qwen3NextRMSNorm`` (decoder / final norm)
-    normalize in fp32, scale by ``(1 + weight)`` in fp32, cast once at the end.
-    The stored weight is zero-centred, so the ``1 +`` offset MUST be applied
-    after the fp32 upcast -- folding it into a bf16 weight first rounds the
-    offset and silently breaks the bitwise claim.
+    Normalize in fp32, scale by ``(1 + weight)`` in fp32, cast once at the end. The
+    stored weight is zero-centred, so the ``1 +`` must be applied after the fp32
+    upcast; folding it into a bf16 weight first rounds the offset.
 
 ``Qwen3NextRMSNormGated`` (inside the Gated DeltaNet block)
-    normalize in fp32, scale by a plain (non zero-centred) weight, then gate by
-    ``silu(gate)``. Where the weight multiply happens is NOT agreed upstream --
-    see below -- so it is an explicit part of the operator identity here.
-
-Divergence at the gated-norm boundary (measured)
--------------------------------------------------
-``transformers`` and vLLM do not agree on the gated variant:
-
-* vLLM (``RMSNormGated``, both ``forward_native`` and the FLA Triton
-  ``forward_cuda``) keeps the normalized value in fp32 for the weight multiply.
-* ``transformers`` (``Qwen3NextRMSNormGated``) casts the normalized value back
-  to the input dtype *before* the weight multiply.
-
-On B200 / bf16 / ``head_v_dim=128`` these differ in 35% of elements with
-``max|diff| = 6.25e-2``; swapping only the cast order reproduces the gap
-(``5.3e-2``), so the cast order -- not the reduction order -- dominates. Because
-RFC #428 claims L2 exactness against **vLLM rollout**, the fp32 multiply is the
-strict default; the transformers convention is kept as a named witness so the
-divergence stays testable instead of being silently picked.
-
-What "agrees with vLLM" means here, precisely
-----------------------------------------------
-Only the *convention* is reproduced, not the bits. vLLM's own two paths are not
-bitwise equal to each other: over 40 seeds (bf16, ``head_v_dim=128``, 512 rows),
-``forward_native`` and ``forward_cuda`` disagreed on 21, worst
-``max|diff| = 1.56e-2``. Against this operator the figures were 6/40 and 18/40.
-So "bitwise equal to vLLM" is undefined until a single provider is named, and
-this operator does not claim it -- see the reduction-order note below.
-
-Both reuse :func:`shape_invariant_rstd` so the reduction order -- and hence the
-result -- never depends on the batch layout (RFC #428 section 6, item 2). The
-reduction order therefore deliberately differs from upstream's ``mean(-1)``;
-what is reproduced exactly is the weight convention and the cast order.
-
-Why the chunked reduction is kept on CUDA too
----------------------------------------------
-:func:`shape_invariant_rstd` was introduced for NPU, where ``mean``/``sum`` pick
-shape-dependent kernels. Measured on B200 (sm_100) it is needed on CUDA as well:
-over 20 seeds at ``H=2048`` in bf16 -- Qwen3-Next's own ``hidden_size`` and dtype
--- a plain ``mean(-1)`` broke slice invariance (``rstd(x[3:5]) != rstd(x)[3:5]``)
-on 1 of 20, while the chunked reduction broke on 0 of 20. Failures were also seen
-at ``H=5120`` in fp32.
-
-The cost is that the decoder norm is NOT bitwise equal to stock vLLM: 7 elements
-of 1048576 differ (``max|diff| = 1.56e-2``, bf16, H=2048). That gap is inherent --
-matching stock vLLM bitwise would mean adopting a reduction that is itself not
-batch-invariant, i.e. trading L1 for L2. These operators therefore claim L0 and L1
-only; an L2 claim needs the strict provider on both sides, per RFC #428 section 1,
-item 1.
+    Normalize in fp32, scale by a plain weight, then gate by ``silu(gate)``.
+    vLLM's ``RMSNormGated`` multiplies the weight in fp32; transformers casts the
+    normalized value back to the input dtype first. ``Qwen3NextRMSNormGatedOp``
+    follows vLLM; ``Qwen3NextRMSNormGatedHFOp`` keeps the transformers convention
+    as a witness, and ``test_gated_conventions_diverge_in_low_precision`` pins that
+    the two differ in bf16 and agree bitwise in fp32.
+
+All three reuse :func:`shape_invariant_rstd`, a fixed-order reduction. They
+reproduce the weight convention and cast order, not vLLM's reduction tree, and are
+not bitwise equal to any vLLM path probed so far. Claim levels, measurements and
+limitations are in ``docs/operators/qwen3-next-rms-norm.md``.
 """
 
 from __future__ import annotations
@@ -100,8 +60,8 @@ class Qwen3NextRMSNormGatedOp:
 
     ``out = (x * rstd * weight) * silu(gate)``, with every multiply in fp32 and
     a single cast on the way out. This is the convention vLLM's ``RMSNormGated``
-    uses with ``norm_before_gate=True``, which is what the RFC #428 L2 claim is
-    measured against.
+    uses with ``norm_before_gate=True``. Which gated convention is the strict
+    default is still open; see the operator page.
 
     Not a subclass of the plain op: it takes an extra tensor and its epilogue
     differs, so it is not a drop-in substitute for one.
diff --git a/tests/check_qwen3_next_norm_providers.py b/tests/check_qwen3_next_norm_providers.py
index 062052cfa..7086aa341 100644
--- a/tests/check_qwen3_next_norm_providers.py
+++ b/tests/check_qwen3_next_norm_providers.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-"""Which vLLM gated-RMSNorm path is the strict provider, and how far apart are they.
+"""How far apart vLLM's eager gated-RMSNorm paths are, and ours from each.
 
 Named ``check_`` rather than ``test_`` on purpose, following
 ``tests/distributed/check_*.py``: this module imports real vLLM, and
@@ -16,13 +16,17 @@
 provider is named, because vLLM ships several and they do not agree bitwise with
 each other.
 
-This module pins two things so a vLLM upgrade cannot move them silently:
+This module records two things so a vLLM upgrade cannot move them silently:
 
-1. **Dispatch facts** -- which provider actually runs, asserted on the env
-   defaults and the registered ops rather than inferred from reading one branch.
+1. **Provider facts** -- the GDN decode env defaults, and that ``RMSNormGated``
+   has distinct ``forward_native`` and ``forward_cuda`` methods. It does NOT
+   determine which path vLLM dispatches at runtime: the checks below call each
+   method directly (eager), and vLLM's default compiled mode traces
+   ``forward_native`` into an inductor graph instead.
 2. **The size of the gap** -- a seed sweep that asserts an upper bound on the
-   disagreement and on the mismatch rate. It deliberately does NOT assert
-   equality; the point is to keep the number honest, not to pretend it is zero.
+   disagreement and on the mismatch rate between the eager paths. The bounds are
+   provider-gap bounds, not ``tolerance_contract.json`` thresholds, and they
+   deliberately do NOT assert equality.
 
 Measured on 2x B200 (sm_100, torch 2.13.0+cu130, vllm 0.30.0), bf16,
 ``head_v_dim=128``, 512 rows, 40 seeds:
@@ -101,21 +105,24 @@ def _disagreement(a: torch.Tensor, b: torch.Tensor) -> tuple[float, float]:
 
 
 # --------------------------------------------------------------------------- #
-# 1. Dispatch facts -- which provider actually runs
+# 1. Provider facts -- recorded, not a runtime dispatch check
 # --------------------------------------------------------------------------- #
 def test_custom_op_has_distinct_native_and_cuda_paths(vllm_config_ctx):
-    """`forward_native` is a reference; `forward_cuda` is what CustomOp dispatches."""
+    """The two methods are distinct. Which one runs depends on the vLLM mode: eager
+    (``custom_ops="all"``) dispatches ``forward_cuda``; the default compiled mode
+    traces ``forward_native``. This test does not check that choice."""
     norm = _make_norm()
     assert type(norm).forward_cuda is not type(norm).forward_native
 
 
 def test_gdn_decode_provider_env_defaults_are_recorded():
-    """Pin the env defaults that decide which GDN decode kernel runs.
+    """Record the GDN decode env defaults.
 
-    These are what make ``fused_recurrent_gated_delta_rule_packed_decode`` (not
-    ``fused_sigmoid_gating_delta_rule_update``) the rollout decode path. If a
-    vLLM bump flips either default, the provider identity behind any L2 claim
-    changes, so this must fail loudly rather than drift.
+    This records the defaults only; it does not assert which kernel runs. For
+    Qwen3-Next the ``VLLM_GDN_DECODE_KERNEL="cuda"`` default does not take effect:
+    vLLM builds its GDN layers with ``gqa_interleaved_layout=True`` and falls back
+    to the Triton decode kernel. A change of either default still fails here, as a
+    prompt to re-derive the decode path.
     """
     pytest.importorskip("vllm", reason="vLLM is required to identify the provider")
     import vllm.envs as envs
@@ -141,12 +148,11 @@ def test_gdn_decode_provider_env_defaults_are_recorded():
 @pytest.mark.parametrize(
     "dtype, max_abs, max_mismatch_rate",
     [
-        # bf16 rounding absorbs most of the reduction-tree difference, so few
-        # elements move -- but each that does moves by a whole bf16 ULP.
+        # Provider-gap bounds, not contract thresholds. bf16 rounding absorbs most
+        # of the reduction-tree difference, so few elements move.
         (torch.bfloat16, 2e-2, 0.05),
-        # fp32 has nothing to absorb it: ~36% of elements differ, every one of
-        # them by an fp32 ULP. Bounding the rate here would be measuring the
-        # wrong thing; the magnitude is what says the two agree semantically.
+        # In fp32 about 36% of elements differ, so the rate is left unbounded and
+        # only the magnitude is bounded (no per-element ULP bound is asserted).
         (torch.float32, 1e-5, 1.0),
     ],
 )
@@ -196,6 +202,7 @@ def test_ours_tracks_each_vllm_path_within_bounds(vllm_config_ctx, path):
         abs_d, rate = _disagreement(ours.forward(x, weight, gate), reference)
         worst_abs, worst_rate = max(worst_abs, abs_d), max(worst_rate, rate)
 
+    # Provider-gap bounds, not contract thresholds.
     assert worst_abs <= 2e-2, f"worst |diff| vs {path} was {worst_abs:.3e}"
     assert worst_rate <= 0.05, f"mismatch rate vs {path} was {worst_rate:.3%}"
 
diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
index 9fdf02e73..a62d8410f 100644
--- a/tests/test_qwen3_next_norm.py
+++ b/tests/test_qwen3_next_norm.py
@@ -5,7 +5,8 @@
 
 Claim levels exercised here (RFC #428 section 2.1):
   * L0 repeatable    -- identical inputs reproduce bitwise identical outputs.
-  * L1 batch-invariant -- a row is unaffected by unrelated rows, padding or order.
+  * L1 batch-invariant -- a row is unaffected by slicing, concurrency and (for the
+    PyTorch reference) padding; packing and order are not exercised.
 
 L2 (train-rollout exact against vLLM) is NOT claimed by this file; it needs the
 rollout engine on the other side.
@@ -22,6 +23,7 @@
 import torch
 import torch.nn.functional as F
 
+from rl_engine.kernels.gtest.tolerance import load_contract, resolve_tolerance
 from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import (
     Qwen3NextRMSNormGatedHFOp,
     Qwen3NextRMSNormGatedOp,
@@ -33,6 +35,16 @@
 _HEAD_V_DIM = 128  # linear_value_head_dim -- the gated norm width
 _EPS = 1e-6  # rms_norm_eps
 
+_CONTRACT = load_contract()
+
+
+def _forward_tol(dtype: torch.dtype) -> dict[str, float]:
+    """C1 forward_accuracy row for the ``reduction`` op class -- no private thresholds."""
+    spec = resolve_tolerance(
+        _CONTRACT, judgment="forward_accuracy", op_class="reduction", dtype=dtype
+    )
+    return {"atol": spec.atol, "rtol": spec.rtol}
+
 
 def _rand(shape, seed):
     g = torch.Generator().manual_seed(seed)
@@ -277,7 +289,7 @@ def test_matches_upstream_formula_low_precision(dtype):
     w = _rand((_HIDDEN,), seed=22).to(dtype)
     got, ref = op.forward(x, w), _hf_rms_norm(x, w)
     assert got.dtype == ref.dtype == dtype
-    torch.testing.assert_close(got.float(), ref.float(), atol=2e-2, rtol=1.6e-2)
+    torch.testing.assert_close(got.float(), ref.float(), **_forward_tol(dtype))
 
 
 @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
@@ -289,7 +301,7 @@ def test_gated_hf_witness_low_precision(dtype):
     gate = _rand((4, 16, _HEAD_V_DIM), seed=25).to(dtype)
     got, ref = op.forward(x, w, gate), _hf_rms_norm_gated(x, w, gate)
     assert got.dtype == ref.dtype == dtype
-    torch.testing.assert_close(got.float(), ref.float(), atol=2e-2, rtol=1.6e-2)
+    torch.testing.assert_close(got.float(), ref.float(), **_forward_tol(dtype))
 
 
 @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
@@ -300,7 +312,7 @@ def test_gated_strict_low_precision(dtype):
     gate = _rand((4, 16, _HEAD_V_DIM), seed=25).to(dtype)
     got, ref = op.forward(x, w, gate), _vllm_rms_norm_gated(x, w, gate)
     assert got.dtype == ref.dtype == dtype
-    torch.testing.assert_close(got.float(), ref.float(), atol=2e-2, rtol=1.6e-2)
+    torch.testing.assert_close(got.float(), ref.float(), **_forward_tol(dtype))
 
 
 # --------------------------------------------------------------------------- #
@@ -421,7 +433,7 @@ def test_cuda_zero_centred_within_tolerance_of_golden():
     w = torch.randn(_HIDDEN, device="cuda", dtype=torch.bfloat16)
     got = Qwen3NextRMSNormCudaOp().forward(x, w, eps=_EPS)
     ref = Qwen3NextRMSNormOp().forward_fp32(x, w, eps=_EPS)
-    torch.testing.assert_close(got.float(), ref, atol=2e-2, rtol=1.6e-2)
+    torch.testing.assert_close(got.float(), ref, **_forward_tol(torch.bfloat16))
 
 
 @requires_cuda_rmsnorm

From 7dd291a9d25757079406989004745ebd3b7a7284 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 18:22:52 +0000
Subject: [PATCH 09/70] fix(norm): enforce gated tensor boundaries and align
 parameter VJP statistics

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 csrc/cuda/rmsnorm.cu                          | 13 +++
 csrc/ops.cpp                                  | 27 ++++++
 docs/operators/qwen3-next-rms-norm-gated.md   | 10 ++-
 rl_engine/_C.pyi                              | 17 ++++
 rl_engine/kernels/ops/cuda/norm/rmsnorm.py    | 37 +++++---
 .../ops/pytorch/norm/qwen3_next_rms_norm.py   |  2 +
 tests/test_qwen3_next_norm.py                 | 88 +++++++++++++++----
 7 files changed, 162 insertions(+), 32 deletions(-)

diff --git a/csrc/cuda/rmsnorm.cu b/csrc/cuda/rmsnorm.cu
index c80945300..2d5c98ec4 100644
--- a/csrc/cuda/rmsnorm.cu
+++ b/csrc/cuda/rmsnorm.cu
@@ -389,6 +389,7 @@ void rmsnorm_forward_cuda(
     // device, which need not be x's.
     const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
     int T = x.size(0);
+    if (T == 0) return;
     int H = x.size(1);
     int threads = choose_threads(H);
     size_t smem = threads * sizeof(float);
@@ -411,6 +412,7 @@ void rmsnorm_forward_cuda(
             );
         });
     });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 
@@ -424,6 +426,7 @@ void rmsnorm_backward_dx_cuda(
 ) {
     const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
     int T = x.size(0);
+    if (T == 0) return;
     int H = x.size(1);
     int threads = choose_threads(H);
     size_t smem = threads * sizeof(float);
@@ -446,6 +449,7 @@ void rmsnorm_backward_dx_cuda(
             );
         });
     });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 
@@ -459,7 +463,9 @@ void rmsnorm_gated_forward_cuda(
     double weight_offset,
     int64_t activation
 ) {
+    const c10::cuda::CUDAGuard device_guard(x.device());
     int T = x.size(0);
+    if (T == 0) return;
     int H = x.size(1);
     int threads = choose_threads(H);
     size_t smem = threads * sizeof(float);
@@ -483,6 +489,7 @@ void rmsnorm_gated_forward_cuda(
             }
         });
     });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 
@@ -496,7 +503,9 @@ void rmsnorm_gated_backward_dx_cuda(
     double weight_offset,
     int64_t activation
 ) {
+    const c10::cuda::CUDAGuard device_guard(x.device());
     int T = x.size(0);
+    if (T == 0) return;
     int H = x.size(1);
     int threads = choose_threads(H);
     size_t smem = threads * sizeof(float);
@@ -520,6 +529,7 @@ void rmsnorm_gated_backward_dx_cuda(
             }
         });
     });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 void rmsnorm_backward_partial_dw_cuda(
@@ -531,6 +541,7 @@ void rmsnorm_backward_partial_dw_cuda(
 ) {
     const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
     int T = x.size(0);
+    if (T == 0) return;
     int H = x.size(1);
 
     int chunks = (T + RMSNORM_DW_ROWS_PER_CHUNK - 1) / RMSNORM_DW_ROWS_PER_CHUNK;
@@ -550,6 +561,7 @@ void rmsnorm_backward_partial_dw_cuda(
             H
         );
     });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 
@@ -572,6 +584,7 @@ void rmsnorm_backward_reduce_dw_cuda(
         chunks,
         H
     );
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 #if !defined(USE_ROCM)
diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index b37ab102c..1efb116fc 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -315,6 +315,24 @@ static void rmsnorm_check_input(const torch::Tensor& x, const char* name) {
   TORCH_CHECK(x.is_contiguous(), name, " must be contiguous");
 }
 
+static void rmsnorm_check_weight(const torch::Tensor& x, const torch::Tensor& weight) {
+  TORCH_CHECK(x.dim() == 2 && x.size(1) > 0, "x must be 2D with positive hidden size");
+  TORCH_CHECK(x.scalar_type() == torch::kFloat32 || x.scalar_type() == torch::kFloat16 ||
+              x.scalar_type() == torch::kBFloat16, "x must be float32, float16 or bfloat16");
+  TORCH_CHECK(weight.dim() == 1 && weight.size(0) == x.size(1), "weight must be [H]");
+  TORCH_CHECK(weight.device() == x.device(), "weight must be on the same device as x");
+  TORCH_CHECK(weight.scalar_type() == x.scalar_type() || weight.scalar_type() == torch::kFloat32,
+              "weight must have x dtype or float32");
+}
+
+static void rmsnorm_check_backward(const torch::Tensor& dy, const torch::Tensor& x,
+                                   const torch::Tensor& rstd) {
+  TORCH_CHECK(dy.device() == x.device() && rstd.device() == x.device(),
+              "dy and rstd must be on the same device as x");
+  TORCH_CHECK(dy.scalar_type() == x.scalar_type(), "dy must have the same dtype as x");
+  TORCH_CHECK(rstd.scalar_type() == torch::kFloat32, "rstd must be float32");
+}
+
 std::vector<torch::Tensor> rmsnorm_forward(
   torch::Tensor x,
   torch::Tensor weight,
@@ -323,6 +341,7 @@ std::vector<torch::Tensor> rmsnorm_forward(
 {
   rmsnorm_check_input(x, "x");
   rmsnorm_check_input(weight, "weight");
+  rmsnorm_check_weight(x, weight);
 
   TORCH_CHECK(x.dim() == 2, "x must be 2D [T, H]");
   TORCH_CHECK(weight.dim() == 1, "weight must be 1D [H]");
@@ -347,7 +366,9 @@ torch::Tensor rmsnorm_backward_dx(
   rmsnorm_check_input(dy, "dy");
   rmsnorm_check_input(x, "x");
   rmsnorm_check_input(weight, "weight");
+  rmsnorm_check_weight(x, weight);
   rmsnorm_check_input(rstd, "rstd");
+  rmsnorm_check_backward(dy, x, rstd);
 
   TORCH_CHECK(dy.sizes() == x.sizes(), "dy and x must have same shape");
   TORCH_CHECK(x.dim() == 2, "x must be 2D [T, H]");
@@ -370,7 +391,9 @@ static void rmsnorm_gated_check(
 {
   rmsnorm_check_input(x, "x");
   rmsnorm_check_input(weight, "weight");
+  rmsnorm_check_weight(x, weight);
   rmsnorm_check_input(gate, "gate");
+  TORCH_CHECK(gate.device() == x.device(), "gate must be on the same device as x");
 
   TORCH_CHECK(x.dim() == 2, "x must be 2D [T, H]");
   TORCH_CHECK(weight.dim() == 1, "weight must be 1D [H]");
@@ -417,6 +440,7 @@ torch::Tensor rmsnorm_gated_backward_dx(
   rmsnorm_gated_check(x, weight, gate, activation);
   rmsnorm_check_input(dy, "dy");
   rmsnorm_check_input(rstd, "rstd");
+  rmsnorm_check_backward(dy, x, rstd);
 
   TORCH_CHECK(dy.sizes() == x.sizes(), "dy must have the same shape as x");
   TORCH_CHECK(rstd.dim() == 1 && rstd.size(0) == x.size(0), "rstd must be [T]");
@@ -438,6 +462,7 @@ torch::Tensor rmsnorm_backward_dw(
   rmsnorm_check_input(dy, "dy");
   rmsnorm_check_input(x, "x");
   rmsnorm_check_input(rstd, "rstd");
+  rmsnorm_check_backward(dy, x, rstd);
   rmsnorm_check_input(mask, "mask");
 
   TORCH_CHECK(dy.sizes() == x.sizes(), "dy and x must have same shape");
@@ -445,6 +470,8 @@ torch::Tensor rmsnorm_backward_dw(
   TORCH_CHECK(rstd.dim() == 1, "rstd must be 1D [T]");
   TORCH_CHECK(mask.dim() == 1, "mask must be 1D [T]");
   TORCH_CHECK(mask.scalar_type() == torch::kBool, "mask must be bool");
+  TORCH_CHECK(mask.device() == x.device(), "mask must be on the same device as x");
+  TORCH_CHECK(x.size(1) > 0, "hidden size must be positive");
   TORCH_CHECK(rstd.size(0) == x.size(0), "rstd.size(0) must equal x.size(0)");
   TORCH_CHECK(mask.size(0) == x.size(0), "mask.size(0) must equal x.size(0)");
 
diff --git a/docs/operators/qwen3-next-rms-norm-gated.md b/docs/operators/qwen3-next-rms-norm-gated.md
index 1bc531ea6..55071d25b 100644
--- a/docs/operators/qwen3-next-rms-norm-gated.md
+++ b/docs/operators/qwen3-next-rms-norm-gated.md
@@ -47,12 +47,16 @@ required tensor, so it cannot stand in for one.
 
 | Argument | Shape | Dtype | Requirements |
 | --- | --- | --- | --- |
-| `x` | `[..., H]` | fp32 / bf16 / fp16 | contiguous on the CUDA path |
-| `weight` | `[H]` | matches `x` | plain, NOT zero-centred |
-| `gate` | same as `x` | same as `x` | shape and dtype are enforced |
+| `x` | `[..., H]` | fp32 / bf16 / fp16 | H > 0; wrapper makes contiguous copies |
+| `weight` | `[H]` | matches `x` or fp32 | plain, NOT zero-centred |
+| `gate` | same as `x` | same as `x` | shape, dtype and device must match before flattening |
 | `eps` | scalar | float | `1e-6` for Qwen3-Next |
 | `activation` | — | `"silu"`/`"swish"`/`"sigmoid"` | anything else is rejected |
 
+All tensors must share a device. Low-level CUDA bindings require contiguous
+2-D inputs, and validate backward gradient dtype and FP32 statistics. Empty
+batches are supported by the bindings.
+
 Only `norm_before_gate=True` and `group_size=None` are implemented — the single
 configuration vLLM's GDN block constructs. Other configurations fail closed rather
 than being approximated (RFC #428 §6 item 7).
diff --git a/rl_engine/_C.pyi b/rl_engine/_C.pyi
index b911feaf7..1e686fb9c 100644
--- a/rl_engine/_C.pyi
+++ b/rl_engine/_C.pyi
@@ -269,6 +269,23 @@ def rmsnorm_backward_dx(
     rstd: torch.Tensor,
     weight_offset: float = ...,
 ) -> torch.Tensor: ...
+def rmsnorm_gated_forward(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    gate: torch.Tensor,
+    eps: float,
+    weight_offset: float = ...,
+    activation: int = ...,
+) -> list[torch.Tensor]: ...
+def rmsnorm_gated_backward_dx(
+    dy: torch.Tensor,
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    gate: torch.Tensor,
+    rstd: torch.Tensor,
+    weight_offset: float = ...,
+    activation: int = ...,
+) -> torch.Tensor: ...
 def rmsnorm_backward_dw(
     dy: torch.Tensor,
     x: torch.Tensor,
diff --git a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
index fc1aa9554..3c66e144c 100644
--- a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
+++ b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
@@ -163,6 +163,7 @@ class Qwen3NextRMSNormCudaOp(RMSNormCudaOp):
 # Gated RMSNorm (Qwen3-Next GDN block)
 # --------------------------------------------------------------------------- #
 
+
 def _require_cuda_symbols(what: str, *names: str) -> None:
     """Raise when the compiled kernels backing ``what`` are missing.
 
@@ -225,9 +226,9 @@ def forward(ctx, x, weight, gate, eps=1e-6, weight_offset=0.0, activation=0):
         assert x.dim() == 2, "x must be [T, H]"
         assert weight.dim() == 1, "weight must be [H]"
         assert gate.shape == x.shape, "gate must match x"
-        assert _EXT_AVAILABLE and hasattr(_C, "rmsnorm_gated_forward"), (
-            "Gated RMSNorm CUDA extension is unavailable. Rebuild with csrc/cuda/rmsnorm.cu."
-        )
+        assert _EXT_AVAILABLE and hasattr(
+            _C, "rmsnorm_gated_forward"
+        ), "Gated RMSNorm CUDA extension is unavailable. Rebuild with csrc/cuda/rmsnorm.cu."
 
         y, rstd = _C.rmsnorm_gated_forward(
             x, weight, gate, float(eps), float(weight_offset), int(activation)
@@ -246,9 +247,7 @@ def backward(ctx, grad_out):
         dy = grad_out.contiguous()
         act = ctx.activation
 
-        dx = _C.rmsnorm_gated_backward_dx(
-            dy, x, weight, gate, rstd, ctx.weight_offset, act
-        )
+        dx = _C.rmsnorm_gated_backward_dx(dy, x, weight, gate, rstd, ctx.weight_offset, act)
 
         # dweight: the gate is a per-element constant here, so the ungated row
         # contributions apply once dy carries act(gate).
@@ -259,9 +258,7 @@ def backward(ctx, grad_out):
         # dgate: row-local and reduction-free.
         normed = x.float() * rstd.unsqueeze(-1)
         scale = weight.float() + ctx.weight_offset
-        dgate = (dy.float() * normed * scale * _gate_activation_grad_fp32(gate, act)).to(
-            gate.dtype
-        )
+        dgate = (dy.float() * normed * scale * _gate_activation_grad_fp32(gate, act)).to(gate.dtype)
 
         record_backward(
             "rms_norm_gated",
@@ -321,6 +318,8 @@ def __call__(self, x, weight, gate, *, eps=1e-6):
         return self.forward(x, weight, gate, eps=eps)
 
     def forward(self, x, weight, gate, *, eps=1e-6):
+        if gate.shape != x.shape:
+            raise ValueError(f"gate must match x, got {tuple(gate.shape)} vs {tuple(x.shape)}")
         hidden = x.shape[-1]
         x_2d = x.contiguous().view(-1, hidden)
         gate_2d = gate.contiguous().view(-1, hidden)
@@ -335,9 +334,21 @@ def forward(self, x, weight, gate, *, eps=1e-6):
         return y_2d.view_as(x)
 
     def parameter_vjp_contributions_fp32(self, *, x, weight, gate, grad_output, eps=1e-6):
-        del weight
-        x32 = x.float()
-        rstd = torch.rsqrt(x32.square().mean(dim=-1) + float(eps))
+        if gate.shape != x.shape:
+            raise ValueError(f"gate must match x, got {tuple(gate.shape)} vs {tuple(x.shape)}")
+        hidden = x.shape[-1]
         act = _GATE_ACTIVATIONS[self.activation]
-        rows = grad_output.float() * _gate_activation_fp32(gate, act) * x32 * rstd.unsqueeze(-1)
+        _, rstd = _C.rmsnorm_gated_forward(
+            x.contiguous().reshape(-1, hidden),
+            weight.contiguous(),
+            gate.contiguous().reshape(-1, hidden),
+            float(eps),
+            float(self.weight_offset),
+            act,
+        )
+        rows = rmsnorm_dweight_rows_fp32(
+            x,
+            grad_output.float() * _gate_activation_fp32(gate, act),
+            rstd=rstd.reshape(x.shape[:-1]),
+        )
         return {"weight": rows}
diff --git a/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py b/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
index 2b3c410a9..1f84f86f3 100644
--- a/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
+++ b/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
@@ -113,6 +113,8 @@ def _normalized(
                 f"gate must match x, got tuple(gate.shape)={tuple(gate.shape)} "
                 f"vs tuple(x.shape)={tuple(x.shape)}"
             )
+        if gate.dtype != x.dtype or gate.device != x.device:
+            raise ValueError("gate must have the same dtype and device as x")
         x_f = x.float()
         rstd = shape_invariant_rstd(x_f, float(eps)).unsqueeze(-1)
         return x_f * rstd
diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
index c1f7e9d73..2f738a1c8 100644
--- a/tests/test_qwen3_next_norm.py
+++ b/tests/test_qwen3_next_norm.py
@@ -535,10 +535,7 @@ def test_cuda_gated_zero_gate_zeroes_output():
 
 @requires_cuda_gated
 def test_cuda_gated_unit_weight_is_plain_norm_times_silu():
-    from rl_engine.kernels.ops.cuda.norm.rmsnorm import (
-        Qwen3NextRMSNormGatedCudaOp,
-        rmsnorm_cuda,
-    )
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp, rmsnorm_cuda
 
     x, _, gate = _gated_cuda_inputs()
     ones = torch.ones(_HEAD_V_DIM, device="cuda", dtype=x.dtype)
@@ -549,7 +546,7 @@ def test_cuda_gated_unit_weight_is_plain_norm_times_silu():
 
 @requires_cuda_gated
 def test_cuda_gated_sigmoid_activation():
-    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda, rmsnorm_cuda
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_cuda, rmsnorm_gated_cuda
 
     x, w, gate = _gated_cuda_inputs()
     got = rmsnorm_gated_cuda(x, w, gate, eps=_EPS, activation="sigmoid").float()
@@ -617,20 +614,35 @@ def test_cuda_gated_weight_offset_is_applied_in_fp32():
 
 
 @requires_cuda_gated
-def test_cuda_gated_parameter_vjp_contributions_match_the_fold():
-    """The harness hook must return the same rows the backward folds."""
-    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
+@pytest.mark.parametrize("activation", ["silu", "sigmoid"])
+def test_cuda_gated_parameter_vjp_contributions_match_the_fold(dtype, activation):
+    """Compare the harness hook with actual autograd, including its CUDA rstd."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import (
+        Qwen3NextRMSNormGatedCudaOp,
+        _fold_dweight_rows,
+    )
 
-    x, w, gate = _gated_cuda_inputs(rows=64)
-    x, w, gate = x.float(), w.float(), gate.float()
+    x, w, gate = (t.to(dtype) for t in _gated_cuda_inputs(rows=512))
+    w = w.float().requires_grad_()
     dy = torch.randn_like(x)
+    op = Qwen3NextRMSNormGatedCudaOp()
+    op.activation = activation
+    op.forward(x, w, gate, eps=_EPS).backward(dy)
+    rows = op.parameter_vjp_contributions_fp32(x=x, weight=w, gate=gate, grad_output=dy, eps=_EPS)[
+        "weight"
+    ]
+    assert torch.equal(_fold_dweight_rows(rows, torch.float32), w.grad)
 
-    rows = Qwen3NextRMSNormGatedCudaOp().parameter_vjp_contributions_fp32(
-        x=x, weight=w, gate=gate, grad_output=dy, eps=_EPS
-    )["weight"]
-    rstd = torch.rsqrt(x.square().mean(dim=-1) + _EPS)
-    expected = dy * F.silu(gate) * x * rstd.unsqueeze(-1)
-    torch.testing.assert_close(rows, expected, atol=1e-6, rtol=1e-6)
+
+@requires_cuda_gated
+def test_cuda_gated_wrapper_rejects_equal_numel_wrong_shape():
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp
+
+    x, w, gate = _gated_cuda_inputs(rows=6)
+    x = x.reshape(2, 3, -1)
+    with pytest.raises(ValueError, match="gate must match x"):
+        Qwen3NextRMSNormGatedCudaOp().forward(x, w, gate)
 
 
 @requires_cuda_gated
@@ -764,3 +776,47 @@ def test_zero_centred_cuda_constructor_rejects_missing_extension(monkeypatch):
     monkeypatch.setattr(rmsnorm, "_C", None)
     with pytest.raises(RuntimeError, match="requires the compiled"):
         rmsnorm.Qwen3NextRMSNormCudaOp()
+
+
+@requires_cuda_gated
+@pytest.mark.parametrize(
+    "fault", ["weight_shape", "weight_dtype", "gate_dtype", "dy_dtype", "rstd_dtype"]
+)
+def test_gated_extension_rejects_invalid_tensor_contract(fault):
+    from rl_engine import _C
+
+    x, w, gate = _gated_cuda_inputs(rows=8)
+    dy = torch.ones_like(x)
+    rstd = torch.ones(8, device=x.device, dtype=torch.float32)
+    if fault == "weight_shape":
+        w = w[:-1]
+    elif fault == "weight_dtype":
+        w = w.half()
+    elif fault == "gate_dtype":
+        gate = gate.float()
+    elif fault == "dy_dtype":
+        dy = dy.float()
+    else:
+        rstd = rstd.bfloat16()
+    with pytest.raises(RuntimeError):
+        _C.rmsnorm_gated_backward_dx(dy, x, w, gate, rstd)
+
+
+@requires_cuda_gated
+def test_gated_extension_handles_empty_batch_and_nondefault_stream():
+    from rl_engine import _C
+
+    stream = torch.cuda.Stream()
+    with torch.cuda.stream(stream):
+        x, w, gate = _gated_cuda_inputs(rows=8)
+        actual, rstd = _C.rmsnorm_gated_forward(x, w, gate, _EPS)
+        dx = _C.rmsnorm_gated_backward_dx(torch.ones_like(x), x, w, gate, rstd)
+        empty, empty_rstd = _C.rmsnorm_gated_forward(x[:0], w, gate[:0], _EPS)
+        empty_dx = _C.rmsnorm_gated_backward_dx(x[:0], x[:0], w, gate[:0], empty_rstd)
+    stream.synchronize()
+    expected, expected_rstd = _C.rmsnorm_gated_forward(x, w, gate, _EPS)
+    expected_dx = _C.rmsnorm_gated_backward_dx(torch.ones_like(x), x, w, gate, expected_rstd)
+    assert torch.equal(actual, expected)
+    assert torch.equal(dx, expected_dx)
+    assert empty.shape == empty_dx.shape == (0, x.shape[-1])
+    assert empty_rstd.numel() == 0

From 8df1ffbc4a3984dd3a25bacd1b46633d915dc88a Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 18:34:21 +0000
Subject: [PATCH 10/70] test(norm): add Qwen3-Next workload and C3 C4 adapters

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 ci/run_ws1_gtest.sh                           |  10 +
 rl_engine/kernels/gtest/gradient_adapters.py  |  58 +-
 .../testing/qwen3_next_norm_manifest.json     | 759 ++++++++++++++++++
 rl_engine/testing/qwen3_next_workload.py      |  90 +++
 rl_engine/testing/ws1_workload.py             |  18 +-
 scripts/check_forward_invariance.py           |  16 +-
 scripts/check_gradient_invariance.py          |  16 +-
 tests/test_qwen3_next_workload.py             |  60 ++
 tests/test_ws1_ascend_closeout.py             |  22 +
 9 files changed, 1035 insertions(+), 14 deletions(-)
 create mode 100644 rl_engine/testing/qwen3_next_norm_manifest.json
 create mode 100644 rl_engine/testing/qwen3_next_workload.py
 create mode 100644 tests/test_qwen3_next_workload.py

diff --git a/ci/run_ws1_gtest.sh b/ci/run_ws1_gtest.sh
index 0e9b172cc..03b77173d 100755
--- a/ci/run_ws1_gtest.sh
+++ b/ci/run_ws1_gtest.sh
@@ -75,3 +75,13 @@ for cell in required:
         )
 print("[ws1-gtest] C8 gate passed")
 PY
+
+echo "[ws1-gtest] Qwen3-Next C3/C4 norms (operator scope)"
+for op in qwen3_next_rms_norm rms_norm_gated; do
+  for gate in forward gradient; do
+    "$PY" "scripts/check_${gate}_invariance.py" \
+      --manifest rl_engine/testing/qwen3_next_norm_manifest.json \
+      --op "$op" --candidate cuda --backend-profile cuda_bf16 \
+      --hidden 2048 --head-dim 128
+  done
+done
diff --git a/rl_engine/kernels/gtest/gradient_adapters.py b/rl_engine/kernels/gtest/gradient_adapters.py
index 74bb2c0a0..a1fb325c0 100644
--- a/rl_engine/kernels/gtest/gradient_adapters.py
+++ b/rl_engine/kernels/gtest/gradient_adapters.py
@@ -53,6 +53,7 @@ class GradientAdapterSpec:
     source_files: tuple[str, ...]
     shape_dependent_bwd_accum: str = "forbidden"
     atomic_add: str = "forbidden"
+    model_id: str | None = None
 
 
 @dataclass(frozen=True)
@@ -115,6 +116,26 @@ def to_dict(self) -> dict[str, Any]:
             "csrc/cuda/rmsnorm.cu",
         ),
     ),
+    "qwen3_next_rms_norm": GradientAdapterSpec(
+        op_name="qwen3_next_rms_norm",
+        chain_node="qwen3_next_rms_norm",
+        op_class="reduction",
+        spec_name="qwen3_next_rms_norm",
+        tensors=(_DX, _DWEIGHT),
+        requirement="required",
+        source_files=("rl_engine/kernels/ops/cuda/norm/rmsnorm.py", "csrc/cuda/rmsnorm.cu"),
+        model_id="Qwen/Qwen3-Next-80B-A3B-Instruct",
+    ),
+    "rms_norm_gated": GradientAdapterSpec(
+        op_name="rms_norm_gated",
+        chain_node="rms_norm_gated",
+        op_class="reduction",
+        spec_name="rms_norm_gated",
+        tensors=(_DX, _DWEIGHT, _DGATE),
+        requirement="required",
+        source_files=("rl_engine/kernels/ops/cuda/norm/rmsnorm.py", "csrc/cuda/rmsnorm.cu"),
+        model_id="Qwen/Qwen3-Next-80B-A3B-Instruct",
+    ),
     "qk_norm": GradientAdapterSpec(
         op_name="qk_norm",
         chain_node="qk_norm",
@@ -291,18 +312,27 @@ def get_adapter(op_name: str) -> GradientAdapterSpec:
         raise KeyError(f"unknown gradient adapter {op_name!r}") from exc
 
 
-def required_gradient_adapters() -> tuple[GradientAdapterSpec, ...]:
+def required_gradient_adapters(
+    manifest: WS1Manifest | None = None,
+) -> tuple[GradientAdapterSpec, ...]:
+    selected = manifest or load_manifest()
+    model_id = selected.model_identity["model_id"]
+    subset = selected.raw.get("scope") == "qwen3_next_norm_operators"
     return tuple(
         spec
         for spec in GRADIENT_ADAPTERS.values()
         if spec.requirement in ("required", "layout_supported")
+        and spec.model_id in (None, model_id)
+        and (not subset or spec.model_id == model_id)
     )
 
 
-def required_forward_adapters() -> tuple[GradientAdapterSpec, ...]:
+def required_forward_adapters(
+    manifest: WS1Manifest | None = None,
+) -> tuple[GradientAdapterSpec, ...]:
     """Same enumerable WS1 ops as C4; C3 reuses the registry, not a second list."""
 
-    return required_gradient_adapters()
+    return required_gradient_adapters(manifest)
 
 
 @dataclass(frozen=True)
@@ -620,9 +650,9 @@ def _row_parameters(
     head_dim: int = 16,
 ) -> dict[str, torch.Tensor]:
     """Config-independent trainable parameters, built in the execution dtype."""
-    if op_name == "rms_norm":
+    if op_name in {"rms_norm", "qwen3_next_rms_norm"}:
         return {"weight": _shared_parameter((hidden,), device=device, dtype=dtype, offset=1)}
-    if op_name == "qk_norm":
+    if op_name in {"qk_norm", "rms_norm_gated"}:
         return {"weight": _shared_parameter((head_dim,), device=device, dtype=dtype, offset=1)}
     if op_name == "det_gemm":
         return {"b": _shared_parameter((hidden, hidden), device=device, dtype=dtype, offset=2)}
@@ -663,12 +693,19 @@ def _row_inputs(
     """
     n = len(keys)
     leading = (n,)
-    if op_name == "rms_norm":
+    if op_name in {"rms_norm", "qwen3_next_rms_norm"}:
         return {
             "x": _stack_rows(keys, leading, (hidden,), device=device, dtype=dtype),
             "weight": params["weight"],
             "eps": 1.0e-6,
         }
+    if op_name == "rms_norm_gated":
+        return {
+            "x": _stack_rows(keys, leading, (head_dim,), device=device, dtype=dtype),
+            "gate": _stack_rows(keys, leading, (head_dim,), device=device, dtype=dtype, offset=7),
+            "weight": params["weight"],
+            "eps": 1.0e-6,
+        }
     if op_name == "qk_norm":
         return {
             "x": _stack_rows(keys, leading, (head_dim,), device=device, dtype=dtype),
@@ -1228,6 +1265,15 @@ def resolve_profile_candidate(
     manifest: WS1Manifest | None = None,
 ) -> dict[str, Any]:
     m = manifest if manifest is not None else load_manifest()
+    subset = m.raw.get("scope") == "qwen3_next_norm_operators"
+    if adapter.model_id not in (None, m.model_identity["model_id"]) or (
+        subset and adapter.model_id != m.model_identity["model_id"]
+    ):
+        return {
+            "status": "absent_not_required",
+            "expected_backend_id": None,
+            "candidate_path": None,
+        }
     if adapter.requirement == "absent_not_required":
         return {
             "status": "absent_not_required",
diff --git a/rl_engine/testing/qwen3_next_norm_manifest.json b/rl_engine/testing/qwen3_next_norm_manifest.json
new file mode 100644
index 000000000..9cf5977ef
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_norm_manifest.json
@@ -0,0 +1,759 @@
+{
+  "version": "1.0",
+  "workload_id": "qwen3-next-80b-a3b-norm-c3-c4-v1",
+  "seed": 20260812,
+  "model_identity": {
+    "model_id": "Qwen/Qwen3-Next-80B-A3B-Instruct",
+    "revision": "9c7f2fbe84465e40164a94cc16cd30b6999b0cc7",
+    "config_fingerprint": {
+      "num_hidden_layers": 48,
+      "hidden_size": 2048,
+      "intermediate_size": 5120,
+      "num_attention_heads": 16,
+      "num_key_value_heads": 2,
+      "head_dim": 256,
+      "vocab_size": 151936,
+      "linear_key_head_dim": 128,
+      "linear_value_head_dim": 128,
+      "linear_num_key_heads": 16,
+      "linear_num_value_heads": 32,
+      "num_experts": 512,
+      "num_experts_per_tok": 10
+    },
+    "exit_forbids_architecture_shrink": true,
+    "weight_snapshot": {
+      "pin_method": "revision-and-sha256",
+      "total_size_bytes": 162649725440,
+      "index_file": "model.safetensors.index.json",
+      "index_sha256": "0e5274f538d750abdd7fd55a36bad64100f9e881d59c65f6a59f364d2f8b6dd7",
+      "content_hash_algorithm": "sha256-of-sorted-shard-records-v1",
+      "content_hash": "00a6a3b5573ccbac2e01fadb673c2cd7bca172d40ddd73f95ed7afa5cbdb9dcd",
+      "shards": [
+        {
+          "filename": "model-00001-of-00041.safetensors",
+          "size_bytes": 3999619256,
+          "sha256": "d8908fd48650169854b5ed815cb01bfc1741152cc58795ec99188862c6a29e11"
+        },
+        {
+          "filename": "model-00002-of-00041.safetensors",
+          "size_bytes": 3999841784,
+          "sha256": "c753c9bfaca220781d4030c3a99e69b4a256434c9d6ec223f5147edc265289df"
+        },
+        {
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+          "algorithm_property": "fixed row reduction and ordered FP32 parameter fold"
+        }
+      ]
+    },
+    "triton_cuda_bf16": {
+      "backend_family": "triton",
+      "execution_dtype": "bfloat16",
+      "required_nodes": [
+        {
+          "node": "qwen3_next_rms_norm",
+          "status": "missing_required",
+          "expected_backend_id": null,
+          "expected_kernel_config_id": null,
+          "algorithm_property": "fixed row reduction and ordered FP32 parameter fold"
+        },
+        {
+          "node": "rms_norm_gated",
+          "status": "missing_required",
+          "expected_backend_id": null,
+          "expected_kernel_config_id": null,
+          "algorithm_property": "fixed row reduction and ordered FP32 parameter fold"
+        }
+      ]
+    },
+    "ascend_bf16": {
+      "backend_family": "ascend",
+      "execution_dtype": "bfloat16",
+      "required_nodes": [
+        {
+          "node": "qwen3_next_rms_norm",
+          "status": "missing_required",
+          "expected_backend_id": null,
+          "expected_kernel_config_id": null,
+          "algorithm_property": "fixed row reduction and ordered FP32 parameter fold"
+        },
+        {
+          "node": "rms_norm_gated",
+          "status": "missing_required",
+          "expected_backend_id": null,
+          "expected_kernel_config_id": null,
+          "algorithm_property": "fixed row reduction and ordered FP32 parameter fold"
+        }
+      ]
+    }
+  },
+  "representative_cases": [
+    {
+      "case_id": "short_full_model_seq8_qwen3_next_rms_norm",
+      "fixture_id": "short_full_model_seq8",
+      "operator_spec": "qwen3_next_rms_norm",
+      "hidden": 2048,
+      "architecture_identity": "qwen3_next_80b_a3b_norm_operators"
+    },
+    {
+      "case_id": "short_full_model_seq8_rms_norm_gated",
+      "fixture_id": "short_full_model_seq8",
+      "operator_spec": "rms_norm_gated",
+      "hidden": 128,
+      "architecture_identity": "qwen3_next_80b_a3b_norm_operators"
+    },
+    {
+      "case_id": "long_full_model_seq32_qwen3_next_rms_norm",
+      "fixture_id": "long_full_model_seq32",
+      "operator_spec": "qwen3_next_rms_norm",
+      "hidden": 2048,
+      "architecture_identity": "qwen3_next_80b_a3b_norm_operators"
+    },
+    {
+      "case_id": "long_full_model_seq32_rms_norm_gated",
+      "fixture_id": "long_full_model_seq32",
+      "operator_spec": "rms_norm_gated",
+      "hidden": 128,
+      "architecture_identity": "qwen3_next_80b_a3b_norm_operators"
+    },
+    {
+      "case_id": "rep_full_model_seq16_qwen3_next_rms_norm",
+      "fixture_id": "rep_full_model_seq16",
+      "operator_spec": "qwen3_next_rms_norm",
+      "hidden": 2048,
+      "architecture_identity": "qwen3_next_80b_a3b_norm_operators"
+    },
+    {
+      "case_id": "rep_full_model_seq16_rms_norm_gated",
+      "fixture_id": "rep_full_model_seq16",
+      "operator_spec": "rms_norm_gated",
+      "hidden": 128,
+      "architecture_identity": "qwen3_next_80b_a3b_norm_operators"
+    }
+  ],
+  "fixture_identity_sha256": "3ac9490ffbb1486edac6ea509811fd4d5affbe880b208bd7d9b052b0ce418442",
+  "provenance_boundary": {
+    "scope": "Synthetic norm inputs at checkpoint dimensions; not checkpoint execution or model-level L2.",
+    "runtime_verified": false
+  },
+  "scope": "qwen3_next_norm_operators",
+  "full_model_evidence": false
+}
diff --git a/rl_engine/testing/qwen3_next_workload.py b/rl_engine/testing/qwen3_next_workload.py
new file mode 100644
index 000000000..ffe3367cd
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_workload.py
@@ -0,0 +1,90 @@
+"""Qwen3-Next norm-only C3/C4 workload, explicitly separate from the Dense chain."""
+
+from collections.abc import Mapping
+from typing import Any
+
+from rl_engine.testing.ws1_workload import (
+    WorkloadError,
+    _validate_backend_profiles,
+    _validate_fixtures,
+    _validate_logical_identity,
+    _validate_model_identity,
+    _validate_primary_matrix,
+    _validate_stochastic_policy,
+    manifest_identity_hash,
+)
+
+MODEL_ID = "Qwen/Qwen3-Next-80B-A3B-Instruct"
+REVISION = "9c7f2fbe84465e40164a94cc16cd30b6999b0cc7"
+FINGERPRINT = {
+    "num_hidden_layers": 48,
+    "hidden_size": 2048,
+    "intermediate_size": 5120,
+    "num_attention_heads": 16,
+    "num_key_value_heads": 2,
+    "head_dim": 256,
+    "vocab_size": 151936,
+    "linear_key_head_dim": 128,
+    "linear_value_head_dim": 128,
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 32,
+    "num_experts": 512,
+    "num_experts_per_tok": 10,
+}
+NORM_OPS = {"qwen3_next_rms_norm": 2048, "rms_norm_gated": 128}
+
+
+def validate_norm_manifest(raw: Mapping[str, Any]) -> None:
+    identity = raw["model_identity"]
+    if identity["model_id"] != MODEL_ID or identity["revision"] != REVISION:
+        raise WorkloadError("Qwen3-Next norm workload requires the pinned official checkpoint")
+    _validate_model_identity(identity, fingerprint=FINGERPRINT, model_label="Qwen3-Next")
+    if (
+        raw.get("scope") != "qwen3_next_norm_operators"
+        or raw.get("full_model_evidence") is not False
+    ):
+        raise WorkloadError("norm workload cannot claim full-model evidence")
+    _validate_stochastic_policy(raw["stochastic_policy"])
+    _validate_primary_matrix(raw["primary_matrix"], raw["fixtures"])
+    _validate_fixtures(raw["fixtures"], raw["primary_matrix"])
+    _validate_logical_identity(raw["logical_identity"])
+    caps = raw["capabilities"]
+    if {e["op"] for e in caps["required_chain_ops"]} != set(NORM_OPS):
+        raise WorkloadError("norm workload must contain exactly the two Qwen3-Next norms")
+    if any(e["status"] != "required" for e in caps["required_chain_ops"]):
+        raise WorkloadError("both norm operators are required")
+    _validate_backend_profiles(raw["backend_profiles"], caps)
+    cases = {case["case_id"]: case for case in raw["representative_cases"]}
+    if len(cases) != len(raw["representative_cases"]):
+        raise WorkloadError("duplicate norm case ID")
+    referenced = set()
+    for key in (
+        "short_full_model_fixture",
+        "long_full_model_fixture",
+        "representative_full_model_fixture",
+    ):
+        fixture = raw["fixtures"][key]
+        for case_id in fixture["candidate_case_ids"]:
+            if case_id not in cases or cases[case_id]["fixture_id"] != fixture["fixture_id"]:
+                raise WorkloadError("norm case fixture binding mismatch")
+            referenced.add(case_id)
+    if referenced != set(cases):
+        raise WorkloadError("unreferenced norm case")
+    if {case["operator_spec"] for case in cases.values()} != set(NORM_OPS):
+        raise WorkloadError("representative cases must cover both norms")
+    for case in cases.values():
+        if case["hidden"] != NORM_OPS[case["operator_spec"]]:
+            raise WorkloadError("norm case hidden dimension does not match checkpoint")
+        if case["architecture_identity"] != "qwen3_next_80b_a3b_norm_operators":
+            raise WorkloadError("norm cases cannot claim Dense or full-model architecture evidence")
+    if raw["fixture_identity_sha256"] != manifest_identity_hash(raw):
+        raise WorkloadError("Qwen3-Next fixture identity hash mismatch")
+
+
+def validate_norm_dimensions(raw: Mapping[str, Any], op: str, hidden: int, head_dim: int) -> None:
+    if raw.get("scope") != "qwen3_next_norm_operators":
+        return
+    if op not in NORM_OPS or hidden != 2048 or head_dim != 128:
+        raise WorkloadError(
+            "Qwen3-Next norm gate requires its two norm ops, --hidden 2048 --head-dim 128"
+        )
diff --git a/rl_engine/testing/ws1_workload.py b/rl_engine/testing/ws1_workload.py
index 376a7a66b..997f441e7 100644
--- a/rl_engine/testing/ws1_workload.py
+++ b/rl_engine/testing/ws1_workload.py
@@ -264,7 +264,12 @@ def load_manifest(path: str | Path | None = None) -> WS1Manifest:
         raw = json.load(fh)
     if not isinstance(raw, dict):
         raise WorkloadError("manifest root must be a JSON object")
-    validate_manifest(raw)
+    if raw.get("scope") == "qwen3_next_norm_operators":
+        from rl_engine.testing.qwen3_next_workload import validate_norm_manifest
+
+        validate_norm_manifest(raw)
+    else:
+        validate_manifest(raw)
     return WS1Manifest(raw=raw, path=manifest_path)
 
 
@@ -292,20 +297,25 @@ def validate_manifest(raw: Mapping[str, Any]) -> None:
         )
 
 
-def _validate_model_identity(identity: Mapping[str, Any]) -> None:
+def _validate_model_identity(
+    identity: Mapping[str, Any],
+    *,
+    fingerprint: Mapping[str, Any] = _OFFICIAL_FINGERPRINT,
+    model_label: str = "Qwen3-8B Dense",
+) -> None:
     for key in ("model_id", "revision", "config_fingerprint", "weight_snapshot"):
         if key not in identity:
             raise WorkloadError(f"model_identity missing {key!r}")
     fp = identity["config_fingerprint"]
     if not isinstance(fp, Mapping):
         raise WorkloadError("config_fingerprint must be an object")
-    for key, expected in _OFFICIAL_FINGERPRINT.items():
+    for key, expected in fingerprint.items():
         if key not in fp:
             raise WorkloadError(f"config_fingerprint missing {key!r}")
         if fp[key] != expected:
             raise WorkloadError(
                 f"config_fingerprint {key}={fp[key]!r} does not match official "
-                f"Qwen3-8B Dense pin {expected!r}; architecture shrink is forbidden"
+                f"{model_label} pin {expected!r}; architecture shrink is forbidden"
             )
     if not identity.get("exit_forbids_architecture_shrink", False):
         raise WorkloadError("exit_forbids_architecture_shrink must be true")
diff --git a/scripts/check_forward_invariance.py b/scripts/check_forward_invariance.py
index 8736bc7bc..57a27088b 100644
--- a/scripts/check_forward_invariance.py
+++ b/scripts/check_forward_invariance.py
@@ -112,6 +112,7 @@ def parse_args() -> argparse.Namespace:
         if adapter.requirement != "absent_not_required"
     ]
     parser = argparse.ArgumentParser(description="WS1 C3 forward invariance GPU gate")
+    parser.add_argument("--manifest", type=pathlib.Path, default=None)
     parser.add_argument("--op", choices=sorted(runnable), default="rms_norm")
     parser.add_argument(
         "--candidate", required=True, help="Manifest-declared CUDA/Triton/Ascend candidate"
@@ -145,7 +146,10 @@ def main() -> None:
         raise SystemExit("ERROR: --vocab must cover every fixed C2 workload token id")
 
     contract = load_contract()
-    manifest = load_manifest()
+    manifest = load_manifest(args.manifest)
+    from rl_engine.testing.qwen3_next_workload import validate_norm_dimensions
+
+    validate_norm_dimensions(manifest.raw, args.op, args.hidden, args.head_dim)
     adapter = get_adapter(args.op)
     if adapter.requirement == "layout_supported":
         raise SystemExit(
@@ -231,7 +235,15 @@ def main() -> None:
     )
 
     if args.json:
-        print(json.dumps(report.to_dict(), indent=2, default=str))
+        payload = report.to_dict()
+        payload["workload"] = {
+            "workload_id": manifest.workload_id,
+            "scope": manifest.raw.get("scope", "qwen3_8b_dense"),
+            "fixture_identity_sha256": manifest.raw["fixture_identity_sha256"],
+            "model_id": manifest.model_identity["model_id"],
+            "full_model_evidence": False,
+        }
+        print(json.dumps(payload, indent=2, default=str))
     else:
         _summarize(report)
     if not report.passed:
diff --git a/scripts/check_gradient_invariance.py b/scripts/check_gradient_invariance.py
index 606be9a54..c2943a4c2 100644
--- a/scripts/check_gradient_invariance.py
+++ b/scripts/check_gradient_invariance.py
@@ -124,6 +124,7 @@ def parse_args() -> argparse.Namespace:
         if adapter.requirement != "absent_not_required"
     ]
     parser = argparse.ArgumentParser(description="WS1 C4 gradient invariance GPU gate")
+    parser.add_argument("--manifest", type=pathlib.Path, default=None)
     parser.add_argument("--op", choices=sorted(runnable), default="rms_norm")
     parser.add_argument(
         "--candidate", required=True, help="Manifest-declared CUDA/Triton/Ascend candidate"
@@ -158,7 +159,10 @@ def main() -> None:
         raise SystemExit(f"ERROR: C4 required-profile evidence needs a real device: {exc}") from exc
 
     contract = load_contract()
-    manifest = load_manifest()
+    manifest = load_manifest(args.manifest)
+    from rl_engine.testing.qwen3_next_workload import validate_norm_dimensions
+
+    validate_norm_dimensions(manifest.raw, args.op, args.hidden, args.head_dim)
     adapter = get_adapter(args.op)
     if adapter.requirement == "layout_supported":
         # Pack is the same PyTorch layout op under both profiles and is not a C2
@@ -253,7 +257,15 @@ def main() -> None:
         ) from exc
 
     if args.json:
-        print(json.dumps(report.to_dict(), indent=2, default=str))
+        payload = report.to_dict()
+        payload["workload"] = {
+            "workload_id": manifest.workload_id,
+            "scope": manifest.raw.get("scope", "qwen3_8b_dense"),
+            "fixture_identity_sha256": manifest.raw["fixture_identity_sha256"],
+            "model_id": manifest.model_identity["model_id"],
+            "full_model_evidence": False,
+        }
+        print(json.dumps(payload, indent=2, default=str))
     else:
         _summarize(report)
     if not report.passed:
diff --git a/tests/test_qwen3_next_workload.py b/tests/test_qwen3_next_workload.py
new file mode 100644
index 000000000..e433de102
--- /dev/null
+++ b/tests/test_qwen3_next_workload.py
@@ -0,0 +1,60 @@
+"""The norm workload cannot stand in for Dense or full-checkpoint evidence."""
+
+import copy
+from pathlib import Path
+
+import pytest
+
+from rl_engine.kernels.gtest.gradient_adapters import get_adapter, resolve_profile_candidate
+from rl_engine.testing.qwen3_next_workload import validate_norm_manifest
+from rl_engine.testing.ws1_workload import WorkloadError, load_manifest, manifest_identity_hash
+
+MANIFEST = Path(__file__).resolve().parents[1] / "rl_engine/testing/qwen3_next_norm_manifest.json"
+
+
+def test_norm_manifest_pins_real_architecture_and_separate_scope():
+    manifest = load_manifest(MANIFEST)
+    assert manifest.model_identity["config_fingerprint"]["hidden_size"] == 2048
+    assert manifest.raw["full_model_evidence"] is False
+    for name, gradients in (
+        ("qwen3_next_rms_norm", ("dx", "dweight")),
+        ("rms_norm_gated", ("dx", "dweight", "dgate")),
+    ):
+        adapter = get_adapter(name)
+        assert tuple(t.name for t in adapter.tensors) == gradients
+        assert resolve_profile_candidate(adapter, "cuda_bf16", manifest)["status"] == "declared"
+        assert (
+            resolve_profile_candidate(adapter, "triton_cuda_bf16", manifest)["status"]
+            == "missing_required"
+        )
+        assert (
+            resolve_profile_candidate(adapter, "cuda_bf16", load_manifest())["status"]
+            == "absent_not_required"
+        )
+
+
+@pytest.mark.parametrize("fault", ["architecture", "full_model", "revision", "shape", "binding"])
+def test_norm_manifest_rejects_false_evidence_even_with_regenerated_hash(fault):
+    raw = copy.deepcopy(load_manifest(MANIFEST).raw)
+    if fault == "architecture":
+        raw["model_identity"]["config_fingerprint"]["hidden_size"] = 4096
+    elif fault == "full_model":
+        raw["full_model_evidence"] = True
+    elif fault == "revision":
+        raw["model_identity"]["revision"] = "main"
+    elif fault == "shape":
+        raw["representative_cases"][0]["hidden"] = 64
+    else:
+        raw["representative_cases"][0]["fixture_id"] = "wrong_fixture"
+    raw["fixture_identity_sha256"] = manifest_identity_hash(raw)
+    with pytest.raises(WorkloadError):
+        validate_norm_manifest(raw)
+
+
+def test_norm_dimension_gate_rejects_shrunk_workload():
+    from rl_engine.testing.qwen3_next_workload import validate_norm_dimensions
+
+    raw = load_manifest(MANIFEST).raw
+    with pytest.raises(WorkloadError, match="hidden 2048"):
+        validate_norm_dimensions(raw, "qwen3_next_rms_norm", 64, 128)
+    validate_norm_dimensions(raw, "rms_norm_gated", 2048, 128)
diff --git a/tests/test_ws1_ascend_closeout.py b/tests/test_ws1_ascend_closeout.py
index 0aed72980..29369eb8a 100644
--- a/tests/test_ws1_ascend_closeout.py
+++ b/tests/test_ws1_ascend_closeout.py
@@ -188,9 +188,15 @@ def test_c2_ascend_cases_pin_real_ascend_kernels_and_sources():
 
 def test_c3_c4_every_required_adapter_resolves_an_ascend_candidate():
     manifest = load_manifest()
+    model_id = manifest.model_identity["model_id"]
     for name, adapter in GRADIENT_ADAPTERS.items():
         if adapter.requirement not in ("required",):
             continue
+        # An adapter scoped to another model (the CUDA-only Qwen3-Next norms)
+        # resolves to `absent_not_required` for this manifest by design; this
+        # test covers the chain of the model whose manifest it loads.
+        if adapter.model_id not in (None, model_id):
+            continue
         resolved = resolve_profile_candidate(adapter, PROFILE, manifest)
         assert resolved["status"] == "declared", name
         assert resolved["expected_backend_id"] == "ascend", name
@@ -198,6 +204,22 @@ def test_c3_c4_every_required_adapter_resolves_an_ascend_candidate():
         assert candidate_family(str(resolved["expected_backend_id"])) == "ascend"
 
 
+def test_model_scoped_adapters_are_absent_not_required_for_other_models():
+    """The scoping the test above relies on must actually hold.
+
+    Without this, skipping scoped adapters could hide one that silently resolves
+    to a real candidate for the wrong model.
+    """
+    manifest = load_manifest()
+    model_id = manifest.model_identity["model_id"]
+    scoped = [a for a in GRADIENT_ADAPTERS.values() if a.model_id not in (None, model_id)]
+    assert scoped, "expected at least the Qwen3-Next norm adapters to be model-scoped"
+    for adapter in scoped:
+        resolved = resolve_profile_candidate(adapter, PROFILE, manifest)
+        assert resolved["status"] == "absent_not_required", adapter.op_name
+        assert resolved["candidate_path"] is None, adapter.op_name
+
+
 def test_c4_adapter_status_matrix_has_no_red_ascend_rows():
     rows = [r for r in gradient_adapter_status_matrix() if r.backend_profile == PROFILE]
     assert rows

From aaf09864cb4a3ce1574ea79eb7c5490697c05db8 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 18:40:19 +0000
Subject: [PATCH 11/70] fix(norm): preserve gated signed-zero weights when
 offset is disabled

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 csrc/cuda/rmsnorm.cu          |  9 ++++++---
 tests/test_qwen3_next_norm.py | 14 ++++++++++++++
 2 files changed, 20 insertions(+), 3 deletions(-)

diff --git a/csrc/cuda/rmsnorm.cu b/csrc/cuda/rmsnorm.cu
index 2d5c98ec4..4249cf525 100644
--- a/csrc/cuda/rmsnorm.cu
+++ b/csrc/cuda/rmsnorm.cu
@@ -266,7 +266,8 @@ __global__ void rmsnorm_gated_fwd_kernel(
 
     for (int col = tid; col < H; col += blockDim.x) {
         float xv = load_as_float<scalar_t>(x_row + col);
-        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
+        float wv = load_as_float<weight_t>(weight + col);
+        if (weight_offset != 0.0f) wv += weight_offset;
         float zv = load_as_float<scalar_t>(gate_row + col);
         float out = xv * row_rstd * wv * gate_activation<ACT>(zv);
         store_from_float<scalar_t>(y_row + col, out);
@@ -301,7 +302,8 @@ __global__ void rmsnorm_gated_bwd_dx_kernel(
     for (int col = tid; col < H; col += blockDim.x) {
         float dyv = load_as_float<scalar_t>(dy_row + col);
         float xv = load_as_float<scalar_t>(x_row + col);
-        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
+        float wv = load_as_float<weight_t>(weight + col);
+        if (weight_offset != 0.0f) wv += weight_offset;
         float zv = load_as_float<scalar_t>(gate_row + col);
         local_dot += dyv * (wv * gate_activation<ACT>(zv)) * xv;
     }
@@ -314,7 +316,8 @@ __global__ void rmsnorm_gated_bwd_dx_kernel(
     for (int col = tid; col < H; col += blockDim.x) {
         float dyv = load_as_float<scalar_t>(dy_row + col);
         float xv = load_as_float<scalar_t>(x_row + col);
-        float wv = load_as_float<weight_t>(weight + col) + weight_offset;
+        float wv = load_as_float<weight_t>(weight + col);
+        if (weight_offset != 0.0f) wv += weight_offset;
         float zv = load_as_float<scalar_t>(gate_row + col);
 
         float out = r * dyv * (wv * gate_activation<ACT>(zv)) - xv * coeff;
diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
index 2f738a1c8..ff82183fc 100644
--- a/tests/test_qwen3_next_norm.py
+++ b/tests/test_qwen3_next_norm.py
@@ -820,3 +820,17 @@ def test_gated_extension_handles_empty_batch_and_nondefault_stream():
     assert torch.equal(dx, expected_dx)
     assert empty.shape == empty_dx.shape == (0, x.shape[-1])
     assert empty_rstd.numel() == 0
+
+
+@requires_cuda_gated
+@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
+def test_cuda_gated_signed_zero_weight_is_preserved(dtype):
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda
+
+    x = torch.ones(2, 128, device="cuda", dtype=dtype)
+    x[1].neg_()
+    weight = torch.full((128,), -0.0, device="cuda", dtype=dtype)
+    actual = rmsnorm_gated_cuda(x, weight, torch.ones_like(x))
+    expected = x * weight
+    bits = torch.int32 if dtype == torch.float32 else torch.int16
+    assert torch.equal(actual.view(bits), expected.view(bits))

From 4ea1611f48eeb45cb31c21804a96bb8f520450e0 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 18:28:40 +0000
Subject: [PATCH 12/70] test(dispatch): cover qwen3_next_rms_norm priority and
 missing-extension fallback

The zero-centred norm was registered CUDA-first alongside the gated one, but only
rms_norm_gated had dispatch tests. Mirror them: pin the priority on all five
platforms, and assert that the inherited RMSNormCudaOp.__init__ symbol check makes
the registry fall through to the PyTorch reference.

Each assertion was checked to fail on CPU with the behaviour removed: PyTorch
first in the CUDA list, the ROCm entry dropped, and the __init__ check removed.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 rl_engine/tests/test_dispatch.py | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/rl_engine/tests/test_dispatch.py b/rl_engine/tests/test_dispatch.py
index ed873c2dd..7f1066ab0 100644
--- a/rl_engine/tests/test_dispatch.py
+++ b/rl_engine/tests/test_dispatch.py
@@ -292,3 +292,31 @@ def test_gated_rms_norm_cuda_backend_reports_absence_by_failing_construction(mon
     # ... and the registry must therefore hand out the reference, not raise.
     resolved = KernelRegistry().get_op("rms_norm_gated")
     assert isinstance(resolved, Qwen3NextRMSNormGatedOp)
+
+
+def test_qwen3_next_rms_norm_priority_is_cuda_first_with_pytorch_fallback():
+    """The zero-centred norm follows the gated one: CUDA kernel first, reference elsewhere."""
+    registry = KernelRegistry()
+
+    assert registry._priority_map["cuda"]["qwen3_next_rms_norm"] == [
+        OpBackend.CUDA_QWEN3_NEXT_RMS_NORM,
+        OpBackend.PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM,
+    ]
+    for platform in ("rocm", "musa", "cpu", "npu"):
+        assert registry._priority_map[platform]["qwen3_next_rms_norm"] == [
+            OpBackend.PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM
+        ], platform
+
+
+def test_qwen3_next_rms_norm_cuda_backend_reports_absence_by_failing_construction(monkeypatch):
+    """The inherited ``RMSNormCudaOp.__init__`` check must reach the subclass."""
+    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import Qwen3NextRMSNormOp
+
+    monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", False)
+    monkeypatch.setattr(cuda_rmsnorm, "_C", None)
+    with pytest.raises(RuntimeError, match="requires the compiled rl_engine._C extension"):
+        cuda_rmsnorm.Qwen3NextRMSNormCudaOp()
+
+    resolved = KernelRegistry().get_op("qwen3_next_rms_norm")
+    assert isinstance(resolved, Qwen3NextRMSNormOp)

From 5eaeca8983f7e057b8e96dc49312020da916e594 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 18:28:40 +0000
Subject: [PATCH 13/70] test(norm): sweep the gated-vs-plain rstd identity
 across dtype, H, activation, offset

The bitwise rstd identity was asserted for one configuration only (bf16, H=128,
silu, offset 0). Parametrize it over fp32/fp16/bf16 x H in {128, 2048, 5120} x
{silu, sigmoid} x offset in {0.0, 1.0}, 36 cases, with the plain kernel taking
the same offset.

All 36 are expected to hold by construction: both kernels run the same
sum-of-squares loop, block_reduce_sum and choose_threads(H) launch shape, and
neither the gate nor the offset enters the statistic. The H values cover both
block sizes (128 and 512 threads) and two per-thread serial lengths. Not yet run
on a GPU; the cases skip without the compiled extension.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 tests/test_qwen3_next_norm.py | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
index ff82183fc..f9c10b46e 100644
--- a/tests/test_qwen3_next_norm.py
+++ b/tests/test_qwen3_next_norm.py
@@ -498,17 +498,27 @@ def test_cuda_gated_matches_golden_within_contract():
 
 
 @requires_cuda_gated
-def test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel():
+@pytest.mark.parametrize("offset", [0.0, 1.0])
+@pytest.mark.parametrize("activation", [0, 1], ids=["silu", "sigmoid"])
+@pytest.mark.parametrize("hidden", [_HEAD_V_DIM, 2048, 5120])
+@pytest.mark.parametrize(
+    "dtype", [torch.float32, torch.float16, torch.bfloat16], ids=["fp32", "fp16", "bf16"]
+)
+def test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel(dtype, hidden, activation, offset):
     """The gate must not perturb the normalization statistic.
 
     Same x, same rstd, bitwise -- otherwise the gate has leaked into the
-    reduction and the two kernels no longer share a contract.
+    reduction and the two kernels no longer share a contract. Both kernels
+    launch with ``choose_threads(H)``: H=128 runs 128 threads with one column
+    each, 2048 and 5120 run 512 threads with 4 and 10 serial columns each.
+    Each activation is its own template instantiation, and the offset is
+    applied only after the statistic, so neither it nor the gate may move it.
     """
     from rl_engine.kernels.ops.base import _C
 
-    x, w, gate = _gated_cuda_inputs()
-    _, rstd_gated = _C.rmsnorm_gated_forward(x, w, gate, _EPS, 0.0, 0)
-    _, rstd_plain = _C.rmsnorm_forward(x, w, _EPS, 0.0)
+    x, w, gate = _gated_cuda_inputs(hidden=hidden, dtype=dtype)
+    _, rstd_gated = _C.rmsnorm_gated_forward(x, w, gate, _EPS, offset, activation)
+    _, rstd_plain = _C.rmsnorm_forward(x, w, _EPS, offset)
     assert torch.equal(rstd_gated, rstd_plain)
 
 

From b0f4c5cae326ce47500efa0237a70bbd41605f8b Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 21:46:15 +0000
Subject: [PATCH 14/70] fix(norm): address the gated-norm review (R2-2, 7, 8,
 9, 12, 13, 14, 16, 17)

R2-1 (private bf16 tolerances cited as the reduction row) was resolved in the
preceding merge, which switched the gated tests to c1's _forward_tol.

- R2-2 docs/operators/qwen3-next-rms-norm-gated.md: Accuracy now points to
  tolerance_contract.json (forward_accuracy, reduction x dtype); the 35% / 6.25e-2
  HF-vs-vLLM figure is labelled a one-off with its setup (one seed, B200, bf16,
  head_v_dim=128, 512 rows, eager); the 40-seed figures are attributed to
  tests/check_qwen3_next_norm_providers.py. Withdrawn, as on the c1 page: the
  cast-order isolation, the per-element fp32 ULP wording, and "fails closed (RFC
  section 6 item 7)" for configurations the op has no parameter for. Claim levels
  now read L1 (prefix slices; batch size, chunking, padding, permutation in the
  C3/C4 gates), L0 not separately tested; the rstd sweep is described.
- R2-7 csrc/ops.cpp: drop the dim and size TORCH_CHECKs that rmsnorm_check_weight
  already performs, in rmsnorm_forward, rmsnorm_backward_dx and
  rmsnorm_gated_check. Error messages for those cases now come from
  rmsnorm_check_weight; no test matches the removed messages.
- R2-8 workload_report() in rl_engine/testing/ws1_workload.py replaces the
  payload block duplicated in both gate scripts. full_model_evidence is read from
  the manifest: False for the Qwen3-Next norm manifest, None (not declared) for the
  Dense manifest, which previously reported a hard-coded False.
- R2-9 the gate scripts import validate_norm_dimensions at module level, and
  --manifest has a help string.
- R2-12 the four dispatch tests become two, parametrized over both norms. The
  fallback test now resolves for device="cuda", so the CUDA-first list is walked
  even on a CPU-only host, and asserts the CUDA backend was tried and failed.
  Before, on CPU, get_op went through the CPU list and the assertion held
  trivially.
- R2-13 SPDX headers on rl_engine/testing/qwen3_next_workload.py and
  tests/test_qwen3_next_workload.py.
- R2-14 ci/run_ws1_gtest.sh runs the four Qwen3-Next gates as explicit commands
  instead of a loop over interpolated script names.
- R2-16 Qwen3NextRMSNormGatedCudaOp takes activation as a constructor argument,
  validated before the extension check; the test no longer mutates the instance.
  "swish" stays as vLLM's alias for "silu", now asserted bitwise on CUDA.
- R2-17 the Python dgate path adds weight_offset only when it is nonzero, like the
  kernels, so a -0.0 weight keeps its sign in dgate.

New tests: activation rejected at construction (CPU), swish == silu (CUDA), dgate
signed zero (CUDA, 3 dtypes). Checked on CPU that the fallback and constructor
tests fail with the behaviour removed. The CUDA tests and the csrc change are not
yet run on a GPU.

Deferred: R2-6 (shared sum-of-squares device function), R2-10 (private validator
imports), R2-11.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 ci/run_ws1_gtest.sh                         | 17 ++--
 csrc/ops.cpp                                | 10 ---
 docs/operators/qwen3-next-rms-norm-gated.md | 54 ++++++++----
 rl_engine/kernels/ops/cuda/norm/rmsnorm.py  | 38 +++++---
 rl_engine/testing/qwen3_next_workload.py    |  3 +
 rl_engine/testing/ws1_workload.py           | 15 ++++
 rl_engine/tests/test_dispatch.py            | 97 +++++++++++----------
 scripts/check_forward_invariance.py         | 20 ++---
 scripts/check_gradient_invariance.py        | 20 ++---
 tests/test_qwen3_next_norm.py               | 44 +++++++++-
 tests/test_qwen3_next_workload.py           |  3 +
 11 files changed, 200 insertions(+), 121 deletions(-)

diff --git a/ci/run_ws1_gtest.sh b/ci/run_ws1_gtest.sh
index 03b77173d..e6d8e0dc0 100755
--- a/ci/run_ws1_gtest.sh
+++ b/ci/run_ws1_gtest.sh
@@ -77,11 +77,12 @@ print("[ws1-gtest] C8 gate passed")
 PY
 
 echo "[ws1-gtest] Qwen3-Next C3/C4 norms (operator scope)"
-for op in qwen3_next_rms_norm rms_norm_gated; do
-  for gate in forward gradient; do
-    "$PY" "scripts/check_${gate}_invariance.py" \
-      --manifest rl_engine/testing/qwen3_next_norm_manifest.json \
-      --op "$op" --candidate cuda --backend-profile cuda_bf16 \
-      --hidden 2048 --head-dim 128
-  done
-done
+QWEN3_NEXT_NORM_MANIFEST=rl_engine/testing/qwen3_next_norm_manifest.json
+"$PY" scripts/check_forward_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
+  --op qwen3_next_rms_norm --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
+"$PY" scripts/check_gradient_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
+  --op qwen3_next_rms_norm --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
+"$PY" scripts/check_forward_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
+  --op rms_norm_gated --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
+"$PY" scripts/check_gradient_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
+  --op rms_norm_gated --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index 1efb116fc..ee9862b28 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -343,10 +343,6 @@ std::vector<torch::Tensor> rmsnorm_forward(
   rmsnorm_check_input(weight, "weight");
   rmsnorm_check_weight(x, weight);
 
-  TORCH_CHECK(x.dim() == 2, "x must be 2D [T, H]");
-  TORCH_CHECK(weight.dim() == 1, "weight must be 1D [H]");
-  TORCH_CHECK(x.size(1) == weight.size(0), "x.size(1) must equal weight.size(0)");
-
   auto T = x.size(0);
   auto y = torch::empty_like(x);
   auto rstd = torch::empty({T}, x.options().dtype(torch::kFloat32));
@@ -371,8 +367,6 @@ torch::Tensor rmsnorm_backward_dx(
   rmsnorm_check_backward(dy, x, rstd);
 
   TORCH_CHECK(dy.sizes() == x.sizes(), "dy and x must have same shape");
-  TORCH_CHECK(x.dim() == 2, "x must be 2D [T, H]");
-  TORCH_CHECK(weight.dim() == 1, "weight must be 1D [H]");
   TORCH_CHECK(rstd.dim() == 1, "rstd must be 1D [T]");
   TORCH_CHECK(rstd.size(0) == x.size(0), "rstd.size(0) must equal x.size(0)");
 
@@ -394,10 +388,6 @@ static void rmsnorm_gated_check(
   rmsnorm_check_weight(x, weight);
   rmsnorm_check_input(gate, "gate");
   TORCH_CHECK(gate.device() == x.device(), "gate must be on the same device as x");
-
-  TORCH_CHECK(x.dim() == 2, "x must be 2D [T, H]");
-  TORCH_CHECK(weight.dim() == 1, "weight must be 1D [H]");
-  TORCH_CHECK(x.size(1) == weight.size(0), "x.size(1) must equal weight.size(0)");
   TORCH_CHECK(gate.sizes() == x.sizes(), "gate must have the same shape as x");
   TORCH_CHECK(
     gate.scalar_type() == x.scalar_type(),
diff --git a/docs/operators/qwen3-next-rms-norm-gated.md b/docs/operators/qwen3-next-rms-norm-gated.md
index 55071d25b..49221c8a0 100644
--- a/docs/operators/qwen3-next-rms-norm-gated.md
+++ b/docs/operators/qwen3-next-rms-norm-gated.md
@@ -29,6 +29,7 @@ from rl_engine.kernels.ops.cuda.norm.rmsnorm import (
 )
 
 y = Qwen3NextRMSNormGatedCudaOp().forward(x, weight, gate, eps=1e-6)
+y = Qwen3NextRMSNormGatedCudaOp(activation="sigmoid").forward(x, weight, gate, eps=1e-6)
 y = rmsnorm_gated_cuda(x, weight, gate, eps=1e-6, activation="sigmoid")
 ```
 
@@ -51,15 +52,15 @@ required tensor, so it cannot stand in for one.
 | `weight` | `[H]` | matches `x` or fp32 | plain, NOT zero-centred |
 | `gate` | same as `x` | same as `x` | shape, dtype and device must match before flattening |
 | `eps` | scalar | float | `1e-6` for Qwen3-Next |
-| `activation` | — | `"silu"`/`"swish"`/`"sigmoid"` | anything else is rejected |
+| `activation` | — | `"silu"`/`"swish"`/`"sigmoid"` | constructor argument; `"swish"` is an alias for `"silu"`, as in vLLM; anything else is rejected |
 
 All tensors must share a device. Low-level CUDA bindings require contiguous
 2-D inputs, and validate backward gradient dtype and FP32 statistics. Empty
 batches are supported by the bindings.
 
-Only `norm_before_gate=True` and `group_size=None` are implemented — the single
-configuration vLLM's GDN block constructs. Other configurations fail closed rather
-than being approximated (RFC #428 §6 item 7).
+Only `norm_before_gate=True` and `group_size=None` are implemented — the
+configuration vLLM's GDN block constructs. The op has no parameter for either, so
+other configurations are not implemented rather than rejected at runtime.
 
 ## Dispatch Behavior
 
@@ -70,22 +71,39 @@ that raises at call time.
 
 ## Accuracy
 
-Claim level: **L0 repeatable, L1 batch-invariant**. L2 is not claimed.
+Claim levels:
+
+- **L1** for `Qwen3NextRMSNormGatedCudaOp`: prefix slices in
+  `tests/test_qwen3_next_norm.py`, and batch size, chunking, padding and permutation
+  in the C3/C4 gates (`ci/run_ws1_gtest.sh`).
+- **L0** is not separately tested for the CUDA op; no test repeats it.
+- L2 is not claimed.
+
+Accuracy tests resolve their tolerances from `tolerance_contract.json`
+(`forward_accuracy`, `reduction` × dtype). The bounds in
+`tests/check_qwen3_next_norm_providers.py` are provider-gap bounds, not contract
+thresholds.
 
 `transformers` casts the normalized value back to the input dtype *before* the weight
-multiply; vLLM keeps it in fp32. On B200 / bf16 / `head_v_dim=128` the two differ in
-35% of elements with `max|diff| = 6.25e-2`, and isolating the cast order alone
-reproduces the gap — so the cast order dominates, not the reduction order. Because
-RFC #428 measures L2 against vLLM rollout, the fp32 multiply is the strict default;
-`Qwen3NextRMSNormGatedHFOp` keeps the other convention as a tested witness.
-
-"Bitwise equal to vLLM" is undefined until a provider is named: vLLM's own
-`forward_native` and `forward_cuda` disagreed on 21 of 40 seeds (worst 1.56e-2 in
-bf16; ~36% of elements at fp32 ULP in fp32). What this reproduces is the convention;
-the residual is the reduction tree.
-
-`rstd` is bitwise identical to the ungated kernel's for the same `x` — asserted, so a
-gate leaking into the statistic would fail.
+multiply; vLLM keeps it in fp32. `Qwen3NextRMSNormGatedOp` follows vLLM and
+`Qwen3NextRMSNormGatedHFOp` keeps the transformers convention as a witness.
+`test_gated_conventions_diverge_in_low_precision` asserts that the two differ in bf16
+(by more than `1e-3`) and agree bitwise in fp32. One earlier measurement, with no
+committed script, found them differing on 35% of elements with `max|diff| = 6.25e-2`
+(one seed, B200, bf16, `head_v_dim=128`, 512 rows, direct eager calls). That is a
+one-off observation, not an assertion. Which convention the strict profile should use
+is an open question for RFC #428.
+
+"Bitwise equal to vLLM" is undefined until a provider is named. Over 40 seeds (bf16,
+`head_v_dim=128`, 512 rows, B200), vLLM's eager `forward_native` and `forward_cuda`
+disagreed on 21, worst `1.56e-2`; in fp32 they stay within the asserted `1e-5`. These
+figures come from `tests/check_qwen3_next_norm_providers.py`, which imports real vLLM
+and must be run explicitly. The PyTorch reference reproduces the convention, not
+vLLM's reduction tree.
+
+`rstd` is bitwise identical to the ungated kernel's for the same `x`, asserted over
+fp32/fp16/bf16 × H ∈ {128, 2048, 5120} × {silu, sigmoid} × offset ∈ {0, 1}, so a gate
+leaking into the statistic would fail.
 
 Backward is assembled from deterministic pieces: `dx` from a row-local kernel,
 `dweight` from fp32 row contributions reduced by the ascending-row left fold, and
diff --git a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
index 3c66e144c..08934d9bb 100644
--- a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
+++ b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
@@ -182,10 +182,20 @@ def _require_cuda_symbols(what: str, *names: str) -> None:
         )
 
 
-#: Gate activations understood by the CUDA kernel, in binding order.
+#: Gate activations understood by the CUDA kernel, in binding order. ``swish`` is
+#: an alias for ``silu``, as in vLLM's GDN block, which maps ``output_gate_type``
+#: "swish" to "silu" before constructing ``RMSNormGated``.
 _GATE_ACTIVATIONS = {"silu": 0, "swish": 0, "sigmoid": 1}
 
 
+def _check_gate_activation(activation: str) -> int:
+    if activation not in _GATE_ACTIVATIONS:
+        raise ValueError(
+            f"activation must be one of {sorted(_GATE_ACTIVATIONS)}, got {activation!r}"
+        )
+    return _GATE_ACTIVATIONS[activation]
+
+
 def _gate_activation_fp32(gate: torch.Tensor, activation: int) -> torch.Tensor:
     """act(gate) in fp32, matching the kernel's ``gate_activation``."""
     gate32 = gate.float()
@@ -257,7 +267,10 @@ def backward(ctx, grad_out):
 
         # dgate: row-local and reduction-free.
         normed = x.float() * rstd.unsqueeze(-1)
-        scale = weight.float() + ctx.weight_offset
+        # Same guard as the kernels: an unconditional `+ 0.0` turns -0.0 weights into +0.0.
+        scale = weight.float()
+        if ctx.weight_offset != 0.0:
+            scale = scale + ctx.weight_offset
         dgate = (dy.float() * normed * scale * _gate_activation_grad_fp32(gate, act)).to(gate.dtype)
 
         record_backward(
@@ -280,13 +293,8 @@ def rmsnorm_gated_cuda(x, weight, gate, eps=1e-6, weight_offset=0.0, activation=
         y = rmsnorm_gated_cuda(x, weight, gate)
         y = rmsnorm_gated_cuda(x, weight, gate, activation="sigmoid")
     """
-    if activation not in _GATE_ACTIVATIONS:
-        raise ValueError(
-            f"activation must be one of {sorted(_GATE_ACTIVATIONS)}, got {activation!r}"
-        )
-    return RMSNormGatedCuda.apply(
-        x, weight, gate, eps, weight_offset, _GATE_ACTIVATIONS[activation]
-    )
+    act = _check_gate_activation(activation)
+    return RMSNormGatedCuda.apply(x, weight, gate, eps, weight_offset, act)
 
 
 class Qwen3NextRMSNormGatedCudaOp:
@@ -298,8 +306,11 @@ class Qwen3NextRMSNormGatedCudaOp:
     ``out = x * rstd * weight * silu(gate)``, every multiply in fp32 with a
     single cast at the store. The weight is plain, not zero-centred, matching
     vLLM's ``RMSNormGated`` with ``norm_before_gate=True`` and ``group_size=None``
-    -- which is exactly how the GDN block constructs it. Other configurations
-    are rejected rather than approximated.
+    -- which is how the GDN block constructs it. The op has no ``norm_before_gate``
+    or ``group_size`` parameter, so other configurations are not implemented.
+
+    ``activation`` is fixed at construction; the registry constructs the
+    released config's ``"silu"``.
     """
 
     backward_impl = "cuda_rmsnorm_gated_dx_declared_fp32_rowfold_dw"
@@ -307,9 +318,10 @@ class Qwen3NextRMSNormGatedCudaOp:
     #: The gated weight is plain; kept as an attribute so the surface matches
     #: the ungated op and a zero-centred variant stays one subclass away.
     weight_offset = 0.0
-    activation = "silu"
 
-    def __init__(self) -> None:
+    def __init__(self, activation: str = "silu") -> None:
+        _check_gate_activation(activation)
+        self.activation = activation
         _require_cuda_symbols(
             "Gated CUDA RMSNorm", "rmsnorm_gated_forward", "rmsnorm_gated_backward_dx"
         )
diff --git a/rl_engine/testing/qwen3_next_workload.py b/rl_engine/testing/qwen3_next_workload.py
index ffe3367cd..63c1f2399 100644
--- a/rl_engine/testing/qwen3_next_workload.py
+++ b/rl_engine/testing/qwen3_next_workload.py
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
 """Qwen3-Next norm-only C3/C4 workload, explicitly separate from the Dense chain."""
 
 from collections.abc import Mapping
diff --git a/rl_engine/testing/ws1_workload.py b/rl_engine/testing/ws1_workload.py
index 997f441e7..9162aa798 100644
--- a/rl_engine/testing/ws1_workload.py
+++ b/rl_engine/testing/ws1_workload.py
@@ -273,6 +273,21 @@ def load_manifest(path: str | Path | None = None) -> WS1Manifest:
     return WS1Manifest(raw=raw, path=manifest_path)
 
 
+def workload_report(manifest: WS1Manifest) -> dict[str, Any]:
+    """The ``workload`` block the C3/C4 gate scripts attach to their JSON report.
+
+    ``full_model_evidence`` is whatever the manifest declares, and ``None`` when
+    it declares nothing.
+    """
+    return {
+        "workload_id": manifest.workload_id,
+        "scope": manifest.raw.get("scope", "qwen3_8b_dense"),
+        "fixture_identity_sha256": manifest.raw["fixture_identity_sha256"],
+        "model_id": manifest.model_identity["model_id"],
+        "full_model_evidence": manifest.raw.get("full_model_evidence"),
+    }
+
+
 def validate_manifest(raw: Mapping[str, Any]) -> None:
     """Hard-fail if any required C2 pin is missing or inconsistent."""
     missing = [k for k in _REQUIRED_TOP_LEVEL if k not in raw]
diff --git a/rl_engine/tests/test_dispatch.py b/rl_engine/tests/test_dispatch.py
index 7f1066ab0..716cb8c09 100644
--- a/rl_engine/tests/test_dispatch.py
+++ b/rl_engine/tests/test_dispatch.py
@@ -259,64 +259,65 @@ def test_executor_flow():
         print(f"\n Test failed with error: {e}")
 
 
-def test_gated_rms_norm_priority_is_cuda_first_with_pytorch_fallback():
-    """The gated norm has a CUDA kernel; every other platform falls back.
-
-    No Triton, ROCm or Ascend gated kernel exists yet, so those platforms must
-    resolve to the PyTorch reference rather than to nothing -- an operator
-    missing from a priority map falls through to ``OpBackend.PYTORCH_NATIVE``,
-    which is the logprob op, not a norm.
-    """
-    registry = KernelRegistry()
-
-    assert registry._priority_map["cuda"]["rms_norm_gated"] == [
+_QWEN3_NEXT_NORMS = [
+    pytest.param(
+        "rms_norm_gated",
         OpBackend.CUDA_RMS_NORM_GATED,
         OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED,
-    ]
-    for platform in ("rocm", "musa", "cpu", "npu"):
-        assert registry._priority_map[platform]["rms_norm_gated"] == [
-            OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED
-        ], platform
-
-
-def test_gated_rms_norm_cuda_backend_reports_absence_by_failing_construction(monkeypatch):
-    """Without the compiled symbols the CUDA backend must be skipped, not returned."""
-    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
-    from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import Qwen3NextRMSNormGatedOp
-
-    monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", False)
-    monkeypatch.setattr(cuda_rmsnorm, "_C", None)
-    with pytest.raises(RuntimeError, match="requires the compiled rl_engine._C extension"):
-        cuda_rmsnorm.Qwen3NextRMSNormGatedCudaOp()
-
-    # ... and the registry must therefore hand out the reference, not raise.
-    resolved = KernelRegistry().get_op("rms_norm_gated")
-    assert isinstance(resolved, Qwen3NextRMSNormGatedOp)
-
-
-def test_qwen3_next_rms_norm_priority_is_cuda_first_with_pytorch_fallback():
-    """The zero-centred norm follows the gated one: CUDA kernel first, reference elsewhere."""
-    registry = KernelRegistry()
-
-    assert registry._priority_map["cuda"]["qwen3_next_rms_norm"] == [
+        "Qwen3NextRMSNormGatedCudaOp",
+        "Qwen3NextRMSNormGatedOp",
+        id="rms_norm_gated",
+    ),
+    pytest.param(
+        "qwen3_next_rms_norm",
         OpBackend.CUDA_QWEN3_NEXT_RMS_NORM,
         OpBackend.PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM,
-    ]
+        "Qwen3NextRMSNormCudaOp",
+        "Qwen3NextRMSNormOp",
+        id="qwen3_next_rms_norm",
+    ),
+]
+
+
+@pytest.mark.parametrize("op_type, cuda_backend, ref_backend, cuda_cls, ref_cls", _QWEN3_NEXT_NORMS)
+def test_qwen3_next_norm_priority_is_cuda_first_with_pytorch_fallback(
+    op_type, cuda_backend, ref_backend, cuda_cls, ref_cls
+):
+    """Both Qwen3-Next norms have a CUDA kernel; every other platform falls back.
+
+    No Triton, ROCm or Ascend kernel exists yet, so those platforms must resolve
+    to the PyTorch reference rather than to nothing -- an operator missing from a
+    priority map falls through to ``OpBackend.PYTORCH_NATIVE``, which is the
+    logprob op, not a norm.
+    """
+    registry = KernelRegistry()
+
+    assert registry._priority_map["cuda"][op_type] == [cuda_backend, ref_backend]
     for platform in ("rocm", "musa", "cpu", "npu"):
-        assert registry._priority_map[platform]["qwen3_next_rms_norm"] == [
-            OpBackend.PYTORCH_NATIVE_QWEN3_NEXT_RMS_NORM
-        ], platform
+        assert registry._priority_map[platform][op_type] == [ref_backend], platform
 
 
-def test_qwen3_next_rms_norm_cuda_backend_reports_absence_by_failing_construction(monkeypatch):
-    """The inherited ``RMSNormCudaOp.__init__`` check must reach the subclass."""
+@pytest.mark.skipif(torch.version.hip is not None, reason="a cuda device maps to rocm on HIP")
+@pytest.mark.parametrize("op_type, cuda_backend, ref_backend, cuda_cls, ref_cls", _QWEN3_NEXT_NORMS)
+def test_qwen3_next_norm_cuda_backend_absence_falls_through_to_reference(
+    monkeypatch, op_type, cuda_backend, ref_backend, cuda_cls, ref_cls
+):
+    """Without the compiled symbols the CUDA backend must be skipped, not returned.
+
+    The zero-centred op inherits its check from ``RMSNormCudaOp.__init__``. The
+    lookup names a CUDA device so the CUDA-first list is walked even on a
+    CPU-only host; resolving for the host's own platform would reach the
+    reference through the CPU list without ever trying the CUDA backend.
+    """
     from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
-    from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import Qwen3NextRMSNormOp
+    from rl_engine.kernels.ops.pytorch.norm import qwen3_next_rms_norm as reference
 
     monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", False)
     monkeypatch.setattr(cuda_rmsnorm, "_C", None)
     with pytest.raises(RuntimeError, match="requires the compiled rl_engine._C extension"):
-        cuda_rmsnorm.Qwen3NextRMSNormCudaOp()
+        getattr(cuda_rmsnorm, cuda_cls)()
 
-    resolved = KernelRegistry().get_op("qwen3_next_rms_norm")
-    assert isinstance(resolved, Qwen3NextRMSNormOp)
+    registry = KernelRegistry()
+    resolved = registry.get_op(op_type, device="cuda")
+    assert type(resolved) is getattr(reference, ref_cls)
+    assert cuda_backend.name in registry._failed_backends
diff --git a/scripts/check_forward_invariance.py b/scripts/check_forward_invariance.py
index 57a27088b..9757946c8 100644
--- a/scripts/check_forward_invariance.py
+++ b/scripts/check_forward_invariance.py
@@ -40,7 +40,8 @@
     resolve_profile_candidate,
 )
 from rl_engine.kernels.gtest.tolerance import resolve_dtype_policy  # noqa: E402
-from rl_engine.testing.ws1_workload import load_manifest  # noqa: E402
+from rl_engine.testing.qwen3_next_workload import validate_norm_dimensions  # noqa: E402
+from rl_engine.testing.ws1_workload import load_manifest, workload_report  # noqa: E402
 
 
 def _object_path(value: Any) -> str:
@@ -112,7 +113,12 @@ def parse_args() -> argparse.Namespace:
         if adapter.requirement != "absent_not_required"
     ]
     parser = argparse.ArgumentParser(description="WS1 C3 forward invariance GPU gate")
-    parser.add_argument("--manifest", type=pathlib.Path, default=None)
+    parser.add_argument(
+        "--manifest",
+        type=pathlib.Path,
+        default=None,
+        help="Workload manifest JSON (default: the Qwen3-8B Dense C2 manifest)",
+    )
     parser.add_argument("--op", choices=sorted(runnable), default="rms_norm")
     parser.add_argument(
         "--candidate", required=True, help="Manifest-declared CUDA/Triton/Ascend candidate"
@@ -147,8 +153,6 @@ def main() -> None:
 
     contract = load_contract()
     manifest = load_manifest(args.manifest)
-    from rl_engine.testing.qwen3_next_workload import validate_norm_dimensions
-
     validate_norm_dimensions(manifest.raw, args.op, args.hidden, args.head_dim)
     adapter = get_adapter(args.op)
     if adapter.requirement == "layout_supported":
@@ -236,13 +240,7 @@ def main() -> None:
 
     if args.json:
         payload = report.to_dict()
-        payload["workload"] = {
-            "workload_id": manifest.workload_id,
-            "scope": manifest.raw.get("scope", "qwen3_8b_dense"),
-            "fixture_identity_sha256": manifest.raw["fixture_identity_sha256"],
-            "model_id": manifest.model_identity["model_id"],
-            "full_model_evidence": False,
-        }
+        payload["workload"] = workload_report(manifest)
         print(json.dumps(payload, indent=2, default=str))
     else:
         _summarize(report)
diff --git a/scripts/check_gradient_invariance.py b/scripts/check_gradient_invariance.py
index c2943a4c2..fb4e8ccdf 100644
--- a/scripts/check_gradient_invariance.py
+++ b/scripts/check_gradient_invariance.py
@@ -40,7 +40,8 @@
 )
 from rl_engine.kernels.gtest.gradient_invariance import MissingBackwardError  # noqa: E402
 from rl_engine.kernels.gtest.tolerance import resolve_dtype_policy  # noqa: E402
-from rl_engine.testing.ws1_workload import load_manifest  # noqa: E402
+from rl_engine.testing.qwen3_next_workload import validate_norm_dimensions  # noqa: E402
+from rl_engine.testing.ws1_workload import load_manifest, workload_report  # noqa: E402
 
 
 def _object_path(value: Any) -> str:
@@ -124,7 +125,12 @@ def parse_args() -> argparse.Namespace:
         if adapter.requirement != "absent_not_required"
     ]
     parser = argparse.ArgumentParser(description="WS1 C4 gradient invariance GPU gate")
-    parser.add_argument("--manifest", type=pathlib.Path, default=None)
+    parser.add_argument(
+        "--manifest",
+        type=pathlib.Path,
+        default=None,
+        help="Workload manifest JSON (default: the Qwen3-8B Dense C2 manifest)",
+    )
     parser.add_argument("--op", choices=sorted(runnable), default="rms_norm")
     parser.add_argument(
         "--candidate", required=True, help="Manifest-declared CUDA/Triton/Ascend candidate"
@@ -160,8 +166,6 @@ def main() -> None:
 
     contract = load_contract()
     manifest = load_manifest(args.manifest)
-    from rl_engine.testing.qwen3_next_workload import validate_norm_dimensions
-
     validate_norm_dimensions(manifest.raw, args.op, args.hidden, args.head_dim)
     adapter = get_adapter(args.op)
     if adapter.requirement == "layout_supported":
@@ -258,13 +262,7 @@ def main() -> None:
 
     if args.json:
         payload = report.to_dict()
-        payload["workload"] = {
-            "workload_id": manifest.workload_id,
-            "scope": manifest.raw.get("scope", "qwen3_8b_dense"),
-            "fixture_identity_sha256": manifest.raw["fixture_identity_sha256"],
-            "model_id": manifest.model_identity["model_id"],
-            "full_model_evidence": False,
-        }
+        payload["workload"] = workload_report(manifest)
         print(json.dumps(payload, indent=2, default=str))
     else:
         _summarize(report)
diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
index f9c10b46e..be48ebd07 100644
--- a/tests/test_qwen3_next_norm.py
+++ b/tests/test_qwen3_next_norm.py
@@ -636,8 +636,7 @@ def test_cuda_gated_parameter_vjp_contributions_match_the_fold(dtype, activation
     x, w, gate = (t.to(dtype) for t in _gated_cuda_inputs(rows=512))
     w = w.float().requires_grad_()
     dy = torch.randn_like(x)
-    op = Qwen3NextRMSNormGatedCudaOp()
-    op.activation = activation
+    op = Qwen3NextRMSNormGatedCudaOp(activation=activation)
     op.forward(x, w, gate, eps=_EPS).backward(dy)
     rows = op.parameter_vjp_contributions_fp32(x=x, weight=w, gate=gate, grad_output=dy, eps=_EPS)[
         "weight"
@@ -684,6 +683,27 @@ def test_cuda_gated_rejects_bad_activation():
         rmsnorm_gated_cuda(x, w, gate, eps=_EPS, activation="gelu")
 
 
+def test_gated_cuda_op_rejects_bad_activation_at_construction():
+    """The activation is validated before the extension check, so this runs on CPU."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp
+
+    with pytest.raises(ValueError, match="activation must be one of"):
+        Qwen3NextRMSNormGatedCudaOp(activation="gelu")
+
+
+@requires_cuda_gated
+def test_cuda_gated_swish_is_an_alias_for_silu():
+    """vLLM maps output_gate_type "swish" to "silu"; the alias must not select sigmoid."""
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp
+
+    x, w, gate = _gated_cuda_inputs(rows=64)
+    swish = Qwen3NextRMSNormGatedCudaOp(activation="swish").forward(x, w, gate, eps=_EPS)
+    silu = Qwen3NextRMSNormGatedCudaOp(activation="silu").forward(x, w, gate, eps=_EPS)
+    sigmoid = Qwen3NextRMSNormGatedCudaOp(activation="sigmoid").forward(x, w, gate, eps=_EPS)
+    assert torch.equal(swish, silu)
+    assert not torch.equal(swish, sigmoid)
+
+
 @requires_cuda_gated
 def test_cuda_gated_rejects_mismatched_gate():
     from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda
@@ -844,3 +864,23 @@ def test_cuda_gated_signed_zero_weight_is_preserved(dtype):
     expected = x * weight
     bits = torch.int32 if dtype == torch.float32 else torch.int16
     assert torch.equal(actual.view(bits), expected.view(bits))
+
+
+@requires_cuda_gated
+@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
+def test_cuda_gated_dgate_keeps_signed_zero_weight(dtype):
+    """dgate scales by the weight too, so it must not turn a -0.0 weight into +0.0.
+
+    dgate = dy * x * rstd * w * act'(gate); with dy, rstd, act'(1) > 0 its sign is
+    the sign of ``x * w``, zeros included.
+    """
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import rmsnorm_gated_cuda
+
+    x = torch.ones(2, 128, device="cuda", dtype=dtype)
+    x[1].neg_()
+    weight = torch.full((128,), -0.0, device="cuda", dtype=dtype)
+    gate = torch.ones_like(x).requires_grad_()
+    rmsnorm_gated_cuda(x, weight, gate).backward(torch.ones_like(x))
+    expected = x * weight
+    bits = torch.int32 if dtype == torch.float32 else torch.int16
+    assert torch.equal(gate.grad.view(bits), expected.view(bits))
diff --git a/tests/test_qwen3_next_workload.py b/tests/test_qwen3_next_workload.py
index e433de102..b0fafe26c 100644
--- a/tests/test_qwen3_next_workload.py
+++ b/tests/test_qwen3_next_workload.py
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
 """The norm workload cannot stand in for Dense or full-checkpoint evidence."""
 
 import copy

From 0cceb8445a81f4b346572bbb387f9ecc4fb10335 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 2 Oct 2026 02:19:47 +0000
Subject: [PATCH 15/70] fix(rmsnorm): use OptionalCUDAGuard in the launchers
 outside the ROCm guard

The six rmsnorm launchers compiled on both CUDA and ROCm (plain forward and
dx, gated forward and dx, partial and reduce dweight) constructed a
non-optional c10::cuda::CUDAGuard. No other file the ROCm build compiles uses
that class outside a USE_ROCM guard, while activation.cu and
deterministic_attention.cu use at::cuda::OptionalCUDAGuard. Match the guard used
by activation.cu so the ROCm build relies only on patterns it already compiles.

The guard follows the same tensor as before (x, or partial_dw for the reduce
launcher). CUDA behaviour is unchanged: these tensors are always on a CUDA
device, so the optional guard always sets the device. The left-fold launcher
sits inside the USE_ROCM guard and keeps its c10::cuda::CUDAGuard. The ROCm
build is still untested.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 csrc/cuda/rmsnorm.cu | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/csrc/cuda/rmsnorm.cu b/csrc/cuda/rmsnorm.cu
index 4249cf525..5a334ea8a 100644
--- a/csrc/cuda/rmsnorm.cu
+++ b/csrc/cuda/rmsnorm.cu
@@ -466,7 +466,7 @@ void rmsnorm_gated_forward_cuda(
     double weight_offset,
     int64_t activation
 ) {
-    const c10::cuda::CUDAGuard device_guard(x.device());
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
     int T = x.size(0);
     if (T == 0) return;
     int H = x.size(1);
@@ -506,7 +506,7 @@ void rmsnorm_gated_backward_dx_cuda(
     double weight_offset,
     int64_t activation
 ) {
-    const c10::cuda::CUDAGuard device_guard(x.device());
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
     int T = x.size(0);
     if (T == 0) return;
     int H = x.size(1);

From cf68fd94cc10e61b8898d9ff3ac41e57639db09a Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 4 Oct 2026 02:07:15 +0000
Subject: [PATCH 16/70] docs(norm): reconcile the C1 norm page, docstrings and
 gated tests with the stacked C1 branch

Carried over from the former merge of the C1 branch into this one: the decoder-norm page again states the registration and the check_operator command, the gated tests use the contract's reduction tolerances through _forward_tol, and the reference docstrings name the gated page. Same final tree as before the re-stack.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/operators/qwen3-next-rms-norm.md            | 16 ++++++++--------
 .../ops/pytorch/norm/qwen3_next_rms_norm.py      |  5 +++--
 tests/test_qwen3_next_norm.py                    |  9 +++------
 3 files changed, 14 insertions(+), 16 deletions(-)

diff --git a/docs/operators/qwen3-next-rms-norm.md b/docs/operators/qwen3-next-rms-norm.md
index e5e9a47c0..420409857 100644
--- a/docs/operators/qwen3-next-rms-norm.md
+++ b/docs/operators/qwen3-next-rms-norm.md
@@ -49,11 +49,11 @@ y = rmsnorm_cuda(x, weight, eps=1e-6, weight_offset=1.0)
 
 ## Dispatch Behavior
 
-Not registered on this branch: there is no `qwen3_next_rms_norm` gtest spec and no
-registry entry yet. Both arrive with the gated-norm PR. Until then, construct the
-ops directly as in "Entry Point". The CUDA op validates the compiled symbols in
-`__init__`, so on a build without the extension construction raises instead of
-handing out an op that fails at call time.
+Registered as the `qwen3_next_rms_norm` gtest operator. On CUDA the registry
+prefers `Qwen3NextRMSNormCudaOp`; every other platform resolves to the PyTorch
+reference. The CUDA op validates the compiled symbols in `__init__`, so on a build
+without the extension construction raises and the registry falls through to the
+reference rather than handing out an op that fails at call time.
 
 ## Accuracy
 
@@ -102,16 +102,16 @@ element and no extra memory traffic.
 
 ```bash
 python -m pytest tests/test_qwen3_next_norm.py -v
+python scripts/check_operator.py --op qwen3_next_rms_norm --candidate cuda \
+    --device cuda --dtype bf16 --check-grad
 ```
 
 ## Known Limitations
 
 - CUDA only; no ROCm, Ascend or Triton backend.
-- Not registered as a gtest operator or in the registry on this branch (see
-  "Dispatch Behavior").
 - Not bitwise against vLLM (see Accuracy). An L2 claim needs a single source of
   truth for the forward on both sides, per RFC #428 §0 item 1.
 - The gated pair (`Qwen3NextRMSNormGatedOp`, `Qwen3NextRMSNormGatedHFOp`) is
-  documented with its CUDA kernel in the gated-norm PR, not on this page.
+  documented with its CUDA kernel on [Gated RMSNorm](qwen3-next-rms-norm-gated.md).
 - Measured on sm_100 (B200). Per RFC #428 §2.2 no claim carries across
   H100/H200/B100/B200.
diff --git a/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py b/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
index 1f84f86f3..598eaf3ad 100644
--- a/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
+++ b/rl_engine/kernels/ops/pytorch/norm/qwen3_next_rms_norm.py
@@ -22,7 +22,8 @@
 All three reuse :func:`shape_invariant_rstd`, a fixed-order reduction. They
 reproduce the weight convention and cast order, not vLLM's reduction tree, and are
 not bitwise equal to any vLLM path probed so far. Claim levels, measurements and
-limitations are in ``docs/operators/qwen3-next-rms-norm.md``.
+limitations are in ``docs/operators/qwen3-next-rms-norm.md`` and, for the gated
+pair, ``docs/operators/qwen3-next-rms-norm-gated.md``.
 """
 
 from __future__ import annotations
@@ -61,7 +62,7 @@ class Qwen3NextRMSNormGatedOp:
     ``out = (x * rstd * weight) * silu(gate)``, with every multiply in fp32 and
     a single cast on the way out. This is the convention vLLM's ``RMSNormGated``
     uses with ``norm_before_gate=True``. Which gated convention is the strict
-    default is still open; see the operator page.
+    default is still open; see ``docs/operators/qwen3-next-rms-norm-gated.md``.
 
     Not a subclass of the plain op: it takes an extra tensor and its epilogue
     differs, so it is not a drop-in substitute for one.
diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
index be48ebd07..ffa20253f 100644
--- a/tests/test_qwen3_next_norm.py
+++ b/tests/test_qwen3_next_norm.py
@@ -475,9 +475,6 @@ def test_cuda_zero_centred_backward_is_offset_aware():
     not _HAS_CUDA_GATED, reason="gated RMSNorm CUDA extension is not available"
 )
 
-# tolerance_contract.json, judgments/forward_accuracy/by_op_class/reduction/bfloat16
-_BF16_ATOL, _BF16_RTOL = 2e-2, 1.6e-2
-
 
 def _gated_cuda_inputs(seed=0, rows=512, hidden=_HEAD_V_DIM, dtype=torch.bfloat16):
     g = torch.Generator(device="cuda").manual_seed(seed)
@@ -494,7 +491,7 @@ def test_cuda_gated_matches_golden_within_contract():
     x, w, gate = _gated_cuda_inputs()
     got = Qwen3NextRMSNormGatedCudaOp().forward(x, w, gate, eps=_EPS)
     ref = Qwen3NextRMSNormGatedOp().forward_fp32(x, w, gate, eps=_EPS)
-    torch.testing.assert_close(got.float(), ref, atol=_BF16_ATOL, rtol=_BF16_RTOL)
+    torch.testing.assert_close(got.float(), ref, **_forward_tol(torch.bfloat16))
 
 
 @requires_cuda_gated
@@ -551,7 +548,7 @@ def test_cuda_gated_unit_weight_is_plain_norm_times_silu():
     ones = torch.ones(_HEAD_V_DIM, device="cuda", dtype=x.dtype)
     got = Qwen3NextRMSNormGatedCudaOp().forward(x, ones, gate, eps=_EPS).float()
     ref = rmsnorm_cuda(x, ones, eps=_EPS).float() * F.silu(gate.float())
-    torch.testing.assert_close(got, ref, atol=_BF16_ATOL, rtol=_BF16_RTOL)
+    torch.testing.assert_close(got, ref, **_forward_tol(torch.bfloat16))
 
 
 @requires_cuda_gated
@@ -561,7 +558,7 @@ def test_cuda_gated_sigmoid_activation():
     x, w, gate = _gated_cuda_inputs()
     got = rmsnorm_gated_cuda(x, w, gate, eps=_EPS, activation="sigmoid").float()
     ref = rmsnorm_cuda(x, w, eps=_EPS).float() * torch.sigmoid(gate.float())
-    torch.testing.assert_close(got, ref, atol=_BF16_ATOL, rtol=_BF16_RTOL)
+    torch.testing.assert_close(got, ref, **_forward_tol(torch.bfloat16))
 
 
 @requires_cuda_gated

From ef4c88de2ec684b329c258c532260a84389c7888 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 16:39:36 +0000
Subject: [PATCH 17/70] feat(ws1): Gated DeltaNet decode-step goldens for RFC
 #428 C6

The trainer-side reference for a Qwen3-Next rollout decode step: the recurrent
Gated DeltaNet update and the causal-conv1d state update that precedes it.
Claim level: L0 repeatable and L1 batch-invariant; L2 is not claimed.

Design notes, measurements and the deferral list are in
docs/design/ws1-c6-428-gdn-recurrent-replay.md.

Provider
--------
`fused_recurrent_gated_delta_rule_packed_decode`, not
`fused_sigmoid_gating_delta_rule_update`. A decode-only, non-speculative batch
returns early at qwen_gdn_linear_attn.py:1295-1307 into the packed path because
VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE defaults to true; the sigmoid-gating
kernel is the fallback for mixed prefill+decode and spec decode. The env
defaults that decide this are asserted, so a vLLM bump fails loudly.

Three places the kernel and the HF model disagree, transcribed from the kernel:

  * The gating is fused -- beta = sigmoid(b) and
    g = -exp(A_log) * softplus(a + dt_bias) are computed in fp32 inside the
    kernel with a threshold branch at 20, not as separate PyTorch ops.
  * No repeat_interleave: the kernel indexes i_h = i_hv // (HV // H).
  * The QK norm is an L2 norm over a plain sum, x / sqrt(sum(x*x) + 1e-6),
    dividing by sqrt rather than multiplying by rsqrt. scale hits q after the
    norm; k is never scaled.

A fourth candidate divergence turned out not to be one: prefill passes
use_qk_l2norm_in_kernel=False only because fused_post_conv_prep(apply_l2norm=True)
already normalized q/k. Both paths normalize exactly once.

Conv accumulation order is load-bearing: taps accumulate sequentially,
acc = acc + win[t] * w[t] from zero. With an fp32 cache that reproduces the
provider bitwise; a tree sum over the same four terms does not, and neither
does an FMA.

State ABI is mirrored: paged [num_blocks, HV, V, K] and
[num_blocks, dim, width-1], NULL_BLOCK_ID (<= 0) skipping, an fp32 accumulator,
and a store that rounds to the cache dtype -- fp32 or bf16, per
FUSED_GDN_STATE_DTYPES. Contractions run in the repo's fixed 32-wide chunk order
via one `_chunked_sum` primitive, which is what the L1 claim rests on.

Agreement, B200, Qwen3-Next dims (H=16, HV=32, K=V=128)
-------------------------------------------------------
  recurrent, fp32 state, B=1..64   max|d out| 1.5e-08..6.1e-05, state <= 3.0e-07
  recurrent, bf16 state, B=1..64   max|d out| 3.7e-09..3.1e-05, state <= 2.0e-03
  conv: the rolled state is BITWISE exact in every configuration
  conv, fp32 cache: output bitwise but for 15 of 524288 elements at B=64
  conv, bf16 cache: output agrees on ~63%, each disagreement one bf16 ULP
  L1: a sequence's output and state block are bitwise identical alone or at any
      position in a batch of 64, for both state dtypes

Where the provider rounds in the bf16-conv-cache case is not reproduced;
recorded as an open gap rather than guessed at.

Decode versus chunked prefill
-----------------------------
  T=8    max|d| 3.66e-04 fp32 state / 5.49e-04 bf16   (7.0e-03 / 1.0e-02 rel)
  T=64   4.88e-04 / 5.49e-04                          (6.6e-03 / 7.5e-03 rel)
  T=256  3.66e-04 / 3.66e-04                          (5.0e-03 / 5.0e-03 rel)

~0.5-1% relative and flat in T. The recurrence is contracting -- per-step decay
exp(g) averages ~0.47 -- so old rounding error is forgotten at roughly the rate
old signal is, and the fp32-vs-bf16 state drift saturates at ~2% relative on the
state (2.67e-03 at step 1, 1.52e-02 at 64, 2.47e-02 at 1024: a 16x longer run
past step 64 grows it 1.6x).

So the two paths are not bitwise, and ~1% relative on logits is still material
for RL importance ratios, but this is a bounded error rather than a divergence.
An fp32 recurrent state remains the right choice; the reason is the plateau, not
a blow-up. Separately, the chunked prefill kernel refuses fp32 q/k/v outright
(chunk.py:213), and causal_conv1d_update does not bounds-check
conv_state_indices under its default validate_data=False.

tests/check_gdn_recurrent_golden.py is named check_ rather than test_, following
tests/distributed/check_*.py: it imports real vLLM, and
tests/test_framework_operator_integrations.py asserts vllm is absent from
sys.modules.

  pytest tests/check_gdn_recurrent_golden.py -q
  pytest tests/ rl_engine/tests/ -q -p no:randomly \
      --ignore=tests/test_rocm_aiter_api_contract.py

The new file passes and the full suite gains no failure.

Absolute suite counts are reported in the PR description against a named base
commit, not here: they shift whenever a sibling test is added, so a count frozen
in a commit message goes stale the moment the branch is rebased.

Not covered, deliberately: speculative decode and MTP, a backward for the
recurrence, and the provider bridge. Reasons in the design doc.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../design/ws1-c6-428-gdn-recurrent-replay.md | 145 +++++
 .../ops/pytorch/linear_attn/__init__.py       |  20 +
 .../ops/pytorch/linear_attn/causal_conv1d.py  | 193 +++++++
 .../pytorch/linear_attn/gated_delta_rule.py   | 294 ++++++++++
 tests/check_gdn_recurrent_golden.py           | 501 ++++++++++++++++++
 5 files changed, 1153 insertions(+)
 create mode 100644 docs/design/ws1-c6-428-gdn-recurrent-replay.md
 create mode 100644 rl_engine/kernels/ops/pytorch/linear_attn/__init__.py
 create mode 100644 rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py
 create mode 100644 rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
 create mode 100644 tests/check_gdn_recurrent_golden.py

diff --git a/docs/design/ws1-c6-428-gdn-recurrent-replay.md b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
new file mode 100644
index 000000000..d0d0f3ea3
--- /dev/null
+++ b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
@@ -0,0 +1,145 @@
+# WS1 C6 — Qwen3-Next Gated DeltaNet recurrent replay
+
+Design notes for RFC #428 work item C6 (GDN recurrent response replay) on the CUDA
+track. Measured on 2× B200 (sm_100), torch 2.13.0+cu130, vllm 0.30.0,
+transformers 5.17.0.
+
+Claim level reached: **L0 repeatable, L1 batch-invariant**. L2 is not claimed.
+
+## 1. Which provider a rollout decode actually takes
+
+vLLM 0.30.0 has three GDN decode paths, selected by env defaults rather than by the
+model:
+
+| condition | path |
+|---|---|
+| `VLLM_GDN_DECODE_KERNEL="cuda"` (default) + MTP | `torch.ops._C.fused_gdn_decode_post_conv_mtp` — conv, recurrence and the gated norm fused |
+| `VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE=1` (default), decode-only, non-spec | `fused_recurrent_gated_delta_rule_packed_decode` |
+| otherwise | `fused_sigmoid_gating_delta_rule_update` |
+
+A pure RL rollout decode — N sequences each emitting one token, no speculative
+decoding — returns early at `qwen_gdn_linear_attn.py:1295-1307` into the **packed**
+path. The golden targets that one. Aligning against the sigmoid-gating kernel would
+validate a path production does not take.
+
+`tests/check_qwen3_next_norm_providers.py` asserts those two env defaults, so a vLLM
+bump that flips either fails loudly rather than silently re-pointing the claim.
+
+## 2. Three places the kernel and the HF model disagree
+
+The golden is transcribed from the kernel, not from `modeling_qwen3_next.py`:
+
+1. **The gating is fused.** `beta = sigmoid(b)` and
+   `g = -exp(A_log) * softplus(a + dt_bias)` are computed inside the Triton kernel,
+   with a `softplus` threshold branch at 20. HF computes them as separate PyTorch
+   ops — a different rounding path.
+2. **No `repeat_interleave`.** The kernel indexes `i_h = i_hv // (HV // H)`, so q/k
+   stay at 16 heads while v has 32. HF materializes the repeat.
+3. **The QK norm is an L2 norm over a plain sum**, `x / sqrt(sum(x*x) + 1e-6)` — not
+   an RMSNorm, not `F.normalize`, and dividing by `sqrt` rather than multiplying by
+   `rsqrt`, which differs in the last bit. `scale` is applied to `q` *after* the
+   norm; `k` is never scaled.
+
+A fourth, checked and found **not** to be a divergence: prefill passes
+`use_qk_l2norm_in_kernel=False` only because `fused_post_conv_prep(apply_l2norm=True)`
+already normalized q/k. Both paths normalize exactly once.
+
+## 3. State ABI
+
+Mirrored rather than reinvented:
+
+- recurrent state `[num_blocks, HV, V, K]`, V-major, addressed by `ssm_state_indices`
+- conv state `[num_blocks, dim, width-1]`, layout chosen by the global
+  `is_conv_state_dim_first()`; the golden takes it as an argument and both are tested
+- `NULL_BLOCK_ID` (index `<= 0`) means skip: zeros out, block untouched
+- the accumulator is fp32 for the whole step; the store rounds to the state tensor's
+  dtype, which `FUSED_GDN_STATE_DTYPES` allows to be fp32 **or** bf16
+- `causal_conv1d_update` casts `x` to the cache dtype before anything else
+
+Contractions run in the repo's fixed 32-wide chunk order rather than `torch.matmul`,
+whose reduction order is unspecified. That is what the L1 claim rests on, and also
+why the golden is not bitwise against the kernel's tree.
+
+## 4. Agreement with the provider
+
+Qwen3-Next dims (H=16, HV=32, K=V=128), `use_qk_l2norm_in_kernel=True`:
+
+| | max\|diff\| out | max\|diff\| state |
+|---|---|---|
+| fp32 state, B=1..64 | 1.5e-08 .. 6.1e-05 | ≤ 3.0e-07 |
+| bf16 state, B=1..64 | 3.7e-09 .. 3.1e-05 | ≤ 2.0e-03 |
+
+Causal conv (`conv_dim=8192`, `W=4`, B ≤ 64): the **rolled state is bitwise exact in
+every configuration**. With an fp32 cache the output is bitwise but for a handful of
+elements (15 of 524288 at B=64); with a bf16 cache it agrees on ~63%, each
+disagreement exactly one bf16 ULP. The conv taps accumulate **sequentially**,
+`acc = acc + win[t] * w[t]` from zero — a tree sum over the same four terms does not
+reproduce the provider, an FMA does not either.
+
+Open: where the provider rounds in the bf16-conv-cache case is not reproduced.
+Recorded rather than guessed at.
+
+## 5. Decode versus chunked prefill
+
+The quantity RFC #428 §4.2 is about. Single sequence, zero initial state:
+
+| T | max\|diff\|, fp32 state | max\|diff\|, bf16 state |
+|---|---|---|
+| 8 | 3.66e-04 (7.0e-03 rel) | 5.49e-04 (1.0e-02 rel) |
+| 64 | 4.88e-04 (6.6e-03 rel) | 5.49e-04 (7.5e-03 rel) |
+| 256 | 3.66e-04 (5.0e-03 rel) | 3.66e-04 (5.0e-03 rel) |
+
+**The gap is ~0.5–1% relative and flat in T** — smaller at T=256 than at T=8 — and the
+state dtype barely moves it.
+
+The reason is that the recurrence is **contracting**: per-step decay `exp(g)` averages
+~0.47 (max 0.996), so old rounding error is forgotten at roughly the rate old signal
+is. Comparing an fp32 state against a bf16 one over 1024 steps shows the same shape:
+
+| step | relative \|d\| state | relative \|d\| out |
+|---|---|---|
+| 1 | 2.67e-03 | 3.47e-03 |
+| 64 | 1.52e-02 | 1.07e-02 |
+| 256 | 2.02e-02 | 1.09e-02 |
+| 1024 | 2.47e-02 | 1.57e-02 |
+
+A 16× longer run past step 64 grows the drift only 1.6×; it saturates at ~2% relative
+on the state and ~1–1.8% on the output.
+
+So the two paths are not bitwise, and ~1% relative on logits is still material for RL
+importance ratios, but this is a bounded, characterizable error rather than a
+divergence. An fp32 recurrent state remains the right choice for exactness work — the
+reason is the 2% plateau, not a blow-up.
+
+Two related constraints: the chunked prefill kernel refuses fp32 q/k/v outright
+(`chunk.py:213`), so the prefill side is bf16-only regardless; and
+`causal_conv1d_update` does not bounds-check `conv_state_indices` under its default
+`validate_data=False` — an index past the cache is an out-of-bounds write, not an
+error.
+
+## 6. Current boundary
+
+Not covered, with reasons:
+
+| deferred | why |
+|---|---|
+| Speculative decode / MTP | `fused_gdn_decode_post_conv_mtp` fuses conv, recurrence and the gated norm; validating it needs a draft model |
+| Backward for the recurrent step | no upstream backward exists, and a naive BPTT through a sequential recurrence is not batch-invariant — it needs its own design |
+| Provider bridge / registry entry | the golden should survive a drift sweep against a real checkpoint first |
+| Paged block allocation policy | the ABI is mirrored; the allocator is not modelled |
+| TP sharding of `A_log` / `dt_bias` | single card only |
+| ROCm / Ascend | CUDA first, per the WS1 order |
+
+`runtime_verified=false` (no checkpoint), `supports_backward=false`,
+`checkpoint=absent`. Op-level agreement says nothing about 48 composed layers.
+
+## 7. Questions for the maintainers
+
+1. Is a **bf16 recurrent state** a supported configuration? The answer decides whether
+   the 2% plateau is a finding or a non-issue.
+2. Is MTP / speculative decode in scope for WS1? If so,
+   `fused_gdn_decode_post_conv_mtp` becomes the primary provider for both the norm and
+   the recurrence, and the largest deferral above reopens.
+3. For the CUDA strict profile, is the single source of truth **vLLM's** arithmetic or
+   **PyTorch eager**? §1 item 1 asks for one provider on both sides but §2.1 does not
+   say which, and the two differ by ~6e-2 in bf16.
diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/__init__.py b/rl_engine/kernels/ops/pytorch/linear_attn/__init__.py
new file mode 100644
index 000000000..28602f18f
--- /dev/null
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/__init__.py
@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Linear-attention operators (Gated DeltaNet and friends)."""
+
+from rl_engine.kernels.ops.pytorch.linear_attn.causal_conv1d import (
+    CausalConv1dUpdateOp,
+)
+from rl_engine.kernels.ops.pytorch.linear_attn.gated_delta_rule import (
+    GatedDeltaRuleRecurrentStepOp,
+    NULL_BLOCK_ID,
+    SOFTPLUS_THRESHOLD,
+)
+
+__all__ = [
+    "CausalConv1dUpdateOp",
+    "GatedDeltaRuleRecurrentStepOp",
+    "NULL_BLOCK_ID",
+    "SOFTPLUS_THRESHOLD",
+]
diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py b/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py
new file mode 100644
index 000000000..ffbac6e13
--- /dev/null
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py
@@ -0,0 +1,193 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Causal depthwise conv1d single-token state update (WS1 ground truth, RFC #428 C6).
+
+The trainer-side reference for ``causal_conv1d_update``, which vLLM calls once
+per decode token before the Gated DeltaNet recurrence.
+
+Semantics were established by differential testing against the provider rather
+than transcribed, and the accumulation order matters:
+
+* ``x`` is cast to ``conv_state.dtype`` **before** anything else, so a bf16
+  conv cache rounds the incoming token (``causal_conv1d.py:1160``).
+* The window is ``[state[..., -(width-1):], x]``; the taps are accumulated
+  **sequentially in tap order**, ``acc = acc + win[t] * w[t]`` starting from
+  zero -- not a tree reduction and not an FMA. With an fp32 conv state this
+  reproduces the provider bitwise; a tree sum does not.
+* Bias and the activation are applied in fp32, with one cast on the way out to
+  the input's original dtype.
+* The new state is the window minus its oldest column.
+
+Measured against the provider on B200 (``conv_dim=8192``, ``W=4``, B up to 64):
+
+* **The rolled state is bitwise exact in every configuration**, fp32 and bf16
+  cache alike -- ``max|diff| = 0``. That is the part the next token consumes,
+  so the recurrence carries no error from here.
+* With an **fp32** conv state the output is bitwise on all but a handful of
+  elements (15 of 524288 at B=64), the residual being fp32 ULP.
+* With a **bf16** conv state the output agrees on ~63% of elements, every
+  disagreement exactly one bf16 ULP.
+
+The bf16 output gap is a rounding-path difference, not a semantic one: none of
+accumulate-in-bf16, bias-in-bf16 or activation-in-bf16 reproduces the provider,
+so where it rounds is left unresolved rather than guessed at. An fp32 conv state
+is what an exactness claim should use anyway -- the same conclusion the
+recurrent state reaches in :mod:`.gated_delta_rule`.
+"""
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+
+from rl_engine.kernels.ops.pytorch.linear_attn.gated_delta_rule import NULL_BLOCK_ID
+
+__all__ = ["CausalConv1dUpdateOp"]
+
+
+class CausalConv1dUpdateOp:
+    """One decode token through the paged causal-conv1d cache.
+
+    ============== ========================== ==========================
+    tensor         shape                      notes
+    ============== ========================== ==========================
+    ``x``          ``[B, dim]``
+    ``conv_state`` ``[num_blocks, dim, W-1]``  paged; ``dim_first`` layout
+    ``weight``     ``[dim, W]``
+    ``bias``       ``[dim]`` or ``None``
+    ``indices``    ``[B]``                     ``<= 0`` skips
+    ============== ========================== ==========================
+
+    Returns ``(out, conv_state)``; the state is updated out of place.
+    """
+
+    def __call__(
+        self,
+        x: torch.Tensor,
+        conv_state: torch.Tensor,
+        weight: torch.Tensor,
+        conv_state_indices: torch.Tensor,
+        *,
+        bias: torch.Tensor | None = None,
+        activation: str | None = "silu",
+        dim_first: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return self.forward(
+            x,
+            conv_state,
+            weight,
+            conv_state_indices,
+            bias=bias,
+            activation=activation,
+            dim_first=dim_first,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        conv_state: torch.Tensor,
+        weight: torch.Tensor,
+        conv_state_indices: torch.Tensor,
+        *,
+        bias: torch.Tensor | None = None,
+        activation: str | None = "silu",
+        dim_first: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return self._update(
+            x,
+            conv_state,
+            weight,
+            conv_state_indices,
+            bias=bias,
+            activation=activation,
+            dim_first=dim_first,
+            output_dtype=x.dtype,
+        )
+
+    def forward_fp32(
+        self,
+        x: torch.Tensor,
+        conv_state: torch.Tensor,
+        weight: torch.Tensor,
+        conv_state_indices: torch.Tensor,
+        *,
+        bias: torch.Tensor | None = None,
+        activation: str | None = "silu",
+        dim_first: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Ground truth: fp32 output, so only the cache dtype rounds."""
+        return self._update(
+            x,
+            conv_state,
+            weight,
+            conv_state_indices,
+            bias=bias,
+            activation=activation,
+            dim_first=dim_first,
+            output_dtype=torch.float32,
+        )
+
+    @staticmethod
+    def _update(
+        x,
+        conv_state,
+        weight,
+        conv_state_indices,
+        *,
+        bias,
+        activation,
+        dim_first,
+        output_dtype,
+    ):
+        if activation not in (None, "silu", "swish"):
+            raise ValueError(f"activation must be None, 'silu' or 'swish', got {activation!r}")
+        if x.dim() != 2:
+            raise ValueError(f"x must be 2-D [B, dim], got {tuple(x.shape)}")
+        if conv_state.dim() != 3:
+            raise ValueError(
+                f"conv_state must be 3-D [num_blocks, dim, W-1], got {tuple(conv_state.shape)}"
+            )
+        if conv_state_indices.dim() != 1 or conv_state_indices.shape[0] != x.shape[0]:
+            raise ValueError("conv_state_indices must be 1-D with one entry per row of x")
+
+        # The "SD" layout stores (state_len, dim); the kernels want (dim, state_len).
+        state = conv_state if dim_first else conv_state.transpose(-1, -2)
+        dim, tail = state.shape[-2], state.shape[-1]
+        width = weight.shape[-1]
+        if weight.shape[0] != dim:
+            raise ValueError(f"weight must be [dim, W] with dim={dim}, got {tuple(weight.shape)}")
+        if tail != width - 1:
+            raise ValueError(f"conv_state tail must be W-1={width - 1}, got {tail}")
+
+        active = conv_state_indices > NULL_BLOCK_ID
+        out = torch.zeros(x.shape, dtype=torch.float32, device=x.device)
+        new_state = conv_state.clone()  # the cache dtype is the contract here
+        if not bool(active.any()):
+            return out.to(output_dtype), new_state
+
+        rows = torch.nonzero(active, as_tuple=False).flatten()
+        blocks = conv_state_indices[rows].long()
+
+        # The incoming token is rounded to the cache dtype before it is used.
+        token = x[rows].to(conv_state.dtype)
+        window = torch.cat([state[blocks], token.unsqueeze(-1)], dim=-1)  # [R, dim, W]
+
+        # Sequential tap accumulation from zero, in fp32. A tree sum over the
+        # same four terms gives a different last bit and does NOT match.
+        acc = torch.zeros(len(rows), dim, dtype=torch.float32, device=x.device)
+        w32 = weight.float()
+        for tap in range(width):
+            acc = acc + window[..., tap].float() * w32[:, tap].unsqueeze(0)
+        if bias is not None:
+            acc = acc + bias.float()
+        if activation in ("silu", "swish"):
+            acc = F.silu(acc)
+
+        out[rows] = acc
+        rolled = window[..., 1:]
+        if dim_first:
+            new_state[blocks] = rolled.to(conv_state.dtype)
+        else:
+            new_state[blocks] = rolled.transpose(-1, -2).to(conv_state.dtype)
+        return out.to(output_dtype), new_state
diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
new file mode 100644
index 000000000..cdf6f17b7
--- /dev/null
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
@@ -0,0 +1,294 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Gated DeltaNet single-token recurrent step (WS1 ground truth for RFC #428 C6).
+
+This is the trainer-side reference for what vLLM runs during rollout decode:
+``fused_recurrent_gated_delta_rule_packed_decode``. That kernel -- not
+``fused_sigmoid_gating_delta_rule_update`` -- is the path a pure RL rollout
+takes, because ``VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE`` defaults to true and
+a decode-only, non-speculative batch returns early into it.
+
+Three things are transcribed from the kernel rather than from the HuggingFace
+model, because they differ:
+
+* **The gating is fused.** ``beta = sigmoid(b)`` and
+  ``g = -exp(A_log) * softplus(a + dt_bias)`` are computed here, in fp32, with
+  the kernel's ``softplus`` threshold branch. HF computes them separately in
+  PyTorch, which is a different rounding path.
+* **No ``repeat_interleave``.** The kernel indexes ``i_h = i_hv // (HV // H)``,
+  so q/k stay at ``H`` heads while v has ``HV``. HF materializes the repeat.
+* **The QK norm is an L2 norm over a plain sum**, ``x / sqrt(sum(x*x) + 1e-6)``,
+  not an RMSNorm and not ``F.normalize``. It divides by ``sqrt`` rather than
+  multiplying by ``rsqrt``; the two differ in the last bit, so the division is
+  kept.
+
+``scale`` is applied to ``q`` *after* the L2 norm; ``k`` is never scaled.
+
+State ABI (mirrored, not reinvented)
+------------------------------------
+The recurrent state is paged: ``[num_blocks, HV, V, K]``, addressed per
+sequence by ``ssm_state_indices``. Index ``<= 0`` is ``NULL_BLOCK_ID`` and means
+"skip": the kernel writes zeros to the output and leaves the block untouched.
+The accumulator is fp32 throughout the step, and the store rounds to the state
+tensor's dtype -- which upstream allows to be fp32 *or* bf16. That rounding is
+part of the recurrence and compounds across tokens, so it is modelled here
+rather than skipped.
+"""
+
+from __future__ import annotations
+
+import torch
+
+__all__ = ["GatedDeltaRuleRecurrentStepOp", "NULL_BLOCK_ID", "SOFTPLUS_THRESHOLD"]
+
+#: Paged-state sentinel: a sequence pointing here is skipped.
+NULL_BLOCK_ID = 0
+
+#: Above this, softplus is the identity (matches the kernel's constexpr).
+SOFTPLUS_THRESHOLD = 20.0
+
+#: Width of the fixed-order reduction chunks. The contraction order must not
+#: depend on the batch layout, so it is pinned here exactly as
+#: :func:`~rl_engine.kernels.ops.pytorch.norm.rms_norm.shape_invariant_rstd`
+#: pins the RMSNorm statistic.
+_REDUCTION_CHUNK = 32
+
+
+def _chunked_sum(x: torch.Tensor) -> torch.Tensor:
+    """Sum the last dim in a fixed 32-wide chunk order.
+
+    The single reduction primitive for this module. Deliberately not
+    ``sum``/``matmul``/``einsum`` on the whole axis: their reduction order is
+    unspecified and may vary with shape, which is what a batch-invariant claim
+    cannot tolerate. Mirrors
+    :func:`~rl_engine.kernels.ops.pytorch.norm.rms_norm.shape_invariant_rstd`.
+    """
+    tail = x.shape[-1]
+    if tail % _REDUCTION_CHUNK != 0:
+        return x.sum(dim=-1)
+    return x.reshape(*x.shape[:-1], -1, _REDUCTION_CHUNK).sum(dim=-1).sum(dim=-1)
+
+
+def _fixed_order_contract(mat: torch.Tensor, vec: torch.Tensor) -> torch.Tensor:
+    """``sum(mat * vec, dim=-1)`` in a fixed chunk order."""
+    return _chunked_sum(mat * vec)
+
+
+def _l2_normalize(x: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
+    """``x / sqrt(sum(x*x) + eps)`` over the last dim, in fp32.
+
+    A plain sum, not a mean: this is an L2 norm, not an RMSNorm. The division is
+    kept rather than folded into a reciprocal-sqrt multiply because the kernel
+    divides.
+    """
+    return x / torch.sqrt(_chunked_sum(x * x) + eps).unsqueeze(-1)
+
+
+def _softplus(x: torch.Tensor, threshold: float = SOFTPLUS_THRESHOLD) -> torch.Tensor:
+    """The kernel's branched softplus; the branch matters for large ``a + dt_bias``."""
+    return torch.where(x <= threshold, torch.log1p(torch.exp(x)), x)
+
+
+class GatedDeltaRuleRecurrentStepOp:
+    """One decode token of the Gated DeltaNet recurrence, over a paged state.
+
+    Shapes follow the provider, not the model definition:
+
+    ============= ====================================== ===================
+    tensor        shape                                  notes
+    ============= ====================================== ===================
+    ``mixed_qkv`` ``[B, H*K + H*K + HV*V]``              q | k | v, packed
+    ``a``, ``b``  ``[B, HV]``
+    ``A_log``     ``[HV]``                               fp32
+    ``dt_bias``   ``[HV]``                               fp32
+    ``state``     ``[num_blocks, HV, V, K]``             paged, V-major
+    ``indices``   ``[B]``                                ``<= 0`` skips
+    ============= ====================================== ===================
+
+    Returns ``(out, state)`` with ``out`` of shape ``[B, 1, HV, V]``. The state
+    is updated out of place; pass the result back to continue the recurrence.
+    """
+
+    def __call__(
+        self,
+        mixed_qkv: torch.Tensor,
+        a: torch.Tensor,
+        b: torch.Tensor,
+        A_log: torch.Tensor,
+        dt_bias: torch.Tensor,
+        state: torch.Tensor,
+        ssm_state_indices: torch.Tensor,
+        *,
+        scale: float,
+        num_k_heads: int,
+        use_qk_l2norm: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return self.forward(
+            mixed_qkv,
+            a,
+            b,
+            A_log,
+            dt_bias,
+            state,
+            ssm_state_indices,
+            scale=scale,
+            num_k_heads=num_k_heads,
+            use_qk_l2norm=use_qk_l2norm,
+        )
+
+    def forward(
+        self,
+        mixed_qkv: torch.Tensor,
+        a: torch.Tensor,
+        b: torch.Tensor,
+        A_log: torch.Tensor,
+        dt_bias: torch.Tensor,
+        state: torch.Tensor,
+        ssm_state_indices: torch.Tensor,
+        *,
+        scale: float,
+        num_k_heads: int,
+        use_qk_l2norm: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Step once, rounding the stored state to ``state.dtype``."""
+        return self._step(
+            mixed_qkv,
+            a,
+            b,
+            A_log,
+            dt_bias,
+            state,
+            ssm_state_indices,
+            scale=scale,
+            num_k_heads=num_k_heads,
+            use_qk_l2norm=use_qk_l2norm,
+            state_dtype=state.dtype,
+            output_dtype=mixed_qkv.dtype,
+        )
+
+    def forward_fp32(
+        self,
+        mixed_qkv: torch.Tensor,
+        a: torch.Tensor,
+        b: torch.Tensor,
+        A_log: torch.Tensor,
+        dt_bias: torch.Tensor,
+        state: torch.Tensor,
+        ssm_state_indices: torch.Tensor,
+        *,
+        scale: float,
+        num_k_heads: int,
+        use_qk_l2norm: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Ground truth: keep the state and the output in fp32.
+
+        The difference from :meth:`forward` is the per-token state rounding, so
+        running both and comparing isolates how much that rounding costs.
+        """
+        return self._step(
+            mixed_qkv,
+            a,
+            b,
+            A_log,
+            dt_bias,
+            state,
+            ssm_state_indices,
+            scale=scale,
+            num_k_heads=num_k_heads,
+            use_qk_l2norm=use_qk_l2norm,
+            state_dtype=torch.float32,
+            output_dtype=torch.float32,
+        )
+
+    @staticmethod
+    def _step(
+        mixed_qkv,
+        a,
+        b,
+        A_log,
+        dt_bias,
+        state,
+        ssm_state_indices,
+        *,
+        scale,
+        num_k_heads,
+        use_qk_l2norm,
+        state_dtype,
+        output_dtype,
+    ):
+        if mixed_qkv.dim() != 2:
+            raise ValueError(f"mixed_qkv must be 2-D [B, D], got {tuple(mixed_qkv.shape)}")
+        if state.dim() != 4:
+            raise ValueError(
+                f"state must be 4-D [num_blocks, HV, V, K], got {tuple(state.shape)}"
+            )
+        if ssm_state_indices.dim() != 1:
+            raise ValueError("ssm_state_indices must be 1-D [B] for packed decode")
+
+        batch = mixed_qkv.shape[0]
+        hv, v_dim, k_dim = state.shape[-3:]
+        heads = int(num_k_heads)
+        if hv % heads != 0:
+            raise ValueError(f"HV={hv} must be a multiple of num_k_heads={heads}")
+        if a.shape != (batch, hv) or b.shape != (batch, hv):
+            raise ValueError(
+                f"a/b must be [B, HV] = {(batch, hv)}, got {tuple(a.shape)} / {tuple(b.shape)}"
+            )
+        expected = heads * k_dim * 2 + hv * v_dim
+        if mixed_qkv.shape[1] != expected:
+            raise ValueError(
+                f"mixed_qkv last dim must be {expected} (q|k|v packed), "
+                f"got {mixed_qkv.shape[1]}"
+            )
+        if ssm_state_indices.shape[0] != batch:
+            raise ValueError("ssm_state_indices must have one entry per sequence")
+
+        group = hv // heads
+        qkv32 = mixed_qkv.float()
+
+        # Unpack q | k | v. q and k carry H heads, v carries HV.
+        q = qkv32[:, : heads * k_dim].reshape(batch, heads, k_dim)
+        k = qkv32[:, heads * k_dim : 2 * heads * k_dim].reshape(batch, heads, k_dim)
+        v = qkv32[:, 2 * heads * k_dim :].reshape(batch, hv, v_dim)
+
+        if use_qk_l2norm:
+            q = _l2_normalize(q)
+            k = _l2_normalize(k)
+        q = q * scale
+
+        # i_h = i_hv // (HV // H): index, do not materialize a repeat.
+        head_of = torch.arange(hv, device=q.device) // group
+        q = q[:, head_of, :]  # [B, HV, K]
+        k = k[:, head_of, :]
+
+        # Fused gating, fp32, with the kernel's softplus branch.
+        decay = -torch.exp(A_log.float()) * _softplus(a.float() + dt_bias.float())
+        beta = torch.sigmoid(b.float())
+
+        active = ssm_state_indices > NULL_BLOCK_ID
+        out = torch.zeros(batch, 1, hv, v_dim, dtype=torch.float32, device=q.device)
+        # The returned state carries `state_dtype`, not the caller's: forward_fp32
+        # exists precisely to run the recurrence without the per-token rounding,
+        # so it must be able to widen a bf16 cache to fp32.
+        new_state = state.to(state_dtype).clone()
+        if not bool(active.any()):
+            return out.to(output_dtype), new_state
+
+        rows = torch.nonzero(active, as_tuple=False).flatten()
+        blocks = ssm_state_indices[rows].long()
+
+        h = state[blocks].float()  # [R, HV, V, K]
+        h = h * torch.exp(decay[rows]).unsqueeze(-1).unsqueeze(-1)
+
+        k_sel, q_sel, v_sel = k[rows], q[rows], v[rows]
+        # v -= h @ k, then v *= beta, then h += outer(v, k), then o = h @ q.
+        v_sel = v_sel - _fixed_order_contract(h, k_sel.unsqueeze(-2))
+        v_sel = v_sel * beta[rows].unsqueeze(-1)
+        h = h + v_sel.unsqueeze(-1) * k_sel.unsqueeze(-2)
+        o = _fixed_order_contract(h, q_sel.unsqueeze(-2))
+
+        out[rows, 0] = o
+        # The store rounds; that rounding is part of the recurrence.
+        new_state[blocks] = h.to(state_dtype)
+        return out.to(output_dtype), new_state
diff --git a/tests/check_gdn_recurrent_golden.py b/tests/check_gdn_recurrent_golden.py
new file mode 100644
index 000000000..e9e18487c
--- /dev/null
+++ b/tests/check_gdn_recurrent_golden.py
@@ -0,0 +1,501 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""The GDN recurrent-step golden against the kernel vLLM actually runs (RFC #428 C6).
+
+Named ``check_`` rather than ``test_``, following ``tests/distributed/check_*.py``:
+this module imports real vLLM, and ``tests/test_framework_operator_integrations.py``
+asserts ``vllm`` is absent from ``sys.modules`` -- an invariant any collected test
+importing vLLM would break for the whole session. Run it explicitly:
+
+    pytest tests/check_gdn_recurrent_golden.py -v
+
+The provider here is ``fused_recurrent_gated_delta_rule_packed_decode``, not
+``fused_sigmoid_gating_delta_rule_update``. A pure RL rollout decode -- N
+sequences each emitting one token, no speculative decoding -- returns early into
+the packed path because ``VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE`` defaults to
+true. Aligning against the sigmoid-gating kernel would be validating a path
+production does not take.
+
+Claim levels (RFC #428 section 2.1): L0 and L1 for the golden. L2 is not claimed;
+the golden agrees with the provider at fp32-ULP scale but not bitwise, because
+its contractions run in a fixed 32-wide chunk order rather than the kernel's
+tree.
+
+Recurrent-state dtype, and how far a decode run actually drifts
+----------------------------------------------------------------
+``FUSED_GDN_STATE_DTYPES`` allows the recurrent state to be fp32 or bf16, and a
+bf16 store rounds the ``[HV, V, K]`` state once per token. The drift that causes
+does **not** compound without bound, because the recurrence is contracting: the
+per-step decay ``exp(g)`` averages ~0.47 here (max 0.996), so old error is
+forgotten at roughly the rate old signal is.
+
+fp32 state vs bf16 state, identical inputs, B=8, B200:
+
+======  =================  ===============
+step    relative |d| state  relative |d| out
+======  =================  ===============
+1       2.67e-03           3.47e-03
+64      1.52e-02           1.07e-02
+256     2.02e-02           1.09e-02
+1024    2.47e-02           1.57e-02
+======  =================  ===============
+
+The curve saturates: a 16x longer run past step 64 grows the state drift only
+1.6x. The plateau is ~2% relative on the state and ~1-1.8% on the output.
+
+Chunked prefill vs step-by-step decode replay, single sequence, from a zero
+state -- this is the quantity RFC #428 section 4.2 is about:
+
+======  ==================  ==================
+T       max|diff|, fp32      max|diff|, bf16
+======  ==================  ==================
+8       3.66e-04 (7.0e-03)  5.49e-04 (1.0e-02)
+64      4.88e-04 (6.6e-03)  5.49e-04 (7.5e-03)
+256     3.66e-04 (5.0e-03)  3.66e-04 (5.0e-03)
+======  ==================  ==================
+
+(relative in parentheses). The gap is ~0.5-1% relative and **flat in T** -- at
+T=256 it is smaller than at T=8 -- and the state dtype barely moves it.
+
+So the two paths are not bitwise, and ~1% relative on logits is still material
+for RL importance ratios, but this is a bounded, characterizable error rather
+than a divergence. An fp32 recurrent state is still the right choice for
+exactness work; the reason is the 2% state plateau, not a blow-up.
+
+Note the chunked prefill kernel refuses fp32 q/k/v outright
+(``chunk.py:213``), so the prefill side is bf16-only regardless.
+"""
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required")
+
+from rl_engine.kernels.ops.pytorch.linear_attn import (  # noqa: E402
+    GatedDeltaRuleRecurrentStepOp,
+)
+
+# Qwen3-Next-80B-A3B-Instruct: linear_num_key_heads / linear_num_value_heads /
+# linear_key_head_dim / linear_value_head_dim.
+_H, _HV, _K, _V = 16, 32, 128, 128
+_SCALE = _K**-0.5
+_PACKED_DIM = _H * _K * 2 + _HV * _V
+
+
+def _vllm_step():
+    pytest.importorskip("vllm", reason="vLLM is required to compare against the provider")
+    from vllm.third_party.flash_linear_attention.ops import (
+        fused_recurrent_gated_delta_rule_packed_decode,
+    )
+
+    return fused_recurrent_gated_delta_rule_packed_decode
+
+
+def _inputs(batch, num_blocks, state_dtype, io_dtype, seed, indices=None):
+    g = torch.Generator(device="cuda").manual_seed(seed)
+    rand = lambda *shape, dtype: torch.randn(  # noqa: E731
+        *shape, device="cuda", dtype=dtype, generator=g
+    )
+    if indices is None:
+        indices = torch.arange(1, batch + 1, device="cuda", dtype=torch.int32)
+    return {
+        "mixed_qkv": rand(batch, _PACKED_DIM, dtype=io_dtype),
+        "a": rand(batch, _HV, dtype=io_dtype),
+        "b": rand(batch, _HV, dtype=io_dtype),
+        "A_log": rand(_HV, dtype=torch.float32),
+        "dt_bias": rand(_HV, dtype=torch.float32),
+        "state": rand(num_blocks, _HV, _V, _K, dtype=state_dtype) * 0.1,
+        "indices": indices,
+    }
+
+
+def _run_provider(inp, io_dtype=torch.bfloat16):
+    """Returns (out, mutated_state). The kernel updates the state in place."""
+    step = _vllm_step()
+    state = inp["state"].clone()
+    out = torch.empty(
+        inp["mixed_qkv"].shape[0], 1, _HV, _V, device="cuda", dtype=io_dtype
+    )
+    step(
+        inp["mixed_qkv"],
+        inp["a"],
+        inp["b"],
+        inp["A_log"],
+        inp["dt_bias"],
+        _SCALE,
+        state,
+        out,
+        inp["indices"],
+        use_qk_l2norm_in_kernel=True,
+    )
+    return out, state
+
+
+def _run_golden(inp):
+    return GatedDeltaRuleRecurrentStepOp().forward(
+        inp["mixed_qkv"],
+        inp["a"],
+        inp["b"],
+        inp["A_log"],
+        inp["dt_bias"],
+        inp["state"].clone(),
+        inp["indices"],
+        scale=_SCALE,
+        num_k_heads=_H,
+    )
+
+
+# --------------------------------------------------------------------------- #
+# 1. Against the provider
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize("batch", [1, 4, 17, 64])
+@pytest.mark.parametrize(
+    "state_dtype, out_atol, state_atol",
+    [
+        # fp32 state: agreement is at fp32-ULP scale.
+        (torch.float32, 1e-3, 1e-5),
+        # bf16 state: the store rounds every token, so the state carries a
+        # bf16 ULP. This is the configuration knob behind the drift probe.
+        (torch.bfloat16, 1e-3, 5e-3),
+    ],
+)
+def test_golden_matches_packed_decode_provider(batch, state_dtype, out_atol, state_atol):
+    inp = _inputs(batch, batch + 2, state_dtype, torch.bfloat16, seed=batch)
+    out_ref, state_ref = _run_provider(inp)
+    out_got, state_got = _run_golden(inp)
+
+    assert (out_got.float() - out_ref.float()).abs().max().item() <= out_atol
+    assert (state_got.float() - state_ref.float()).abs().max().item() <= state_atol
+
+
+def test_null_block_id_is_skipped_by_both():
+    """Index 0 means "no state": zeros out, and the block is left alone."""
+    indices = torch.tensor([1, 0, 2, 0], device="cuda", dtype=torch.int32)
+    inp = _inputs(4, 4, torch.float32, torch.bfloat16, seed=7, indices=indices)
+
+    out_ref, state_ref = _run_provider(inp)
+    out_got, state_got = _run_golden(inp)
+
+    for row in (1, 3):
+        assert bool((out_ref[row] == 0).all()), f"provider row {row}"
+        assert bool((out_got[row] == 0).all()), f"golden row {row}"
+    assert torch.equal(state_got[0], inp["state"][0]), "block 0 must be untouched"
+
+
+# --------------------------------------------------------------------------- #
+# 2. L1 -- a sequence is unaffected by the others sharing the batch
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize("state_dtype", [torch.float32, torch.bfloat16])
+def test_golden_is_batch_invariant(state_dtype):
+    """Bitwise: the same sequence, alone or in a batch of 64, at any position."""
+    batch = 64
+    inp = _inputs(batch, batch + 2, state_dtype, torch.bfloat16, seed=3)
+    full_out, full_state = _run_golden(inp)
+
+    op = GatedDeltaRuleRecurrentStepOp()
+    for row in (0, 1, 31, 63):
+        alone = {
+            key: (value[row : row + 1] if key in ("mixed_qkv", "a", "b", "indices") else value)
+            for key, value in inp.items()
+        }
+        out, state = op.forward(
+            alone["mixed_qkv"],
+            alone["a"],
+            alone["b"],
+            alone["A_log"],
+            alone["dt_bias"],
+            alone["state"].clone(),
+            alone["indices"],
+            scale=_SCALE,
+            num_k_heads=_H,
+        )
+        assert torch.equal(out[0], full_out[row]), f"out row {row}"
+        block = int(inp["indices"][row])
+        assert torch.equal(state[block], full_state[block]), f"state block {block}"
+
+
+# --------------------------------------------------------------------------- #
+# 3. The state-dtype rounding is modelled, not skipped
+# --------------------------------------------------------------------------- #
+def test_bf16_state_rounding_is_visible_but_saturates():
+    """A bf16 state rounds from the first store, and the drift then flattens.
+
+    The recurrence is contracting -- ``exp(g)`` is well below 1 for most heads --
+    so old rounding error is forgotten at roughly the rate old signal is. The
+    drift therefore grows quickly at first and then plateaus, rather than
+    compounding without bound. Asserting a bound is the honest form: asserting
+    monotone growth would have been true over a truncated run and false over a
+    long one.
+    """
+    batch, steps = 8, 128
+    inp = _inputs(batch, batch + 2, torch.float32, torch.bfloat16, seed=11)
+    op = GatedDeltaRuleRecurrentStepOp()
+
+    # Both runs start from the SAME bf16-representable state, so the initial
+    # cast is a no-op and drift[0] can only come from a per-token store.
+    seed_state = inp["state"].to(torch.bfloat16).float()
+    state_fp32 = seed_state.clone()
+    state_bf16 = seed_state.to(torch.bfloat16).clone()
+    assert torch.equal(state_fp32, state_bf16.float()), "the two runs must start equal"
+    drift = []
+    for step in range(steps):
+        g = torch.Generator(device="cuda").manual_seed(100 + step)
+        token = torch.randn(
+            batch, _PACKED_DIM, device="cuda", dtype=torch.bfloat16, generator=g
+        )
+        common = (inp["a"], inp["b"], inp["A_log"], inp["dt_bias"])
+        _, state_fp32 = op.forward(
+            token, *common, state_fp32, inp["indices"], scale=_SCALE, num_k_heads=_H
+        )
+        _, state_bf16 = op.forward(
+            token, *common, state_bf16, inp["indices"], scale=_SCALE, num_k_heads=_H
+        )
+        scale = max(state_fp32.float().abs().max().item(), 1e-9)
+        drift.append((state_fp32.float() - state_bf16.float()).abs().max().item() / scale)
+
+    assert drift[0] > 0.0, "a bf16 state must round on the very first store"
+    # Measured plateau is ~2% relative; the bound leaves room without hiding a
+    # genuine divergence.
+    assert max(drift) < 0.05, f"relative state drift reached {max(drift):.3e}"
+    # The back half must not be materially worse than the front half.
+    assert max(drift[steps // 2 :]) < 2.0 * max(drift[: steps // 2]) + 1e-3
+
+
+# --------------------------------------------------------------------------- #
+# 4. The causal-conv1d state update, the other half of a decode step
+# --------------------------------------------------------------------------- #
+_CONV_DIM, _CONV_WIDTH = 8192, 4  # Qwen3-Next: key_dim*2 + value_dim, linear_conv_kernel_dim
+
+
+def _conv_update():
+    pytest.importorskip("vllm", reason="vLLM is required to compare against the provider")
+    from vllm.model_executor.layers.mamba.ops.causal_conv1d import causal_conv1d_update
+
+    return causal_conv1d_update
+
+
+def _conv_inputs(batch, state_dtype, seed, with_bias=True, indices=None):
+    g = torch.Generator(device="cuda").manual_seed(seed)
+    rand = lambda *shape, dtype: torch.randn(  # noqa: E731
+        *shape, device="cuda", dtype=dtype, generator=g
+    )
+    if indices is None:
+        indices = torch.arange(1, batch + 1, device="cuda", dtype=torch.int32)
+    return {
+        "x": rand(batch, _CONV_DIM, dtype=torch.bfloat16),
+        # The cache must have room for every index used, plus the null block.
+        "state": rand(batch + 2, _CONV_DIM, _CONV_WIDTH - 1, dtype=state_dtype),
+        "weight": rand(_CONV_DIM, _CONV_WIDTH, dtype=torch.bfloat16),
+        "bias": rand(_CONV_DIM, dtype=torch.bfloat16) if with_bias else None,
+        "indices": indices,
+    }
+
+
+def _run_conv_pair(inp, activation="silu"):
+    from rl_engine.kernels.ops.pytorch.linear_attn import CausalConv1dUpdateOp
+
+    state_ref, out_ref = inp["state"].clone(), torch.empty_like(inp["x"])
+    _conv_update()(
+        inp["x"],
+        state_ref,
+        inp["weight"],
+        inp["bias"],
+        activation,
+        conv_state_indices=inp["indices"],
+        out=out_ref,
+    )
+    out_got, state_got = CausalConv1dUpdateOp().forward(
+        inp["x"],
+        inp["state"].clone(),
+        inp["weight"],
+        inp["indices"],
+        bias=inp["bias"],
+        activation=activation,
+    )
+    return (out_ref, state_ref), (out_got, state_got)
+
+
+@pytest.mark.parametrize("batch", [1, 4, 17, 64])
+def test_conv_state_update_is_bitwise_exact(batch):
+    """The rolled window is what the next token consumes, so it must be exact.
+
+    Holds for an fp32 and a bf16 cache alike -- the rolling is a copy, not a
+    computation, which is also why bias and activation are not varied here: they
+    cannot reach the state.
+    """
+    for state_dtype in (torch.float32, torch.bfloat16):
+        inp = _conv_inputs(batch, state_dtype, seed=batch)
+        (_, state_ref), (_, state_got) = _run_conv_pair(inp)
+        assert torch.equal(state_got, state_ref), f"{state_dtype} batch={batch}"
+
+
+@pytest.mark.parametrize("batch", [1, 4, 17, 64])
+def test_conv_output_matches_provider_with_fp32_cache(batch):
+    """An fp32 cache reproduces the provider up to fp32 ULP on a few elements.
+
+    Measured: 1 element of 8192 at B=1, 15 of 524288 at B=64. The bound is on
+    the magnitude and on a handful of elements rather than on a tight rate --
+    at small batches a single straddling element is already 1.2e-4 of the
+    tensor, which says nothing about accuracy.
+    """
+    inp = _conv_inputs(batch, torch.float32, seed=batch)
+    (out_ref, _), (out_got, _) = _run_conv_pair(inp)
+    mismatch = int(
+        (out_got.float().view(torch.int32) != out_ref.float().view(torch.int32)).sum()
+    )
+    assert mismatch <= 32, f"{mismatch} of {out_got.numel()} elements differ"
+    assert (out_got.float() - out_ref.float()).abs().max().item() <= 1e-2
+
+
+@pytest.mark.parametrize("batch", [1, 17])
+def test_conv_output_bf16_cache_differs_only_by_one_ulp(batch):
+    """A bf16 cache disagrees often but never by more than a bf16 ULP.
+
+    Bounded rather than asserted equal: where the provider rounds in this
+    configuration is not reproduced, and pretending otherwise would hide it.
+    """
+    inp = _conv_inputs(batch, torch.bfloat16, seed=batch)
+    (out_ref, _), (out_got, _) = _run_conv_pair(inp)
+    assert (out_got.float() - out_ref.float()).abs().max().item() <= 7e-2
+
+
+def test_conv_null_block_id_is_skipped():
+    from rl_engine.kernels.ops.pytorch.linear_attn import CausalConv1dUpdateOp
+
+    indices = torch.tensor([1, 0, 2, 0], device="cuda", dtype=torch.int32)
+    inp = _conv_inputs(4, torch.float32, seed=5, with_bias=False, indices=indices)
+    out_got, state_got = CausalConv1dUpdateOp().forward(
+        inp["x"], inp["state"].clone(), inp["weight"], indices, bias=None
+    )
+    for row in (1, 3):
+        assert bool((out_got[row] == 0).all())
+    assert torch.equal(state_got[0], inp["state"][0])
+
+
+def test_conv_rejects_unknown_activation_and_bad_shapes():
+    from rl_engine.kernels.ops.pytorch.linear_attn import CausalConv1dUpdateOp
+
+    op = CausalConv1dUpdateOp()
+    inp = _conv_inputs(2, torch.float32, seed=1)
+    with pytest.raises(ValueError, match="activation must be"):
+        op.forward(inp["x"], inp["state"], inp["weight"], inp["indices"], activation="relu")
+    with pytest.raises(ValueError, match="conv_state tail must be"):
+        op.forward(
+            inp["x"],
+            inp["state"][..., :1],
+            inp["weight"],
+            inp["indices"],
+            activation=None,
+        )
+
+
+# --------------------------------------------------------------------------- #
+# 5. forward_fp32, and the branches the provider comparison never reaches
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize("state_dtype", [torch.float32, torch.bfloat16])
+def test_recurrent_forward_fp32_widens_the_state(state_dtype):
+    """forward_fp32 must run the recurrence without the per-token rounding.
+
+    With a bf16 cache that means widening it -- the whole point of having the
+    method is to be able to compare against the rounded path.
+    """
+    op = GatedDeltaRuleRecurrentStepOp()
+    inp = _inputs(4, 6, state_dtype, torch.bfloat16, seed=1)
+    out, state = op.forward_fp32(
+        inp["mixed_qkv"],
+        inp["a"],
+        inp["b"],
+        inp["A_log"],
+        inp["dt_bias"],
+        inp["state"].clone(),
+        inp["indices"],
+        scale=_SCALE,
+        num_k_heads=_H,
+    )
+    assert out.dtype is torch.float32 and state.dtype is torch.float32
+
+    rounded, _ = _run_golden(inp)
+    if state_dtype is torch.bfloat16:
+        # The rounded path went through bf16; the fp32 path did not.
+        assert not torch.equal(rounded.float(), out)
+
+
+def test_recurrent_call_matches_forward():
+    op = GatedDeltaRuleRecurrentStepOp()
+    inp = _inputs(4, 6, torch.float32, torch.bfloat16, seed=2)
+    args = (
+        inp["mixed_qkv"],
+        inp["a"],
+        inp["b"],
+        inp["A_log"],
+        inp["dt_bias"],
+        inp["state"].clone(),
+        inp["indices"],
+    )
+    kwargs = dict(scale=_SCALE, num_k_heads=_H)
+    a_out, a_state = op(*args, **kwargs)
+    b_out, b_state = op.forward(*args, **kwargs)
+    assert torch.equal(a_out, b_out) and torch.equal(a_state, b_state)
+
+
+def test_recurrent_without_in_kernel_l2norm_differs():
+    """`use_qk_l2norm=False` is the prefill convention and must be reachable."""
+    op = GatedDeltaRuleRecurrentStepOp()
+    inp = _inputs(4, 6, torch.float32, torch.bfloat16, seed=3)
+    common = (
+        inp["mixed_qkv"],
+        inp["a"],
+        inp["b"],
+        inp["A_log"],
+        inp["dt_bias"],
+    )
+    normed, _ = op.forward(
+        *common, inp["state"].clone(), inp["indices"], scale=_SCALE, num_k_heads=_H
+    )
+    raw, _ = op.forward(
+        *common,
+        inp["state"].clone(),
+        inp["indices"],
+        scale=_SCALE,
+        num_k_heads=_H,
+        use_qk_l2norm=False,
+    )
+    assert not torch.equal(normed, raw)
+
+
+def test_conv_dim_first_false_matches_the_transposed_layout():
+    """`dim_first=False` is vLLM's "SD" cache layout, not a dead branch."""
+    from rl_engine.kernels.ops.pytorch.linear_attn import CausalConv1dUpdateOp
+
+    op = CausalConv1dUpdateOp()
+    inp = _conv_inputs(4, torch.float32, seed=9)
+    ds_out, ds_state = op.forward(
+        inp["x"], inp["state"].clone(), inp["weight"], inp["indices"], bias=inp["bias"]
+    )
+    sd_out, sd_state = op.forward(
+        inp["x"],
+        inp["state"].transpose(-1, -2).contiguous(),
+        inp["weight"],
+        inp["indices"],
+        bias=inp["bias"],
+        dim_first=False,
+    )
+    assert torch.equal(ds_out, sd_out)
+    assert torch.equal(ds_state, sd_state.transpose(-1, -2))
+
+
+def test_conv_forward_fp32_and_call_entry_points():
+    from rl_engine.kernels.ops.pytorch.linear_attn import CausalConv1dUpdateOp
+
+    op = CausalConv1dUpdateOp()
+    inp = _conv_inputs(4, torch.float32, seed=10)
+    args = (inp["x"], inp["state"].clone(), inp["weight"], inp["indices"])
+    out, _ = op.forward(*args, bias=inp["bias"])
+    called, _ = op(*args, bias=inp["bias"])
+    fp32, _ = op.forward_fp32(*args, bias=inp["bias"])
+    assert torch.equal(out, called)
+    assert fp32.dtype is torch.float32
+    torch.testing.assert_close(fp32, out.float(), atol=1e-2, rtol=1e-2)

From dbb8d00ae8cc7c5d8dcd772be6715e8500e9d14b Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 18:28:34 +0000
Subject: [PATCH 18/70] fix(gdn): validate cache ownership and reproduce conv
 product rounding

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/ws1-gtest-gpu.yml           |   8 ++
 ci/run_ws1_gtest.sh                           |  21 ++--
 .../design/ws1-c6-428-gdn-recurrent-replay.md |  78 +++++---------
 .../ops/pytorch/linear_attn/__init__.py       |   6 +-
 .../ops/pytorch/linear_attn/causal_conv1d.py  |  66 ++++++------
 .../pytorch/linear_attn/gated_delta_rule.py   |  45 +++++++-
 tests/check_gdn_recurrent_golden.py           | 101 +++++-------------
 tests/test_gdn_state_contract.py              |  98 +++++++++++++++++
 8 files changed, 245 insertions(+), 178 deletions(-)
 create mode 100644 tests/test_gdn_state_contract.py

diff --git a/.github/workflows/ws1-gtest-gpu.yml b/.github/workflows/ws1-gtest-gpu.yml
index 646d80bdf..d8f32b714 100644
--- a/.github/workflows/ws1-gtest-gpu.yml
+++ b/.github/workflows/ws1-gtest-gpu.yml
@@ -20,6 +20,10 @@ on:
       - "scripts/check_gradient_invariance.py"
       - "scripts/ws1_candidate_evidence.py"
       - "tests/test_ws1_*.py"
+      - "tests/test_gdn_state_contract.py"
+      - "tests/test_qwen3_next_norm.py"
+      - "tests/check_qwen3_next_norm_providers.py"
+      - "tests/check_gdn_recurrent_golden.py"
       - "tests/test_forward_invariance.py"
       - "tests/test_gradient_invariance.py"
       - "tests/test_four_judgment_matrix.py"
@@ -38,6 +42,10 @@ on:
       - "scripts/check_gradient_invariance.py"
       - "scripts/ws1_candidate_evidence.py"
       - "tests/test_ws1_*.py"
+      - "tests/test_gdn_state_contract.py"
+      - "tests/test_qwen3_next_norm.py"
+      - "tests/check_qwen3_next_norm_providers.py"
+      - "tests/check_gdn_recurrent_golden.py"
       - "tests/test_forward_invariance.py"
       - "tests/test_gradient_invariance.py"
       - "tests/test_four_judgment_matrix.py"
diff --git a/ci/run_ws1_gtest.sh b/ci/run_ws1_gtest.sh
index e6d8e0dc0..c9daa1118 100755
--- a/ci/run_ws1_gtest.sh
+++ b/ci/run_ws1_gtest.sh
@@ -77,12 +77,15 @@ print("[ws1-gtest] C8 gate passed")
 PY
 
 echo "[ws1-gtest] Qwen3-Next C3/C4 norms (operator scope)"
-QWEN3_NEXT_NORM_MANIFEST=rl_engine/testing/qwen3_next_norm_manifest.json
-"$PY" scripts/check_forward_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
-  --op qwen3_next_rms_norm --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
-"$PY" scripts/check_gradient_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
-  --op qwen3_next_rms_norm --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
-"$PY" scripts/check_forward_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
-  --op rms_norm_gated --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
-"$PY" scripts/check_gradient_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
-  --op rms_norm_gated --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
+for op in qwen3_next_rms_norm rms_norm_gated; do
+  for gate in forward gradient; do
+    "$PY" "scripts/check_${gate}_invariance.py" \
+      --manifest rl_engine/testing/qwen3_next_norm_manifest.json \
+      --op "$op" --candidate cuda --backend-profile cuda_bf16 \
+      --hidden 2048 --head-dim 128
+  done
+done
+# Provider imports run in a separate process from framework-isolation tests.
+echo "[ws1-gtest] Qwen3-Next provider comparisons (required)"
+"$PY" -c 'import torch, vllm; assert torch.cuda.is_available(), "CUDA is required"'
+"$PY" -m pytest -q tests/check_qwen3_next_norm_providers.py tests/check_gdn_recurrent_golden.py
diff --git a/docs/design/ws1-c6-428-gdn-recurrent-replay.md b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
index d0d0f3ea3..2aa1ddf23 100644
--- a/docs/design/ws1-c6-428-gdn-recurrent-replay.md
+++ b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
@@ -69,53 +69,28 @@ Qwen3-Next dims (H=16, HV=32, K=V=128), `use_qk_l2norm_in_kernel=True`:
 | fp32 state, B=1..64 | 1.5e-08 .. 6.1e-05 | ≤ 3.0e-07 |
 | bf16 state, B=1..64 | 3.7e-09 .. 3.1e-05 | ≤ 2.0e-03 |
 
-Causal conv (`conv_dim=8192`, `W=4`, B ≤ 64): the **rolled state is bitwise exact in
-every configuration**. With an fp32 cache the output is bitwise but for a handful of
-elements (15 of 524288 at B=64); with a bf16 cache it agrees on ~63%, each
-disagreement exactly one bf16 ULP. The conv taps accumulate **sequentially**,
-`acc = acc + win[t] * w[t]` from zero — a tree sum over the same four terms does not
-reproduce the provider, an FMA does not either.
-
-Open: where the provider rounds in the bf16-conv-cache case is not reproduced.
-Recorded rather than guessed at.
+Causal conv uses sequential FP32 accumulation **starting from bias**, with
+products first rounded to the operand dtype. The previous BF16 path incorrectly
+promoted both operands to FP32; its disagreements were not limited to one BF16
+ULP. Cancellation and bias-order CPU tests now cover these errors. Provider
+comparisons preserve the existing absolute bounds and additionally limit BF16
+mismatches to 32 elements on the checked fixtures. This is not a bitwise claim.
 
 ## 5. Decode versus chunked prefill
 
-The quantity RFC #428 §4.2 is about. Single sequence, zero initial state:
-
-| T | max\|diff\|, fp32 state | max\|diff\|, bf16 state |
-|---|---|---|
-| 8 | 3.66e-04 (7.0e-03 rel) | 5.49e-04 (1.0e-02 rel) |
-| 64 | 4.88e-04 (6.6e-03 rel) | 5.49e-04 (7.5e-03 rel) |
-| 256 | 3.66e-04 (5.0e-03 rel) | 3.66e-04 (5.0e-03 rel) |
+The earlier 1024-step drift and prefill tables did not have a checked-in runner;
+they are withdrawn as acceptance evidence. The existing 128-step synthetic test
+only bounds its fixed seed and gate inputs. It does not establish a universal
+plateau, prefill/decode equality, or any bound on model logits.
 
-**The gap is ~0.5–1% relative and flat in T** — smaller at T=256 than at T=8 — and the
-state dtype barely moves it.
+The strict profile uses FP32 recurrent state. BF16 state remains a differential
+experiment. Full checkpoint prefill, response replay, optimizer updates and
+reload all remain required before L2 can pass.
 
-The reason is that the recurrence is **contracting**: per-step decay `exp(g)` averages
-~0.47 (max 0.996), so old rounding error is forgotten at roughly the rate old signal
-is. Comparing an fp32 state against a bf16 one over 1024 steps shows the same shape:
-
-| step | relative \|d\| state | relative \|d\| out |
-|---|---|---|
-| 1 | 2.67e-03 | 3.47e-03 |
-| 64 | 1.52e-02 | 1.07e-02 |
-| 256 | 2.02e-02 | 1.09e-02 |
-| 1024 | 2.47e-02 | 1.57e-02 |
-
-A 16× longer run past step 64 grows the drift only 1.6×; it saturates at ~2% relative
-on the state and ~1–1.8% on the output.
-
-So the two paths are not bitwise, and ~1% relative on logits is still material for RL
-importance ratios, but this is a bounded, characterizable error rather than a
-divergence. An fp32 recurrent state remains the right choice for exactness work — the
-reason is the 2% plateau, not a blow-up.
-
-Two related constraints: the chunked prefill kernel refuses fp32 q/k/v outright
-(`chunk.py:213`), so the prefill side is bf16-only regardless; and
-`causal_conv1d_update` does not bounds-check `conv_state_indices` under its default
-`validate_data=False` — an index past the cache is an out-of-bounds write, not an
-error.
+Cache indices must be int32/int64, on the input device, and positive active
+indices must be unique and in range. Nonpositive sentinels may repeat. The golden
+validates before any cache update; raw provider calls remain the caller's
+responsibility (the provider's default does not bounds-check).
 
 ## 6. Current boundary
 
@@ -133,13 +108,14 @@ Not covered, with reasons:
 `runtime_verified=false` (no checkpoint), `supports_backward=false`,
 `checkpoint=absent`. Op-level agreement says nothing about 48 composed layers.
 
-## 7. Questions for the maintainers
+## 7. Accepted execution boundaries
+
+Use a shared, explicitly pinned vLLM-compatible forward provider on both sides,
+with independent VIME recomputation. Disable MTP and prefix reuse. Keep FP32
+recurrent state for strict acceptance; BF16 is experimental. Require bitwise
+logits/logprobs at a common topology, at least two real optimizer updates, weight
+synchronization and checkpoint reload. No operator-level test closes these gates.
 
-1. Is a **bf16 recurrent state** a supported configuration? The answer decides whether
-   the 2% plateau is a finding or a non-issue.
-2. Is MTP / speculative decode in scope for WS1? If so,
-   `fused_gdn_decode_post_conv_mtp` becomes the primary provider for both the norm and
-   the recurrence, and the largest deferral above reopens.
-3. For the CUDA strict profile, is the single source of truth **vLLM's** arithmetic or
-   **PyTorch eager**? §1 item 1 asks for one provider on both sides but §2.1 does not
-   say which, and the two differ by ~6e-2 in bf16.
+The 2026-09-30 real-checkpoint startup attempt with vLLM 0.30.0 failed before
+inference: `VLLM batch_invariant mode is not supported for GDN_ATTN`. A shared
+provider integration must resolve this; disabling the check is not L2 evidence.
diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/__init__.py b/rl_engine/kernels/ops/pytorch/linear_attn/__init__.py
index 28602f18f..c974a89b1 100644
--- a/rl_engine/kernels/ops/pytorch/linear_attn/__init__.py
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/__init__.py
@@ -3,13 +3,11 @@
 
 """Linear-attention operators (Gated DeltaNet and friends)."""
 
-from rl_engine.kernels.ops.pytorch.linear_attn.causal_conv1d import (
-    CausalConv1dUpdateOp,
-)
+from rl_engine.kernels.ops.pytorch.linear_attn.causal_conv1d import CausalConv1dUpdateOp
 from rl_engine.kernels.ops.pytorch.linear_attn.gated_delta_rule import (
-    GatedDeltaRuleRecurrentStepOp,
     NULL_BLOCK_ID,
     SOFTPLUS_THRESHOLD,
+    GatedDeltaRuleRecurrentStepOp,
 )
 
 __all__ = [
diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py b/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py
index ffbac6e13..fb171af49 100644
--- a/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py
@@ -6,42 +6,22 @@
 The trainer-side reference for ``causal_conv1d_update``, which vLLM calls once
 per decode token before the Gated DeltaNet recurrence.
 
-Semantics were established by differential testing against the provider rather
-than transcribed, and the accumulation order matters:
-
-* ``x`` is cast to ``conv_state.dtype`` **before** anything else, so a bf16
-  conv cache rounds the incoming token (``causal_conv1d.py:1160``).
-* The window is ``[state[..., -(width-1):], x]``; the taps are accumulated
-  **sequentially in tap order**, ``acc = acc + win[t] * w[t]`` starting from
-  zero -- not a tree reduction and not an FMA. With an fp32 conv state this
-  reproduces the provider bitwise; a tree sum does not.
-* Bias and the activation are applied in fp32, with one cast on the way out to
-  the input's original dtype.
-* The new state is the window minus its oldest column.
-
-Measured against the provider on B200 (``conv_dim=8192``, ``W=4``, B up to 64):
-
-* **The rolled state is bitwise exact in every configuration**, fp32 and bf16
-  cache alike -- ``max|diff| = 0``. That is the part the next token consumes,
-  so the recurrence carries no error from here.
-* With an **fp32** conv state the output is bitwise on all but a handful of
-  elements (15 of 524288 at B=64), the residual being fp32 ULP.
-* With a **bf16** conv state the output agrees on ~63% of elements, every
-  disagreement exactly one bf16 ULP.
-
-The bf16 output gap is a rounding-path difference, not a semantic one: none of
-accumulate-in-bf16, bias-in-bf16 or activation-in-bf16 reproduces the provider,
-so where it rounds is left unresolved rather than guessed at. An fp32 conv state
-is what an exactness claim should use anyway -- the same conclusion the
-recurrent state reaches in :mod:`.gated_delta_rule`.
+The incoming token is rounded to the cache dtype. Products are rounded in
+operand dtype before sequential FP32 accumulation, starting from bias (or zero).
+This matters for BF16 caches: promoting operands before multiplication loses the
+provider's product rounding. Activation may still differ at transcendental ULP
+scale; this reference does not establish model-level bitwise equality.
+
 """
 
 from __future__ import annotations
 
 import torch
-import torch.nn.functional as F
 
-from rl_engine.kernels.ops.pytorch.linear_attn.gated_delta_rule import NULL_BLOCK_ID
+from rl_engine.kernels.ops.pytorch.linear_attn.gated_delta_rule import (
+    NULL_BLOCK_ID,
+    _validate_state_indices,
+)
 
 __all__ = ["CausalConv1dUpdateOp"]
 
@@ -154,6 +134,21 @@ def _update(
         # The "SD" layout stores (state_len, dim); the kernels want (dim, state_len).
         state = conv_state if dim_first else conv_state.transpose(-1, -2)
         dim, tail = state.shape[-2], state.shape[-1]
+        if weight.ndim != 2 or dim <= 0 or weight.shape[-1] <= 0:
+            raise ValueError("weight must be 2-D [dim, W] with positive dimensions")
+        if x.shape[1] != dim:
+            raise ValueError("x and conv_state must have the same dim")
+        _validate_state_indices(conv_state_indices, x.shape[0], state.shape[0], x.device)
+        for name, tensor in (
+            ("x", x),
+            ("conv_state", conv_state),
+            ("weight", weight),
+            ("bias", bias),
+        ):
+            if tensor is not None and (tensor.device != x.device or not tensor.is_floating_point()):
+                raise ValueError(f"{name} must be floating point on the input device")
+        if bias is not None and bias.shape != (dim,):
+            raise ValueError("bias must have shape [dim]")
         width = weight.shape[-1]
         if weight.shape[0] != dim:
             raise ValueError(f"weight must be [dim, W] with dim={dim}, got {tuple(weight.shape)}")
@@ -173,16 +168,15 @@ def _update(
         token = x[rows].to(conv_state.dtype)
         window = torch.cat([state[blocks], token.unsqueeze(-1)], dim=-1)  # [R, dim, W]
 
-        # Sequential tap accumulation from zero, in fp32. A tree sum over the
-        # same four terms gives a different last bit and does NOT match.
+        # Match the provider's product rounding and bias-before-taps order.
         acc = torch.zeros(len(rows), dim, dtype=torch.float32, device=x.device)
-        w32 = weight.float()
-        for tap in range(width):
-            acc = acc + window[..., tap].float() * w32[:, tap].unsqueeze(0)
         if bias is not None:
             acc = acc + bias.float()
+        for tap in range(width):
+            product = window[..., tap] * weight[:, tap].unsqueeze(0)
+            acc = acc + product.float()
         if activation in ("silu", "swish"):
-            acc = F.silu(acc)
+            acc = acc / (1.0 + torch.exp(-acc))
 
         out[rows] = acc
         rolled = window[..., 1:]
diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
index cdf6f17b7..bfab76047 100644
--- a/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
@@ -55,6 +55,21 @@
 _REDUCTION_CHUNK = 32
 
 
+def _validate_state_indices(indices, batch, blocks, device):
+    """Each active row owns one cache block; inactive sentinels may repeat."""
+    if indices.ndim != 1 or indices.shape[0] != batch:
+        raise ValueError("state indices must be 1-D with one entry per sequence")
+    if indices.dtype not in (torch.int32, torch.int64):
+        raise ValueError("state indices must have int32 or int64 dtype")
+    if indices.device != device:
+        raise ValueError("state indices must be on the input device")
+    active = indices[indices > NULL_BLOCK_ID]
+    if bool((active >= blocks).any()):
+        raise ValueError("active state index is out of range")
+    if active.unique().numel() != active.numel():
+        raise ValueError("active state indices must be unique")
+
+
 def _chunked_sum(x: torch.Tensor) -> torch.Tensor:
     """Sum the last dim in a fixed 32-wide chunk order.
 
@@ -67,7 +82,9 @@ def _chunked_sum(x: torch.Tensor) -> torch.Tensor:
     tail = x.shape[-1]
     if tail % _REDUCTION_CHUNK != 0:
         return x.sum(dim=-1)
-    return x.reshape(*x.shape[:-1], -1, _REDUCTION_CHUNK).sum(dim=-1).sum(dim=-1)
+    return (
+        x.reshape(*x.shape[:-1], tail // _REDUCTION_CHUNK, _REDUCTION_CHUNK).sum(dim=-1).sum(dim=-1)
+    )
 
 
 def _fixed_order_contract(mat: torch.Tensor, vec: torch.Tensor) -> torch.Tensor:
@@ -220,15 +237,33 @@ def _step(
         if mixed_qkv.dim() != 2:
             raise ValueError(f"mixed_qkv must be 2-D [B, D], got {tuple(mixed_qkv.shape)}")
         if state.dim() != 4:
-            raise ValueError(
-                f"state must be 4-D [num_blocks, HV, V, K], got {tuple(state.shape)}"
-            )
+            raise ValueError(f"state must be 4-D [num_blocks, HV, V, K], got {tuple(state.shape)}")
         if ssm_state_indices.dim() != 1:
             raise ValueError("ssm_state_indices must be 1-D [B] for packed decode")
 
         batch = mixed_qkv.shape[0]
         hv, v_dim, k_dim = state.shape[-3:]
-        heads = int(num_k_heads)
+        if isinstance(num_k_heads, bool) or not isinstance(num_k_heads, int) or num_k_heads <= 0:
+            raise ValueError("num_k_heads must be a positive integer")
+        if min(hv, v_dim, k_dim) <= 0:
+            raise ValueError("state head dimensions must be positive")
+        heads = num_k_heads
+        _validate_state_indices(ssm_state_indices, batch, state.shape[0], mixed_qkv.device)
+        for name, tensor in (
+            ("a", a),
+            ("b", b),
+            ("A_log", A_log),
+            ("dt_bias", dt_bias),
+            ("state", state),
+        ):
+            if tensor.device != mixed_qkv.device:
+                raise ValueError(f"{name} must be on the input device")
+            if not tensor.is_floating_point():
+                raise ValueError(f"{name} must be floating point")
+        if not mixed_qkv.is_floating_point():
+            raise ValueError("mixed_qkv must be floating point")
+        if A_log.shape != (hv,) or dt_bias.shape != (hv,):
+            raise ValueError("A_log and dt_bias must have shape [HV]")
         if hv % heads != 0:
             raise ValueError(f"HV={hv} must be a multiple of num_k_heads={heads}")
         if a.shape != (batch, hv) or b.shape != (batch, hv):
diff --git a/tests/check_gdn_recurrent_golden.py b/tests/check_gdn_recurrent_golden.py
index e9e18487c..7c7e929ac 100644
--- a/tests/check_gdn_recurrent_golden.py
+++ b/tests/check_gdn_recurrent_golden.py
@@ -22,49 +22,11 @@
 its contractions run in a fixed 32-wide chunk order rather than the kernel's
 tree.
 
-Recurrent-state dtype, and how far a decode run actually drifts
-----------------------------------------------------------------
-``FUSED_GDN_STATE_DTYPES`` allows the recurrent state to be fp32 or bf16, and a
-bf16 store rounds the ``[HV, V, K]`` state once per token. The drift that causes
-does **not** compound without bound, because the recurrence is contracting: the
-per-step decay ``exp(g)`` averages ~0.47 here (max 0.996), so old error is
-forgotten at roughly the rate old signal is.
-
-fp32 state vs bf16 state, identical inputs, B=8, B200:
-
-======  =================  ===============
-step    relative |d| state  relative |d| out
-======  =================  ===============
-1       2.67e-03           3.47e-03
-64      1.52e-02           1.07e-02
-256     2.02e-02           1.09e-02
-1024    2.47e-02           1.57e-02
-======  =================  ===============
-
-The curve saturates: a 16x longer run past step 64 grows the state drift only
-1.6x. The plateau is ~2% relative on the state and ~1-1.8% on the output.
-
-Chunked prefill vs step-by-step decode replay, single sequence, from a zero
-state -- this is the quantity RFC #428 section 4.2 is about:
-
-======  ==================  ==================
-T       max|diff|, fp32      max|diff|, bf16
-======  ==================  ==================
-8       3.66e-04 (7.0e-03)  5.49e-04 (1.0e-02)
-64      4.88e-04 (6.6e-03)  5.49e-04 (7.5e-03)
-256     3.66e-04 (5.0e-03)  3.66e-04 (5.0e-03)
-======  ==================  ==================
-
-(relative in parentheses). The gap is ~0.5-1% relative and **flat in T** -- at
-T=256 it is smaller than at T=8 -- and the state dtype barely moves it.
-
-So the two paths are not bitwise, and ~1% relative on logits is still material
-for RL importance ratios, but this is a bounded, characterizable error rather
-than a divergence. An fp32 recurrent state is still the right choice for
-exactness work; the reason is the 2% state plateau, not a blow-up.
-
-Note the chunked prefill kernel refuses fp32 q/k/v outright
-(``chunk.py:213``), so the prefill side is bf16-only regardless.
+The fixed-seed 128-step rounding test below is a bounded synthetic regression,
+not evidence of a universal drift plateau or model-logit agreement. Earlier
+1024-step and prefill tables had no reproducible runner and are withdrawn until
+those experiments are checked in. Operator outputs are not model logits.
+
 """
 
 from __future__ import annotations
@@ -74,9 +36,7 @@
 
 pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required")
 
-from rl_engine.kernels.ops.pytorch.linear_attn import (  # noqa: E402
-    GatedDeltaRuleRecurrentStepOp,
-)
+from rl_engine.kernels.ops.pytorch.linear_attn import GatedDeltaRuleRecurrentStepOp  # noqa: E402
 
 # Qwen3-Next-80B-A3B-Instruct: linear_num_key_heads / linear_num_value_heads /
 # linear_key_head_dim / linear_value_head_dim.
@@ -116,9 +76,7 @@ def _run_provider(inp, io_dtype=torch.bfloat16):
     """Returns (out, mutated_state). The kernel updates the state in place."""
     step = _vllm_step()
     state = inp["state"].clone()
-    out = torch.empty(
-        inp["mixed_qkv"].shape[0], 1, _HV, _V, device="cuda", dtype=io_dtype
-    )
+    out = torch.empty(inp["mixed_qkv"].shape[0], 1, _HV, _V, device="cuda", dtype=io_dtype)
     step(
         inp["mixed_qkv"],
         inp["a"],
@@ -220,16 +178,8 @@ def test_golden_is_batch_invariant(state_dtype):
 # --------------------------------------------------------------------------- #
 # 3. The state-dtype rounding is modelled, not skipped
 # --------------------------------------------------------------------------- #
-def test_bf16_state_rounding_is_visible_but_saturates():
-    """A bf16 state rounds from the first store, and the drift then flattens.
-
-    The recurrence is contracting -- ``exp(g)`` is well below 1 for most heads --
-    so old rounding error is forgotten at roughly the rate old signal is. The
-    drift therefore grows quickly at first and then plateaus, rather than
-    compounding without bound. Asserting a bound is the honest form: asserting
-    monotone growth would have been true over a truncated run and false over a
-    long one.
-    """
+def test_bf16_state_rounding_stays_within_fixed_fixture_bound():
+    """Bound this fixed 128-step fixture; no general contraction claim."""
     batch, steps = 8, 128
     inp = _inputs(batch, batch + 2, torch.float32, torch.bfloat16, seed=11)
     op = GatedDeltaRuleRecurrentStepOp()
@@ -243,9 +193,7 @@ def test_bf16_state_rounding_is_visible_but_saturates():
     drift = []
     for step in range(steps):
         g = torch.Generator(device="cuda").manual_seed(100 + step)
-        token = torch.randn(
-            batch, _PACKED_DIM, device="cuda", dtype=torch.bfloat16, generator=g
-        )
+        token = torch.randn(batch, _PACKED_DIM, device="cuda", dtype=torch.bfloat16, generator=g)
         common = (inp["a"], inp["b"], inp["A_log"], inp["dt_bias"])
         _, state_fp32 = op.forward(
             token, *common, state_fp32, inp["indices"], scale=_SCALE, num_k_heads=_H
@@ -257,8 +205,7 @@ def test_bf16_state_rounding_is_visible_but_saturates():
         drift.append((state_fp32.float() - state_bf16.float()).abs().max().item() / scale)
 
     assert drift[0] > 0.0, "a bf16 state must round on the very first store"
-    # Measured plateau is ~2% relative; the bound leaves room without hiding a
-    # genuine divergence.
+    # Preserve the original regression bound for this fixed fixture.
     assert max(drift) < 0.05, f"relative state drift reached {max(drift):.3e}"
     # The back half must not be materially worse than the front half.
     assert max(drift[steps // 2 :]) < 2.0 * max(drift[: steps // 2]) + 1e-3
@@ -343,23 +290,18 @@ def test_conv_output_matches_provider_with_fp32_cache(batch):
     """
     inp = _conv_inputs(batch, torch.float32, seed=batch)
     (out_ref, _), (out_got, _) = _run_conv_pair(inp)
-    mismatch = int(
-        (out_got.float().view(torch.int32) != out_ref.float().view(torch.int32)).sum()
-    )
+    mismatch = int((out_got.float().view(torch.int32) != out_ref.float().view(torch.int32)).sum())
     assert mismatch <= 32, f"{mismatch} of {out_got.numel()} elements differ"
     assert (out_got.float() - out_ref.float()).abs().max().item() <= 1e-2
 
 
 @pytest.mark.parametrize("batch", [1, 17])
-def test_conv_output_bf16_cache_differs_only_by_one_ulp(batch):
-    """A bf16 cache disagrees often but never by more than a bf16 ULP.
-
-    Bounded rather than asserted equal: where the provider rounds in this
-    configuration is not reproduced, and pretending otherwise would hide it.
-    """
+def test_conv_output_bf16_cache_matches_rounded_product_path(batch):
+    """Product rounding is reproduced; activation ULP residuals remain allowed."""
     inp = _conv_inputs(batch, torch.bfloat16, seed=batch)
     (out_ref, _), (out_got, _) = _run_conv_pair(inp)
     assert (out_got.float() - out_ref.float()).abs().max().item() <= 7e-2
+    assert int((out_got != out_ref).sum()) <= 32
 
 
 def test_conv_null_block_id_is_skipped():
@@ -499,3 +441,16 @@ def test_conv_forward_fp32_and_call_entry_points():
     assert torch.equal(out, called)
     assert fp32.dtype is torch.float32
     torch.testing.assert_close(fp32, out.float(), atol=1e-2, rtol=1e-2)
+
+
+def test_conv_provider_preserves_bf16_product_cancellation():
+    inp = _conv_inputs(1, torch.bfloat16, seed=1, with_bias=False)
+    inp["state"].zero_()
+    inp["state"][1, :, -1] = 1.0078125
+    inp["weight"].zero_()
+    inp["weight"][:, -2] = 1.0078125
+    inp["weight"][:, -1] = 1.0
+    inp["x"].fill_(-1.015625)
+    (provider, _), (golden, _) = _run_conv_pair(inp, activation=None)
+    assert torch.count_nonzero(provider) == 0
+    assert torch.equal(provider, golden)
diff --git a/tests/test_gdn_state_contract.py b/tests/test_gdn_state_contract.py
new file mode 100644
index 000000000..9e84ba288
--- /dev/null
+++ b/tests/test_gdn_state_contract.py
@@ -0,0 +1,98 @@
+"""Provider-independent cache contracts, collected in the ordinary CPU suite."""
+
+import pytest
+import torch
+
+from rl_engine.kernels.ops.pytorch.linear_attn import (
+    CausalConv1dUpdateOp,
+    GatedDeltaRuleRecurrentStepOp,
+)
+
+
+def _call(kind, indices, heads=1):
+    batch = indices.numel()
+    if kind == "conv":
+        state = torch.randn(4, 2, 3)
+        before = state.clone()
+        result = CausalConv1dUpdateOp()(torch.randn(batch, 2), state, torch.randn(2, 4), indices)
+    else:
+        state = torch.randn(4, 2, 2, 32)
+        before = state.clone()
+        result = GatedDeltaRuleRecurrentStepOp()(
+            torch.randn(batch, 68),
+            torch.randn(batch, 2),
+            torch.randn(batch, 2),
+            torch.zeros(2),
+            torch.zeros(2),
+            state,
+            indices,
+            scale=32**-0.5,
+            num_k_heads=heads,
+        )
+    assert torch.equal(state, before)
+    return result, before
+
+
+@pytest.mark.parametrize("kind", ["conv", "gdn"])
+@pytest.mark.parametrize(
+    "indices,message",
+    [
+        (torch.tensor([1.5, 2.0]), "int32 or int64"),
+        (torch.tensor([True, False]), "int32 or int64"),
+        (torch.tensor([1, 1]), "unique"),
+        (torch.tensor([1, 4]), "out of range"),
+    ],
+)
+def test_invalid_cache_indices_are_rejected(kind, indices, message):
+    with pytest.raises(ValueError, match=message):
+        _call(kind, indices)
+
+
+@pytest.mark.parametrize("kind", ["conv", "gdn"])
+@pytest.mark.parametrize("indices", [torch.tensor([0, -1, 0]), torch.empty(0, dtype=torch.int64)])
+def test_inactive_and_empty_batches_preserve_cache(kind, indices):
+    (out, state), before = _call(kind, indices)
+    assert torch.equal(state, before)
+    assert torch.count_nonzero(out) == 0
+
+
+@pytest.mark.parametrize("heads", [0, -1, 1.5, True])
+def test_gdn_requires_positive_integer_heads(heads):
+    with pytest.raises(ValueError, match="positive integer"):
+        _call("gdn", torch.tensor([1, 2]), heads=heads)
+
+
+def test_conv_bias_is_accumulated_before_taps():
+    # Adding bias last would yield 1.0 instead of 0.0.
+    out, _ = CausalConv1dUpdateOp()(
+        torch.tensor([[-1e8]]),
+        torch.zeros(2, 1, 1),
+        torch.tensor([[0.0, 1.0]]),
+        torch.tensor([1]),
+        bias=torch.tensor([1e8]),
+        activation=None,
+    )
+    assert out.item() == 0.0
+    out, _ = CausalConv1dUpdateOp()(
+        torch.tensor([[-1e8]]),
+        torch.ones(2, 1, 1),
+        torch.ones(1, 2),
+        torch.tensor([1]),
+        bias=torch.tensor([1e8]),
+        activation=None,
+    )
+    assert out.item() == 0.0
+
+
+def test_conv_bf16_products_round_before_fp32_accumulation():
+    state = torch.zeros(2, 1, 1, dtype=torch.bfloat16)
+    state[1, 0, 0] = 1.0078125
+    out, _ = CausalConv1dUpdateOp().forward_fp32(
+        torch.tensor([[-1.015625]], dtype=torch.bfloat16),
+        state,
+        torch.tensor([[1.0078125, 1.0]], dtype=torch.bfloat16),
+        torch.tensor([1]),
+        activation=None,
+    )
+    # The first exact product is 1.015686..., rounded to 1.015625 in BF16.
+    assert out.item() == 0.0

From dcb926310546af8f25b558e69014e052406b2c1b Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 18:35:04 +0000
Subject: [PATCH 19/70] fix(gdn): avoid overflow in inactive softplus gradient
 branch

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../kernels/ops/pytorch/linear_attn/gated_delta_rule.py   | 4 +++-
 tests/test_gdn_state_contract.py                          | 8 ++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
index bfab76047..e2af3a0c3 100644
--- a/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
@@ -104,7 +104,9 @@ def _l2_normalize(x: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
 
 def _softplus(x: torch.Tensor, threshold: float = SOFTPLUS_THRESHOLD) -> torch.Tensor:
     """The kernel's branched softplus; the branch matters for large ``a + dt_bias``."""
-    return torch.where(x <= threshold, torch.log1p(torch.exp(x)), x)
+    small = x <= threshold
+    safe = torch.where(small, x, torch.zeros_like(x))
+    return torch.where(small, torch.log1p(torch.exp(safe)), x)
 
 
 class GatedDeltaRuleRecurrentStepOp:
diff --git a/tests/test_gdn_state_contract.py b/tests/test_gdn_state_contract.py
index 9e84ba288..70255bb0d 100644
--- a/tests/test_gdn_state_contract.py
+++ b/tests/test_gdn_state_contract.py
@@ -96,3 +96,11 @@ def test_conv_bf16_products_round_before_fp32_accumulation():
     )
     # The first exact product is 1.015686..., rounded to 1.015625 in BF16.
     assert out.item() == 0.0
+
+
+def test_softplus_large_branch_has_finite_gradient():
+    from rl_engine.kernels.ops.pytorch.linear_attn.gated_delta_rule import _softplus
+
+    x = torch.tensor([100.0, 21.0, 0.0], requires_grad=True)
+    _softplus(x).sum().backward()
+    assert torch.equal(x.grad, torch.tensor([1.0, 1.0, 0.5]))

From 16204ab1de6d3f22d5a568c56dd8866c2278e44f Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 30 Sep 2026 18:42:32 +0000
Subject: [PATCH 20/70] ci(qwen3-next): separate audited provider runtime from
 legacy CUDA gate

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/qwen3-next-provider-gpu.yml | 42 +++++++++++++++++++
 ci/run_ws1_gtest.sh                           |  4 --
 2 files changed, 42 insertions(+), 4 deletions(-)
 create mode 100644 .github/workflows/qwen3-next-provider-gpu.yml

diff --git a/.github/workflows/qwen3-next-provider-gpu.yml b/.github/workflows/qwen3-next-provider-gpu.yml
new file mode 100644
index 000000000..ee1851881
--- /dev/null
+++ b/.github/workflows/qwen3-next-provider-gpu.yml
@@ -0,0 +1,42 @@
+name: Qwen3-Next-provider-GPU
+
+on:
+  pull_request:
+    branches: [main]
+    paths:
+      - "rl_engine/kernels/ops/**"
+      - "rl_engine/integrations/qwen3_next_gdn.py"
+      - "rl_engine/testing/qwen3_next*"
+      - "tests/check_*qwen3_next*.py"
+      - "tests/check_gdn_recurrent_golden.py"
+      - "ci/run_qwen3_next_operator_gates.sh"
+      - ".github/workflows/qwen3-next-provider-gpu.yml"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  provider:
+    # This label must identify a maintained CUDA 13 / vLLM 0.30.0 environment.
+    # Never run untrusted fork PR code on the self-hosted runner.
+    if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name == github.repository
+    runs-on: [self-hosted, linux, x64, rl-kernel-qwen3-next]
+    timeout-minutes: 30
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+      - name: Require the audited runtime
+        run: |
+          python3 - <<'PY'
+          import torch, vllm
+          assert torch.__version__ == "2.13.0+cu130"
+          assert vllm.__version__ == "0.30.0"
+          assert torch.cuda.is_available()
+          PY
+      - name: Build the extension against that runtime
+        run: python3 setup.py build_ext --inplace
+      - name: Check pinned providers in their own process
+        run: |
+          python3 -m pytest -q tests/check_qwen3_next_norm_providers.py tests/check_gdn_recurrent_golden.py
diff --git a/ci/run_ws1_gtest.sh b/ci/run_ws1_gtest.sh
index c9daa1118..03b77173d 100755
--- a/ci/run_ws1_gtest.sh
+++ b/ci/run_ws1_gtest.sh
@@ -85,7 +85,3 @@ for op in qwen3_next_rms_norm rms_norm_gated; do
       --hidden 2048 --head-dim 128
   done
 done
-# Provider imports run in a separate process from framework-isolation tests.
-echo "[ws1-gtest] Qwen3-Next provider comparisons (required)"
-"$PY" -c 'import torch, vllm; assert torch.cuda.is_available(), "CUDA is required"'
-"$PY" -m pytest -q tests/check_qwen3_next_norm_providers.py tests/check_gdn_recurrent_golden.py

From 697bea887281354d13f7f81af5ed3417e8b1e994 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 18:26:46 +0000
Subject: [PATCH 21/70] test(gdn): assert the packed-decode provider leaves the
 null block untouched

test_null_block_id_is_skipped_by_both checked that both sides write zeros
for a NULL_BLOCK_ID row, but asserted "block untouched" only for the golden.
The provider returns before any store for state_idx <= 0
(fused_recurrent.py:299-303 in vllm 0.30.0); assert it on the provider's
state as well so the test name matches what it checks.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 tests/check_gdn_recurrent_golden.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/check_gdn_recurrent_golden.py b/tests/check_gdn_recurrent_golden.py
index 7c7e929ac..aefecaddf 100644
--- a/tests/check_gdn_recurrent_golden.py
+++ b/tests/check_gdn_recurrent_golden.py
@@ -141,6 +141,7 @@ def test_null_block_id_is_skipped_by_both():
         assert bool((out_ref[row] == 0).all()), f"provider row {row}"
         assert bool((out_got[row] == 0).all()), f"golden row {row}"
     assert torch.equal(state_got[0], inp["state"][0]), "block 0 must be untouched"
+    assert torch.equal(state_ref[0], inp["state"][0]), "provider must leave block 0 untouched"
 
 
 # --------------------------------------------------------------------------- #

From 12a22256e69496a9600577bb267be2d97e0e1046 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 18:27:08 +0000
Subject: [PATCH 22/70] ci(qwen3-next): drop stale and forward-referencing
 workflow paths

ws1-gtest-gpu.yml: 158fc75 added four Qwen3-Next/GDN test files to the
pull_request and push path filters together with a step in
ci/run_ws1_gtest.sh that ran the two check_ files. 03176a3 moved that step
to qwen3-next-provider-gpu.yml but left the filters, so editing those files
triggered a RunPod job that never executed them. Remove the filters; the
file is back to its state on feat/cuda-qwen3-next-gated-rmsnorm.

qwen3-next-provider-gpu.yml: drop rl_engine/integrations/qwen3_next_gdn.py
and ci/run_qwen3_next_operator_gates.sh from the path filter. Neither file
exists on this branch.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/qwen3-next-provider-gpu.yml | 2 --
 .github/workflows/ws1-gtest-gpu.yml           | 8 --------
 2 files changed, 10 deletions(-)

diff --git a/.github/workflows/qwen3-next-provider-gpu.yml b/.github/workflows/qwen3-next-provider-gpu.yml
index ee1851881..b53c73441 100644
--- a/.github/workflows/qwen3-next-provider-gpu.yml
+++ b/.github/workflows/qwen3-next-provider-gpu.yml
@@ -5,11 +5,9 @@ on:
     branches: [main]
     paths:
       - "rl_engine/kernels/ops/**"
-      - "rl_engine/integrations/qwen3_next_gdn.py"
       - "rl_engine/testing/qwen3_next*"
       - "tests/check_*qwen3_next*.py"
       - "tests/check_gdn_recurrent_golden.py"
-      - "ci/run_qwen3_next_operator_gates.sh"
       - ".github/workflows/qwen3-next-provider-gpu.yml"
   workflow_dispatch:
 
diff --git a/.github/workflows/ws1-gtest-gpu.yml b/.github/workflows/ws1-gtest-gpu.yml
index d8f32b714..646d80bdf 100644
--- a/.github/workflows/ws1-gtest-gpu.yml
+++ b/.github/workflows/ws1-gtest-gpu.yml
@@ -20,10 +20,6 @@ on:
       - "scripts/check_gradient_invariance.py"
       - "scripts/ws1_candidate_evidence.py"
       - "tests/test_ws1_*.py"
-      - "tests/test_gdn_state_contract.py"
-      - "tests/test_qwen3_next_norm.py"
-      - "tests/check_qwen3_next_norm_providers.py"
-      - "tests/check_gdn_recurrent_golden.py"
       - "tests/test_forward_invariance.py"
       - "tests/test_gradient_invariance.py"
       - "tests/test_four_judgment_matrix.py"
@@ -42,10 +38,6 @@ on:
       - "scripts/check_gradient_invariance.py"
       - "scripts/ws1_candidate_evidence.py"
       - "tests/test_ws1_*.py"
-      - "tests/test_gdn_state_contract.py"
-      - "tests/test_qwen3_next_norm.py"
-      - "tests/check_qwen3_next_norm_providers.py"
-      - "tests/check_gdn_recurrent_golden.py"
       - "tests/test_forward_invariance.py"
       - "tests/test_gradient_invariance.py"
       - "tests/test_four_judgment_matrix.py"

From b54ac72644cdb75680c94a04d07a4cc19780d031 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 18:29:46 +0000
Subject: [PATCH 23/70] feat(gdn): add a runner for the C6 provider-agreement
 measurements

The design note quoted provider-vs-golden figures (max|d out|, max|d state|,
conv mismatch counts) that no committed code printed, so they could not be
reproduced. scripts/ws1_gdn_provider_agreement.py prints them as JSON,
using tests/check_gdn_recurrent_golden.py's own input helpers and seeds
(imported, not copied):

- recurrent: per (batch, state dtype), max|d out|, max|d state| and
  bitwise mismatch counts, next to the bounds the check file asserts;
- conv: per (batch, cache dtype), output mismatch count, max|diff| and
  whether the rolled state is bitwise equal;
- the conv comparison again with triton.knobs.language.default_fp_fusion
  off, plus the fma.rn.f32 count in each compiled variant of the
  provider's conv-update kernel, to test whether the fp32-cache mismatches
  are FMA contraction.

Provenance (git commit and dirty flag, torch/triton/vllm versions, device)
is recorded with the results. Requires CUDA and vLLM; not run in CI.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 scripts/ws1_gdn_provider_agreement.py | 238 ++++++++++++++++++++++++++
 1 file changed, 238 insertions(+)
 create mode 100644 scripts/ws1_gdn_provider_agreement.py

diff --git a/scripts/ws1_gdn_provider_agreement.py b/scripts/ws1_gdn_provider_agreement.py
new file mode 100644
index 000000000..a3a433575
--- /dev/null
+++ b/scripts/ws1_gdn_provider_agreement.py
@@ -0,0 +1,238 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Measure how closely the GDN decode-step goldens track vLLM's providers (RFC #428 C6).
+
+``tests/check_gdn_recurrent_golden.py`` asserts loose bounds; this runner prints the
+measured values behind them, so the numbers quoted in
+``docs/design/ws1-c6-428-gdn-recurrent-replay.md`` can be reproduced. Inputs and seeds
+are the check file's own helpers, imported from it rather than copied.
+
+Three measurements, emitted as one JSON document on stdout:
+
+* ``recurrent``: provider vs golden for the packed recurrent decode, per
+  (batch, state dtype): max|d out|, max|d state| and bitwise mismatch counts.
+* ``conv``: provider vs golden for ``causal_conv1d_update``, per (batch, cache dtype):
+  output mismatch count and max|diff|, and whether the rolled state is bitwise equal.
+* ``conv`` again with Triton FP fusion disabled
+  (``triton.knobs.language.default_fp_fusion = False``), plus ``conv_ptx``: the number
+  of ``fma.rn.f32`` instructions in each compiled variant of the provider's conv
+  kernel. If the fp32-cache mismatches are FMA contraction, they should vanish with
+  fusion off and the fusion-off PTX should contain no ``fma.rn.f32``.
+
+Requires CUDA and vLLM 0.30.0. Run it from a clean checkout so ``git_dirty`` is false:
+
+    python scripts/ws1_gdn_provider_agreement.py > gdn_provider_agreement.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import contextlib
+import importlib.util
+import json
+import platform
+import subprocess
+import sys
+from pathlib import Path
+from typing import Any
+
+import torch
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+_CHECK_FILE = REPO_ROOT / "tests" / "check_gdn_recurrent_golden.py"
+_STATE_DTYPES = {"fp32": torch.float32, "bf16": torch.bfloat16}
+_INT_VIEW = {2: torch.int16, 4: torch.int32}
+
+
+def _load_check_module():
+    """Import the check file by path; ``tests/`` is not a package."""
+    spec = importlib.util.spec_from_file_location("check_gdn_recurrent_golden", _CHECK_FILE)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _bit_mismatches(a: torch.Tensor, b: torch.Tensor) -> int:
+    """Elements whose bit patterns differ (NaN-safe, unlike ``a != b``)."""
+    assert a.dtype == b.dtype and a.shape == b.shape, (a.dtype, b.dtype, a.shape, b.shape)
+    view = _INT_VIEW[a.element_size()]
+    return int((a.contiguous().view(view) != b.contiguous().view(view)).sum())
+
+
+def _max_abs_diff(a: torch.Tensor, b: torch.Tensor) -> float:
+    return (a.float() - b.float()).abs().max().item()
+
+
+def _git(*args: str) -> str:
+    try:
+        return subprocess.run(
+            ["git", *args], cwd=REPO_ROOT, capture_output=True, text=True, check=True
+        ).stdout.strip()
+    except (OSError, subprocess.CalledProcessError) as exc:
+        return f"unavailable: {exc}"
+
+
+def _provenance() -> dict[str, Any]:
+    import triton
+    import vllm
+
+    return {
+        "git_commit": _git("rev-parse", "HEAD"),
+        "git_dirty": bool(_git("status", "--porcelain")),
+        "python": platform.python_version(),
+        "torch": torch.__version__,
+        "triton": triton.__version__,
+        "vllm": vllm.__version__,
+        "device": torch.cuda.get_device_name(),
+        "capability": list(torch.cuda.get_device_capability()),
+        "default_fp_fusion": bool(triton.knobs.language.default_fp_fusion),
+    }
+
+
+@contextlib.contextmanager
+def _fp_fusion(enabled: bool):
+    """Set Triton's default FP fusion for kernels compiled inside the block.
+
+    ``enable_fp_fusion`` is part of the options string in Triton's kernel cache key,
+    so flipping it compiles a fresh variant rather than reusing the fused one.
+    """
+    import triton
+
+    knobs = triton.knobs.language
+    previous = knobs.default_fp_fusion
+    knobs.default_fp_fusion = enabled
+    try:
+        yield
+    finally:
+        knobs.default_fp_fusion = previous
+
+
+def _measure_recurrent(check, batches: list[int]) -> list[dict[str, Any]]:
+    bounds = {"fp32": (1e-3, 1e-5), "bf16": (1e-3, 5e-3)}  # check file :117, :120
+    rows = []
+    for batch in batches:
+        for name, state_dtype in _STATE_DTYPES.items():
+            # Same call as test_golden_matches_packed_decode_provider.
+            inp = check._inputs(batch, batch + 2, state_dtype, torch.bfloat16, seed=batch)
+            out_ref, state_ref = check._run_provider(inp)
+            out_got, state_got = check._run_golden(inp)
+            rows.append(
+                {
+                    "batch": batch,
+                    "state_dtype": name,
+                    "max_abs_diff_out": _max_abs_diff(out_got, out_ref),
+                    "max_abs_diff_state": _max_abs_diff(state_got, state_ref),
+                    "out_mismatch_elements": _bit_mismatches(out_got, out_ref),
+                    "out_elements": out_ref.numel(),
+                    "state_mismatch_elements": _bit_mismatches(state_got, state_ref),
+                    "state_elements": state_ref.numel(),
+                    "asserted_out_atol": bounds[name][0],
+                    "asserted_state_atol": bounds[name][1],
+                }
+            )
+    return rows
+
+
+def _measure_conv(check, batches: list[int], fp_fusion: bool) -> list[dict[str, Any]]:
+    rows = []
+    with _fp_fusion(fp_fusion):
+        for batch in batches:
+            for name, cache_dtype in _STATE_DTYPES.items():
+                # Same call as the test_conv_* provider comparisons.
+                inp = check._conv_inputs(batch, cache_dtype, seed=batch)
+                (out_ref, state_ref), (out_got, state_got) = check._run_conv_pair(inp)
+                rows.append(
+                    {
+                        "batch": batch,
+                        "cache_dtype": name,
+                        "fp_fusion": fp_fusion,
+                        "out_mismatch_elements": _bit_mismatches(out_got, out_ref),
+                        "out_elements": out_ref.numel(),
+                        "max_abs_diff_out": _max_abs_diff(out_got, out_ref),
+                        "state_bitwise_equal": torch.equal(state_got, state_ref),
+                    }
+                )
+    return rows
+
+
+def _conv_ptx_fma_counts() -> list[dict[str, Any]] | dict[str, str]:
+    """``fma.rn.f32`` count per compiled variant of the provider's conv-update kernel."""
+    try:
+        from vllm.model_executor.layers.mamba.ops import causal_conv1d as conv_module
+
+        kernel = conv_module._causal_conv1d_update_kernel
+        while not hasattr(kernel, "device_caches") and hasattr(kernel, "fn"):
+            kernel = kernel.fn
+        variants = []
+        for device, cache in kernel.device_caches.items():
+            for compiled in cache[0].values():
+                ptx = compiled.asm["ptx"]
+                variants.append(
+                    {
+                        "device": str(device),
+                        "enable_fp_fusion": getattr(compiled.metadata, "enable_fp_fusion", None),
+                        "fma_rn_f32": ptx.count("fma.rn.f32"),
+                        "mul_rn_f32": ptx.count("mul.rn.f32"),
+                        "add_rn_f32": ptx.count("add.rn.f32"),
+                    }
+                )
+        return variants
+    except Exception as exc:  # introspection of Triton internals; report, do not fail
+        return {"unavailable": f"{type(exc).__name__}: {exc}"}
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    parser.add_argument(
+        "--batches",
+        default="1,4,17,64",
+        help="comma-separated batch sizes (default: the check file's 1,4,17,64)",
+    )
+    parser.add_argument(
+        "--skip-fusion-off",
+        action="store_true",
+        help="do not rerun the conv comparison with Triton FP fusion disabled",
+    )
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    if not torch.cuda.is_available():
+        print("CUDA is required", file=sys.stderr)
+        return 2
+    try:
+        import vllm  # noqa: F401
+    except ImportError:
+        print("vLLM is required", file=sys.stderr)
+        return 2
+
+    batches = [int(b) for b in args.batches.split(",") if b.strip()]
+    check = _load_check_module()
+    report: dict[str, Any] = {
+        "runner": "scripts/ws1_gdn_provider_agreement.py",
+        "provenance": _provenance(),
+        "inputs": {
+            "source": "tests/check_gdn_recurrent_golden.py (_inputs, _conv_inputs)",
+            "seed": "seed=batch for every case",
+            "batches": batches,
+            "recurrent": "bf16 I/O, use_qk_l2norm_in_kernel=True, num_blocks=batch+2",
+            "conv": "bias=True, activation=silu, dim_first=True, W=4, dim=8192",
+        },
+        "recurrent": _measure_recurrent(check, batches),
+        "conv": _measure_conv(check, batches, fp_fusion=True),
+    }
+    if not args.skip_fusion_off:
+        report["conv"] += _measure_conv(check, batches, fp_fusion=False)
+    report["conv_ptx"] = _conv_ptx_fma_counts()
+    print(json.dumps(report, indent=2, sort_keys=True))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

From 7b30633a80b9192538176f8aa9798e7748c1ca9d Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 19:01:57 +0000
Subject: [PATCH 24/70] docs(gdn): correct the C6 design note against vLLM
 0.30.0 source
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Review after 03176a3 found nine statements in the note that the source
does not support. Fix them:

- §1: the decode path is chosen per engine step and by the model, not by
  env defaults alone. Tabulate decode-only / mixed / draft-token steps with
  their conv and recurrent kernels. Qwen3-Next's gqa_interleaved_layout=True
  makes the fused CUDA decode unsupported, so the layer falls back to triton
  and fused_gdn_decode_post_conv_mtp is unreachable (:1834). The env-default
  assertion guards the packed-decode default but not VLLM_GDN_DECODE_KERNEL.
- §2: the golden follows the kernel's arithmetic but is not a transcription
  everywhere: softplus uses log1p where the kernel uses log(1 + exp), and
  FLA_USE_FAST_OPS swaps in fast exp/log.
- §3: split NULL_BLOCK_ID semantics. The recurrent provider skips <= 0 and
  writes zeros; the conv provider skips only == null_block_id (0) and does
  not write the output row; both goldens skip <= 0 and write zeros.
- §4: replace the "measured" table, which had no runner, with the bounds the
  check file asserts and point to scripts/ws1_gdn_provider_agreement.py.
  State the fp32-cache conv mismatch and the FMA hypothesis the runner tests.
- §5: add the mixed decode-and-prefill caveat on the provider side, including
  vllm-project/vllm#49827, and state that validate_data=True does not check
  index values either.
- §6: restate the MTP deferral against RFC #428 §2.2 and the actual
  Qwen3-Next MTP path.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../design/ws1-c6-428-gdn-recurrent-replay.md | 179 ++++++++++++++----
 1 file changed, 137 insertions(+), 42 deletions(-)

diff --git a/docs/design/ws1-c6-428-gdn-recurrent-replay.md b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
index 2aa1ddf23..7832bbb1b 100644
--- a/docs/design/ws1-c6-428-gdn-recurrent-replay.md
+++ b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
@@ -2,34 +2,65 @@
 
 Design notes for RFC #428 work item C6 (GDN recurrent response replay) on the CUDA
 track. Measured on 2× B200 (sm_100), torch 2.13.0+cu130, vllm 0.30.0,
-transformers 5.17.0.
+transformers 5.17.0. vLLM paths below are relative to the installed `vllm` 0.30.0
+package; two files are named `causal_conv1d.py`, and each citation says whether it
+means vLLM's (`model_executor/layers/mamba/ops/causal_conv1d.py`) or the golden's
+(`rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py`).
 
 Claim level reached: **L0 repeatable, L1 batch-invariant**. L2 is not claimed.
 
 ## 1. Which provider a rollout decode actually takes
 
-vLLM 0.30.0 has three GDN decode paths, selected by env defaults rather than by the
-model:
+vLLM 0.30.0 picks the GDN decode path per engine step. The choice depends on env
+defaults, on what else the step contains, and on how the model constructs the layer:
 
-| condition | path |
-|---|---|
-| `VLLM_GDN_DECODE_KERNEL="cuda"` (default) + MTP | `torch.ops._C.fused_gdn_decode_post_conv_mtp` — conv, recurrence and the gated norm fused |
-| `VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE=1` (default), decode-only, non-spec | `fused_recurrent_gated_delta_rule_packed_decode` |
-| otherwise | `fused_sigmoid_gating_delta_rule_update` |
-
-A pure RL rollout decode — N sequences each emitting one token, no speculative
-decoding — returns early at `qwen_gdn_linear_attn.py:1295-1307` into the **packed**
-path. The golden targets that one. Aligning against the sigmoid-gating kernel would
-validate a path production does not take.
-
-`tests/check_qwen3_next_norm_providers.py` asserts those two env defaults, so a vLLM
-bump that flips either fails loudly rather than silently re-pointing the claim.
-
-## 2. Three places the kernel and the HF model disagree
-
-The golden is transcribed from the kernel, not from `modeling_qwen3_next.py`:
-
-1. **The gating is fused.** `beta = sigmoid(b)` and
+| step contains | conv | recurrence |
+|---|---|---|
+| decodes only, no draft tokens | `causal_conv1d_update` | `fused_recurrent_gated_delta_rule_packed_decode` |
+| decodes and at least one prefill, no draft tokens | `causal_conv1d_fn` | `fused_sigmoid_gating_delta_rule_update` for the cached decode rows |
+| draft tokens (speculative decode / MTP) | `causal_conv1d_update` with `num_accepted_tokens` | `fused_sigmoid_gating_delta_rule_update`; plain decodes in the step are reclassified as prefills |
+
+- **Decode-only.** With `VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE` at its default of on
+  (`envs.py:1199-1200`), a decode-only step returns early at
+  `model_executor/layers/mamba/gdn/qwen_gdn_linear_attn.py:1295-1307` into
+  `_forward_core_decode_non_spec`, which calls the packed kernel (`:1697-1708`). **The
+  goldens target this path.** With the variable off, the same step goes through
+  `fused_sigmoid_gating_delta_rule_update` instead (`:1553-1572`).
+- **Mixed decode and prefill.** `_forward_core` runs the conv for the whole non-spec
+  batch through `causal_conv1d_fn` (branch at `:1373`, call at `:1378-1388`) and the
+  cached decode rows through `fused_sigmoid_gating_delta_rule_update`
+  (`split_non_spec` defined at `:1408-1412`, branch at `:1493`, call at `:1497-1512`).
+  See §5 for what that does to the claim.
+- **Draft tokens.** Spec rows go through `causal_conv1d_update` with
+  `num_accepted_tokens` (`:1357-1370`) and `fused_sigmoid_gating_delta_rule_update`
+  (`:1470-1487`). Plain decodes in the same step are reclassified as prefills
+  (`v1/attention/backends/gdn_attn.py:283-289`). A step with speculative decoding
+  enabled but zero draft tokens sets `spec_sequence_masks` to `None`
+  (`gdn_attn.py:236-243`) and is treated as decode-only.
+
+`VLLM_GDN_DECODE_KERNEL` defaults to `"cuda"`, but that is not what Qwen3-Next runs.
+`model_executor/models/qwen3_next.py:495` constructs the layer with
+`gqa_interleaved_layout=True`, which makes `_fused_gdn_decode_unsupported_reason`
+(`qwen_gdn_linear_attn.py:535-554`) return a reason. The layer then logs a fallback
+to `"triton"` (`:520-523`), or raises `ValueError` if `VLLM_GDN_DECODE_KERNEL` was
+explicitly set to `cuda` (`:516-519`). As a consequence the fused
+`torch.ops._C.fused_gdn_decode_post_conv_mtp` path is unreachable for Qwen3-Next:
+`_can_use_fused_gdn_mtp_decode` requires `gdn_decode_kernel == "cuda"` (`:1834`).
+This section is read from the source.
+
+`tests/check_qwen3_next_norm_providers.py` asserts both env defaults (`:124-131`). For
+`VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE` that is a useful guard: a vLLM bump that
+flips it fails loudly. For `VLLM_GDN_DECODE_KERNEL` it guards nothing for Qwen3-Next,
+because the interleaved layout, not the default, decides the kernel. Neither
+assertion checks which kernel actually runs.
+
+## 2. How the golden relates to the kernel
+
+The golden follows the kernel's arithmetic, not `modeling_qwen3_next.py`'s. Checked
+line by line against `third_party/flash_linear_attention/ops/fused_recurrent.py:288-335`,
+three places where the kernel and the HF model differ:
+
+1. **The gating is fused, in fp32.** `beta = sigmoid(b)` and
    `g = -exp(A_log) * softplus(a + dt_bias)` are computed inside the Triton kernel,
    with a `softplus` threshold branch at 20. HF computes them as separate PyTorch
    ops — a different rounding path.
@@ -40,9 +71,19 @@ The golden is transcribed from the kernel, not from `modeling_qwen3_next.py`:
    `rsqrt`, which differs in the last bit. `scale` is applied to `q` *after* the
    norm; `k` is never scaled.
 
-A fourth, checked and found **not** to be a divergence: prefill passes
-`use_qk_l2norm_in_kernel=False` only because `fused_post_conv_prep(apply_l2norm=True)`
-already normalized q/k. Both paths normalize exactly once.
+Where the golden is **not** a transcription:
+
+- softplus: the kernel computes `tl.log(1.0 + tl.exp(x))` (`fused_recurrent.py:327`);
+  the golden computes `torch.log1p(torch.exp(safe))`, where `safe` is `x` on the taken
+  branch (`gated_delta_rule.py:107-109`). These round differently.
+- The kernel's `exp`/`log` become `fast_expf`/`fast_logf` when `FLA_USE_FAST_OPS=1`
+  (`third_party/flash_linear_attention/ops/op.py:16-25`). The golden models the
+  default.
+
+Checked and found **not** to be a divergence: prefill passes
+`use_qk_l2norm_in_kernel=False` (`qwen_gdn_linear_attn.py:1542`) only because
+`fused_post_conv_prep(apply_l2norm=True)` (`:1450`) already normalized q/k. Decode
+passes `True` (`:1707`). Both paths normalize exactly once.
 
 ## 3. State ABI
 
@@ -51,30 +92,60 @@ Mirrored rather than reinvented:
 - recurrent state `[num_blocks, HV, V, K]`, V-major, addressed by `ssm_state_indices`
 - conv state `[num_blocks, dim, width-1]`, layout chosen by the global
   `is_conv_state_dim_first()`; the golden takes it as an argument and both are tested
-- `NULL_BLOCK_ID` (index `<= 0`) means skip: zeros out, block untouched
 - the accumulator is fp32 for the whole step; the store rounds to the state tensor's
-  dtype, which `FUSED_GDN_STATE_DTYPES` allows to be fp32 **or** bf16
-- `causal_conv1d_update` casts `x` to the cache dtype before anything else
+  dtype, which `FUSED_GDN_STATE_DTYPES` (`qwen_gdn_linear_attn.py:91`) allows to be
+  fp32 **or** bf16
+- `causal_conv1d_update` casts `x` to the cache dtype before computing
+
+`NULL_BLOCK_ID` is **not** one contract across the two providers:
+
+| | recurrent provider | conv provider | both goldens |
+|---|---|---|---|
+| which indices skip | `<= 0` (`fused_recurrent.py:300`) | `== null_block_id` only, default `NULL_BLOCK_ID` = 0 (vLLM `causal_conv1d.py:835`; `v1/attention/backends/utils.py:47`) | `<= 0` |
+| output for a skipped row | zeros (`fused_recurrent.py:301-302`) | not written (vLLM `causal_conv1d.py:835-839` returns before any store) | zeros |
+| state block | not touched | not touched | not touched |
 
-Contractions run in the repo's fixed 32-wide chunk order rather than `torch.matmul`,
-whose reduction order is unspecified. That is what the L1 claim rests on, and also
-why the golden is not bitwise against the kernel's tree.
+A negative conv index is therefore inactive in the golden
+(`tests/test_gdn_state_contract.py:51-56`) but a real, out-of-range index to the conv
+provider.
+
+Contractions use `_chunked_sum` (`gated_delta_rule.py:73-87`): fixed 32-wide chunks,
+so the reduction shape per row does not depend on the batch size, rather than
+`torch.matmul`, whose reduction order is unspecified. That is the argument for the
+L1 claim; L1 itself is established empirically by `test_golden_is_batch_invariant`.
+The same choice is why the golden is not bitwise against the kernel's reduction.
 
 ## 4. Agreement with the provider
 
-Qwen3-Next dims (H=16, HV=32, K=V=128), `use_qk_l2norm_in_kernel=True`:
+Qwen3-Next dims (H=16, HV=32, K=V=128), bf16 I/O, `use_qk_l2norm_in_kernel=True`,
+random inputs, B ∈ {1, 4, 17, 64}, one seed per batch. Bounds asserted by
+`tests/check_gdn_recurrent_golden.py`:
 
 | | max\|diff\| out | max\|diff\| state |
 |---|---|---|
-| fp32 state, B=1..64 | 1.5e-08 .. 6.1e-05 | ≤ 3.0e-07 |
-| bf16 state, B=1..64 | 3.7e-09 .. 3.1e-05 | ≤ 2.0e-03 |
+| fp32 state | ≤ 1e-3 | ≤ 1e-5 |
+| bf16 state | ≤ 1e-3 | ≤ 5e-3 |
+
+`scripts/ws1_gdn_provider_agreement.py` prints the measured values behind these
+bounds for the same inputs. An earlier table here quoted tighter figures from a
+single run with no committed runner; it is withdrawn in favour of the runner's
+output.
 
 Causal conv uses sequential FP32 accumulation **starting from bias**, with
-products first rounded to the operand dtype. The previous BF16 path incorrectly
-promoted both operands to FP32; its disagreements were not limited to one BF16
-ULP. Cancellation and bias-order CPU tests now cover these errors. Provider
-comparisons preserve the existing absolute bounds and additionally limit BF16
-mismatches to 32 elements on the checked fixtures. This is not a bitwise claim.
+products first rounded to the operand dtype (golden `causal_conv1d.py:167-177`;
+the provider initialises from bias at vLLM `causal_conv1d.py:960-967`, `1000`). The
+previous BF16 path incorrectly promoted both operands to FP32; its disagreements
+were not limited to one BF16 ULP. The CPU tests in `tests/test_gdn_state_contract.py`
+cover bias order and bf16 product rounding, and
+`test_conv_provider_preserves_bf16_product_cancellation` checks the provider on a
+constructed cancellation input. Provider comparisons limit mismatches to 32 elements
+on the checked fixtures. This is not a bitwise claim.
+
+With an fp32 cache a few output elements still differ. The cause is not pinned. One
+candidate is FP contraction: Triton's default `enable_fp_fusion=True` may contract the
+provider's `acc += matrix_x * matrix_w` (vLLM `causal_conv1d.py:1061`) into an FMA,
+whereas the golden rounds the product first. The runner reruns the comparison with
+fusion off and counts `fma.rn.f32` in the compiled kernel to test this.
 
 ## 5. Decode versus chunked prefill
 
@@ -83,14 +154,38 @@ they are withdrawn as acceptance evidence. The existing 128-step synthetic test
 only bounds its fixed seed and gate inputs. It does not establish a universal
 plateau, prefill/decode equality, or any bound on model logits.
 
+**Mixed decode-and-prefill steps (provider side).** The goldens target the
+decode-only path (§1). In a step that also holds a prefill, vLLM 0.30.0 sends the
+cached decode rows through `causal_conv1d_fn` and
+`fused_sigmoid_gating_delta_rule_update` instead, so in those steps neither golden
+matches what rollout runs. The two recurrent kernels do not agree bitwise. A
+measurement on B200 with Qwen3-Next TP1 shapes, driving the real
+`GDNAttentionMetadataBuilder.build()` and `_forward_core`, found the step's bf16
+output equal but the fp32 state different in 75,146 of 524,288 elements (8.8e-08
+relative). In 4 of 20 seeds the difference reached a bf16 output within the next 16
+decode steps. This was measured outside this change and is not yet published; treat it
+as a reported observation.
+
+vllm-project/vllm#49827, open and unmerged, would route mixed-step *recurrent*
+decodes through the packed kernel too, according to its description. It was
+validated on Qwen3.5 (non-interleaved), H100, TP1, and it does not change the conv
+path in mixed steps, which stays `causal_conv1d_fn`. Disabling
+`VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE` instead sends every decode row through
+`fused_sigmoid_gating_delta_rule_update`, and the recurrent golden's target would
+have to change.
+
 The strict profile uses FP32 recurrent state. BF16 state remains a differential
 experiment. Full checkpoint prefill, response replay, optimizer updates and
 reload all remain required before L2 can pass.
 
 Cache indices must be int32/int64, on the input device, and positive active
 indices must be unique and in range. Nonpositive sentinels may repeat. The golden
-validates before any cache update; raw provider calls remain the caller's
-responsibility (the provider's default does not bounds-check).
+validates before any cache update. Raw provider calls remain the caller's
+responsibility: neither provider checks index values. `causal_conv1d_update`'s
+`validate_data=True` adds only shape and stride asserts and a
+`null_block_id is not None` assert (vLLM `causal_conv1d.py:1151-1153`, `1188-1201`),
+so an out-of-range index is an unchecked memory access in either mode (read from
+the source, not exercised).
 
 ## 6. Current boundary
 
@@ -98,7 +193,7 @@ Not covered, with reasons:
 
 | deferred | why |
 |---|---|
-| Speculative decode / MTP | `fused_gdn_decode_post_conv_mtp` fuses conv, recurrence and the gated norm; validating it needs a draft model |
+| Speculative decode / MTP | RFC #428 §2.2 excludes speculative decoding from the first claim. Qwen3-Next's MTP head ships in the checkpoint (`mtp.*`, loaded by `model_executor/models/qwen3_next_mtp.py`) and is full attention (`qwen3_next_mtp.py:90-92`). Enabling it changes the target model's GDN path in steps that carry draft tokens (§1); `fused_gdn_decode_post_conv_mtp` is unreachable for Qwen3-Next |
 | Backward for the recurrent step | no upstream backward exists, and a naive BPTT through a sequential recurrence is not batch-invariant — it needs its own design |
 | Provider bridge / registry entry | the golden should survive a drift sweep against a real checkpoint first |
 | Paged block allocation policy | the ABI is mirrored; the allocator is not modelled |

From 9da9c36e47241114b2bd980fb90bea5d6461480b Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 19:05:59 +0000
Subject: [PATCH 25/70] fix(gdn): report unknown git state as null, not dirty,
 in the C6 runner

_git() returned the string "unavailable: ..." on failure, so
bool(_git("status", "--porcelain")) turned "git missing / not a checkout"
into git_dirty=True. Return None on failure and record git_dirty and
git_commit as null in that case, so the provenance distinguishes unknown
from dirty.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 scripts/ws1_gdn_provider_agreement.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/scripts/ws1_gdn_provider_agreement.py b/scripts/ws1_gdn_provider_agreement.py
index a3a433575..393d342cd 100644
--- a/scripts/ws1_gdn_provider_agreement.py
+++ b/scripts/ws1_gdn_provider_agreement.py
@@ -68,22 +68,25 @@ def _max_abs_diff(a: torch.Tensor, b: torch.Tensor) -> float:
     return (a.float() - b.float()).abs().max().item()
 
 
-def _git(*args: str) -> str:
+def _git(*args: str) -> str | None:
+    """Stripped stdout, or ``None`` if git is missing or the command fails."""
     try:
         return subprocess.run(
             ["git", *args], cwd=REPO_ROOT, capture_output=True, text=True, check=True
         ).stdout.strip()
-    except (OSError, subprocess.CalledProcessError) as exc:
-        return f"unavailable: {exc}"
+    except (OSError, subprocess.CalledProcessError):
+        return None
 
 
 def _provenance() -> dict[str, Any]:
     import triton
     import vllm
 
+    status = _git("status", "--porcelain")
     return {
+        # None means "unknown" (no git, or not a checkout), never "clean".
         "git_commit": _git("rev-parse", "HEAD"),
-        "git_dirty": bool(_git("status", "--porcelain")),
+        "git_dirty": None if status is None else bool(status),
         "python": platform.python_version(),
         "torch": torch.__version__,
         "triton": triton.__version__,

From 5216a6c3443519e85f6d01a91500fcab60dbf450 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 19:06:16 +0000
Subject: [PATCH 26/70] docs(gdn): restate the backward deferral against RFC
 #428
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The §6 row deferred backward because "a naive BPTT through a sequential
recurrence is not batch-invariant". RFC #428 does not ask backward to be:
§2.2 item 4 says backward need not match rollout, only be correct for the
replayed forward, and §9.1 makes the GDN backward its own work item, C7.
Cite those instead, matching the PR description.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/design/ws1-c6-428-gdn-recurrent-replay.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/design/ws1-c6-428-gdn-recurrent-replay.md b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
index 7832bbb1b..efe2b08aa 100644
--- a/docs/design/ws1-c6-428-gdn-recurrent-replay.md
+++ b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
@@ -194,7 +194,7 @@ Not covered, with reasons:
 | deferred | why |
 |---|---|
 | Speculative decode / MTP | RFC #428 §2.2 excludes speculative decoding from the first claim. Qwen3-Next's MTP head ships in the checkpoint (`mtp.*`, loaded by `model_executor/models/qwen3_next_mtp.py`) and is full attention (`qwen3_next_mtp.py:90-92`). Enabling it changes the target model's GDN path in steps that carry draft tokens (§1); `fused_gdn_decode_post_conv_mtp` is unreachable for Qwen3-Next |
-| Backward for the recurrent step | no upstream backward exists, and a naive BPTT through a sequential recurrence is not batch-invariant — it needs its own design |
+| Backward for the recurrent step | RFC #428 §2.2 item 4: backward need not match rollout, only be correct for the replayed forward. RFC §9.1 makes it a separate work item, **C7** (GDN backward/recompute adapter including prompt-state gradient). `supports_backward=false` here; `_softplus`'s NaN-gradient fix (`08969ac`) adds no backward claim |
 | Provider bridge / registry entry | the golden should survive a drift sweep against a real checkpoint first |
 | Paged block allocation policy | the ABI is mirrored; the allocator is not modelled |
 | TP sharding of `A_log` / `dt_bias` | single card only |

From 10e523056bfcab6d16bc9e0e9791c1ceaa0e0cc2 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 19:47:57 +0000
Subject: [PATCH 27/70] fix(gdn): make the C6 runner's fusion-off arm and FMA
 count real

A run of the runner at d66e595 showed both of its FMA probes were ineffective:

- The fusion-off arm reused the fused kernel. Triton 3.7.1 keys its
  in-memory kernel cache on the launch kwargs; the default_fp_fusion knob
  is read in parse_options only after a cache miss. Flipping the knob
  in-process never missed, so all six recorded variants had
  enable_fp_fusion=True and the "off" counts were the "on" counts again.
  Run the fusion-off arm in a child process with TRITON_DEFAULT_FP_FUSION=0
  and a private TRITON_CACHE_DIR instead.
- fma.rn.f32 = 0 in the PTX does not rule out FMA. With fusion on, Triton
  emits plain mul.f32/add.f32 and lets ptxas contract them into FFMA; with
  fusion off it passes --fmad=false. Count plain and .rn PTX ops, and
  FFMA/FMUL/FADD in the SASS (Triton's bundled cuobjdump).

Report per-arm kernel variants under conv_kernels and add fusion_check,
which says whether the fusion-off variants were actually compiled without
fusion, so a silent no-op cannot pass for a result again.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 scripts/ws1_gdn_provider_agreement.py | 166 +++++++++++++++++---------
 1 file changed, 109 insertions(+), 57 deletions(-)

diff --git a/scripts/ws1_gdn_provider_agreement.py b/scripts/ws1_gdn_provider_agreement.py
index 393d342cd..539fa9979 100644
--- a/scripts/ws1_gdn_provider_agreement.py
+++ b/scripts/ws1_gdn_provider_agreement.py
@@ -15,11 +15,19 @@
   (batch, state dtype): max|d out|, max|d state| and bitwise mismatch counts.
 * ``conv``: provider vs golden for ``causal_conv1d_update``, per (batch, cache dtype):
   output mismatch count and max|diff|, and whether the rolled state is bitwise equal.
-* ``conv`` again with Triton FP fusion disabled
-  (``triton.knobs.language.default_fp_fusion = False``), plus ``conv_ptx``: the number
-  of ``fma.rn.f32`` instructions in each compiled variant of the provider's conv
-  kernel. If the fp32-cache mismatches are FMA contraction, they should vanish with
-  fusion off and the fusion-off PTX should contain no ``fma.rn.f32``.
+* ``conv`` again with Triton FP fusion disabled, plus ``conv_kernels``: per compiled
+  variant of the provider's conv-update kernel, its ``enable_fp_fusion`` option and
+  instruction counts from the PTX and from the SASS.
+
+The fusion-off arm runs in a child process with ``TRITON_DEFAULT_FP_FUSION=0`` and a
+private ``TRITON_CACHE_DIR``. Flipping ``triton.knobs.language.default_fp_fusion``
+in-process does not work: Triton's in-memory kernel cache is keyed on the launch
+kwargs, and the knob is read only after a cache miss, so the fused variant is reused.
+
+Count FMAs in the SASS, not only the PTX. With fusion on, Triton emits plain
+``mul.f32``/``add.f32`` and leaves ptxas free to contract them into ``FFMA``; with
+fusion off it passes ``--fmad=false`` to ptxas. A PTX with no ``fma.rn.f32`` can still
+run FMAs. ``fusion_check`` reports whether each arm compiled what it claims to.
 
 Requires CUDA and vLLM 0.30.0. Run it from a clean checkout so ``git_dirty`` is false:
 
@@ -29,12 +37,14 @@
 from __future__ import annotations
 
 import argparse
-import contextlib
 import importlib.util
 import json
+import os
 import platform
+import re
 import subprocess
 import sys
+import tempfile
 from pathlib import Path
 from typing import Any
 
@@ -47,6 +57,16 @@
 _CHECK_FILE = REPO_ROOT / "tests" / "check_gdn_recurrent_golden.py"
 _STATE_DTYPES = {"fp32": torch.float32, "bf16": torch.bfloat16}
 _INT_VIEW = {2: torch.int16, 4: torch.int32}
+# Opcode patterns. PTX: a rounding-qualified op (".rn") may not be contracted by ptxas;
+# the plain form may. SASS: count opcode tokens, including modifiers such as FFMA.FTZ.
+_PTX_OPS = {
+    "fma_rn_f32": r"\bfma\.rn\.f32\b",
+    "mul_f32": r"\bmul\.f32\b",
+    "add_f32": r"\badd\.f32\b",
+    "mul_rn_f32": r"\bmul\.rn\.f32\b",
+    "add_rn_f32": r"\badd\.rn\.f32\b",
+}
+_SASS_OPS = {"FFMA": r"\bFFMA[\w.]*", "FMUL": r"\bFMUL[\w.]*", "FADD": r"\bFADD[\w.]*"}
 
 
 def _load_check_module():
@@ -97,24 +117,6 @@ def _provenance() -> dict[str, Any]:
     }
 
 
-@contextlib.contextmanager
-def _fp_fusion(enabled: bool):
-    """Set Triton's default FP fusion for kernels compiled inside the block.
-
-    ``enable_fp_fusion`` is part of the options string in Triton's kernel cache key,
-    so flipping it compiles a fresh variant rather than reusing the fused one.
-    """
-    import triton
-
-    knobs = triton.knobs.language
-    previous = knobs.default_fp_fusion
-    knobs.default_fp_fusion = enabled
-    try:
-        yield
-    finally:
-        knobs.default_fp_fusion = previous
-
-
 def _measure_recurrent(check, batches: list[int]) -> list[dict[str, Any]]:
     bounds = {"fp32": (1e-3, 1e-5), "bf16": (1e-3, 5e-3)}  # check file :117, :120
     rows = []
@@ -141,30 +143,36 @@ def _measure_recurrent(check, batches: list[int]) -> list[dict[str, Any]]:
     return rows
 
 
-def _measure_conv(check, batches: list[int], fp_fusion: bool) -> list[dict[str, Any]]:
+def _measure_conv(check, batches: list[int]) -> list[dict[str, Any]]:
+    import triton
+
+    fp_fusion = bool(triton.knobs.language.default_fp_fusion)  # this process's setting
     rows = []
-    with _fp_fusion(fp_fusion):
-        for batch in batches:
-            for name, cache_dtype in _STATE_DTYPES.items():
-                # Same call as the test_conv_* provider comparisons.
-                inp = check._conv_inputs(batch, cache_dtype, seed=batch)
-                (out_ref, state_ref), (out_got, state_got) = check._run_conv_pair(inp)
-                rows.append(
-                    {
-                        "batch": batch,
-                        "cache_dtype": name,
-                        "fp_fusion": fp_fusion,
-                        "out_mismatch_elements": _bit_mismatches(out_got, out_ref),
-                        "out_elements": out_ref.numel(),
-                        "max_abs_diff_out": _max_abs_diff(out_got, out_ref),
-                        "state_bitwise_equal": torch.equal(state_got, state_ref),
-                    }
-                )
+    for batch in batches:
+        for name, cache_dtype in _STATE_DTYPES.items():
+            # Same call as the test_conv_* provider comparisons.
+            inp = check._conv_inputs(batch, cache_dtype, seed=batch)
+            (out_ref, state_ref), (out_got, state_got) = check._run_conv_pair(inp)
+            rows.append(
+                {
+                    "batch": batch,
+                    "cache_dtype": name,
+                    "fp_fusion": fp_fusion,
+                    "out_mismatch_elements": _bit_mismatches(out_got, out_ref),
+                    "out_elements": out_ref.numel(),
+                    "max_abs_diff_out": _max_abs_diff(out_got, out_ref),
+                    "state_bitwise_equal": torch.equal(state_got, state_ref),
+                }
+            )
     return rows
 
 
-def _conv_ptx_fma_counts() -> list[dict[str, Any]] | dict[str, str]:
-    """``fma.rn.f32`` count per compiled variant of the provider's conv-update kernel."""
+def _count(patterns: dict[str, str], text: str) -> dict[str, int]:
+    return {name: len(re.findall(pattern, text)) for name, pattern in patterns.items()}
+
+
+def _conv_kernel_variants() -> list[dict[str, Any]] | dict[str, str]:
+    """Options and instruction counts per compiled variant of the conv-update kernel."""
     try:
         from vllm.model_executor.layers.mamba.ops import causal_conv1d as conv_module
 
@@ -174,21 +182,55 @@ def _conv_ptx_fma_counts() -> list[dict[str, Any]] | dict[str, str]:
         variants = []
         for device, cache in kernel.device_caches.items():
             for compiled in cache[0].values():
-                ptx = compiled.asm["ptx"]
-                variants.append(
-                    {
-                        "device": str(device),
-                        "enable_fp_fusion": getattr(compiled.metadata, "enable_fp_fusion", None),
-                        "fma_rn_f32": ptx.count("fma.rn.f32"),
-                        "mul_rn_f32": ptx.count("mul.rn.f32"),
-                        "add_rn_f32": ptx.count("add.rn.f32"),
-                    }
-                )
+                row: dict[str, Any] = {
+                    "device": str(device),
+                    "enable_fp_fusion": getattr(compiled.metadata, "enable_fp_fusion", None),
+                    "ptx": _count(_PTX_OPS, compiled.asm["ptx"]),
+                }
+                try:
+                    row["sass"] = _count(_SASS_OPS, compiled.asm["sass"])
+                except Exception as exc:  # needs cuobjdump; report, do not fail
+                    row["sass"] = {"unavailable": f"{type(exc).__name__}: {exc}"}
+                variants.append(row)
         return variants
     except Exception as exc:  # introspection of Triton internals; report, do not fail
         return {"unavailable": f"{type(exc).__name__}: {exc}"}
 
 
+def _conv_report(check, batches: list[int]) -> dict[str, Any]:
+    return {"conv": _measure_conv(check, batches), "conv_kernels": _conv_kernel_variants()}
+
+
+def _conv_report_without_fusion(batches: str) -> dict[str, Any]:
+    """Rerun the conv arm in a child process that compiles with fusion off."""
+    with tempfile.TemporaryDirectory(prefix="triton-nofusion-") as cache_dir:
+        env = dict(os.environ, TRITON_DEFAULT_FP_FUSION="0", TRITON_CACHE_DIR=cache_dir)
+        child = subprocess.run(
+            [sys.executable, str(Path(__file__).resolve()), "--conv-only", "--batches", batches],
+            env=env,
+            stdout=subprocess.PIPE,
+            text=True,
+            check=True,
+        )
+    return json.loads(child.stdout)
+
+
+def _fusion_check(on: Any, off: Any) -> dict[str, Any]:
+    """Whether each arm's compiled variants carry the fusion setting it claims."""
+
+    def flags(variants: Any) -> list[Any] | None:
+        if not isinstance(variants, list):
+            return None
+        return [v["enable_fp_fusion"] for v in variants]
+
+    on_flags, off_flags = flags(on), flags(off)
+    return {
+        "fusion_on_variants": on_flags,
+        "fusion_off_variants": off_flags,
+        "fusion_off_effective": bool(off_flags) and all(f is False for f in off_flags),
+    }
+
+
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
     parser.add_argument(
@@ -201,6 +243,8 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help="do not rerun the conv comparison with Triton FP fusion disabled",
     )
+    # Internal: the fusion-off child process prints only the conv arm.
+    parser.add_argument("--conv-only", action="store_true", help=argparse.SUPPRESS)
     return parser.parse_args()
 
 
@@ -217,6 +261,10 @@ def main() -> int:
 
     batches = [int(b) for b in args.batches.split(",") if b.strip()]
     check = _load_check_module()
+    if args.conv_only:
+        print(json.dumps(_conv_report(check, batches), sort_keys=True))
+        return 0
+
     report: dict[str, Any] = {
         "runner": "scripts/ws1_gdn_provider_agreement.py",
         "provenance": _provenance(),
@@ -228,11 +276,15 @@ def main() -> int:
             "conv": "bias=True, activation=silu, dim_first=True, W=4, dim=8192",
         },
         "recurrent": _measure_recurrent(check, batches),
-        "conv": _measure_conv(check, batches, fp_fusion=True),
     }
+    fused = _conv_report(check, batches)
+    report["conv"] = fused["conv"]
+    report["conv_kernels"] = {"fusion_on": fused["conv_kernels"]}
     if not args.skip_fusion_off:
-        report["conv"] += _measure_conv(check, batches, fp_fusion=False)
-    report["conv_ptx"] = _conv_ptx_fma_counts()
+        unfused = _conv_report_without_fusion(args.batches)
+        report["conv"] += unfused["conv"]
+        report["conv_kernels"]["fusion_off"] = unfused["conv_kernels"]
+        report["fusion_check"] = _fusion_check(fused["conv_kernels"], unfused["conv_kernels"])
     print(json.dumps(report, indent=2, sort_keys=True))
     return 0
 

From 2d895d09da9c22c69594cc8b7c55e8ced7c519bb Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 19:48:57 +0000
Subject: [PATCH 28/70] docs(gdn): quote the runner's measured C6 agreement,
 not stale counts

The runner on d66e595 (B200, clean checkout) reproduced the recurrent
figures the note had withdrawn for lack of a runner, so restore them as a
per-batch table with their provenance and bitwise output-mismatch counts.

The conv counts quoted in test_conv_output_matches_provider_with_fp32_cache's
docstring (1 of 8192 at B=1, 15 of 524288 at B=64) predate the product-
rounding fix; the runner measures 0, 0, 1 and 5 for B = 1, 4, 17, 64.
Replace them, and record the bf16-cache counts in the note.

State plainly that the FMA explanation for the fp32-cache mismatches is not
determined: that run's fusion-off arm reused the fused kernel, and a PTX
without fma.rn.f32 cannot exclude ptxas contraction. The runner now tests
both (previous commit).

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../design/ws1-c6-428-gdn-recurrent-replay.md | 42 +++++++++++++++----
 tests/check_gdn_recurrent_golden.py           |  8 ++--
 2 files changed, 38 insertions(+), 12 deletions(-)

diff --git a/docs/design/ws1-c6-428-gdn-recurrent-replay.md b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
index efe2b08aa..44c6c5a36 100644
--- a/docs/design/ws1-c6-428-gdn-recurrent-replay.md
+++ b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
@@ -126,10 +126,24 @@ random inputs, B ∈ {1, 4, 17, 64}, one seed per batch. Bounds asserted by
 | fp32 state | ≤ 1e-3 | ≤ 1e-5 |
 | bf16 state | ≤ 1e-3 | ≤ 5e-3 |
 
-`scripts/ws1_gdn_provider_agreement.py` prints the measured values behind these
-bounds for the same inputs. An earlier table here quoted tighter figures from a
-single run with no committed runner; it is withdrawn in favour of the runner's
-output.
+Measured values for the same inputs, from `scripts/ws1_gdn_provider_agreement.py` on
+B200 at commit `acf38b6` (clean checkout). The last column counts output elements
+whose bits differ:
+
+| state | B | max\|diff\| out | max\|diff\| state | out elements differing |
+|---|---|---|---|---|
+| fp32 | 1 | 1.49e-08 | 1.19e-07 | 1 / 4096 |
+| fp32 | 4 | 9.54e-07 | 1.79e-07 | 2 / 16384 |
+| fp32 | 17 | 3.81e-06 | 2.38e-07 | 10 / 69632 |
+| fp32 | 64 | 6.10e-05 | 2.98e-07 | 40 / 262144 |
+| bf16 | 1 | 3.73e-09 | 9.77e-04 | 2 / 4096 |
+| bf16 | 4 | 1.53e-05 | 9.77e-04 | 2 / 16384 |
+| bf16 | 17 | 3.05e-05 | 1.95e-03 | 14 / 69632 |
+| bf16 | 64 | 3.05e-05 | 1.95e-03 | 36 / 262144 |
+
+This reproduces the figures an earlier version of this note quoted without a runner
+(out 1.5e-08 .. 6.1e-05 and state ≤ 3.0e-07 for fp32; out 3.7e-09 .. 3.1e-05 and
+state ≤ 2.0e-03 for bf16). It is one seed per batch on one device.
 
 Causal conv uses sequential FP32 accumulation **starting from bias**, with
 products first rounded to the operand dtype (golden `causal_conv1d.py:167-177`;
@@ -141,11 +155,21 @@ cover bias order and bf16 product rounding, and
 constructed cancellation input. Provider comparisons limit mismatches to 32 elements
 on the checked fixtures. This is not a bitwise claim.
 
-With an fp32 cache a few output elements still differ. The cause is not pinned. One
-candidate is FP contraction: Triton's default `enable_fp_fusion=True` may contract the
-provider's `acc += matrix_x * matrix_w` (vLLM `causal_conv1d.py:1061`) into an FMA,
-whereas the golden rounds the product first. The runner reruns the comparison with
-fusion off and counts `fma.rn.f32` in the compiled kernel to test this.
+Measured with the same runner and commit: the rolled conv state is bitwise equal in
+all 8 (batch, cache dtype) cases. Output elements differing, with an fp32 cache:
+0 / 8192 (B=1), 0 / 32768 (B=4), 1 / 139264 (B=17, max|diff| 2.44e-04) and
+5 / 524288 (B=64, max|diff| 3.91e-03). With a bf16 cache: 0 at B=1, 4 and 17, and
+3 / 524288 at B=64 (max|diff| 1.56e-02).
+
+Why a few fp32-cache elements differ is **not determined**. One candidate is FP
+contraction: with Triton's default `enable_fp_fusion=True`, ptxas may contract the
+provider's `acc += matrix_x * matrix_w` (vLLM `causal_conv1d.py:1061`) into an `FFMA`,
+whereas the golden rounds the product first. A PTX without `fma.rn.f32` does not rule
+this out, because the contraction can happen in ptxas. The runner tests it by
+rerunning the comparison in a child process compiled with fusion off and by counting
+`FFMA` in each variant's SASS; its `fusion_check` field says whether the fusion-off
+variants were really compiled that way. The run above predates that arm working (its
+fusion-off pass reused the fused kernel), so it is no evidence either way.
 
 ## 5. Decode versus chunked prefill
 
diff --git a/tests/check_gdn_recurrent_golden.py b/tests/check_gdn_recurrent_golden.py
index aefecaddf..103098fc5 100644
--- a/tests/check_gdn_recurrent_golden.py
+++ b/tests/check_gdn_recurrent_golden.py
@@ -282,10 +282,12 @@ def test_conv_state_update_is_bitwise_exact(batch):
 
 @pytest.mark.parametrize("batch", [1, 4, 17, 64])
 def test_conv_output_matches_provider_with_fp32_cache(batch):
-    """An fp32 cache reproduces the provider up to fp32 ULP on a few elements.
+    """An fp32 cache reproduces the provider except on a few output elements.
 
-    Measured: 1 element of 8192 at B=1, 15 of 524288 at B=64. The bound is on
-    the magnitude and on a handful of elements rather than on a tight rate --
+    Measured by scripts/ws1_gdn_provider_agreement.py (B200, acf38b6): 0 of 8192
+    at B=1, 0 at B=4, 1 of 139264 at B=17, 5 of 524288 at B=64; max|diff| 3.9e-3.
+    The bound is on the magnitude and on a handful of elements rather than on a
+    tight rate --
     at small batches a single straddling element is already 1.2e-4 of the
     tensor, which says nothing about accuracy.
     """

From 52f7f61951104f38ada05e71978a6445952ab690 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 20:08:52 +0000
Subject: [PATCH 29/70] docs(gdn): record that FP contraction does not explain
 the conv mismatches
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The fixed runner (b54e50f) on 7622616, B200, settles the FMA hypothesis
left open in §4. Its fusion-off arm took effect: all six variants compiled
with enable_fp_fusion=False, and their PTX uses only .rn-qualified f32
mul/add, which ptxas may not contract. With contraction verifiably off,
the fp32-cache mismatch counts (0, 0, 1, 5 for B = 1, 4, 17, 64) and
max|diff| are identical to the fused run, and neither arm's SASS has an
FFMA. So FP contraction is ruled out; the actual cause stays open.

The note bases the conclusion on the A/B outcome, not on the opcode
counts: each variant's PTX has only two f32 multiplies, too few to be the
four tap products, so the counters do not show where the products are
computed.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../design/ws1-c6-428-gdn-recurrent-replay.md | 26 ++++++++++++-------
 1 file changed, 17 insertions(+), 9 deletions(-)

diff --git a/docs/design/ws1-c6-428-gdn-recurrent-replay.md b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
index 44c6c5a36..25febbb8c 100644
--- a/docs/design/ws1-c6-428-gdn-recurrent-replay.md
+++ b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
@@ -161,15 +161,23 @@ all 8 (batch, cache dtype) cases. Output elements differing, with an fp32 cache:
 5 / 524288 (B=64, max|diff| 3.91e-03). With a bf16 cache: 0 at B=1, 4 and 17, and
 3 / 524288 at B=64 (max|diff| 1.56e-02).
 
-Why a few fp32-cache elements differ is **not determined**. One candidate is FP
-contraction: with Triton's default `enable_fp_fusion=True`, ptxas may contract the
-provider's `acc += matrix_x * matrix_w` (vLLM `causal_conv1d.py:1061`) into an `FFMA`,
-whereas the golden rounds the product first. A PTX without `fma.rn.f32` does not rule
-this out, because the contraction can happen in ptxas. The runner tests it by
-rerunning the comparison in a child process compiled with fusion off and by counting
-`FFMA` in each variant's SASS; its `fusion_check` field says whether the fusion-off
-variants were really compiled that way. The run above predates that arm working (its
-fusion-off pass reused the fused kernel), so it is no evidence either way.
+**FP contraction is ruled out** as the cause of the few differing fp32-cache
+elements; what does cause them is **not determined**. The candidate was that, with
+Triton's default `enable_fp_fusion=True`, ptxas contracts the provider's
+`acc += matrix_x * matrix_w` (vLLM `causal_conv1d.py:1061`) into an `FFMA`, whereas the
+golden rounds the product first. The runner tests this by rerunning the comparison in
+a child process compiled with fusion off. On B200 at commit `cf6be1a`:
+
+- the fusion-off arm took effect: all 6 compiled variants carry
+  `enable_fp_fusion=False`, and their PTX has only `.rn`-qualified f32 `mul`/`add`,
+  which ptxas may not contract;
+- the mismatch counts and max\|diff\| are **identical** with fusion on and off
+  (0, 0, 1, 5 for B = 1, 4, 17, 64);
+- neither arm's SASS contains `FFMA`.
+
+The conclusion rests on the second point. The opcode counts are supporting evidence
+only: each variant's PTX has 2 f32 multiplies, too few to be the four tap products, so
+the counters do not show which instructions compute those products.
 
 ## 5. Decode versus chunked prefill
 

From 7862a1263e0079d4ae72fd6a7e3346d8214ef1c8 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 20:24:02 +0000
Subject: [PATCH 30/70] docs(gdn): scope the mixed-step caveat to what was
 measured
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

§5 said a mixed decode-and-prefill step leaves the decode row's bf16 output
equal. That holds only at TP1 head counts (H=16, HV=32). At TP4 per-rank
head counts (H=4, HV=8) the same step already moves one output element
(4.8e-05 relative) and 32,663 of 131,072 state elements (1.7e-07), with the
same numbers for all three prefill-bearing compositions.

Tabulate both head counts and state the measurement's limits: one
standalone layer from config.json, synthetic parameters and cache, single
process with no engine, scheduler or CUDA graph, metadata built by the test.
The 4-of-20 propagation figure is TP1-only and does not carry over to real
weights. Conv output and state matched bitwise, so the difference is in the
recurrent kernels. vllm-project/vllm#49827's two commits on 0.30.0 close the
decode-row gap at both head counts (scheduler part untested); with packed
decode disabled the decode row is bitwise equal in every composition.

Source: the GDN evidence branch's committed results (not yet published).
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../design/ws1-c6-428-gdn-recurrent-replay.md | 51 +++++++++++++------
 1 file changed, 35 insertions(+), 16 deletions(-)

diff --git a/docs/design/ws1-c6-428-gdn-recurrent-replay.md b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
index 25febbb8c..21abba4aa 100644
--- a/docs/design/ws1-c6-428-gdn-recurrent-replay.md
+++ b/docs/design/ws1-c6-428-gdn-recurrent-replay.md
@@ -189,22 +189,41 @@ plateau, prefill/decode equality, or any bound on model logits.
 **Mixed decode-and-prefill steps (provider side).** The goldens target the
 decode-only path (§1). In a step that also holds a prefill, vLLM 0.30.0 sends the
 cached decode rows through `causal_conv1d_fn` and
-`fused_sigmoid_gating_delta_rule_update` instead, so in those steps neither golden
-matches what rollout runs. The two recurrent kernels do not agree bitwise. A
-measurement on B200 with Qwen3-Next TP1 shapes, driving the real
-`GDNAttentionMetadataBuilder.build()` and `_forward_core`, found the step's bf16
-output equal but the fp32 state different in 75,146 of 524,288 elements (8.8e-08
-relative). In 4 of 20 seeds the difference reached a bf16 output within the next 16
-decode steps. This was measured outside this change and is not yet published; treat it
-as a reported observation.
-
-vllm-project/vllm#49827, open and unmerged, would route mixed-step *recurrent*
-decodes through the packed kernel too, according to its description. It was
-validated on Qwen3.5 (non-interleaved), H100, TP1, and it does not change the conv
-path in mixed steps, which stays `causal_conv1d_fn`. Disabling
-`VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE` instead sends every decode row through
-`fused_sigmoid_gating_delta_rule_update`, and the recurrent golden's target would
-have to change.
+`fused_sigmoid_gating_delta_rule_update` instead, so in those steps the recurrent
+golden does not target what rollout runs.
+
+A measurement on B200 drove the real `GDNAttentionMetadataBuilder.build()` and
+`_forward_core` of **one standalone layer** built from the checkpoint's
+`config.json`. Its limits: parameters and cache were synthetic (no weights loaded); it
+ran in a single process, with no engine, scheduler or CUDA graph; and the test, not the
+scheduler, built the attention metadata (decode rows first). For one target decode
+request with an fp32 recurrent state, three prefill-bearing step compositions gave
+identical numbers:
+
+| head counts | step's bf16 output | fp32 state |
+|---|---|---|
+| TP1 (H=16, HV=32) | matched | 75,146 of 524,288 elements differ (8.8e-08 relative) |
+| TP4 per-rank (H=4, HV=8) | 1 element differs (4.8e-05 relative) | 32,663 of 131,072 differ (1.7e-07) |
+
+The convolution output and conv state matched bitwise in every composition, so the
+difference comes from the recurrent kernels, not from `causal_conv1d_fn`. At TP1 the
+state difference reached a bf16 output within the next 16 plain decode steps in 4 of
+20 seeds; that was measured at TP1 only, and with synthetic parameters the frequency
+does not carry over to real weights. At TP4 per-rank head counts it appears in the
+same step's output. This is a provider-side batch-composition dependence: whether a
+prefill shares the step changes a decode row's state, and at TP4 per-rank head counts
+its output too, which no golden can fix. The measurement was made outside this change
+and is not yet published; treat it as a reported observation.
+
+vllm-project/vllm#49827, open and unmerged, routes mixed-step *recurrent* decodes
+through the packed kernel too. Its two commits, applied to 0.30.0, closed the gap to 0
+at both head counts above; its scheduler part was not tested. It does not change the
+conv path in mixed steps, which stays `causal_conv1d_fn`, and its own validation is on
+Qwen3.5 (non-interleaved), H100, TP1. Disabling
+`VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE` instead sends every non-speculative decode
+row through `fused_sigmoid_gating_delta_rule_update`; the same measurement found the
+decode row's output and state bitwise equal in every step composition in that
+configuration, and the recurrent golden's target would then have to change.
 
 The strict profile uses FP32 recurrent state. BF16 state remains a differential
 experiment. Full checkpoint prefill, response replay, optimizer updates and

From 1d693124e6ba5d0ab24e2ecb6371e460d74e0c76 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 22:33:35 +0000
Subject: [PATCH 31/70] fix(gdn): address the C6 review (R3 F1-4, F6, F10-14,
 F17, F20)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- F1: the runner's usage example writes its JSON to $TMPDIR, outside the
  checkout. Redirecting into the repo left an untracked file that made the
  next run report git_dirty: true.
- F2: label the §5 mixed-step measurements and the §7 startup failure as
  reported observations with no checked-in runner, hence not acceptance
  evidence, by the same rule that withdrew the earlier tables.
- F3: mark every bound in the check file as a regression bound against the
  provider (the drift bound: against the golden), not gate evidence, and give
  the contract's forward_accuracy/by_op_class/reduction row
  (tolerance_contract.json: fp32 1e-4/1e-4, bf16 5e-2/2e-2) for scale, in
  the check file and in design note §4.
- F4: run tests/test_gdn_state_contract.py in ci.yml's unit-tests list.
- F6: rename the note to docs/design/rfc428-c6-gdn-recurrent-replay.md,
  retitle it "RFC #428 C6", and write the backward item as "RFC #428 C7", so
  neither collides with upstream's WS1 C6 (#272) / C7 (#273). The runner's
  docstring was the only in-repo reference to the old path.
- F10: _RECURRENT_BOUNDS is a module constant in the check file; the
  parametrization and the runner both read it instead of copying values.
- F11: derive H, HV, K, V from qwen3_next_workload.FINGERPRINT;
  _CONV_DIM is _PACKED_DIM.
- F12: runner provenance records rl_engine.__file__ and the
  FLA_USE_FAST_OPS / TRITON_DEFAULT_FP_FUSION environment.
- F13: qwen3-next-provider-gpu.yml gets a header (purpose, security),
  concurrency, a fork-pr-notice job, and a check after the build that
  rl_engine._C was loaded from inside $GITHUB_WORKSPACE.
- F14: the NULL_BLOCK_ID comment says <= 0 is the recurrent provider's
  semantics and vLLM's conv provider skips only == 0.
- F17: drop two index checks in GatedDeltaRuleRecurrentStepOp that
  _validate_state_indices repeats. The length check could not fire; the 1-D
  check could, so a 2-D index now raises the validator's message ("state
  indices must be 1-D with one entry per sequence") instead.
- F20: SPDX header on tests/test_gdn_state_contract.py.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/ci.yml                      |  2 +-
 .github/workflows/qwen3-next-provider-gpu.yml | 46 ++++++++++++++++++-
 ...y.md => rfc428-c6-gdn-recurrent-replay.md} | 23 +++++++---
 .../ops/pytorch/linear_attn/causal_conv1d.py  |  2 +-
 .../pytorch/linear_attn/gated_delta_rule.py   |  9 ++--
 scripts/ws1_gdn_provider_agreement.py         | 22 ++++++---
 tests/check_gdn_recurrent_golden.py           | 46 +++++++++++++------
 tests/test_gdn_state_contract.py              |  3 ++
 8 files changed, 117 insertions(+), 36 deletions(-)
 rename docs/design/{ws1-c6-428-gdn-recurrent-replay.md => rfc428-c6-gdn-recurrent-replay.md} (92%)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 5f8c900b3..814a4ae22 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -67,7 +67,7 @@ jobs:
         run: |
           python -m pytest rl_engine/tests/test_dispatch.py -v
           PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 python -m pytest tests/test_attention_correctness.py -q -rs
-          python -m pytest tests/test_forward_invariance.py tests/test_tolerance_contract.py tests/test_ws1_workload.py tests/test_gradient_invariance.py tests/test_elementwise_inventory.py tests/test_four_judgment_matrix.py tests/test_op_checks.py tests/test_operator_inputs.py tests/test_profiler.py tests/test_kv_consistency.py tests/test_ws1_qwen3_dense.py tests/test_ws1_chain_integration.py tests/test_qwen3_next_norm.py -q
+          python -m pytest tests/test_forward_invariance.py tests/test_tolerance_contract.py tests/test_ws1_workload.py tests/test_gradient_invariance.py tests/test_elementwise_inventory.py tests/test_four_judgment_matrix.py tests/test_op_checks.py tests/test_operator_inputs.py tests/test_profiler.py tests/test_kv_consistency.py tests/test_ws1_qwen3_dense.py tests/test_ws1_chain_integration.py tests/test_qwen3_next_norm.py tests/test_gdn_state_contract.py -q
 
       - name: Run Cross-Configuration Contract Tests (CPU-safe)
         run: |
diff --git a/.github/workflows/qwen3-next-provider-gpu.yml b/.github/workflows/qwen3-next-provider-gpu.yml
index b53c73441..146818019 100644
--- a/.github/workflows/qwen3-next-provider-gpu.yml
+++ b/.github/workflows/qwen3-next-provider-gpu.yml
@@ -1,3 +1,17 @@
+# SPDX-License-Identifier: Apache-2.0
+# RFC #428 Qwen3-Next provider comparisons: the C1/C3/C4 norm providers and the C6
+# GDN decode-step goldens, each checked against the real vLLM 0.30.0 provider.
+#
+# These check_ files import vLLM, so the default pytest collection never runs them
+# (tests/test_framework_operator_integrations.py asserts vLLM is not imported). This
+# workflow runs them in their own process on a self-hosted runner that a maintainer
+# registers with the labels below and keeps on the audited runtime (CUDA 13,
+# torch 2.13.0+cu130, vLLM 0.30.0). Without such a runner the job queues rather than
+# reporting a false pass.
+#
+# Security: do not use pull_request_target. Fork PRs never reach the self-hosted
+# runner; a maintainer dispatches the reviewed commit from a trusted branch.
+
 name: Qwen3-Next-provider-GPU
 
 on:
@@ -11,13 +25,26 @@ on:
       - ".github/workflows/qwen3-next-provider-gpu.yml"
   workflow_dispatch:
 
+concurrency:
+  group: qwen3-next-provider-gpu-${{ github.ref }}
+  cancel-in-progress: false
+
 permissions:
   contents: read
 
 jobs:
+  fork-pr-notice:
+    if: github.event_name == 'pull_request' && github.event.pull_request.head.repo.full_name != github.repository
+    runs-on: ubuntu-latest
+    steps:
+      - name: Report required trusted execution
+        run: |
+          echo "Fork code does not run on the self-hosted Qwen3-Next provider runner."
+          echo "A maintainer must dispatch this workflow from a trusted upstream branch."
+          echo "source_repository=${{ github.event.pull_request.head.repo.full_name }}"
+          echo "source_sha=${{ github.event.pull_request.head.sha }}"
+
   provider:
-    # This label must identify a maintained CUDA 13 / vLLM 0.30.0 environment.
-    # Never run untrusted fork PR code on the self-hosted runner.
     if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name == github.repository
     runs-on: [self-hosted, linux, x64, rl-kernel-qwen3-next]
     timeout-minutes: 30
@@ -35,6 +62,21 @@ jobs:
           PY
       - name: Build the extension against that runtime
         run: python3 setup.py build_ext --inplace
+      - name: Require the extension built from this checkout
+        # A stale editable install elsewhere on the runner would otherwise satisfy the
+        # import and test some other tree's _C.
+        run: |
+          python3 - <<'PY'
+          import os
+          from pathlib import Path
+
+          from rl_engine import _C
+
+          workspace = Path(os.environ["GITHUB_WORKSPACE"]).resolve()
+          loaded = Path(_C.__file__).resolve()
+          assert workspace in loaded.parents, f"rl_engine._C loaded from {loaded}, outside {workspace}"
+          print("rl_engine._C:", loaded)
+          PY
       - name: Check pinned providers in their own process
         run: |
           python3 -m pytest -q tests/check_qwen3_next_norm_providers.py tests/check_gdn_recurrent_golden.py
diff --git a/docs/design/ws1-c6-428-gdn-recurrent-replay.md b/docs/design/rfc428-c6-gdn-recurrent-replay.md
similarity index 92%
rename from docs/design/ws1-c6-428-gdn-recurrent-replay.md
rename to docs/design/rfc428-c6-gdn-recurrent-replay.md
index 21abba4aa..718e5ab65 100644
--- a/docs/design/ws1-c6-428-gdn-recurrent-replay.md
+++ b/docs/design/rfc428-c6-gdn-recurrent-replay.md
@@ -1,4 +1,4 @@
-# WS1 C6 — Qwen3-Next Gated DeltaNet recurrent replay
+# RFC #428 C6 — Qwen3-Next Gated DeltaNet recurrent replay
 
 Design notes for RFC #428 work item C6 (GDN recurrent response replay) on the CUDA
 track. Measured on 2× B200 (sm_100), torch 2.13.0+cu130, vllm 0.30.0,
@@ -119,7 +119,12 @@ The same choice is why the golden is not bitwise against the kernel's reduction.
 
 Qwen3-Next dims (H=16, HV=32, K=V=128), bf16 I/O, `use_qk_l2norm_in_kernel=True`,
 random inputs, B ∈ {1, 4, 17, 64}, one seed per batch. Bounds asserted by
-`tests/check_gdn_recurrent_golden.py`:
+`tests/check_gdn_recurrent_golden.py` (`_RECURRENT_BOUNDS`). They, and every other
+bound in that file, are **regression bounds against the provider, not gate evidence**;
+they do not go through `resolve_tolerance`. For scale, the gate contract's
+`forward_accuracy/by_op_class/reduction` row in
+`rl_engine/kernels/gtest/tolerance_contract.json` is atol = rtol = 1e-4 for float32
+and atol = 5e-2, rtol = 2e-2 for bfloat16.
 
 | | max\|diff\| out | max\|diff\| state |
 |---|---|---|
@@ -153,7 +158,7 @@ were not limited to one BF16 ULP. The CPU tests in `tests/test_gdn_state_contrac
 cover bias order and bf16 product rounding, and
 `test_conv_provider_preserves_bf16_product_cancellation` checks the provider on a
 constructed cancellation input. Provider comparisons limit mismatches to 32 elements
-on the checked fixtures. This is not a bitwise claim.
+on the checked fixtures (a regression bound, not gate evidence). This is not a bitwise claim.
 
 Measured with the same runner and commit: the rolled conv state is bitwise equal in
 all 8 (batch, cache dtype) cases. Output elements differing, with an fp32 cache:
@@ -192,6 +197,10 @@ cached decode rows through `causal_conv1d_fn` and
 `fused_sigmoid_gating_delta_rule_update` instead, so in those steps the recurrent
 golden does not target what rollout runs.
 
+*The numbers in the rest of this subsection are a reported observation with no
+checked-in runner in this repository. By the same rule that withdrew the tables above,
+they are not acceptance evidence.*
+
 A measurement on B200 drove the real `GDNAttentionMetadataBuilder.build()` and
 `_forward_core` of **one standalone layer** built from the checkpoint's
 `config.json`. Its limits: parameters and cache were synthetic (no weights loaded); it
@@ -213,7 +222,7 @@ does not carry over to real weights. At TP4 per-rank head counts it appears in t
 same step's output. This is a provider-side batch-composition dependence: whether a
 prefill shares the step changes a decode row's state, and at TP4 per-rank head counts
 its output too, which no golden can fix. The measurement was made outside this change
-and is not yet published; treat it as a reported observation.
+and is not yet published.
 
 vllm-project/vllm#49827, open and unmerged, routes mixed-step *recurrent* decodes
 through the packed kernel too. Its two commits, applied to 0.30.0, closed the gap to 0
@@ -245,7 +254,7 @@ Not covered, with reasons:
 | deferred | why |
 |---|---|
 | Speculative decode / MTP | RFC #428 §2.2 excludes speculative decoding from the first claim. Qwen3-Next's MTP head ships in the checkpoint (`mtp.*`, loaded by `model_executor/models/qwen3_next_mtp.py`) and is full attention (`qwen3_next_mtp.py:90-92`). Enabling it changes the target model's GDN path in steps that carry draft tokens (§1); `fused_gdn_decode_post_conv_mtp` is unreachable for Qwen3-Next |
-| Backward for the recurrent step | RFC #428 §2.2 item 4: backward need not match rollout, only be correct for the replayed forward. RFC §9.1 makes it a separate work item, **C7** (GDN backward/recompute adapter including prompt-state gradient). `supports_backward=false` here; `_softplus`'s NaN-gradient fix (`08969ac`) adds no backward claim |
+| Backward for the recurrent step | RFC #428 §2.2 item 4: backward need not match rollout, only be correct for the replayed forward. RFC §9.1 makes it a separate work item, **RFC #428 C7** (GDN backward/recompute adapter including prompt-state gradient). `supports_backward=false` here; `_softplus`'s NaN-gradient fix (`08969ac`) adds no backward claim |
 | Provider bridge / registry entry | the golden should survive a drift sweep against a real checkpoint first |
 | Paged block allocation policy | the ABI is mirrored; the allocator is not modelled |
 | TP sharding of `A_log` / `dt_bias` | single card only |
@@ -264,4 +273,6 @@ synchronization and checkpoint reload. No operator-level test closes these gates
 
 The 2026-09-30 real-checkpoint startup attempt with vLLM 0.30.0 failed before
 inference: `VLLM batch_invariant mode is not supported for GDN_ATTN`. A shared
-provider integration must resolve this; disabling the check is not L2 evidence.
+provider integration must resolve this; disabling the check is not L2 evidence. *This
+failure is a reported observation: the attempt's log and launcher are not checked into
+this repository, so it is not acceptance evidence either.*
diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py b/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py
index fb171af49..99036f6de 100644
--- a/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/causal_conv1d.py
@@ -36,7 +36,7 @@ class CausalConv1dUpdateOp:
     ``conv_state`` ``[num_blocks, dim, W-1]``  paged; ``dim_first`` layout
     ``weight``     ``[dim, W]``
     ``bias``       ``[dim]`` or ``None``
-    ``indices``    ``[B]``                     ``<= 0`` skips
+    ``indices``    ``[B]``                     ``<= 0`` skips (vLLM: ``== 0``)
     ============== ========================== ==========================
 
     Returns ``(out, conv_state)``; the state is updated out of place.
diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
index e2af3a0c3..27708f319 100644
--- a/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
@@ -42,7 +42,10 @@
 
 __all__ = ["GatedDeltaRuleRecurrentStepOp", "NULL_BLOCK_ID", "SOFTPLUS_THRESHOLD"]
 
-#: Paged-state sentinel: a sequence pointing here is skipped.
+#: Paged-state sentinel. Both goldens skip a row whose index is ``<= NULL_BLOCK_ID``:
+#: that is the recurrent provider's semantics (``state_idx <= 0``). vLLM's conv
+#: provider skips only ``== null_block_id`` (0), so a negative index is a real index
+#: there; see the design note's NULL_BLOCK_ID table.
 NULL_BLOCK_ID = 0
 
 #: Above this, softplus is the identity (matches the kernel's constexpr).
@@ -240,8 +243,6 @@ def _step(
             raise ValueError(f"mixed_qkv must be 2-D [B, D], got {tuple(mixed_qkv.shape)}")
         if state.dim() != 4:
             raise ValueError(f"state must be 4-D [num_blocks, HV, V, K], got {tuple(state.shape)}")
-        if ssm_state_indices.dim() != 1:
-            raise ValueError("ssm_state_indices must be 1-D [B] for packed decode")
 
         batch = mixed_qkv.shape[0]
         hv, v_dim, k_dim = state.shape[-3:]
@@ -278,8 +279,6 @@ def _step(
                 f"mixed_qkv last dim must be {expected} (q|k|v packed), "
                 f"got {mixed_qkv.shape[1]}"
             )
-        if ssm_state_indices.shape[0] != batch:
-            raise ValueError("ssm_state_indices must have one entry per sequence")
 
         group = hv // heads
         qkv32 = mixed_qkv.float()
diff --git a/scripts/ws1_gdn_provider_agreement.py b/scripts/ws1_gdn_provider_agreement.py
index 539fa9979..83727fdb5 100644
--- a/scripts/ws1_gdn_provider_agreement.py
+++ b/scripts/ws1_gdn_provider_agreement.py
@@ -4,9 +4,9 @@
 
 """Measure how closely the GDN decode-step goldens track vLLM's providers (RFC #428 C6).
 
-``tests/check_gdn_recurrent_golden.py`` asserts loose bounds; this runner prints the
-measured values behind them, so the numbers quoted in
-``docs/design/ws1-c6-428-gdn-recurrent-replay.md`` can be reproduced. Inputs and seeds
+``tests/check_gdn_recurrent_golden.py`` asserts loose regression bounds; this runner
+prints the measured values behind them, so the numbers quoted in
+``docs/design/rfc428-c6-gdn-recurrent-replay.md`` can be reproduced. Inputs and seeds
 are the check file's own helpers, imported from it rather than copied.
 
 Three measurements, emitted as one JSON document on stdout:
@@ -29,9 +29,11 @@
 fusion off it passes ``--fmad=false`` to ptxas. A PTX with no ``fma.rn.f32`` can still
 run FMAs. ``fusion_check`` reports whether each arm compiled what it claims to.
 
-Requires CUDA and vLLM 0.30.0. Run it from a clean checkout so ``git_dirty`` is false:
+Requires CUDA and vLLM 0.30.0. Run it from a clean checkout so ``git_dirty`` is false,
+and write the result outside the checkout: an untracked file inside it would itself
+make the next run report ``git_dirty: true``.
 
-    python scripts/ws1_gdn_provider_agreement.py > gdn_provider_agreement.json
+    python scripts/ws1_gdn_provider_agreement.py > "${TMPDIR:-/tmp}/gdn_provider_agreement.json"
 """
 
 from __future__ import annotations
@@ -57,6 +59,9 @@
 _CHECK_FILE = REPO_ROOT / "tests" / "check_gdn_recurrent_golden.py"
 _STATE_DTYPES = {"fp32": torch.float32, "bf16": torch.bfloat16}
 _INT_VIEW = {2: torch.int16, 4: torch.int32}
+# FLA_USE_FAST_OPS swaps the kernel's exp/log for fast_expf/fast_logf;
+# TRITON_DEFAULT_FP_FUSION decides whether ptxas may contract mul/add.
+_PROVENANCE_ENV = ("FLA_USE_FAST_OPS", "TRITON_DEFAULT_FP_FUSION")
 # Opcode patterns. PTX: a rounding-qualified op (".rn") may not be contracted by ptxas;
 # the plain form may. SASS: count opcode tokens, including modifiers such as FFMA.FTZ.
 _PTX_OPS = {
@@ -102,11 +107,16 @@ def _provenance() -> dict[str, Any]:
     import triton
     import vllm
 
+    import rl_engine
+
     status = _git("status", "--porcelain")
     return {
         # None means "unknown" (no git, or not a checkout), never "clean".
         "git_commit": _git("rev-parse", "HEAD"),
         "git_dirty": None if status is None else bool(status),
+        # Which tree was imported, and the env knobs that change the provider's code.
+        "rl_engine_file": rl_engine.__file__,
+        "env": {key: os.environ.get(key) for key in _PROVENANCE_ENV},
         "python": platform.python_version(),
         "torch": torch.__version__,
         "triton": triton.__version__,
@@ -118,7 +128,7 @@ def _provenance() -> dict[str, Any]:
 
 
 def _measure_recurrent(check, batches: list[int]) -> list[dict[str, Any]]:
-    bounds = {"fp32": (1e-3, 1e-5), "bf16": (1e-3, 5e-3)}  # check file :117, :120
+    bounds = {name: check._RECURRENT_BOUNDS[dtype] for name, dtype in _STATE_DTYPES.items()}
     rows = []
     for batch in batches:
         for name, state_dtype in _STATE_DTYPES.items():
diff --git a/tests/check_gdn_recurrent_golden.py b/tests/check_gdn_recurrent_golden.py
index 103098fc5..7815939c0 100644
--- a/tests/check_gdn_recurrent_golden.py
+++ b/tests/check_gdn_recurrent_golden.py
@@ -37,13 +37,33 @@
 pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required")
 
 from rl_engine.kernels.ops.pytorch.linear_attn import GatedDeltaRuleRecurrentStepOp  # noqa: E402
+from rl_engine.testing.qwen3_next_workload import FINGERPRINT  # noqa: E402
 
-# Qwen3-Next-80B-A3B-Instruct: linear_num_key_heads / linear_num_value_heads /
-# linear_key_head_dim / linear_value_head_dim.
-_H, _HV, _K, _V = 16, 32, 128, 128
+# Qwen3-Next-80B-A3B-Instruct dims, from the pinned checkpoint fingerprint.
+_H = FINGERPRINT["linear_num_key_heads"]
+_HV = FINGERPRINT["linear_num_value_heads"]
+_K = FINGERPRINT["linear_key_head_dim"]
+_V = FINGERPRINT["linear_value_head_dim"]
 _SCALE = _K**-0.5
 _PACKED_DIM = _H * _K * 2 + _HV * _V
 
+# Every bound in this file is a regression bound against the vLLM provider (or, for
+# the bf16-state drift test, against the golden itself), set from measurements. None
+# is gate evidence, and none goes through rl_engine.kernels.gtest.tolerance's
+# resolve_tolerance. For scale, the gate contract's
+# forward_accuracy/by_op_class/reduction row in
+# rl_engine/kernels/gtest/tolerance_contract.json is atol = rtol = 1e-4 for float32
+# and atol = 5e-2, rtol = 2e-2 for bfloat16.
+#
+# (max|d out|, max|d state|) per recurrent-state dtype. Read by
+# scripts/ws1_gdn_provider_agreement.py, so the runner reports against the same values.
+_RECURRENT_BOUNDS = {
+    # fp32 state: agreement is at fp32-ULP scale.
+    torch.float32: (1e-3, 1e-5),
+    # bf16 state: the store rounds every token, so the state carries a bf16 ULP.
+    torch.bfloat16: (1e-3, 5e-3),
+}
+
 
 def _vllm_step():
     pytest.importorskip("vllm", reason="vLLM is required to compare against the provider")
@@ -110,17 +130,9 @@ def _run_golden(inp):
 # 1. Against the provider
 # --------------------------------------------------------------------------- #
 @pytest.mark.parametrize("batch", [1, 4, 17, 64])
-@pytest.mark.parametrize(
-    "state_dtype, out_atol, state_atol",
-    [
-        # fp32 state: agreement is at fp32-ULP scale.
-        (torch.float32, 1e-3, 1e-5),
-        # bf16 state: the store rounds every token, so the state carries a
-        # bf16 ULP. This is the configuration knob behind the drift probe.
-        (torch.bfloat16, 1e-3, 5e-3),
-    ],
-)
-def test_golden_matches_packed_decode_provider(batch, state_dtype, out_atol, state_atol):
+@pytest.mark.parametrize("state_dtype", list(_RECURRENT_BOUNDS), ids=["fp32", "bf16"])
+def test_golden_matches_packed_decode_provider(batch, state_dtype):
+    out_atol, state_atol = _RECURRENT_BOUNDS[state_dtype]
     inp = _inputs(batch, batch + 2, state_dtype, torch.bfloat16, seed=batch)
     out_ref, state_ref = _run_provider(inp)
     out_got, state_got = _run_golden(inp)
@@ -207,6 +219,7 @@ def test_bf16_state_rounding_stays_within_fixed_fixture_bound():
 
     assert drift[0] > 0.0, "a bf16 state must round on the very first store"
     # Preserve the original regression bound for this fixed fixture.
+    # Regression bound for this fixture, golden against golden; not gate evidence.
     assert max(drift) < 0.05, f"relative state drift reached {max(drift):.3e}"
     # The back half must not be materially worse than the front half.
     assert max(drift[steps // 2 :]) < 2.0 * max(drift[: steps // 2]) + 1e-3
@@ -215,7 +228,8 @@ def test_bf16_state_rounding_stays_within_fixed_fixture_bound():
 # --------------------------------------------------------------------------- #
 # 4. The causal-conv1d state update, the other half of a decode step
 # --------------------------------------------------------------------------- #
-_CONV_DIM, _CONV_WIDTH = 8192, 4  # Qwen3-Next: key_dim*2 + value_dim, linear_conv_kernel_dim
+# The conv runs over the packed q|k|v channels; Qwen3-Next's linear_conv_kernel_dim is 4.
+_CONV_DIM, _CONV_WIDTH = _PACKED_DIM, 4
 
 
 def _conv_update():
@@ -294,6 +308,7 @@ def test_conv_output_matches_provider_with_fp32_cache(batch):
     inp = _conv_inputs(batch, torch.float32, seed=batch)
     (out_ref, _), (out_got, _) = _run_conv_pair(inp)
     mismatch = int((out_got.float().view(torch.int32) != out_ref.float().view(torch.int32)).sum())
+    # Regression bounds against the provider, not gate evidence (see the module note).
     assert mismatch <= 32, f"{mismatch} of {out_got.numel()} elements differ"
     assert (out_got.float() - out_ref.float()).abs().max().item() <= 1e-2
 
@@ -303,6 +318,7 @@ def test_conv_output_bf16_cache_matches_rounded_product_path(batch):
     """Product rounding is reproduced; activation ULP residuals remain allowed."""
     inp = _conv_inputs(batch, torch.bfloat16, seed=batch)
     (out_ref, _), (out_got, _) = _run_conv_pair(inp)
+    # Regression bounds against the provider, not gate evidence (see the module note).
     assert (out_got.float() - out_ref.float()).abs().max().item() <= 7e-2
     assert int((out_got != out_ref).sum()) <= 32
 
diff --git a/tests/test_gdn_state_contract.py b/tests/test_gdn_state_contract.py
index 70255bb0d..7af1e083d 100644
--- a/tests/test_gdn_state_contract.py
+++ b/tests/test_gdn_state_contract.py
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
 """Provider-independent cache contracts, collected in the ordinary CPU suite."""
 
 import pytest

From c0ab21c20abacb0627e07df45fb49c5d6ebd3fc2 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 1 Oct 2026 23:35:23 +0000
Subject: [PATCH 32/70] docs(gdn): refresh the design note's in-repo line
 references

f128870 added three comment lines above NULL_BLOCK_ID and an SPDX header to
tests/test_gdn_state_contract.py, and the gated merge (c4b451c) rewrote the
docstring of the env-default test in tests/check_qwen3_next_norm_providers.py,
so four references pointed at the wrong lines:

- gated_delta_rule.py:107-109 -> :110-112 (_softplus body)
- gated_delta_rule.py:73-87   -> :76-90   (_chunked_sum)
- tests/test_gdn_state_contract.py:51-56 -> :54-59 (inactive-index test)
- check_qwen3_next_norm_providers.py:124-131 -> :129-142 (env-default assert)

The golden causal_conv1d.py:167-177 reference is still right. vLLM-package
references are unaffected.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/design/rfc428-c6-gdn-recurrent-replay.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/design/rfc428-c6-gdn-recurrent-replay.md b/docs/design/rfc428-c6-gdn-recurrent-replay.md
index 718e5ab65..4b9ca767e 100644
--- a/docs/design/rfc428-c6-gdn-recurrent-replay.md
+++ b/docs/design/rfc428-c6-gdn-recurrent-replay.md
@@ -48,7 +48,7 @@ explicitly set to `cuda` (`:516-519`). As a consequence the fused
 `_can_use_fused_gdn_mtp_decode` requires `gdn_decode_kernel == "cuda"` (`:1834`).
 This section is read from the source.
 
-`tests/check_qwen3_next_norm_providers.py` asserts both env defaults (`:124-131`). For
+`tests/check_qwen3_next_norm_providers.py` asserts both env defaults (`:129-142`). For
 `VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE` that is a useful guard: a vLLM bump that
 flips it fails loudly. For `VLLM_GDN_DECODE_KERNEL` it guards nothing for Qwen3-Next,
 because the interleaved layout, not the default, decides the kernel. Neither
@@ -75,7 +75,7 @@ Where the golden is **not** a transcription:
 
 - softplus: the kernel computes `tl.log(1.0 + tl.exp(x))` (`fused_recurrent.py:327`);
   the golden computes `torch.log1p(torch.exp(safe))`, where `safe` is `x` on the taken
-  branch (`gated_delta_rule.py:107-109`). These round differently.
+  branch (`gated_delta_rule.py:110-112`). These round differently.
 - The kernel's `exp`/`log` become `fast_expf`/`fast_logf` when `FLA_USE_FAST_OPS=1`
   (`third_party/flash_linear_attention/ops/op.py:16-25`). The golden models the
   default.
@@ -106,10 +106,10 @@ Mirrored rather than reinvented:
 | state block | not touched | not touched | not touched |
 
 A negative conv index is therefore inactive in the golden
-(`tests/test_gdn_state_contract.py:51-56`) but a real, out-of-range index to the conv
+(`tests/test_gdn_state_contract.py:54-59`) but a real, out-of-range index to the conv
 provider.
 
-Contractions use `_chunked_sum` (`gated_delta_rule.py:73-87`): fixed 32-wide chunks,
+Contractions use `_chunked_sum` (`gated_delta_rule.py:76-90`): fixed 32-wide chunks,
 so the reduction shape per row does not depend on the batch size, rather than
 `torch.matmul`, whose reduction order is unspecified. That is the argument for the
 L1 claim; L1 itself is established empirically by `test_golden_is_batch_invariant`.

From f8d93993d809ded2dc65391ac745bf5a32ca4da2 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 2 Oct 2026 02:22:32 +0000
Subject: [PATCH 33/70] feat(gdn): localise the fp32-cache conv mismatches in
 the C6 runner

The runner reported that a few conv outputs differ from vLLM with an fp32
cache but not why. Add two sections, reusing its inputs, provenance and the
fusion-off child process:

- conv_silu (fusion on): the conv inputs widened to fp32, fp32 cache and
  output. Compares the pre-activation values (activation off), the SiLU
  outputs with a ULP histogram, and four Triton SiLU formulations --
  x / (1 + tl.exp(-x)) as in the provider, and the variants with div_rn
  and/or libdevice.exp -- applied to the golden's pre-activation values,
  each compared bitwise with both sides.
- conv_noact_bf16 (fusion on and off): the no-activation conv with a bf16
  output against the golden, whether it equals its fp32-output twin
  rounded to bf16, each mismatch's magnitude and size in bf16 ULPs, and the
  compiled variants of both specializations. The two run in separate loops
  so the variants each one adds can be told apart.

The PTX counts gain the packed f32x2 forms, ex2.approx and div.full.f32.
fusion_check now covers every variant an arm compiles.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 scripts/ws1_gdn_provider_agreement.py | 234 +++++++++++++++++++++++---
 1 file changed, 212 insertions(+), 22 deletions(-)

diff --git a/scripts/ws1_gdn_provider_agreement.py b/scripts/ws1_gdn_provider_agreement.py
index 83727fdb5..715cd5627 100644
--- a/scripts/ws1_gdn_provider_agreement.py
+++ b/scripts/ws1_gdn_provider_agreement.py
@@ -9,7 +9,7 @@
 ``docs/design/rfc428-c6-gdn-recurrent-replay.md`` can be reproduced. Inputs and seeds
 are the check file's own helpers, imported from it rather than copied.
 
-Three measurements, emitted as one JSON document on stdout:
+Measurements, emitted as one JSON document on stdout:
 
 * ``recurrent``: provider vs golden for the packed recurrent decode, per
   (batch, state dtype): max|d out|, max|d state| and bitwise mismatch counts.
@@ -18,6 +18,13 @@
 * ``conv`` again with Triton FP fusion disabled, plus ``conv_kernels``: per compiled
   variant of the provider's conv-update kernel, its ``enable_fp_fusion`` option and
   instruction counts from the PTX and from the SASS.
+* ``conv_silu`` localises the fp32-cache conv mismatches, on the same inputs widened to
+  fp32 with an fp32 output: the pre-activation comparison (activation off), the SiLU
+  comparison with its ULP histogram, and four Triton SiLU variants applied to the
+  golden's pre-activation values, each compared bitwise with both sides.
+* ``conv_noact_bf16``: the no-activation, bf16-output specialization against the golden,
+  whether it equals the fp32-output run rounded to bf16, each mismatch's size, and the
+  compiled variants of both specializations; with fusion on and off.
 
 The fusion-off arm runs in a child process with ``TRITON_DEFAULT_FP_FUSION=0`` and a
 private ``TRITON_CACHE_DIR``. Flipping ``triton.knobs.language.default_fp_fusion``
@@ -41,6 +48,7 @@
 import argparse
 import importlib.util
 import json
+import math
 import os
 import platform
 import re
@@ -70,8 +78,16 @@
     "add_f32": r"\badd\.f32\b",
     "mul_rn_f32": r"\bmul\.rn\.f32\b",
     "add_rn_f32": r"\badd\.rn\.f32\b",
+    # Packed pairs (sm_100), approximate exp and division.
+    "fma_rn_f32x2": r"\bfma\.rn\.f32x2\b",
+    "mul_f32x2": r"\bmul(\.rn)?\.f32x2\b",
+    "add_f32x2": r"\badd(\.rn)?\.f32x2\b",
+    "ex2_approx": r"\bex2\.approx",
+    "div_full_f32": r"\bdiv\.full\.f32\b",
 }
 _SASS_OPS = {"FFMA": r"\bFFMA[\w.]*", "FMUL": r"\bFMUL[\w.]*", "FADD": r"\bFADD[\w.]*"}
+# Triton SiLU variants for conv_silu, by MODE of _triton_silu's kernel.
+_SILU_VARIANTS = ("div_exp", "divrn_exp", "div_libexp", "divrn_libexp")
 
 
 def _load_check_module():
@@ -181,34 +197,203 @@ def _count(patterns: dict[str, str], text: str) -> dict[str, int]:
     return {name: len(re.findall(pattern, text)) for name, pattern in patterns.items()}
 
 
-def _conv_kernel_variants() -> list[dict[str, Any]] | dict[str, str]:
-    """Options and instruction counts per compiled variant of the conv-update kernel."""
-    try:
-        from vllm.model_executor.layers.mamba.ops import causal_conv1d as conv_module
+def _compiled_conv_variants() -> dict[tuple[str, Any], Any]:
+    """Every compiled variant of vLLM's conv-update kernel in this process, by cache key."""
+    from vllm.model_executor.layers.mamba.ops import causal_conv1d as conv_module
+
+    kernel = conv_module._causal_conv1d_update_kernel
+    while not hasattr(kernel, "device_caches") and hasattr(kernel, "fn"):
+        kernel = kernel.fn
+    return {
+        (str(device), key): compiled
+        for device, cache in kernel.device_caches.items()
+        for key, compiled in cache[0].items()
+    }
+
 
-        kernel = conv_module._causal_conv1d_update_kernel
-        while not hasattr(kernel, "device_caches") and hasattr(kernel, "fn"):
-            kernel = kernel.fn
+def _conv_kernel_variants(keys: Any = None) -> list[dict[str, Any]] | dict[str, str]:
+    """Options and instruction counts per compiled variant (only ``keys``, if given)."""
+    try:
         variants = []
-        for device, cache in kernel.device_caches.items():
-            for compiled in cache[0].values():
-                row: dict[str, Any] = {
-                    "device": str(device),
-                    "enable_fp_fusion": getattr(compiled.metadata, "enable_fp_fusion", None),
-                    "ptx": _count(_PTX_OPS, compiled.asm["ptx"]),
-                }
-                try:
-                    row["sass"] = _count(_SASS_OPS, compiled.asm["sass"])
-                except Exception as exc:  # needs cuobjdump; report, do not fail
-                    row["sass"] = {"unavailable": f"{type(exc).__name__}: {exc}"}
-                variants.append(row)
+        for (device, key), compiled in _compiled_conv_variants().items():
+            if keys is not None and (device, key) not in keys:
+                continue
+            row: dict[str, Any] = {
+                "device": device,
+                "enable_fp_fusion": getattr(compiled.metadata, "enable_fp_fusion", None),
+                "ptx": _count(_PTX_OPS, compiled.asm["ptx"]),
+            }
+            try:
+                row["sass"] = _count(_SASS_OPS, compiled.asm["sass"])
+            except Exception as exc:  # needs cuobjdump; report, do not fail
+                row["sass"] = {"unavailable": f"{type(exc).__name__}: {exc}"}
+            variants.append(row)
         return variants
     except Exception as exc:  # introspection of Triton internals; report, do not fail
         return {"unavailable": f"{type(exc).__name__}: {exc}"}
 
 
+def _new_variant_keys(before: set[Any]) -> set[Any] | None:
+    try:
+        return set(_compiled_conv_variants()) - before
+    except Exception:  # introspection of Triton internals; report, do not fail
+        return None
+
+
+def _bf16_ulps(diff: float, ref: float) -> float | None:
+    """``diff`` in units of one bf16 ULP at the magnitude of ``ref`` (None at zero)."""
+    if ref == 0.0:
+        return None if diff else 0.0
+    return diff / 2.0 ** (math.floor(math.log2(abs(ref))) - 7)
+
+
+def _conv_noact_bf16(check, batches: list[int]) -> dict[str, Any]:
+    """The no-activation conv with a bf16 output, against its fp32-output twin.
+
+    The provider casts x to the fp32 cache dtype before launching, so the two runs
+    compute the same thing and differ only in the output dtype, i.e. in which Triton
+    specialization runs. Each specialization runs in its own loop so that the compiled
+    variants it adds can be told apart.
+    """
+    import triton
+
+    fp_fusion = bool(triton.knobs.language.default_fp_fusion)
+    try:
+        before = set(_compiled_conv_variants())
+    except Exception:  # introspection of Triton internals; report, do not fail
+        before = set()
+    bf16_out = {}
+    for batch in batches:
+        inp = check._conv_inputs(batch, torch.float32, seed=batch)
+        bf16_out[batch] = check._run_conv_pair(inp, activation=None)
+    bf16_keys = _new_variant_keys(before)
+    fp32_out = {}
+    for batch in batches:
+        inp = check._conv_inputs(batch, torch.float32, seed=batch)
+        fp32_out[batch] = check._run_conv_pair(dict(inp, x=inp["x"].float()), activation=None)
+    fp32_keys = _new_variant_keys(before | (bf16_keys or set()))
+
+    rows = []
+    for batch in batches:
+        (b_ref, _), (b_got, _) = bf16_out[batch]
+        (d_ref, _), (d_got, _) = fp32_out[batch]
+        differ = torch.nonzero(b_ref.view(torch.int16) != b_got.view(torch.int16)).tolist()
+        mismatches = []
+        for r, ch in differ:
+            provider, golden = b_ref[r, ch].item(), b_got[r, ch].item()
+            mismatches.append(
+                {
+                    "abs_out": abs(golden),
+                    "abs_diff": abs(provider - golden),
+                    "bf16_ulps": _bf16_ulps(abs(provider - golden), golden),
+                }
+            )
+        rows.append(
+            {
+                "batch": batch,
+                "fp_fusion": fp_fusion,
+                "out_mismatch_elements": len(differ),
+                "out_elements": b_ref.numel(),
+                "provider_eq_rne_of_fp32_out": torch.equal(b_ref, d_ref.to(torch.bfloat16)),
+                "fp32_out_mismatch_elements": _bit_mismatches(d_got, d_ref),
+                "mismatches": mismatches,
+            }
+        )
+    return {
+        "batches": rows,
+        "bf16_out_kernels": _conv_kernel_variants(bf16_keys) if bf16_keys is not None else None,
+        "fp32_out_kernels": _conv_kernel_variants(fp32_keys) if fp32_keys is not None else None,
+    }
+
+
+def _triton_silu():
+    """``apply(x, mode)``: one of the four SiLU formulations, evaluated by Triton."""
+    import triton
+    import triton.language as tl
+    from triton.language.extra import libdevice
+
+    @triton.jit
+    def silu(x_ptr, y_ptr, n, MODE: tl.constexpr, BLOCK: tl.constexpr):
+        offs = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
+        mask = offs < n
+        x = tl.load(x_ptr + offs, mask=mask, other=0.0)
+        if MODE == 0:  # the provider's source: x / (1 + tl.exp(-x))
+            y = x / (1 + tl.exp(-x))
+        elif MODE == 1:
+            y = tl.math.div_rn(x, 1 + tl.exp(-x))
+        elif MODE == 2:
+            y = x / (1 + libdevice.exp(-x))
+        else:
+            y = tl.math.div_rn(x, 1 + libdevice.exp(-x))
+        tl.store(y_ptr + offs, y, mask=mask)
+
+    def apply(x: torch.Tensor, mode: int) -> torch.Tensor:
+        flat = x.contiguous().view(-1)
+        y = torch.empty_like(flat)
+        silu[(triton.cdiv(flat.numel(), 1024),)](flat, y, flat.numel(), MODE=mode, BLOCK=1024)
+        return y.view_as(x)
+
+    return apply
+
+
+def _ulp_histogram(a: torch.Tensor, b: torch.Tensor) -> dict[str, int]:
+    """Differing fp32 elements by bit-pattern distance."""
+    d = (a.contiguous().view(torch.int32).long() - b.contiguous().view(torch.int32).long()).abs()
+    d = d[d != 0]
+    return {
+        "1": int((d == 1).sum()),
+        "2": int((d == 2).sum()),
+        "3-4": int(((d == 3) | (d == 4)).sum()),
+        ">4": int((d > 4).sum()),
+    }
+
+
+def _conv_silu(check, batches: list[int]) -> list[dict[str, Any]]:
+    """Where the fp32-cache conv mismatches come from, on fp32 inputs and outputs."""
+    silu = _triton_silu()
+    rows = []
+    for batch in batches:
+        inp = check._conv_inputs(batch, torch.float32, seed=batch)
+        inp32 = dict(inp, x=inp["x"].float())  # the same values, kept in fp32 throughout
+        (pre_ref, _), (pre_got, _) = check._run_conv_pair(inp32, activation=None)
+        (out_ref, _), (out_got, _) = check._run_conv_pair(inp32)
+        variants = {name: silu(pre_got, mode) for mode, name in enumerate(_SILU_VARIANTS)}
+        rows.append(
+            {
+                "batch": batch,
+                "elements": out_ref.numel(),
+                "preactivation_mismatch_elements": _bit_mismatches(pre_got, pre_ref),
+                "silu_mismatch_elements": _bit_mismatches(out_got, out_ref),
+                "silu_ulp_histogram": _ulp_histogram(out_got, out_ref),
+                # Applied to the golden's pre-activation values.
+                "triton_silu_vs_provider": {
+                    k: _bit_mismatches(v, out_ref) for k, v in variants.items()
+                },
+                "triton_silu_vs_golden": {
+                    k: _bit_mismatches(v, out_got) for k, v in variants.items()
+                },
+            }
+        )
+    return rows
+
+
 def _conv_report(check, batches: list[int]) -> dict[str, Any]:
-    return {"conv": _measure_conv(check, batches), "conv_kernels": _conv_kernel_variants()}
+    conv = _measure_conv(check, batches)
+    kernels = _conv_kernel_variants()  # before the no-activation runs add their own
+    return {
+        "conv": conv,
+        "conv_kernels": kernels,
+        "conv_noact_bf16": _conv_noact_bf16(check, batches),
+    }
+
+
+def _arm_variants(arm: dict[str, Any]) -> list[Any] | None:
+    """Every compiled variant an arm reports, or None if any listing is unavailable."""
+    noact = arm["conv_noact_bf16"]
+    lists = [arm["conv_kernels"], noact["bf16_out_kernels"], noact["fp32_out_kernels"]]
+    if not all(isinstance(x, list) for x in lists):
+        return None
+    return [v for x in lists for v in x]
 
 
 def _conv_report_without_fusion(batches: str) -> dict[str, Any]:
@@ -284,17 +469,22 @@ def main() -> int:
             "batches": batches,
             "recurrent": "bf16 I/O, use_qk_l2norm_in_kernel=True, num_blocks=batch+2",
             "conv": "bias=True, activation=silu, dim_first=True, W=4, dim=8192",
+            "conv_silu": "the conv inputs with x widened to fp32; fp32 cache and output",
+            "conv_noact_bf16": "the conv inputs with activation=None; fp32 cache",
         },
         "recurrent": _measure_recurrent(check, batches),
     }
     fused = _conv_report(check, batches)
     report["conv"] = fused["conv"]
     report["conv_kernels"] = {"fusion_on": fused["conv_kernels"]}
+    report["conv_noact_bf16"] = {"fusion_on": fused["conv_noact_bf16"]}
+    report["conv_silu"] = _conv_silu(check, batches)
     if not args.skip_fusion_off:
         unfused = _conv_report_without_fusion(args.batches)
         report["conv"] += unfused["conv"]
         report["conv_kernels"]["fusion_off"] = unfused["conv_kernels"]
-        report["fusion_check"] = _fusion_check(fused["conv_kernels"], unfused["conv_kernels"])
+        report["conv_noact_bf16"]["fusion_off"] = unfused["conv_noact_bf16"]
+        report["fusion_check"] = _fusion_check(_arm_variants(fused), _arm_variants(unfused))
     print(json.dumps(report, indent=2, sort_keys=True))
     return 0
 

From 610f1b7d8f8ba807ad4a0714c588a2a80a48eaed Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 2 Oct 2026 02:36:59 +0000
Subject: [PATCH 34/70] docs(gdn): state the conv mismatch causes, measured by
 the runner
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

§4 said FP contraction was ruled out and the cause of the few fp32-cache
conv mismatches was not determined. The runner's conv_silu and
conv_noact_bf16 sections (bb89750) now settle both, on B200:

- With SiLU (Qwen3-Next's configuration): everything before the activation
  matches bitwise; the mismatches come from Triton lowering the activation's
  exp and division to ex2.approx and div.full.f32, where the golden matches
  libdevice exp with IEEE division. Triton's x / (1 + tl.exp(-x)) on the
  golden's pre-activation values reproduces the provider bitwise. No FFMA,
  and fusion off changes nothing, so "contraction ruled out" now holds for
  this path only.
- Without an activation and with a bf16 output: that specialization does
  contract the tap multiply-adds (fma.rn.f32x2 / FFMA); its fp32-output twin
  does not and matches bitwise. 1/0/4/14 mismatches, mostly near-cancelling
  outputs; 0 with TRITON_DEFAULT_FP_FUSION=0. Not on Qwen3-Next's decode path.

The recurrent provider's Triton exp/sigmoid is noted as open.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/design/rfc428-c6-gdn-recurrent-replay.md | 59 +++++++++++++------
 1 file changed, 42 insertions(+), 17 deletions(-)

diff --git a/docs/design/rfc428-c6-gdn-recurrent-replay.md b/docs/design/rfc428-c6-gdn-recurrent-replay.md
index 4b9ca767e..31475af42 100644
--- a/docs/design/rfc428-c6-gdn-recurrent-replay.md
+++ b/docs/design/rfc428-c6-gdn-recurrent-replay.md
@@ -166,23 +166,48 @@ all 8 (batch, cache dtype) cases. Output elements differing, with an fp32 cache:
 5 / 524288 (B=64, max|diff| 3.91e-03). With a bf16 cache: 0 at B=1, 4 and 17, and
 3 / 524288 at B=64 (max|diff| 1.56e-02).
 
-**FP contraction is ruled out** as the cause of the few differing fp32-cache
-elements; what does cause them is **not determined**. The candidate was that, with
-Triton's default `enable_fp_fusion=True`, ptxas contracts the provider's
-`acc += matrix_x * matrix_w` (vLLM `causal_conv1d.py:1061`) into an `FFMA`, whereas the
-golden rounds the product first. The runner tests this by rerunning the comparison in
-a child process compiled with fusion off. On B200 at commit `cf6be1a`:
-
-- the fusion-off arm took effect: all 6 compiled variants carry
-  `enable_fp_fusion=False`, and their PTX has only `.rn`-qualified f32 `mul`/`add`,
-  which ptxas may not contract;
-- the mismatch counts and max\|diff\| are **identical** with fusion on and off
-  (0, 0, 1, 5 for B = 1, 4, 17, 64);
-- neither arm's SASS contains `FFMA`.
-
-The conclusion rests on the second point. The opcode counts are supporting evidence
-only: each variant's PTX has 2 f32 multiplies, too few to be the four tap products, so
-the counters do not show which instructions compute those products.
+**Why a few fp32-cache outputs differ.** There are two mechanisms, both on the provider
+side, and which one applies depends on the Triton specialization. Measured by
+`scripts/ws1_gdn_provider_agreement.py` (`conv_silu`, `conv_noact_bf16`) on B200 at commit
+`bb89750`:
+
+*With SiLU -- Qwen3-Next's configuration and the check file's -- the activation's
+implementation.*
+
+- With the activation off and the output kept in fp32, provider and golden agree bitwise
+  on every element at B = 1, 4, 17 and 64: the cast to the cache dtype, the bias start,
+  the product rounding and the tap order all match.
+- Both sides compute `acc / (1 + exp(-acc))` (vLLM `causal_conv1d.py:1085`), but Triton
+  lowers the exp and the fp32 division to `ex2.approx` and `div.full.f32`, while the
+  golden's PyTorch result equals the same expression with `libdevice.exp` and IEEE
+  division (`div_rn`). Applying Triton's `x / (1 + tl.exp(-x))` to the golden's
+  pre-activation values reproduces the provider's fp32 output bitwise; replacing only the
+  exp, or only the division, reproduces neither side.
+- In fp32 about 38% of outputs differ: 86% of those by 1 ULP, 12% by 2, 3% by 3 or 4,
+  0.2% by more. Only values on opposite sides of a bf16 rounding midpoint survive the bf16
+  store: 0, 0, 1 and 5 elements, each one bf16 ULP apart.
+- FP contraction plays no part on this path. These specializations compute the tap
+  products as packed `mul.f32x2` with separate adds and contain no `FFMA`; recompiling
+  with `TRITON_DEFAULT_FP_FUSION=0` leaves every count unchanged.
+
+*Without an activation and with a bf16 output -- FP contraction.*
+
+- With fusion at its default, this specialization contracts each tap's multiply-add
+  (`fma.rn.f32x2` in the PTX, `FFMA` in the SASS). Its fp32-output twin computes the same
+  values (the provider casts x to the fp32 cache dtype first) but is not contracted, and
+  matches the golden bitwise.
+- The bf16 outputs differ in 1, 0, 4 and 14 elements (max |diff| 3.9e-3). 18 of the 19
+  have |out| < 0.11, where the taps nearly cancel; there the gap can span several bf16
+  ULPs (up to 96 at |out| ≈ 2.4e-7) while staying at most 2.4e-7 in absolute terms.
+- With `TRITON_DEFAULT_FP_FUSION=0` this specialization compiles to separate multiplies
+  and adds, and the mismatches drop to 0. The golden is self-consistent across output
+  dtypes. Qwen3-Next's conv applies SiLU, so this specialization is not on its decode
+  path.
+
+*Open:* the recurrent provider computes `exp(g)` and `sigmoid(b)` with Triton (`tl.exp`
+via the vendored FLA `op.py`), the golden with PyTorch; the same kind of difference may
+account for part of the recurrent output mismatches in the table above. Not
+investigated.
 
 ## 5. Decode versus chunked prefill
 

From 395fef838ad8cd2ba5ccd376b6151c2225bfd753 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 4 Oct 2026 02:16:59 +0000
Subject: [PATCH 35/70] ci(qwen3-next): reconcile the WS1 gtest script with the
 stacked gated-norm branch

Carried over from the former merges of the gated-norm branch into this one; the final tree is the same as before the re-stack.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 ci/run_ws1_gtest.sh | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/ci/run_ws1_gtest.sh b/ci/run_ws1_gtest.sh
index 03b77173d..e6d8e0dc0 100755
--- a/ci/run_ws1_gtest.sh
+++ b/ci/run_ws1_gtest.sh
@@ -77,11 +77,12 @@ print("[ws1-gtest] C8 gate passed")
 PY
 
 echo "[ws1-gtest] Qwen3-Next C3/C4 norms (operator scope)"
-for op in qwen3_next_rms_norm rms_norm_gated; do
-  for gate in forward gradient; do
-    "$PY" "scripts/check_${gate}_invariance.py" \
-      --manifest rl_engine/testing/qwen3_next_norm_manifest.json \
-      --op "$op" --candidate cuda --backend-profile cuda_bf16 \
-      --hidden 2048 --head-dim 128
-  done
-done
+QWEN3_NEXT_NORM_MANIFEST=rl_engine/testing/qwen3_next_norm_manifest.json
+"$PY" scripts/check_forward_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
+  --op qwen3_next_rms_norm --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
+"$PY" scripts/check_gradient_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
+  --op qwen3_next_rms_norm --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
+"$PY" scripts/check_forward_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
+  --op rms_norm_gated --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128
+"$PY" scripts/check_gradient_invariance.py --manifest "$QWEN3_NEXT_NORM_MANIFEST" \
+  --op rms_norm_gated --candidate cuda --backend-profile cuda_bf16 --hidden 2048 --head-dim 128

From 0b41350536a738c76af5b20c5a409b166289f276 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 4 Oct 2026 11:46:39 +0000
Subject: [PATCH 36/70] fix(gdn): keep _chunked_sum fixed-order for widths not
 divisible by 32

The non-multiple-of-32 branch fell back to a whole-axis sum, whose
reduction order is unspecified, which contradicts the batch-invariance
contract the helper exists to enforce. Zero-pad the last dim to the next
chunk boundary instead and reduce in the same fixed chunk order for every
width. Qwen3-Next uses K=128 so the shipped goldens are unaffected; a CPU
test covers an odd width.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../ops/pytorch/linear_attn/gated_delta_rule.py   |  6 +++++-
 tests/test_gdn_state_contract.py                  | 15 +++++++++++++++
 2 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
index 27708f319..181bfca5d 100644
--- a/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
+++ b/rl_engine/kernels/ops/pytorch/linear_attn/gated_delta_rule.py
@@ -84,7 +84,11 @@ def _chunked_sum(x: torch.Tensor) -> torch.Tensor:
     """
     tail = x.shape[-1]
     if tail % _REDUCTION_CHUNK != 0:
-        return x.sum(dim=-1)
+        # Zero-pad to the next chunk boundary instead of falling back to an
+        # unpinned ``sum``: exact zeros add nothing, so the result is the same
+        # fixed chunk order for every width.
+        x = torch.nn.functional.pad(x, (0, _REDUCTION_CHUNK - tail % _REDUCTION_CHUNK))
+        tail = x.shape[-1]
     return (
         x.reshape(*x.shape[:-1], tail // _REDUCTION_CHUNK, _REDUCTION_CHUNK).sum(dim=-1).sum(dim=-1)
     )
diff --git a/tests/test_gdn_state_contract.py b/tests/test_gdn_state_contract.py
index 7af1e083d..a154efd35 100644
--- a/tests/test_gdn_state_contract.py
+++ b/tests/test_gdn_state_contract.py
@@ -107,3 +107,18 @@ def test_softplus_large_branch_has_finite_gradient():
     x = torch.tensor([100.0, 21.0, 0.0], requires_grad=True)
     _softplus(x).sum().backward()
     assert torch.equal(x.grad, torch.tensor([1.0, 1.0, 0.5]))
+
+
+def test_chunked_sum_keeps_fixed_order_for_non_multiple_of_32_width():
+    from rl_engine.kernels.ops.pytorch.linear_attn.gated_delta_rule import (
+        _REDUCTION_CHUNK,
+        _chunked_sum,
+    )
+
+    torch.manual_seed(0)
+    x = torch.randn(3, 5, 2 * _REDUCTION_CHUNK + 7)
+    padded = torch.nn.functional.pad(x, (0, _REDUCTION_CHUNK - 7))
+    chunked = padded.reshape(3, 5, 3, _REDUCTION_CHUNK).sum(dim=-1).sum(dim=-1)
+    assert torch.equal(_chunked_sum(x), chunked)
+    # The reduction must not depend on the leading shape either.
+    assert torch.equal(_chunked_sum(x[:1]), _chunked_sum(x)[:1])

From bec7e7adc01763c26dd7325689ed5f3b4ca06239 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Wed, 7 Oct 2026 16:35:28 +0000
Subject: [PATCH 37/70] fix(norm): reject stale RMSNorm bindings and enable GPU
 coverage

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/ws1-gtest-gpu.yml        |  6 +++
 ci/run_ws1_gtest.sh                        |  1 +
 csrc/ops.cpp                               |  2 +
 rl_engine/_C.pyi                           |  3 ++
 rl_engine/kernels/ops/cuda/norm/rmsnorm.py | 29 ++++++------
 tests/test_qwen3_next_norm.py              |  6 ++-
 tests/test_rms_norm.py                     | 51 +++++++++++++++++++---
 7 files changed, 77 insertions(+), 21 deletions(-)

diff --git a/.github/workflows/ws1-gtest-gpu.yml b/.github/workflows/ws1-gtest-gpu.yml
index 646d80bdf..f48d62382 100644
--- a/.github/workflows/ws1-gtest-gpu.yml
+++ b/.github/workflows/ws1-gtest-gpu.yml
@@ -12,6 +12,8 @@ on:
   pull_request:
     branches: [ main ]
     paths:
+      - "csrc/cuda/rmsnorm.cu"
+      - "csrc/ops.cpp"
       - "rl_engine/kernels/gtest/**"
       - "rl_engine/kernels/ops/**"
       - "rl_engine/testing/**"
@@ -20,6 +22,7 @@ on:
       - "scripts/check_gradient_invariance.py"
       - "scripts/ws1_candidate_evidence.py"
       - "tests/test_ws1_*.py"
+      - "tests/test_qwen3_next_norm.py"
       - "tests/test_forward_invariance.py"
       - "tests/test_gradient_invariance.py"
       - "tests/test_four_judgment_matrix.py"
@@ -30,6 +33,8 @@ on:
   push:
     branches: [ main ]
     paths:
+      - "csrc/cuda/rmsnorm.cu"
+      - "csrc/ops.cpp"
       - "rl_engine/kernels/gtest/**"
       - "rl_engine/kernels/ops/**"
       - "rl_engine/testing/**"
@@ -38,6 +43,7 @@ on:
       - "scripts/check_gradient_invariance.py"
       - "scripts/ws1_candidate_evidence.py"
       - "tests/test_ws1_*.py"
+      - "tests/test_qwen3_next_norm.py"
       - "tests/test_forward_invariance.py"
       - "tests/test_gradient_invariance.py"
       - "tests/test_four_judgment_matrix.py"
diff --git a/ci/run_ws1_gtest.sh b/ci/run_ws1_gtest.sh
index 0e9b172cc..e4d8a36fa 100755
--- a/ci/run_ws1_gtest.sh
+++ b/ci/run_ws1_gtest.sh
@@ -19,6 +19,7 @@ echo "[ws1-gtest] interpreter=$PY out=$OUT"
 
 "$PY" -m pytest -q \
   tests/test_ws1_gtest_gpu.py \
+  tests/test_qwen3_next_norm.py \
   tests/test_triton_batch_invariant_attention.py \
   tests/test_four_judgment_matrix.py \
   tests/test_ws1_candidate_evidence.py \
diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index 4e786e5b3..7ae1523f6 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -716,6 +716,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         &det_gemm_db_transposed,
         "Batch-invariant deterministic GEMM backward in canonical [N,K] layout");
     // registry RMSNorm
+    // API version 2 includes weight_offset in both entry points.
+    m.attr("rmsnorm_api_version") = 2;
     m.def("rmsnorm_forward", &rmsnorm_forward, "Batch-invariant RMSNorm forward CUDA",
           py::arg("x"), py::arg("weight"), py::arg("eps"),
           py::arg("weight_offset") = 0.0);
diff --git a/rl_engine/_C.pyi b/rl_engine/_C.pyi
index b911feaf7..225f997b6 100644
--- a/rl_engine/_C.pyi
+++ b/rl_engine/_C.pyi
@@ -256,6 +256,9 @@ def swiglu_backward(
     gate: torch.Tensor,
     up: torch.Tensor,
 ) -> list[torch.Tensor]: ...
+
+rmsnorm_api_version: int
+
 def rmsnorm_forward(
     x: torch.Tensor,
     weight: torch.Tensor,
diff --git a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
index af9a31358..a0da2ddd4 100644
--- a/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
+++ b/rl_engine/kernels/ops/cuda/norm/rmsnorm.py
@@ -4,9 +4,11 @@
 from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
 from rl_engine.kernels.ops.vjp_fp32 import reduce_rows_fp32, rmsnorm_dweight_rows_fp32
 
+_RMSNORM_API_VERSION = 2
 
-def _require_cuda_symbols(what: str, *names: str) -> None:
-    """Raise when the compiled kernels backing ``what`` are missing.
+
+def _require_cuda_rmsnorm() -> None:
+    """Raise when the compiled RMSNorm bindings are missing or incompatible.
 
     The registry treats a backend whose construction raises as unavailable and
     falls through to the next candidate, so calling this from ``__init__`` is
@@ -15,13 +17,21 @@ def _require_cuda_symbols(what: str, *names: str) -> None:
     activation ops.
     """
     if not _EXT_AVAILABLE or _C is None:
-        raise RuntimeError(f"{what} requires the compiled rl_engine._C extension.")
+        raise RuntimeError("CUDA RMSNorm requires the compiled rl_engine._C extension.")
+    names = ("rmsnorm_forward", "rmsnorm_backward_dx")
     missing = [name for name in names if not hasattr(_C, name)]
     if missing:
         raise RuntimeError(
-            f"{what} symbols ({', '.join(missing)}) are not compiled into _C. "
+            f"CUDA RMSNorm symbols ({', '.join(missing)}) are not compiled into _C. "
             "Rebuild the extension with csrc/cuda/rmsnorm.cu."
         )
+    api_version = getattr(_C, "rmsnorm_api_version", None)
+    if api_version != _RMSNORM_API_VERSION:
+        raise RuntimeError(
+            f"CUDA RMSNorm requires rl_engine._C RMSNorm API version {_RMSNORM_API_VERSION} "
+            f"(loaded {api_version!r}). Rebuild the extension with csrc/cuda/rmsnorm.cu "
+            "for weight_offset support."
+        )
 
 
 class RMSNormCuda(torch.autograd.Function):
@@ -46,6 +56,7 @@ def forward(ctx, x, weight, mask=None, eps=1e-6, weight_offset=0.0):
         Output:
           y: [T, H]
         """
+        _require_cuda_rmsnorm()
         assert x.is_cuda, "x must be CUDA tensor"
         assert weight.is_cuda, "weight must be CUDA tensor"
         assert x.is_contiguous(), "x must be contiguous"
@@ -53,10 +64,6 @@ def forward(ctx, x, weight, mask=None, eps=1e-6, weight_offset=0.0):
         assert x.dim() == 2, "x must be [T, H]"
         assert weight.dim() == 1, "weight must be [H]"
         assert x.shape[1] == weight.shape[0], "hidden size mismatch"
-        assert _EXT_AVAILABLE and hasattr(
-            _C, "rmsnorm_forward"
-        ), "RMSNorm CUDA extension is unavailable. Please rebuild with rmsnorm.cu."
-
         if mask is None:
             mask = torch.ones((x.shape[0],), device=x.device, dtype=torch.bool)
         else:
@@ -124,11 +131,7 @@ class RMSNormCudaOp:
     backward_impl = "cuda_rmsnorm_dx_declared_fp32_rowfold_dw"
 
     def __init__(self) -> None:
-        _require_cuda_symbols(
-            "CUDA RMSNorm",
-            "rmsnorm_forward",
-            "rmsnorm_backward_dx",
-        )
+        _require_cuda_rmsnorm()
 
     #: Added to the weight in fp32 inside the kernel. Subclasses override it;
     #: 0.0 is the plain convention.
diff --git a/tests/test_qwen3_next_norm.py b/tests/test_qwen3_next_norm.py
index a62d8410f..d45670a7d 100644
--- a/tests/test_qwen3_next_norm.py
+++ b/tests/test_qwen3_next_norm.py
@@ -355,7 +355,11 @@ def test_gated_shape_mismatch_raises():
     try:
         from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
 
-        _CUDA_RMSNORM = _EXT_AVAILABLE and hasattr(_C, "rmsnorm_forward")
+        _CUDA_RMSNORM = (
+            _EXT_AVAILABLE
+            and getattr(_C, "rmsnorm_api_version", None) == 2
+            and all(hasattr(_C, name) for name in ("rmsnorm_forward", "rmsnorm_backward_dx"))
+        )
     except ImportError:  # pragma: no cover
         _CUDA_RMSNORM = False
 
diff --git a/tests/test_rms_norm.py b/tests/test_rms_norm.py
index b17a3f302..ec3046593 100644
--- a/tests/test_rms_norm.py
+++ b/tests/test_rms_norm.py
@@ -17,10 +17,11 @@
 try:
     from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
 
-    # The same two symbols RMSNormCudaOp.__init__ requires; a build that has them
-    # dispatches to the CUDA op, so the dispatch test must agree with that guard.
-    _HAS_CUDA_RMSNORM = _EXT_AVAILABLE and all(
-        hasattr(_C, name) for name in ("rmsnorm_forward", "rmsnorm_backward_dx")
+    # Keep dispatch expectations aligned with the constructor's capability guard.
+    _HAS_CUDA_RMSNORM = (
+        _EXT_AVAILABLE
+        and getattr(_C, "rmsnorm_api_version", None) == 2
+        and all(hasattr(_C, name) for name in ("rmsnorm_forward", "rmsnorm_backward_dx"))
     )
 except ImportError:  # pragma: no cover - import can fail when the extension is not built.
     _HAS_CUDA_RMSNORM = False
@@ -263,7 +264,7 @@ def test_cuda_op_construction_fails_when_symbols_missing(monkeypatch):
     from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
 
     class _WithoutRMSNorm:  # a built extension that lacks the rmsnorm symbols
-        pass
+        rmsnorm_api_version = 2
 
     monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", True)
     monkeypatch.setattr(cuda_rmsnorm, "_C", _WithoutRMSNorm())
@@ -291,12 +292,43 @@ def test_registry_falls_back_when_required_symbol_is_missing(monkeypatch, missin
 
     symbols = {name: object() for name in ("rmsnorm_forward", "rmsnorm_backward_dx")}
     del symbols[missing]
+    symbols["rmsnorm_api_version"] = 2
     monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", True)
     monkeypatch.setattr(cuda_rmsnorm, "_C", SimpleNamespace(**symbols))
     assert isinstance(KernelRegistry().get_op("rms_norm", device="cuda"), NativeRMSNormOp)
 
 
-def test_registry_cuda_requires_only_used_symbols_and_cpu_stays_native(monkeypatch):
+@pytest.mark.parametrize("api_version", [None, 1, 3])
+def test_cuda_rejects_incompatible_rmsnorm_api_before_dispatch(monkeypatch, api_version):
+    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.kernels.registry import KernelRegistry
+
+    class _LegacyRMSNorm:
+        def rmsnorm_forward(self, x, weight, eps):
+            pytest.fail("an incompatible extension must not be invoked")
+
+        def rmsnorm_backward_dx(self, dy, x, weight, rstd):
+            pytest.fail("an incompatible extension must not be invoked")
+
+    extension = _LegacyRMSNorm()
+    if api_version is not None:
+        extension.rmsnorm_api_version = api_version
+    monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", True)
+    monkeypatch.setattr(cuda_rmsnorm, "_C", extension)
+
+    for op_type in (cuda_rmsnorm.RMSNormCudaOp, cuda_rmsnorm.Qwen3NextRMSNormCudaOp):
+        with pytest.raises(RuntimeError, match="RMSNorm API version 2.*Rebuild"):
+            op_type()
+
+    registry_op = KernelRegistry().get_op("rms_norm", device="cuda")
+    assert isinstance(registry_op, NativeRMSNormOp)
+    x, weight = torch.ones(2, 8), torch.ones(8)
+    assert torch.isfinite(registry_op(x, weight)).all()
+    with pytest.raises(RuntimeError, match="RMSNorm API version 2.*Rebuild"):
+        rmsnorm_cuda(x, weight, weight_offset=1.0)
+
+
+def test_registry_cuda_requires_current_api_and_only_used_symbols(monkeypatch):
     from types import SimpleNamespace
 
     from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
@@ -306,10 +338,15 @@ def test_registry_cuda_requires_only_used_symbols_and_cpu_stays_native(monkeypat
     monkeypatch.setattr(
         cuda_rmsnorm,
         "_C",
-        SimpleNamespace(rmsnorm_forward=object(), rmsnorm_backward_dx=object()),
+        SimpleNamespace(
+            rmsnorm_api_version=2,
+            rmsnorm_forward=object(),
+            rmsnorm_backward_dx=object(),
+        ),
     )
     registry = KernelRegistry()
     assert isinstance(registry.get_op("rms_norm", device="cuda"), RMSNormCudaOp)
+    cuda_rmsnorm.Qwen3NextRMSNormCudaOp()
     assert isinstance(registry.get_op("rms_norm", device="cpu"), NativeRMSNormOp)
 
 

From 53767ab89110fe53a70d4c6abcfff626b10f37b5 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 8 Oct 2026 17:27:59 +0000
Subject: [PATCH 38/70] feat(scripts): Qwen3-Next norm evidence runner vs
 existing implementations

Compares the zero-centred RMSNorm CUDA op with the PyTorch reference,
transformers, vLLM and FlashInfer (optional providers are skipped when
absent): error vs an FP64 golden, row invariance (256 rows alone vs a
4096-row batch, three seeds, bitwise) and forward/backward latency.
The plot script renders the report as one figure per op.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 scripts/plot_qwen3_next_norm_evidence.py | 117 ++++++++++
 scripts/qwen3_next_norm_evidence.py      | 276 +++++++++++++++++++++++
 2 files changed, 393 insertions(+)
 create mode 100644 scripts/plot_qwen3_next_norm_evidence.py
 create mode 100644 scripts/qwen3_next_norm_evidence.py

diff --git a/scripts/plot_qwen3_next_norm_evidence.py b/scripts/plot_qwen3_next_norm_evidence.py
new file mode 100644
index 000000000..54dab1a67
--- /dev/null
+++ b/scripts/plot_qwen3_next_norm_evidence.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Plot a report from scripts/qwen3_next_norm_evidence.py: one 2x2 figure per op.
+
+    python scripts/plot_qwen3_next_norm_evidence.py report.json  # figure[-<op>].png beside it
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+
+COLORS = ["#2a6fdb", "#7a7a7a", "#e07b39", "#3aa676", "#9b59b6", "#c0392b"]
+
+
+def _short(name: str) -> str:
+    return name.replace(" (forward only)", "*")
+
+
+def plot_op(op: str, data: dict, title: str, out: Path) -> None:
+    names = list(data["row_invariance"])
+    color = {n: COLORS[i % len(COLORS)] for i, n in enumerate(names)}
+    fig, axes = plt.subplots(2, 2, figsize=(13, 9))
+    fig.suptitle(title, fontsize=13)
+
+    for ax, key, label in (
+        (axes[0, 0], "forward_us", "forward latency"),
+        (axes[0, 1], "backward_us", "backward latency (training-capable only)"),
+    ):
+        for name in names:
+            rows = sorted(int(r) for r in data["latency"][name])
+            ys = [data["latency"][name][str(r)].get(key) for r in rows]
+            if all(y is None for y in ys):
+                continue
+            ax.plot(rows, ys, "o-", color=color[name], label=_short(name))
+        ax.set_xscale("log", base=2)
+        ax.set_yscale("log")
+        ax.set_xlabel("rows")
+        ax.set_ylabel("µs (median)")
+        ax.set_title(label)
+        ax.grid(True, which="both", alpha=0.3)
+        ax.legend(fontsize=8)
+
+    ax = axes[1, 0]
+    acc = data["accuracy"][max(data["accuracy"], key=int)]
+    metrics = [
+        ("forward_max_abs", "forward max |err|"),
+        ("dx_max_abs_over_absmax", "dx max |err| / max|dx|"),
+        ("dweight_max_abs_over_absmax", "dweight max |err| / max|dw|"),
+    ]
+    width = 0.8 / len(names)
+    for i, name in enumerate(names):
+        vals = [acc[name].get(m) for m, _ in metrics]
+        xs = [j + (i - (len(names) - 1) / 2) * width for j in range(len(metrics))]
+        ax.bar(
+            [x for x, v in zip(xs, vals) if v is not None],
+            [v for v in vals if v is not None],
+            width,
+            color=color[name],
+            label=_short(name),
+        )
+    ax.set_xticks(range(len(metrics)), [label for _, label in metrics], fontsize=9)
+    ax.set_yscale("log")
+    ax.set_title(f"error vs FP64 golden ({max(data['accuracy'], key=int)} rows, BF16)")
+    ax.grid(True, axis="y", alpha=0.3)
+    ax.legend(fontsize=8)
+
+    ax = axes[1, 1]
+    bi = data["row_invariance"]
+    checked = next(iter(bi.values()))["rows_checked"]
+    ys = range(len(names))
+    fwd = [bi[n]["forward_rows_differing"] for n in names]
+    dx = [
+        bi[n]["dx_rows_differing"] if bi[n]["dx_rows_differing"] is not None else 0 for n in names
+    ]
+    ax.barh([y - 0.2 for y in ys], fwd, 0.4, color="#2a6fdb", label="forward")
+    ax.barh([y + 0.2 for y in ys], dx, 0.4, color="#e07b39", label="dx")
+    for y, n, f, d in zip(ys, names, fwd, dx):
+        no_bwd = bi[n]["dx_rows_differing"] is None
+        ax.text(max(f, d) + 0.2, y, f"{f} / {'n/a' if no_bwd else d}", va="center", fontsize=8)
+    ax.set_yticks(list(ys), [_short(n) for n in names], fontsize=8)
+    ax.invert_yaxis()
+    ax.set_xlabel(f"rows differing (of {checked}; row alone vs inside a batch, bitwise)")
+    ax.set_title("row invariance (0 = batch-invariant)")
+    ax.legend(fontsize=8)
+    ax.grid(True, axis="x", alpha=0.3)
+
+    fig.text(0.01, 0.005, "* forward only (no backward)", fontsize=8)
+    fig.tight_layout(rect=(0, 0.02, 1, 0.96))
+    fig.savefig(out, dpi=130)
+    print(f"wrote {out}")
+
+
+def main() -> None:
+    path = Path(sys.argv[1])
+    report = json.loads(path.read_text())
+    env = report["environment"]
+    ops = report["ops"]
+    for op, data in ops.items():
+        name = "figure.png" if len(ops) == 1 else f"figure-{op}.png"
+        title = (
+            f"Qwen3-Next {op.replace('_', ' ')} — {env['gpu']}, hidden {report['hidden']}, "
+            f"BF16, commit {report['git_commit'][:7]}"
+        )
+        plot_op(op, data, title, path.parent / name)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_norm_evidence.py b/scripts/qwen3_next_norm_evidence.py
new file mode 100644
index 000000000..27a063df6
--- /dev/null
+++ b/scripts/qwen3_next_norm_evidence.py
@@ -0,0 +1,276 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Accuracy, row invariance and latency of the Qwen3-Next norms vs existing implementations.
+
+Writes one JSON report (RFC #428, reuse rule of #420): every candidate is compared with
+an FP64 golden, checked for row invariance (a row computed alone vs inside a batch,
+bitwise), and timed. Optional providers (transformers, vLLM, FlashInfer) are skipped
+when they are not installed; the report says which ran.
+
+    python scripts/qwen3_next_norm_evidence.py --out report.json
+    python scripts/plot_qwen3_next_norm_evidence.py report.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import platform
+import statistics
+import subprocess
+import sys
+from pathlib import Path
+from typing import Any, Callable
+
+import torch
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(REPO_ROOT))
+
+from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp  # noqa: E402
+from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import Qwen3NextRMSNormOp  # noqa: E402
+
+EPS = 1e-6
+HIDDEN = 2048  # Qwen3-Next hidden size (decoder and final norms)
+TIMED_ROWS = (1024, 4096, 16384, 65536)
+
+
+# --------------------------------------------------------------------------- #
+# Candidates: name -> (forward fn(x, w) -> y, has_backward)
+# --------------------------------------------------------------------------- #
+
+
+def zero_centred_candidates() -> dict[str, dict[str, Any]]:
+    cands: dict[str, dict[str, Any]] = {
+        "rl-kernel CUDA": {"fn": Qwen3NextRMSNormCudaOp(), "backward": True},
+        "rl-kernel PyTorch reference": {"fn": Qwen3NextRMSNormOp(), "backward": True},
+    }
+    try:
+        from transformers.models.qwen3_next.modeling_qwen3_next import Qwen3NextRMSNorm
+
+        module = Qwen3NextRMSNorm(HIDDEN, eps=EPS).cuda()
+
+        def hf(x, w, module=module):
+            return torch.func.functional_call(module, {"weight": w}, (x,))
+
+        cands["transformers Qwen3NextRMSNorm"] = {"fn": hf, "backward": True}
+    except ImportError:
+        pass
+    try:
+        from vllm.config import VllmConfig, set_current_vllm_config
+
+        with set_current_vllm_config(VllmConfig()):
+            from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+
+            gemma = GemmaRMSNorm(HIDDEN, eps=EPS).cuda()
+
+        def vllm_fwd(x, w, gemma=gemma):
+            gemma.weight.data = w.detach()
+            return gemma.forward_cuda(x)
+
+        cands["vLLM GemmaRMSNorm (forward only)"] = {"fn": vllm_fwd, "backward": False}
+    except ImportError:
+        pass
+    try:
+        import flashinfer
+
+        cands["FlashInfer gemma_rmsnorm (forward only)"] = {
+            "fn": lambda x, w: flashinfer.norm.gemma_rmsnorm(x, w, EPS),
+            "backward": False,
+        }
+    except ImportError:
+        pass
+    return cands
+
+
+def zero_centred_golden(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
+    x64 = x.double()
+    return x64 * torch.rsqrt(x64.square().mean(-1, keepdim=True) + EPS) * (1.0 + w.double())
+
+
+# --------------------------------------------------------------------------- #
+# Measurements
+# --------------------------------------------------------------------------- #
+
+
+def _inputs(rows: int, seed: int, dtype=torch.bfloat16):
+    g = torch.Generator(device="cuda").manual_seed(seed)
+    x = (torch.randn(rows, HIDDEN, device="cuda", generator=g) * 2).to(dtype)
+    w = (torch.randn(HIDDEN, device="cuda", generator=g) * 0.1).to(dtype)
+    up = torch.randn(rows, HIDDEN, device="cuda", generator=g).to(dtype)
+    return x, w, up
+
+
+def _grads(fn, x, w, up, dtype=None):
+    xl = (x if dtype is None else x.to(dtype)).detach().clone().requires_grad_(True)
+    wl = (w if dtype is None else w.to(dtype)).detach().clone().requires_grad_(True)
+    out = fn(xl, wl)
+    out.backward(up if dtype is None else up.to(dtype))
+    return out.detach(), xl.grad, wl.grad
+
+
+def accuracy(cands, golden, rows: int, seed: int) -> dict[str, Any]:
+    x, w, up = _inputs(rows, seed)
+    ref_out, ref_dx, ref_dw = _grads(golden, x, w, up, torch.float64)
+    result = {}
+    for name, c in cands.items():
+        entry: dict[str, Any] = {}
+        if c["backward"]:
+            out, dx, dw = _grads(c["fn"], x, w, up)
+            for key, got, ref in (("dx", dx, ref_dx), ("dweight", dw, ref_dw)):
+                err = (got.double() - ref).abs().max().item()
+                entry[f"{key}_max_abs_over_absmax"] = err / ref.abs().max().item()
+        else:
+            with torch.no_grad():
+                out = c["fn"](x, w)
+        err = (out.double() - ref_out).abs()
+        entry["forward_max_abs"] = err.max().item()
+        entry["forward_correctly_rounded_fraction"] = (
+            (out == ref_out.to(out.dtype)).float().mean().item()
+        )
+        result[name] = entry
+    return result
+
+
+def row_invariance(cands, seeds=(3, 4, 5), rows: int = 4096, step: int = 16) -> dict[str, Any]:
+    """256 rows computed alone vs the same rows inside a batch, bitwise."""
+
+    result = {}
+    for name, c in cands.items():
+        fwd_bad = dx_bad = checked = 0
+        for seed in seeds:
+            x, w, up = _inputs(rows, seed)
+            if c["backward"]:
+                full_out, full_dx, _ = _grads(c["fn"], x, w, up)
+            else:
+                with torch.no_grad():
+                    full_out = c["fn"](x, w)
+            for i in range(0, rows, step):
+                sl = slice(i, i + 1)
+                if c["backward"]:
+                    out, dx, _ = _grads(c["fn"], x[sl], w, up[sl])
+                    dx_bad += not torch.equal(dx[0], full_dx[i])
+                else:
+                    with torch.no_grad():
+                        out = c["fn"](x[sl], w)
+                fwd_bad += not torch.equal(out[0], full_out[i])
+                checked += 1
+        result[name] = {
+            "rows_checked": checked,
+            "forward_rows_differing": fwd_bad,
+            "dx_rows_differing": dx_bad if c["backward"] else None,
+            "batch_rows": rows,
+            "seeds": list(seeds),
+        }
+    return result
+
+
+def _time_us(fn: Callable[[], Any], warmup: int = 10, iters: int = 50) -> float:
+    for _ in range(warmup):
+        fn()
+    torch.cuda.synchronize()
+    samples = []
+    for _ in range(iters):
+        start, end = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
+        start.record()
+        fn()
+        end.record()
+        end.synchronize()
+        samples.append(start.elapsed_time(end) * 1e3)
+    return statistics.median(samples)
+
+
+def latency(cands, rows_list=TIMED_ROWS) -> dict[str, Any]:
+    result: dict[str, Any] = {name: {} for name in cands}
+    for rows in rows_list:
+        x, w, up = _inputs(rows, seed=11)
+        for name, c in cands.items():
+            row: dict[str, float] = {}
+            with torch.no_grad():
+                row["forward_us"] = _time_us(lambda f=c["fn"]: f(x, w))
+            if c["backward"]:
+                xl = x.detach().clone().requires_grad_(True)
+                wl = w.detach().clone().requires_grad_(True)
+                out = c["fn"](xl, wl)
+                row["backward_us"] = _time_us(
+                    lambda o=out: torch.autograd.grad(o, (xl, wl), up, retain_graph=True)
+                )
+            result[name][str(rows)] = row
+    return result
+
+
+# --------------------------------------------------------------------------- #
+
+
+def _git(*args: str) -> str:
+    try:
+        return subprocess.check_output(["git", *args], cwd=REPO_ROOT, text=True).strip()
+    except (OSError, subprocess.CalledProcessError):
+        return ""
+
+
+def environment() -> dict[str, Any]:
+    env = {
+        "gpu": torch.cuda.get_device_name(),
+        "capability": list(torch.cuda.get_device_capability()),
+        "torch": torch.__version__,
+        "cuda": torch.version.cuda,
+        "python": platform.python_version(),
+    }
+    for mod in ("transformers", "vllm", "flashinfer"):
+        try:
+            env[mod] = __import__(mod).__version__
+        except ImportError:
+            env[mod] = None
+    return env
+
+
+def run_op(name: str, cands, golden) -> dict[str, Any]:
+    print(f"[{name}] candidates: {', '.join(cands)}", flush=True)
+    report = {
+        "accuracy": {str(r): accuracy(cands, golden, r, seed=r) for r in (257, 4096)},
+        "row_invariance": row_invariance(cands),
+        "latency": latency(cands),
+    }
+    for cand, entry in report["row_invariance"].items():
+        print(f"  BI {cand}: {entry}", flush=True)
+    return report
+
+
+def build_report(ops: dict[str, tuple[Callable, Callable]]) -> dict[str, Any]:
+    torch.backends.cuda.matmul.allow_tf32 = False
+    report = {
+        "kind": "qwen3_next_norm_evidence",
+        "rfc": "RL-Align/RL-Kernel#428",
+        "git_commit": _git("rev-parse", "HEAD") or "unknown",
+        "git_dirty": bool(_git("status", "--porcelain", "--untracked-files=no")),
+        "environment": environment(),
+        "hidden": HIDDEN,
+        "eps": EPS,
+        "dtype": "bfloat16",
+        "ops": {},
+    }
+    for name, (make_cands, golden) in ops.items():
+        report["ops"][name] = run_op(name, make_cands(), golden)
+    return report
+
+
+OPS = {"zero_centred_rmsnorm": (zero_centred_candidates, zero_centred_golden)}
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--out", type=Path, required=True)
+    args = parser.parse_args()
+    if not torch.cuda.is_available():
+        raise SystemExit("needs a CUDA device")
+    report = build_report(OPS)
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.out.write_text(json.dumps(report, indent=2) + "\n")
+    print(f"wrote {args.out} (commit {report['git_commit'][:7]}, dirty={report['git_dirty']})")
+
+
+if __name__ == "__main__":
+    main()

From a441ff972d52cf56b180ea00758404e986f55013 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 8 Oct 2026 18:10:56 +0000
Subject: [PATCH 39/70] feat(scripts): extend the Qwen3-Next norm evidence
 runner to the gated RMSNorm

Adds the gated op (hidden 128, rows = tokens x heads) against the PyTorch
reference, transformers' cast-first Qwen3NextRMSNormGated and vLLM's
RMSNormGated, with an FP64 golden in vLLM's convention. Row inputs are now
generic, so the gate is sliced, differentiated and row-checked like x.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 scripts/plot_qwen3_next_norm_evidence.py |  20 +--
 scripts/qwen3_next_norm_evidence.py      | 198 +++++++++++++++--------
 2 files changed, 146 insertions(+), 72 deletions(-)

diff --git a/scripts/plot_qwen3_next_norm_evidence.py b/scripts/plot_qwen3_next_norm_evidence.py
index 54dab1a67..41c444100 100644
--- a/scripts/plot_qwen3_next_norm_evidence.py
+++ b/scripts/plot_qwen3_next_norm_evidence.py
@@ -22,13 +22,13 @@
 
 
 def _short(name: str) -> str:
-    return name.replace(" (forward only)", "*")
+    return name.replace(" (forward only)", "*").replace(" (cast-first)", "\n(cast-first)")
 
 
 def plot_op(op: str, data: dict, title: str, out: Path) -> None:
     names = list(data["row_invariance"])
     color = {n: COLORS[i % len(COLORS)] for i, n in enumerate(names)}
-    fig, axes = plt.subplots(2, 2, figsize=(13, 9))
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10), layout="constrained")
     fig.suptitle(title, fontsize=13)
 
     for ax, key, label in (
@@ -52,10 +52,12 @@ def plot_op(op: str, data: dict, title: str, out: Path) -> None:
     ax = axes[1, 0]
     acc = data["accuracy"][max(data["accuracy"], key=int)]
     metrics = [
-        ("forward_max_abs", "forward max |err|"),
-        ("dx_max_abs_over_absmax", "dx max |err| / max|dx|"),
-        ("dweight_max_abs_over_absmax", "dweight max |err| / max|dw|"),
+        ("forward_max_abs", "forward\nmax |err|"),
+        ("dx_max_abs_over_absmax", "dx\nmax |err| / max"),
+        ("dweight_max_abs_over_absmax", "dweight\nmax |err| / max"),
+        ("dgate_max_abs_over_absmax", "dgate\nmax |err| / max"),
     ]
+    metrics = [m for m in metrics if any(acc[n].get(m[0]) is not None for n in names)]
     width = 0.8 / len(names)
     for i, name in enumerate(names):
         vals = [acc[name].get(m) for m, _ in metrics]
@@ -87,14 +89,13 @@ def plot_op(op: str, data: dict, title: str, out: Path) -> None:
         no_bwd = bi[n]["dx_rows_differing"] is None
         ax.text(max(f, d) + 0.2, y, f"{f} / {'n/a' if no_bwd else d}", va="center", fontsize=8)
     ax.set_yticks(list(ys), [_short(n) for n in names], fontsize=8)
+    ax.set_xlim(0, max(3, max(fwd + dx) * 1.4))
     ax.invert_yaxis()
     ax.set_xlabel(f"rows differing (of {checked}; row alone vs inside a batch, bitwise)")
     ax.set_title("row invariance (0 = batch-invariant)")
     ax.legend(fontsize=8)
     ax.grid(True, axis="x", alpha=0.3)
 
-    fig.text(0.01, 0.005, "* forward only (no backward)", fontsize=8)
-    fig.tight_layout(rect=(0, 0.02, 1, 0.96))
     fig.savefig(out, dpi=130)
     print(f"wrote {out}")
 
@@ -106,9 +107,10 @@ def main() -> None:
     ops = report["ops"]
     for op, data in ops.items():
         name = "figure.png" if len(ops) == 1 else f"figure-{op}.png"
+        hidden = data.get("hidden", report.get("hidden"))
         title = (
-            f"Qwen3-Next {op.replace('_', ' ')} — {env['gpu']}, hidden {report['hidden']}, "
-            f"BF16, commit {report['git_commit'][:7]}"
+            f"Qwen3-Next {op.replace('_', ' ')} — {env['gpu']}, hidden {hidden}, "
+            f"BF16, commit {report['git_commit'][:7]}  (* forward only)"
         )
         plot_op(op, data, title, path.parent / name)
 
diff --git a/scripts/qwen3_next_norm_evidence.py b/scripts/qwen3_next_norm_evidence.py
index 27a063df6..d29f249ee 100644
--- a/scripts/qwen3_next_norm_evidence.py
+++ b/scripts/qwen3_next_norm_evidence.py
@@ -25,24 +25,30 @@
 from typing import Any, Callable
 
 import torch
+import torch.nn.functional as F
 
 REPO_ROOT = Path(__file__).resolve().parents[1]
 sys.path.insert(0, str(REPO_ROOT))
 
-from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp  # noqa: E402
-from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import Qwen3NextRMSNormOp  # noqa: E402
+from rl_engine.kernels.ops.cuda.norm.rmsnorm import (  # noqa: E402
+    Qwen3NextRMSNormCudaOp,
+    Qwen3NextRMSNormGatedCudaOp,
+)
+from rl_engine.kernels.ops.pytorch.norm.qwen3_next_rms_norm import (  # noqa: E402
+    Qwen3NextRMSNormGatedOp,
+    Qwen3NextRMSNormOp,
+)
 
 EPS = 1e-6
-HIDDEN = 2048  # Qwen3-Next hidden size (decoder and final norms)
-TIMED_ROWS = (1024, 4096, 16384, 65536)
 
 
 # --------------------------------------------------------------------------- #
-# Candidates: name -> (forward fn(x, w) -> y, has_backward)
+# Ops. Each candidate is fn(*row_inputs, weight) -> y, plus whether it has a
+# backward. Row inputs are sliced together for the row-invariance check.
 # --------------------------------------------------------------------------- #
 
 
-def zero_centred_candidates() -> dict[str, dict[str, Any]]:
+def zero_centred_candidates(hidden: int) -> dict[str, dict[str, Any]]:
     cands: dict[str, dict[str, Any]] = {
         "rl-kernel CUDA": {"fn": Qwen3NextRMSNormCudaOp(), "backward": True},
         "rl-kernel PyTorch reference": {"fn": Qwen3NextRMSNormOp(), "backward": True},
@@ -50,7 +56,7 @@ def zero_centred_candidates() -> dict[str, dict[str, Any]]:
     try:
         from transformers.models.qwen3_next.modeling_qwen3_next import Qwen3NextRMSNorm
 
-        module = Qwen3NextRMSNorm(HIDDEN, eps=EPS).cuda()
+        module = Qwen3NextRMSNorm(hidden, eps=EPS).cuda()
 
         def hf(x, w, module=module):
             return torch.func.functional_call(module, {"weight": w}, (x,))
@@ -64,7 +70,7 @@ def hf(x, w, module=module):
         with set_current_vllm_config(VllmConfig()):
             from vllm.model_executor.layers.layernorm import GemmaRMSNorm
 
-            gemma = GemmaRMSNorm(HIDDEN, eps=EPS).cuda()
+            gemma = GemmaRMSNorm(hidden, eps=EPS).cuda()
 
         def vllm_fwd(x, w, gemma=gemma):
             gemma.weight.data = w.detach()
@@ -85,46 +91,121 @@ def vllm_fwd(x, w, gemma=gemma):
     return cands
 
 
-def zero_centred_golden(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
+def zero_centred_golden(x, w):
     x64 = x.double()
     return x64 * torch.rsqrt(x64.square().mean(-1, keepdim=True) + EPS) * (1.0 + w.double())
 
 
+def gated_candidates(hidden: int) -> dict[str, dict[str, Any]]:
+    cuda_op, ref_op = Qwen3NextRMSNormGatedCudaOp(), Qwen3NextRMSNormGatedOp()
+    cands: dict[str, dict[str, Any]] = {
+        "rl-kernel CUDA": {"fn": lambda x, g, w: cuda_op(x, w, g, eps=EPS), "backward": True},
+        "rl-kernel PyTorch reference": {
+            "fn": lambda x, g, w: ref_op(x, w, g, eps=EPS),
+            "backward": True,
+        },
+    }
+    try:
+        from transformers.models.qwen3_next.modeling_qwen3_next import Qwen3NextRMSNormGated
+
+        module = Qwen3NextRMSNormGated(hidden, eps=EPS).cuda()
+
+        def hf(x, g, w, module=module):
+            return torch.func.functional_call(module, {"weight": w}, (x, g))
+
+        cands["transformers Qwen3NextRMSNormGated (cast-first)"] = {"fn": hf, "backward": True}
+    except ImportError:
+        pass
+    try:
+        from vllm.config import VllmConfig, set_current_vllm_config
+
+        with set_current_vllm_config(VllmConfig()):
+            from vllm.model_executor.layers.layernorm import RMSNormGated
+
+            gated = RMSNormGated(hidden, eps=EPS, norm_before_gate=True).cuda()
+
+        def vllm_fwd(x, g, w, gated=gated):
+            gated.weight.data = w.detach()
+            return gated.forward_cuda(x, g)
+
+        cands["vLLM RMSNormGated (forward only)"] = {"fn": vllm_fwd, "backward": False}
+    except ImportError:
+        pass
+    return cands
+
+
+def gated_golden(x, g, w):
+    """vLLM's convention (the one #468 implements) in FP64: x * rstd * w * silu(gate)."""
+
+    x64 = x.double()
+    return (x64 * torch.rsqrt(x64.square().mean(-1, keepdim=True) + EPS) * w.double()) * F.silu(
+        g.double()
+    )
+
+
+OPS: dict[str, dict[str, Any]] = {
+    "zero_centred_rmsnorm": {
+        "hidden": 2048,  # decoder and final norms
+        "row_inputs": 1,
+        "weight": lambda h, gen: torch.randn(h, device="cuda", generator=gen) * 0.1,
+        "candidates": zero_centred_candidates,
+        "golden": zero_centred_golden,
+        "timed_rows": (1024, 4096, 16384, 65536),
+    },
+    "gated_rmsnorm": {
+        "hidden": 128,  # GDN value head dim; rows are tokens x heads
+        "row_inputs": 2,
+        "weight": lambda h, gen: 1.0 + torch.randn(h, device="cuda", generator=gen) * 0.1,
+        "candidates": gated_candidates,
+        "golden": gated_golden,
+        "timed_rows": (4096, 16384, 65536, 262144),
+    },
+}
+
+
 # --------------------------------------------------------------------------- #
 # Measurements
 # --------------------------------------------------------------------------- #
 
 
-def _inputs(rows: int, seed: int, dtype=torch.bfloat16):
+def _inputs(spec, rows: int, seed: int, dtype=torch.bfloat16):
     g = torch.Generator(device="cuda").manual_seed(seed)
-    x = (torch.randn(rows, HIDDEN, device="cuda", generator=g) * 2).to(dtype)
-    w = (torch.randn(HIDDEN, device="cuda", generator=g) * 0.1).to(dtype)
-    up = torch.randn(rows, HIDDEN, device="cuda", generator=g).to(dtype)
-    return x, w, up
+    h = spec["hidden"]
+    row_inputs = [(torch.randn(rows, h, device="cuda", generator=g) * 2).to(dtype)]
+    for _ in range(spec["row_inputs"] - 1):
+        row_inputs.append(torch.randn(rows, h, device="cuda", generator=g).to(dtype))
+    w = spec["weight"](h, g).to(dtype)
+    up = torch.randn(rows, h, device="cuda", generator=g).to(dtype)
+    return row_inputs, w, up
 
 
-def _grads(fn, x, w, up, dtype=None):
-    xl = (x if dtype is None else x.to(dtype)).detach().clone().requires_grad_(True)
-    wl = (w if dtype is None else w.to(dtype)).detach().clone().requires_grad_(True)
-    out = fn(xl, wl)
+def _grads(fn, row_inputs, w, up, dtype=None):
+    def leaf(t):
+        return (t if dtype is None else t.to(dtype)).detach().clone().requires_grad_(True)
+
+    rl, wl = [leaf(t) for t in row_inputs], leaf(w)
+    out = fn(*rl, wl)
     out.backward(up if dtype is None else up.to(dtype))
-    return out.detach(), xl.grad, wl.grad
+    return out.detach(), [t.grad for t in rl], wl.grad
 
 
-def accuracy(cands, golden, rows: int, seed: int) -> dict[str, Any]:
-    x, w, up = _inputs(rows, seed)
-    ref_out, ref_dx, ref_dw = _grads(golden, x, w, up, torch.float64)
+def accuracy(spec, cands, rows: int, seed: int) -> dict[str, Any]:
+    row_inputs, w, up = _inputs(spec, rows, seed)
+    ref_out, ref_drows, ref_dw = _grads(spec["golden"], row_inputs, w, up, torch.float64)
     result = {}
     for name, c in cands.items():
         entry: dict[str, Any] = {}
         if c["backward"]:
-            out, dx, dw = _grads(c["fn"], x, w, up)
-            for key, got, ref in (("dx", dx, ref_dx), ("dweight", dw, ref_dw)):
+            out, drows, dw = _grads(c["fn"], row_inputs, w, up)
+            pairs = [("dx", drows[0], ref_drows[0]), ("dweight", dw, ref_dw)]
+            if len(drows) > 1:
+                pairs.append(("dgate", drows[1], ref_drows[1]))
+            for key, got, ref in pairs:
                 err = (got.double() - ref).abs().max().item()
                 entry[f"{key}_max_abs_over_absmax"] = err / ref.abs().max().item()
         else:
             with torch.no_grad():
-                out = c["fn"](x, w)
+                out = c["fn"](*row_inputs, w)
         err = (out.double() - ref_out).abs()
         entry["forward_max_abs"] = err.max().item()
         entry["forward_correctly_rounded_fraction"] = (
@@ -134,27 +215,27 @@ def accuracy(cands, golden, rows: int, seed: int) -> dict[str, Any]:
     return result
 
 
-def row_invariance(cands, seeds=(3, 4, 5), rows: int = 4096, step: int = 16) -> dict[str, Any]:
+def row_invariance(spec, cands, seeds=(3, 4, 5), rows: int = 4096, step: int = 16):
     """256 rows computed alone vs the same rows inside a batch, bitwise."""
 
     result = {}
     for name, c in cands.items():
         fwd_bad = dx_bad = checked = 0
         for seed in seeds:
-            x, w, up = _inputs(rows, seed)
+            row_inputs, w, up = _inputs(spec, rows, seed)
             if c["backward"]:
-                full_out, full_dx, _ = _grads(c["fn"], x, w, up)
+                full_out, full_drows, _ = _grads(c["fn"], row_inputs, w, up)
             else:
                 with torch.no_grad():
-                    full_out = c["fn"](x, w)
+                    full_out = c["fn"](*row_inputs, w)
             for i in range(0, rows, step):
-                sl = slice(i, i + 1)
+                part = [t[i : i + 1] for t in row_inputs]
                 if c["backward"]:
-                    out, dx, _ = _grads(c["fn"], x[sl], w, up[sl])
-                    dx_bad += not torch.equal(dx[0], full_dx[i])
+                    out, drows, _ = _grads(c["fn"], part, w, up[i : i + 1])
+                    dx_bad += not all(torch.equal(d[0], fd[i]) for d, fd in zip(drows, full_drows))
                 else:
                     with torch.no_grad():
-                        out = c["fn"](x[sl], w)
+                        out = c["fn"](*part, w)
                 fwd_bad += not torch.equal(out[0], full_out[i])
                 checked += 1
         result[name] = {
@@ -182,20 +263,19 @@ def _time_us(fn: Callable[[], Any], warmup: int = 10, iters: int = 50) -> float:
     return statistics.median(samples)
 
 
-def latency(cands, rows_list=TIMED_ROWS) -> dict[str, Any]:
+def latency(spec, cands) -> dict[str, Any]:
     result: dict[str, Any] = {name: {} for name in cands}
-    for rows in rows_list:
-        x, w, up = _inputs(rows, seed=11)
+    for rows in spec["timed_rows"]:
+        row_inputs, w, up = _inputs(spec, rows, seed=11)
         for name, c in cands.items():
             row: dict[str, float] = {}
             with torch.no_grad():
-                row["forward_us"] = _time_us(lambda f=c["fn"]: f(x, w))
+                row["forward_us"] = _time_us(lambda f=c["fn"]: f(*row_inputs, w))
             if c["backward"]:
-                xl = x.detach().clone().requires_grad_(True)
-                wl = w.detach().clone().requires_grad_(True)
-                out = c["fn"](xl, wl)
+                leaves = [t.detach().clone().requires_grad_(True) for t in [*row_inputs, w]]
+                out = c["fn"](*leaves)
                 row["backward_us"] = _time_us(
-                    lambda o=out: torch.autograd.grad(o, (xl, wl), up, retain_graph=True)
+                    lambda o=out, lv=leaves: torch.autograd.grad(o, lv, up, retain_graph=True)
                 )
             result[name][str(rows)] = row
     return result
@@ -227,19 +307,27 @@ def environment() -> dict[str, Any]:
     return env
 
 
-def run_op(name: str, cands, golden) -> dict[str, Any]:
+def run_op(name: str, spec) -> dict[str, Any]:
+    cands = spec["candidates"](spec["hidden"])
     print(f"[{name}] candidates: {', '.join(cands)}", flush=True)
     report = {
-        "accuracy": {str(r): accuracy(cands, golden, r, seed=r) for r in (257, 4096)},
-        "row_invariance": row_invariance(cands),
-        "latency": latency(cands),
+        "hidden": spec["hidden"],
+        "accuracy": {str(r): accuracy(spec, cands, r, seed=r) for r in (257, 4096)},
+        "row_invariance": row_invariance(spec, cands),
+        "latency": latency(spec, cands),
     }
     for cand, entry in report["row_invariance"].items():
         print(f"  BI {cand}: {entry}", flush=True)
     return report
 
 
-def build_report(ops: dict[str, tuple[Callable, Callable]]) -> dict[str, Any]:
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--out", type=Path, required=True)
+    parser.add_argument("--ops", default=",".join(OPS), help="comma list of ops")
+    args = parser.parse_args()
+    if not torch.cuda.is_available():
+        raise SystemExit("needs a CUDA device")
     torch.backends.cuda.matmul.allow_tf32 = False
     report = {
         "kind": "qwen3_next_norm_evidence",
@@ -247,26 +335,10 @@ def build_report(ops: dict[str, tuple[Callable, Callable]]) -> dict[str, Any]:
         "git_commit": _git("rev-parse", "HEAD") or "unknown",
         "git_dirty": bool(_git("status", "--porcelain", "--untracked-files=no")),
         "environment": environment(),
-        "hidden": HIDDEN,
         "eps": EPS,
         "dtype": "bfloat16",
-        "ops": {},
+        "ops": {name: run_op(name, OPS[name]) for name in args.ops.split(",")},
     }
-    for name, (make_cands, golden) in ops.items():
-        report["ops"][name] = run_op(name, make_cands(), golden)
-    return report
-
-
-OPS = {"zero_centred_rmsnorm": (zero_centred_candidates, zero_centred_golden)}
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--out", type=Path, required=True)
-    args = parser.parse_args()
-    if not torch.cuda.is_available():
-        raise SystemExit("needs a CUDA device")
-    report = build_report(OPS)
     args.out.parent.mkdir(parents=True, exist_ok=True)
     args.out.write_text(json.dumps(report, indent=2) + "\n")
     print(f"wrote {args.out} (commit {report['git_commit'][:7]}, dirty={report['git_dirty']})")

From 3d0bae7174c09a133ea1288d820b63e4a7db8f57 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 8 Oct 2026 18:11:14 +0000
Subject: [PATCH 40/70] fix(scripts): readable Qwen3-Next norm evidence figure
 layout

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 scripts/plot_qwen3_next_norm_evidence.py | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/scripts/plot_qwen3_next_norm_evidence.py b/scripts/plot_qwen3_next_norm_evidence.py
index 54dab1a67..41c444100 100644
--- a/scripts/plot_qwen3_next_norm_evidence.py
+++ b/scripts/plot_qwen3_next_norm_evidence.py
@@ -22,13 +22,13 @@
 
 
 def _short(name: str) -> str:
-    return name.replace(" (forward only)", "*")
+    return name.replace(" (forward only)", "*").replace(" (cast-first)", "\n(cast-first)")
 
 
 def plot_op(op: str, data: dict, title: str, out: Path) -> None:
     names = list(data["row_invariance"])
     color = {n: COLORS[i % len(COLORS)] for i, n in enumerate(names)}
-    fig, axes = plt.subplots(2, 2, figsize=(13, 9))
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10), layout="constrained")
     fig.suptitle(title, fontsize=13)
 
     for ax, key, label in (
@@ -52,10 +52,12 @@ def plot_op(op: str, data: dict, title: str, out: Path) -> None:
     ax = axes[1, 0]
     acc = data["accuracy"][max(data["accuracy"], key=int)]
     metrics = [
-        ("forward_max_abs", "forward max |err|"),
-        ("dx_max_abs_over_absmax", "dx max |err| / max|dx|"),
-        ("dweight_max_abs_over_absmax", "dweight max |err| / max|dw|"),
+        ("forward_max_abs", "forward\nmax |err|"),
+        ("dx_max_abs_over_absmax", "dx\nmax |err| / max"),
+        ("dweight_max_abs_over_absmax", "dweight\nmax |err| / max"),
+        ("dgate_max_abs_over_absmax", "dgate\nmax |err| / max"),
     ]
+    metrics = [m for m in metrics if any(acc[n].get(m[0]) is not None for n in names)]
     width = 0.8 / len(names)
     for i, name in enumerate(names):
         vals = [acc[name].get(m) for m, _ in metrics]
@@ -87,14 +89,13 @@ def plot_op(op: str, data: dict, title: str, out: Path) -> None:
         no_bwd = bi[n]["dx_rows_differing"] is None
         ax.text(max(f, d) + 0.2, y, f"{f} / {'n/a' if no_bwd else d}", va="center", fontsize=8)
     ax.set_yticks(list(ys), [_short(n) for n in names], fontsize=8)
+    ax.set_xlim(0, max(3, max(fwd + dx) * 1.4))
     ax.invert_yaxis()
     ax.set_xlabel(f"rows differing (of {checked}; row alone vs inside a batch, bitwise)")
     ax.set_title("row invariance (0 = batch-invariant)")
     ax.legend(fontsize=8)
     ax.grid(True, axis="x", alpha=0.3)
 
-    fig.text(0.01, 0.005, "* forward only (no backward)", fontsize=8)
-    fig.tight_layout(rect=(0, 0.02, 1, 0.96))
     fig.savefig(out, dpi=130)
     print(f"wrote {out}")
 
@@ -106,9 +107,10 @@ def main() -> None:
     ops = report["ops"]
     for op, data in ops.items():
         name = "figure.png" if len(ops) == 1 else f"figure-{op}.png"
+        hidden = data.get("hidden", report.get("hidden"))
         title = (
-            f"Qwen3-Next {op.replace('_', ' ')} — {env['gpu']}, hidden {report['hidden']}, "
-            f"BF16, commit {report['git_commit'][:7]}"
+            f"Qwen3-Next {op.replace('_', ' ')} — {env['gpu']}, hidden {hidden}, "
+            f"BF16, commit {report['git_commit'][:7]}  (* forward only)"
         )
         plot_op(op, data, title, path.parent / name)
 

From db420ab3fcf0cb49cf667b27bd17d8ce9414eb07 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 8 Oct 2026 18:18:42 +0000
Subject: [PATCH 41/70] test(gdn): L1 batch-invariance check for the
 causal-conv1d golden

Every sequence of a batch of 257 is compared, bitwise, alone and inside
sub-batches of 2, 7 and 64 at other offsets and a shuffled batch order,
with contiguous and shuffled cache blocks, fp32 and bf16 caches, with and
without bias and SiLU, over three seeds: output rows and rolled state
blocks are unchanged.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 tests/check_gdn_recurrent_golden.py | 46 +++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/tests/check_gdn_recurrent_golden.py b/tests/check_gdn_recurrent_golden.py
index 7815939c0..d7aa3f34f 100644
--- a/tests/check_gdn_recurrent_golden.py
+++ b/tests/check_gdn_recurrent_golden.py
@@ -294,6 +294,52 @@ def test_conv_state_update_is_bitwise_exact(batch):
         assert torch.equal(state_got, state_ref), f"{state_dtype} batch={batch}"
 
 
+@pytest.mark.parametrize("state_dtype", [torch.float32, torch.bfloat16])
+@pytest.mark.parametrize("activation", ["silu", None])
+@pytest.mark.parametrize("with_bias", [True, False])
+def test_conv_golden_is_batch_invariant(state_dtype, activation, with_bias):
+    """L1 for the conv golden, bitwise: a sequence's output row and state block
+    do not depend on which other sequences share the step.
+
+    Every sequence of a batch of 257 is run alone; sub-batches of 2, 7 and 64 at
+    other offsets and a shuffled batch order are run too, with contiguous and
+    shuffled cache blocks, over three seeds.
+    """
+    from rl_engine.kernels.ops.pytorch.linear_attn import CausalConv1dUpdateOp
+
+    op = CausalConv1dUpdateOp()
+    batch = 257
+    for seed in (3, 4, 5):
+        g = torch.Generator(device="cpu").manual_seed(seed)
+        perm = torch.randperm(batch, generator=g).cuda()
+        shuffled_blocks = (torch.randperm(batch, generator=g) + 1).to("cuda", torch.int32)
+        for indices in (None, shuffled_blocks):
+            inp = _conv_inputs(batch, state_dtype, seed, with_bias=with_bias, indices=indices)
+
+            def run(rows, inp=inp):
+                return op.forward(
+                    inp["x"][rows],
+                    inp["state"].clone(),
+                    inp["weight"],
+                    inp["indices"][rows],
+                    bias=inp["bias"],
+                    activation=activation,
+                )
+
+            full_out, full_state = run(torch.arange(batch, device="cuda"))
+            picks = [torch.tensor([r], device="cuda") for r in range(batch)]
+            picks += [
+                torch.arange(o, o + s, device="cuda") for s, o in ((2, 0), (7, 100), (64, 193))
+            ]
+            picks.append(perm)
+            for rows in picks:
+                out, state = run(rows)
+                blocks = inp["indices"][rows].long()
+                where = f"seed={seed} rows[{int(rows[0])}..] n={rows.numel()}"
+                assert torch.equal(out, full_out[rows]), f"out {where}"
+                assert torch.equal(state[blocks], full_state[blocks]), f"state {where}"
+
+
 @pytest.mark.parametrize("batch", [1, 4, 17, 64])
 def test_conv_output_matches_provider_with_fp32_cache(batch):
     """An fp32 cache reproduces the provider except on a few output elements.

From ba467508cb935d4049552436c4beef2a60c1a75f Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 8 Oct 2026 18:32:22 +0000
Subject: [PATCH 42/70] docs(norm): zero-centred RMSNorm evidence vs existing
 implementations on B200

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/operators/qwen3-next-rms-norm.md         |  28 +++
 .../qwen3-next-rms-norm-b200/figure.png       | Bin 0 -> 241252 bytes
 .../qwen3-next-rms-norm-b200/report.json      | 225 ++++++++++++++++++
 3 files changed, 253 insertions(+)
 create mode 100644 docs/usage/evidence/qwen3-next-rms-norm-b200/figure.png
 create mode 100644 docs/usage/evidence/qwen3-next-rms-norm-b200/report.json

diff --git a/docs/operators/qwen3-next-rms-norm.md b/docs/operators/qwen3-next-rms-norm.md
index 81fdde9b3..ede972b3a 100644
--- a/docs/operators/qwen3-next-rms-norm.md
+++ b/docs/operators/qwen3-next-rms-norm.md
@@ -98,6 +98,34 @@ The CUDA path reuses the existing `rmsnorm_fwd_kernel` reduction
 (`block_reduce_sum` over `choose_threads(H)`), so the offset costs one fp32 add per
 element and no extra memory traffic.
 
+## Evidence
+
+![zero-centred RMSNorm vs existing implementations on B200](../usage/evidence/qwen3-next-rms-norm-b200/figure.png)
+
+[`report.json`](../usage/evidence/qwen3-next-rms-norm-b200/report.json) was written by
+`scripts/qwen3_next_norm_evidence.py` from a clean tree at `3d0bae7`, on an otherwise idle
+B200 (torch 2.13.0+cu130, transformers 5.17.0, vLLM 0.30.0, FlashInfer 0.6.18). Hidden 2048,
+BF16.
+
+| | rl-kernel CUDA | PyTorch reference | transformers | vLLM `GemmaRMSNorm`* | FlashInfer `gemma_rmsnorm`* |
+|---|---|---|---|---|---|
+| rows differing alone vs in a batch, forward / `dx` (of 768) | **0 / 0** | 0 / 12 | 2 / 11 | 2 / n/a | 0 / n/a |
+| forward, 65536 rows | 425 µs | 2148 µs | 1449 µs | 1449 µs | **90 µs** |
+| backward, 65536 rows | 30.9 ms | **3.0 ms** | 3.1 ms | n/a | n/a |
+
+\* forward only, no backward.
+
+- **Accuracy is the same for every implementation:** forward max error 1.56e-2 against the
+  FP64 golden (BF16 output rounding; 99.999% of elements correctly rounded), `dx` and
+  `dweight` within 2.8e-3 and 2.1e-3 of their maximum.
+- **Row invariance:** only this op and FlashInfer give every row the same bits alone and in a
+  batch; FlashInfer has no backward. transformers and vLLM differ on 2 forward rows in 768.
+- **The backward is about 10× slower than transformers at 65536 rows.** `dweight` is folded
+  over rows in ascending order by `reduce_rows_fp32_left_fold`, one thread per column, so
+  that the batch `dweight` equals the in-order sum of single-row contributions bitwise (the
+  gradient-invariance contract's singleton-aggregate check). That serial fold over rows is
+  the cost; a faster tree fold would not pass that check.
+
 ## Tests
 
 ```bash
diff --git a/docs/usage/evidence/qwen3-next-rms-norm-b200/figure.png b/docs/usage/evidence/qwen3-next-rms-norm-b200/figure.png
new file mode 100644
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GIT binary patch
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diff --git a/docs/usage/evidence/qwen3-next-rms-norm-b200/report.json b/docs/usage/evidence/qwen3-next-rms-norm-b200/report.json
new file mode 100644
index 000000000..0fc8452ae
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-rms-norm-b200/report.json
@@ -0,0 +1,225 @@
+{
+  "kind": "qwen3_next_norm_evidence",
+  "rfc": "RL-Align/RL-Kernel#428",
+  "git_commit": "3d0bae7174c09a133ea1288d820b63e4a7db8f57",
+  "git_dirty": false,
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "capability": [
+      10,
+      0
+    ],
+    "torch": "2.13.0+cu130",
+    "cuda": "13.0",
+    "python": "3.12.14",
+    "transformers": "5.17.0",
+    "vllm": "0.30.0",
+    "flashinfer": "0.6.18.post1"
+  },
+  "hidden": 2048,
+  "eps": 1e-06,
+  "dtype": "bfloat16",
+  "ops": {
+    "zero_centred_rmsnorm": {
+      "accuracy": {
+        "257": {
+          "rl-kernel CUDA": {
+            "dx_max_abs_over_absmax": 0.002701418159021442,
+            "dweight_max_abs_over_absmax": 0.0023356239188215265,
+            "forward_max_abs": 0.01552825644384992,
+            "forward_correctly_rounded_fraction": 0.9999942779541016
+          },
+          "rl-kernel PyTorch reference": {
+            "dx_max_abs_over_absmax": 0.002701418159021442,
+            "dweight_max_abs_over_absmax": 0.0023356239188215265,
+            "forward_max_abs": 0.01552825644384992,
+            "forward_correctly_rounded_fraction": 0.9999905228614807
+          },
+          "transformers Qwen3NextRMSNorm": {
+            "dx_max_abs_over_absmax": 0.002701418159021442,
+            "dweight_max_abs_over_absmax": 0.0023356239188215265,
+            "forward_max_abs": 0.01552825644384992,
+            "forward_correctly_rounded_fraction": 0.9999905228614807
+          },
+          "vLLM GemmaRMSNorm (forward only)": {
+            "forward_max_abs": 0.01552825644384992,
+            "forward_correctly_rounded_fraction": 0.9999905228614807
+          },
+          "FlashInfer gemma_rmsnorm (forward only)": {
+            "forward_max_abs": 0.01552825644384992,
+            "forward_correctly_rounded_fraction": 0.9999886155128479
+          }
+        },
+        "4096": {
+          "rl-kernel CUDA": {
+            "dx_max_abs_over_absmax": 0.0027934846392837836,
+            "dweight_max_abs_over_absmax": 0.002058919545322908,
+            "forward_max_abs": 0.015599696412158082,
+            "forward_correctly_rounded_fraction": 0.999991774559021
+          },
+          "rl-kernel PyTorch reference": {
+            "dx_max_abs_over_absmax": 0.0027934846392837836,
+            "dweight_max_abs_over_absmax": 0.002058919545322908,
+            "forward_max_abs": 0.015599696412158082,
+            "forward_correctly_rounded_fraction": 0.9999927282333374
+          },
+          "transformers Qwen3NextRMSNorm": {
+            "dx_max_abs_over_absmax": 0.0027934846392837836,
+            "dweight_max_abs_over_absmax": 0.002058919545322908,
+            "forward_max_abs": 0.015599696412158082,
+            "forward_correctly_rounded_fraction": 0.9999921321868896
+          },
+          "vLLM GemmaRMSNorm (forward only)": {
+            "forward_max_abs": 0.015599696412158082,
+            "forward_correctly_rounded_fraction": 0.9999921321868896
+          },
+          "FlashInfer gemma_rmsnorm (forward only)": {
+            "forward_max_abs": 0.015599696412158082,
+            "forward_correctly_rounded_fraction": 0.999992847442627
+          }
+        }
+      },
+      "row_invariance": {
+        "rl-kernel CUDA": {
+          "rows_checked": 768,
+          "forward_rows_differing": 0,
+          "dx_rows_differing": 0,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "rl-kernel PyTorch reference": {
+          "rows_checked": 768,
+          "forward_rows_differing": 0,
+          "dx_rows_differing": 12,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "transformers Qwen3NextRMSNorm": {
+          "rows_checked": 768,
+          "forward_rows_differing": 2,
+          "dx_rows_differing": 11,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "vLLM GemmaRMSNorm (forward only)": {
+          "rows_checked": 768,
+          "forward_rows_differing": 2,
+          "dx_rows_differing": null,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "FlashInfer gemma_rmsnorm (forward only)": {
+          "rows_checked": 768,
+          "forward_rows_differing": 0,
+          "dx_rows_differing": null,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        }
+      },
+      "latency": {
+        "rl-kernel CUDA": {
+          "1024": {
+            "forward_us": 24.70399998128414,
+            "backward_us": 568.7519907951355
+          },
+          "4096": {
+            "forward_us": 39.16800022125244,
+            "backward_us": 1259.3119740486145
+          },
+          "16384": {
+            "forward_us": 122.36800044775009,
+            "backward_us": 7879.024028778076
+          },
+          "65536": {
+            "forward_us": 425.80799758434296,
+            "backward_us": 30937.551498413086
+          }
+        },
+        "rl-kernel PyTorch reference": {
+          "1024": {
+            "forward_us": 70.54400071501732,
+            "backward_us": 644.7039842605591
+          },
+          "4096": {
+            "forward_us": 155.90400248765945,
+            "backward_us": 624.2719888687134
+          },
+          "16384": {
+            "forward_us": 577.5039792060852,
+            "backward_us": 986.7520034313202
+          },
+          "65536": {
+            "forward_us": 2148.0319499969482,
+            "backward_us": 2990.224003791809
+          }
+        },
+        "transformers Qwen3NextRMSNorm": {
+          "1024": {
+            "forward_us": 69.34399902820587,
+            "backward_us": 608.5599958896637
+          },
+          "4096": {
+            "forward_us": 117.3119992017746,
+            "backward_us": 606.3359975814819
+          },
+          "16384": {
+            "forward_us": 407.50400722026825,
+            "backward_us": 1027.888000011444
+          },
+          "65536": {
+            "forward_us": 1449.4240283966064,
+            "backward_us": 3144.6080207824707
+          }
+        },
+        "vLLM GemmaRMSNorm (forward only)": {
+          "1024": {
+            "forward_us": 65.61600044369698
+          },
+          "4096": {
+            "forward_us": 118.40000003576279
+          },
+          "16384": {
+            "forward_us": 405.90400993824005
+          },
+          "65536": {
+            "forward_us": 1449.6000409126282
+          }
+        },
+        "FlashInfer gemma_rmsnorm (forward only)": {
+          "1024": {
+            "forward_us": 14.448000118136406
+          },
+          "4096": {
+            "forward_us": 16.24000072479248
+          },
+          "16384": {
+            "forward_us": 32.207999378442764
+          },
+          "65536": {
+            "forward_us": 90.55999666452408
+          }
+        }
+      }
+    }
+  }
+}

From 024823d81ec15c5722c1bcdc3e94cf102220bfa0 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 8 Oct 2026 18:33:00 +0000
Subject: [PATCH 43/70] docs(norm): gated RMSNorm evidence vs existing
 implementations on B200

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/operators/qwen3-next-rms-norm-gated.md   |  28 ++
 .../figure-gated_rmsnorm.png                  | Bin 0 -> 246981 bytes
 .../figure-zero_centred_rmsnorm.png           | Bin 0 -> 241545 bytes
 .../report.json                               | 400 ++++++++++++++++++
 4 files changed, 428 insertions(+)
 create mode 100644 docs/usage/evidence/qwen3-next-rms-norm-gated-b200/figure-gated_rmsnorm.png
 create mode 100644 docs/usage/evidence/qwen3-next-rms-norm-gated-b200/figure-zero_centred_rmsnorm.png
 create mode 100644 docs/usage/evidence/qwen3-next-rms-norm-gated-b200/report.json

diff --git a/docs/operators/qwen3-next-rms-norm-gated.md b/docs/operators/qwen3-next-rms-norm-gated.md
index 49221c8a0..ba5d13501 100644
--- a/docs/operators/qwen3-next-rms-norm-gated.md
+++ b/docs/operators/qwen3-next-rms-norm-gated.md
@@ -119,6 +119,34 @@ python scripts/check_operator.py --op rms_norm_gated --candidate cuda \
     --device cuda --dtype bf16 --check-grad
 ```
 
+## Evidence
+
+![gated RMSNorm vs existing implementations on B200](../usage/evidence/qwen3-next-rms-norm-gated-b200/figure-gated_rmsnorm.png)
+
+[`report.json`](../usage/evidence/qwen3-next-rms-norm-gated-b200/report.json) was written by
+`scripts/qwen3_next_norm_evidence.py` from a clean tree at `822b085`, on an otherwise idle
+B200 (torch 2.13.0+cu130, transformers 5.17.0, vLLM 0.30.0). Head dim 128, BF16; the FP64
+golden uses vLLM's convention. The same report re-measures the zero-centred op
+([figure](../usage/evidence/qwen3-next-rms-norm-gated-b200/figure-zero_centred_rmsnorm.png)).
+
+| | rl-kernel CUDA | PyTorch reference | transformers (cast-first) | vLLM `RMSNormGated`* |
+|---|---|---|---|---|
+| rows differing alone vs in a batch, forward / `dx`,`dgate` (of 768) | **0 / 0** | 0 / 0 | 0 / 0 | 0 / n/a |
+| forward elements correctly rounded vs the golden | **99.999%** | 99.999% | 65.6% | 99.999% |
+| forward, 262144 rows | 235 µs | 774 µs | 697 µs | **81 µs** |
+| backward, 262144 rows | 114 ms | **1.4 ms** | 1.5 ms | n/a |
+
+\* forward only, no backward.
+
+- **Every implementation is row-invariant** for this op.
+- **transformers computes a different function:** it casts to BF16 before the weight
+  multiply, so 34% of its forward elements differ from vLLM's convention, and its gradients
+  are about twice as far from the golden.
+- **The backward is about 80× slower than transformers at 262144 rows**, for the same reason
+  as the zero-centred op: `dweight` (128 columns here) is folded over the rows in ascending
+  order, one thread per column, so that it meets the gradient-invariance contract's
+  singleton-aggregate check bitwise.
+
 ## Tests
 
 ```bash
diff --git a/docs/usage/evidence/qwen3-next-rms-norm-gated-b200/figure-gated_rmsnorm.png b/docs/usage/evidence/qwen3-next-rms-norm-gated-b200/figure-gated_rmsnorm.png
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diff --git a/docs/usage/evidence/qwen3-next-rms-norm-gated-b200/report.json b/docs/usage/evidence/qwen3-next-rms-norm-gated-b200/report.json
new file mode 100644
index 000000000..8a931b315
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-rms-norm-gated-b200/report.json
@@ -0,0 +1,400 @@
+{
+  "kind": "qwen3_next_norm_evidence",
+  "rfc": "RL-Align/RL-Kernel#428",
+  "git_commit": "822b085cd5b042661b2bda021414a2caa796bef0",
+  "git_dirty": false,
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "capability": [
+      10,
+      0
+    ],
+    "torch": "2.13.0+cu130",
+    "cuda": "13.0",
+    "python": "3.12.14",
+    "transformers": "5.17.0",
+    "vllm": "0.30.0",
+    "flashinfer": "0.6.18.post1"
+  },
+  "eps": 1e-06,
+  "dtype": "bfloat16",
+  "ops": {
+    "zero_centred_rmsnorm": {
+      "hidden": 2048,
+      "accuracy": {
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+          "rl-kernel CUDA": {
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+            "dweight_max_abs_over_absmax": 0.0023356239188215265,
+            "forward_max_abs": 0.01552825644384992,
+            "forward_correctly_rounded_fraction": 0.9999942779541016
+          },
+          "rl-kernel PyTorch reference": {
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+            "forward_max_abs": 0.01552825644384992,
+            "forward_correctly_rounded_fraction": 0.9999905228614807
+          },
+          "transformers Qwen3NextRMSNorm": {
+            "dx_max_abs_over_absmax": 0.002701418159021442,
+            "dweight_max_abs_over_absmax": 0.0023356239188215265,
+            "forward_max_abs": 0.01552825644384992,
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+          "vLLM GemmaRMSNorm (forward only)": {
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+          },
+          "FlashInfer gemma_rmsnorm (forward only)": {
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+            "forward_correctly_rounded_fraction": 0.9999886155128479
+          }
+        },
+        "4096": {
+          "rl-kernel CUDA": {
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+            "forward_max_abs": 0.015599696412158082,
+            "forward_correctly_rounded_fraction": 0.999991774559021
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+          "rl-kernel PyTorch reference": {
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+            "forward_max_abs": 0.015599696412158082,
+            "forward_correctly_rounded_fraction": 0.9999927282333374
+          },
+          "transformers Qwen3NextRMSNorm": {
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+            "dweight_max_abs_over_absmax": 0.002058919545322908,
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+          "vLLM GemmaRMSNorm (forward only)": {
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+            "forward_correctly_rounded_fraction": 0.9999921321868896
+          },
+          "FlashInfer gemma_rmsnorm (forward only)": {
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+            "forward_correctly_rounded_fraction": 0.999992847442627
+          }
+        }
+      },
+      "row_invariance": {
+        "rl-kernel CUDA": {
+          "rows_checked": 768,
+          "forward_rows_differing": 0,
+          "dx_rows_differing": 0,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "rl-kernel PyTorch reference": {
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+          "forward_rows_differing": 0,
+          "dx_rows_differing": 12,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "transformers Qwen3NextRMSNorm": {
+          "rows_checked": 768,
+          "forward_rows_differing": 2,
+          "dx_rows_differing": 11,
+          "batch_rows": 4096,
+          "seeds": [
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+            4,
+            5
+          ]
+        },
+        "vLLM GemmaRMSNorm (forward only)": {
+          "rows_checked": 768,
+          "forward_rows_differing": 2,
+          "dx_rows_differing": null,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "FlashInfer gemma_rmsnorm (forward only)": {
+          "rows_checked": 768,
+          "forward_rows_differing": 0,
+          "dx_rows_differing": null,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        }
+      },
+      "latency": {
+        "rl-kernel CUDA": {
+          "1024": {
+            "forward_us": 25.200000032782555,
+            "backward_us": 556.6880106925964
+          },
+          "4096": {
+            "forward_us": 39.503999054431915,
+            "backward_us": 1260.4320049285889
+          },
+          "16384": {
+            "forward_us": 122.41600081324577,
+            "backward_us": 7879.663944244385
+          },
+          "65536": {
+            "forward_us": 427.5680035352707,
+            "backward_us": 30925.631523132324
+          }
+        },
+        "rl-kernel PyTorch reference": {
+          "1024": {
+            "forward_us": 71.45600020885468,
+            "backward_us": 595.3599810600281
+          },
+          "4096": {
+            "forward_us": 156.92799538373947,
+            "backward_us": 537.9360020160675
+          },
+          "16384": {
+            "forward_us": 578.4800052642822,
+            "backward_us": 992.5920069217682
+          },
+          "65536": {
+            "forward_us": 2150.12788772583,
+            "backward_us": 2983.504056930542
+          }
+        },
+        "transformers Qwen3NextRMSNorm": {
+          "1024": {
+            "forward_us": 70.25599852204323,
+            "backward_us": 599.3280112743378
+          },
+          "4096": {
+            "forward_us": 118.12799796462059,
+            "backward_us": 541.1999821662903
+          },
+          "16384": {
+            "forward_us": 409.2479944229126,
+            "backward_us": 1025.5680084228516
+          },
+          "65536": {
+            "forward_us": 1452.351987361908,
+            "backward_us": 3140.112042427063
+          }
+        },
+        "vLLM GemmaRMSNorm (forward only)": {
+          "1024": {
+            "forward_us": 67.4239993095398
+          },
+          "4096": {
+            "forward_us": 119.00799721479416
+          },
+          "16384": {
+            "forward_us": 407.3439985513687
+          },
+          "65536": {
+            "forward_us": 1451.9200325012207
+          }
+        },
+        "FlashInfer gemma_rmsnorm (forward only)": {
+          "1024": {
+            "forward_us": 14.944000169634819
+          },
+          "4096": {
+            "forward_us": 17.008000053465366
+          },
+          "16384": {
+            "forward_us": 32.94399939477444
+          },
+          "65536": {
+            "forward_us": 90.43200314044952
+          }
+        }
+      }
+    },
+    "gated_rmsnorm": {
+      "hidden": 128,
+      "accuracy": {
+        "257": {
+          "rl-kernel CUDA": {
+            "dx_max_abs_over_absmax": 0.0018348192997423702,
+            "dweight_max_abs_over_absmax": 0.0021439847223899285,
+            "dgate_max_abs_over_absmax": 0.0027467772330278155,
+            "forward_max_abs": 0.01814649124332668,
+            "forward_correctly_rounded_fraction": 0.9999392032623291
+          },
+          "rl-kernel PyTorch reference": {
+            "dx_max_abs_over_absmax": 0.0018348192997423702,
+            "dweight_max_abs_over_absmax": 0.0021439847223899285,
+            "dgate_max_abs_over_absmax": 0.0027467772330278155,
+            "forward_max_abs": 0.01814649124332668,
+            "forward_correctly_rounded_fraction": 0.9999392032623291
+          },
+          "transformers Qwen3NextRMSNormGated (cast-first)": {
+            "dx_max_abs_over_absmax": 0.004283006956510838,
+            "dweight_max_abs_over_absmax": 0.0053612828842400685,
+            "dgate_max_abs_over_absmax": 0.004464247864523204,
+            "forward_max_abs": 0.041932799726739134,
+            "forward_correctly_rounded_fraction": 0.6546084880828857
+          },
+          "vLLM RMSNormGated (forward only)": {
+            "forward_max_abs": 0.01814649124332668,
+            "forward_correctly_rounded_fraction": 0.9999392032623291
+          }
+        },
+        "4096": {
+          "rl-kernel CUDA": {
+            "dx_max_abs_over_absmax": 0.0028905074752616756,
+            "dweight_max_abs_over_absmax": 0.0017551573197679504,
+            "dgate_max_abs_over_absmax": 0.002440494893713147,
+            "forward_max_abs": 0.028763107099734952,
+            "forward_correctly_rounded_fraction": 0.9999942779541016
+          },
+          "rl-kernel PyTorch reference": {
+            "dx_max_abs_over_absmax": 0.0028905074752616756,
+            "dweight_max_abs_over_absmax": 0.0017551573197679504,
+            "dgate_max_abs_over_absmax": 0.002440494893713147,
+            "forward_max_abs": 0.028763107099734952,
+            "forward_correctly_rounded_fraction": 0.9999942779541016
+          },
+          "transformers Qwen3NextRMSNormGated (cast-first)": {
+            "dx_max_abs_over_absmax": 0.006106839188157589,
+            "dweight_max_abs_over_absmax": 0.002835271706901685,
+            "dgate_max_abs_over_absmax": 0.003959744684751284,
+            "forward_max_abs": 0.06659807537061546,
+            "forward_correctly_rounded_fraction": 0.6562480926513672
+          },
+          "vLLM RMSNormGated (forward only)": {
+            "forward_max_abs": 0.028763107099734952,
+            "forward_correctly_rounded_fraction": 0.9999923706054688
+          }
+        }
+      },
+      "row_invariance": {
+        "rl-kernel CUDA": {
+          "rows_checked": 768,
+          "forward_rows_differing": 0,
+          "dx_rows_differing": 0,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "rl-kernel PyTorch reference": {
+          "rows_checked": 768,
+          "forward_rows_differing": 0,
+          "dx_rows_differing": 0,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "transformers Qwen3NextRMSNormGated (cast-first)": {
+          "rows_checked": 768,
+          "forward_rows_differing": 0,
+          "dx_rows_differing": 0,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        },
+        "vLLM RMSNormGated (forward only)": {
+          "rows_checked": 768,
+          "forward_rows_differing": 0,
+          "dx_rows_differing": null,
+          "batch_rows": 4096,
+          "seeds": [
+            3,
+            4,
+            5
+          ]
+        }
+      },
+      "latency": {
+        "rl-kernel CUDA": {
+          "4096": {
+            "forward_us": 20.800000056624413,
+            "backward_us": 1269.7439789772034
+          },
+          "16384": {
+            "forward_us": 27.168000116944313,
+            "backward_us": 3861.135959625244
+          },
+          "65536": {
+            "forward_us": 64.00000303983688,
+            "backward_us": 14388.879776000977
+          },
+          "262144": {
+            "forward_us": 235.23200303316116,
+            "backward_us": 114274.65438842773
+          }
+        },
+        "rl-kernel PyTorch reference": {
+          "4096": {
+            "forward_us": 81.31200075149536,
+            "backward_us": 560.6080293655396
+          },
+          "16384": {
+            "forward_us": 89.72799777984619,
+            "backward_us": 550.2559840679169
+          },
+          "65536": {
+            "forward_us": 209.3760073184967,
+            "backward_us": 573.7600028514862
+          },
+          "262144": {
+            "forward_us": 774.8000025749207,
+            "backward_us": 1376.08003616333
+          }
+        },
+        "transformers Qwen3NextRMSNormGated (cast-first)": {
+          "4096": {
+            "forward_us": 90.01599997282028,
+            "backward_us": 577.888011932373
+          },
+          "16384": {
+            "forward_us": 91.80799871683121,
+            "backward_us": 601.1359989643097
+          },
+          "65536": {
+            "forward_us": 196.46400213241577,
+            "backward_us": 623.5039830207825
+          },
+          "262144": {
+            "forward_us": 697.2479820251465,
+            "backward_us": 1528.384029865265
+          }
+        },
+        "vLLM RMSNormGated (forward only)": {
+          "4096": {
+            "forward_us": 49.775999039411545
+          },
+          "16384": {
+            "forward_us": 51.024001091718674
+          },
+          "65536": {
+            "forward_us": 50.20799860358238
+          },
+          "262144": {
+            "forward_us": 81.4880020916462
+          }
+        }
+      }
+    }
+  }
+}

From b4bb977cdf73150a543525e27bdc01ee2756a9aa Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Thu, 8 Oct 2026 18:33:10 +0000
Subject: [PATCH 44/70] docs(norm): correct the gated backward slowdown ratio

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/operators/qwen3-next-rms-norm-gated.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/operators/qwen3-next-rms-norm-gated.md b/docs/operators/qwen3-next-rms-norm-gated.md
index ba5d13501..41d7616a1 100644
--- a/docs/operators/qwen3-next-rms-norm-gated.md
+++ b/docs/operators/qwen3-next-rms-norm-gated.md
@@ -142,7 +142,7 @@ golden uses vLLM's convention. The same report re-measures the zero-centred op
 - **transformers computes a different function:** it casts to BF16 before the weight
   multiply, so 34% of its forward elements differ from vLLM's convention, and its gradients
   are about twice as far from the golden.
-- **The backward is about 80× slower than transformers at 262144 rows**, for the same reason
+- **The backward is about 75× slower than transformers at 262144 rows**, for the same reason
   as the zero-centred op: `dweight` (128 columns here) is folded over the rows in ascending
   order, one thread per column, so that it meets the gradient-invariance contract's
   singleton-aggregate check bitwise.

From 2e950f386bd3c4c2c07f41472f48878be94ea3b8 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 01:43:43 +0000
Subject: [PATCH 45/70] feat(qwen3-next): MoE route/combine contract with a
 batch-invariant grouped expert GEMM

RFC #428 moe_route_combine_contract (CUDA).

- Routing: FP32 router through the pinned vLLM batch-invariant GEMM, a
  fixed-order softmax, stable descending argsort (ties go to the lower
  expert id) and FP32 renormalisation of the ten selected weights.
- Experts: per-route gate_up and down projections through vLLM 0.30.0's
  fused_moe Triton kernel with the batch-invariant tile passed explicitly
  (BLOCK 64/64/32, SPLIT_K=1); no routed-weight multiply and no sum inside
  the kernel, so every (token, route) row has one writer. Bitwise equal to
  the previous per-expert GEMM loop at TP4 and TP1 widths; fails closed if
  VLLM_TRITON_USE_TD is enabled.
- Combine: ten ordered FP32 multiply-adds and one BF16 cast; no atomics.
- Backward: per-expert pinned GEMMs into one dense gradient buffer; dx is
  summed in ascending expert order.
- TP4 block: HF load/export for all 512 experts, replicated router and
  shared-expert gate, Megatron-style copy/reduce autograd boundaries moved
  into qwen3_next_tp.py.
- Prior-art runner and plot comparing HF transformers, vLLM fused_moe
  (BI=0/1) and FlashInfer cutlass_fused_moe; TP4 real-weight gate script.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/ci.yml                      |   2 +-
 .github/workflows/qwen3-next-provider-gpu.yml |  11 +-
 rl_engine/integrations/qwen3_next_forward.py  | 338 ++++++++++++++
 rl_engine/integrations/qwen3_next_tp.py       |  65 +++
 .../integrations/qwen3_next_tp_blocks.py      | 138 ++++++
 rl_engine/testing/qwen3_next_moe_prior_art.py | 440 ++++++++++++++++++
 rl_engine/testing/tensor_identity.py          |  38 ++
 scripts/plot_qwen3_next_moe_prior_art.py      | 159 +++++++
 scripts/qwen3_next_moe_prior_art.py           |  87 ++++
 scripts/qwen3_next_tp_moe_check.py            | 207 ++++++++
 tests/check_qwen3_next_forward.py             | 238 ++++++++++
 tests/test_qwen3_next_forward_contract.py     |  62 +++
 tests/test_qwen3_next_tp_blocks.py            |  93 ++++
 tests/test_tensor_identity.py                 |  41 ++
 14 files changed, 1915 insertions(+), 4 deletions(-)
 create mode 100644 rl_engine/integrations/qwen3_next_forward.py
 create mode 100644 rl_engine/integrations/qwen3_next_tp.py
 create mode 100644 rl_engine/integrations/qwen3_next_tp_blocks.py
 create mode 100644 rl_engine/testing/qwen3_next_moe_prior_art.py
 create mode 100644 rl_engine/testing/tensor_identity.py
 create mode 100644 scripts/plot_qwen3_next_moe_prior_art.py
 create mode 100644 scripts/qwen3_next_moe_prior_art.py
 create mode 100644 scripts/qwen3_next_tp_moe_check.py
 create mode 100644 tests/check_qwen3_next_forward.py
 create mode 100644 tests/test_qwen3_next_forward_contract.py
 create mode 100644 tests/test_qwen3_next_tp_blocks.py
 create mode 100644 tests/test_tensor_identity.py

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 814a4ae22..2fb92d641 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -67,7 +67,7 @@ jobs:
         run: |
           python -m pytest rl_engine/tests/test_dispatch.py -v
           PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 python -m pytest tests/test_attention_correctness.py -q -rs
-          python -m pytest tests/test_forward_invariance.py tests/test_tolerance_contract.py tests/test_ws1_workload.py tests/test_gradient_invariance.py tests/test_elementwise_inventory.py tests/test_four_judgment_matrix.py tests/test_op_checks.py tests/test_operator_inputs.py tests/test_profiler.py tests/test_kv_consistency.py tests/test_ws1_qwen3_dense.py tests/test_ws1_chain_integration.py tests/test_qwen3_next_norm.py tests/test_gdn_state_contract.py -q
+          python -m pytest tests/test_forward_invariance.py tests/test_tolerance_contract.py tests/test_ws1_workload.py tests/test_gradient_invariance.py tests/test_elementwise_inventory.py tests/test_four_judgment_matrix.py tests/test_op_checks.py tests/test_operator_inputs.py tests/test_profiler.py tests/test_kv_consistency.py tests/test_ws1_qwen3_dense.py tests/test_ws1_chain_integration.py tests/test_qwen3_next_norm.py tests/test_gdn_state_contract.py tests/test_tensor_identity.py tests/test_qwen3_next_forward_contract.py tests/test_qwen3_next_tp_blocks.py -q
 
       - name: Run Cross-Configuration Contract Tests (CPU-safe)
         run: |
diff --git a/.github/workflows/qwen3-next-provider-gpu.yml b/.github/workflows/qwen3-next-provider-gpu.yml
index 146818019..691489402 100644
--- a/.github/workflows/qwen3-next-provider-gpu.yml
+++ b/.github/workflows/qwen3-next-provider-gpu.yml
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
-# RFC #428 Qwen3-Next provider comparisons: the C1/C3/C4 norm providers and the C6
-# GDN decode-step goldens, each checked against the real vLLM 0.30.0 provider.
+# RFC #428 Qwen3-Next provider comparisons: the C1/C3/C4 norm providers, the C6
+# GDN decode-step goldens and the shared GEMM/MoE route-combine provider, each
+# checked against the real vLLM 0.30.0 provider.
 #
 # These check_ files import vLLM, so the default pytest collection never runs them
 # (tests/test_framework_operator_integrations.py asserts vLLM is not imported). This
@@ -19,6 +20,7 @@ on:
     branches: [main]
     paths:
       - "rl_engine/kernels/ops/**"
+      - "rl_engine/integrations/qwen3_next*"
       - "rl_engine/testing/qwen3_next*"
       - "tests/check_*qwen3_next*.py"
       - "tests/check_gdn_recurrent_golden.py"
@@ -78,5 +80,8 @@ jobs:
           print("rl_engine._C:", loaded)
           PY
       - name: Check pinned providers in their own process
+        # The FP32 router needs IEEE Triton dots from process start.
+        env:
+          TRITON_F32_DEFAULT: ieee
         run: |
-          python3 -m pytest -q tests/check_qwen3_next_norm_providers.py tests/check_gdn_recurrent_golden.py
+          python3 -m pytest -q tests/check_qwen3_next_norm_providers.py tests/check_gdn_recurrent_golden.py tests/check_qwen3_next_forward.py
diff --git a/rl_engine/integrations/qwen3_next_forward.py b/rl_engine/integrations/qwen3_next_forward.py
new file mode 100644
index 000000000..b5a1e20e0
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_forward.py
@@ -0,0 +1,338 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Explicit CUDA forward primitives for the Qwen3-Next shared TP4 profile.
+
+These functions do not install framework hooks or advertise engine capabilities.
+The caller owns parameter sharding, TP collectives, positions and cache identity.
+Weights remain BF16; only the router explicitly computes in FP32, as in VIME's
+Qwen3-Next configuration. No CPU or alternate-kernel fallback is permitted.
+"""
+
+from dataclasses import dataclass
+from functools import lru_cache
+from importlib.metadata import version
+
+import torch
+import torch.nn.functional as F
+from torch.autograd.function import once_differentiable
+
+FORWARD_PROVIDER_ID = "qwen3-next-shared-cuda-forward-v1"
+
+
+@lru_cache(maxsize=1)
+def _vllm_linear():
+    if version("vllm") != "0.30.0":
+        raise RuntimeError("Shared Qwen3-Next linear requires pinned vLLM 0.30.0")
+    from vllm.model_executor.determinism.batch_invariant import linear_batch_invariant
+
+    return linear_batch_invariant
+
+
+# The batch-invariant tile vLLM itself selects under VLLM_BATCH_INVARIANT=1
+# (fused_moe.get_default_config), passed explicitly so no environment variable,
+# tuned-config file or token count can change it.
+_GROUPED_CONFIG = {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 32,
+    "GROUP_SIZE_M": 8,
+    "SPLIT_K": 1,
+}
+
+
+@lru_cache(maxsize=1)
+def _vllm_grouped():
+    if version("vllm") != "0.30.0":
+        raise RuntimeError("Shared Qwen3-Next experts require pinned vLLM 0.30.0")
+    from vllm.model_executor.layers.fused_moe.fused_moe import (
+        _prepare_expert_assignment,
+        dispatch_fused_moe_kernel,
+    )
+    from vllm.model_executor.layers.fused_moe.utils import resolve_moe_use_td
+
+    return _prepare_expert_assignment, dispatch_fused_moe_kernel, resolve_moe_use_td
+
+
+def grouped_route_linear(rows, weight, route_indices):
+    """Per-route ``rows @ weight[expert].T`` through vLLM's fused MoE Triton kernel.
+
+    ``rows`` is [T, K] (one row per token, shared by its ten routes) or
+    [T*10, K] (one row per route); the result is [T, 10, N] in route order.
+    No routed weight is applied and nothing is summed: every output row has
+    exactly one writer, and its K loop has the fixed order of the fixed tile.
+    """
+    prepare, dispatch, use_td = _vllm_grouped()
+    if use_td():
+        raise RuntimeError("VLLM_TRITON_USE_TD changes the MoE kernel; strict mode requires 0")
+    import triton.language as tl
+
+    tokens, top_k = route_indices.shape
+    out = rows.new_empty((tokens, top_k, weight.shape[1]))
+    if tokens == 0:
+        return out
+    indices = route_indices.to(torch.int32).contiguous()
+    sorted_ids, expert_ids, padded = prepare(
+        indices, _GROUPED_CONFIG, tokens, top_k, weight.shape[0], None, ignore_invalid_experts=True
+    )
+    dispatch(
+        rows.contiguous(),
+        weight,
+        out,
+        None,
+        None,
+        None,
+        None,
+        sorted_ids,
+        expert_ids,
+        padded,
+        False,
+        top_k if rows.shape[0] == tokens else 1,
+        _GROUPED_CONFIG,
+        compute_type=tl.bfloat16,
+        use_fp8_w8a8=False,
+        use_int8_w8a8=False,
+        use_int8_w8a16=False,
+        use_int4_w4a16=False,
+        per_channel_quant=False,
+    )
+    return out
+
+
+def _cuda_tensors(*tensors):
+    if torch.version.hip is not None or any(not value.is_cuda for value in tensors):
+        raise ValueError("Shared Qwen3-Next forward requires CUDA tensors; no CPU fallback")
+    if any(value.device != tensors[0].device for value in tensors[1:]):
+        raise ValueError("Shared Qwen3-Next tensors must be on the same device")
+
+
+class _SharedLinear(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, weight, bias):
+        ctx.save_for_backward(x, weight)
+        ctx.has_bias = bias is not None
+        if x.shape[0] == 0:
+            return x.new_empty((0, weight.shape[0]))
+        with torch.cuda.device(x.device):
+            return _vllm_linear()(x, weight, bias)
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        x, weight = ctx.saved_tensors
+        if x.shape[0] == 0:
+            return (
+                torch.empty_like(x) if ctx.needs_input_grad[0] else None,
+                torch.zeros_like(weight) if ctx.needs_input_grad[1] else None,
+                weight.new_zeros(weight.shape[0]) if ctx.has_bias else None,
+            )
+        grad = grad_output.contiguous()
+        with torch.cuda.device(x.device):
+            linear = _vllm_linear()
+            dx = linear(grad, weight.t()) if ctx.needs_input_grad[0] else None
+            dw = linear(grad.t(), x.t()) if ctx.needs_input_grad[1] else None
+            db = grad.sum(dim=0) if ctx.has_bias and ctx.needs_input_grad[2] else None
+        return dx, dw, db
+
+
+def shared_linear(x, weight, bias=None):
+    """Pinned vLLM deterministic GEMM with an explicit first-order VJP.
+
+    x is [..., K], weight is [N, K], output is [..., N]. Operands have one
+    common BF16 or FP32 dtype. Empty token dimensions preserve autograd.
+    Backward executes dX=dY@W and dW=dY.T@X through the same fixed provider.
+    """
+    _cuda_tensors(x, weight, *(() if bias is None else (bias,)))
+    if x.ndim < 2 or weight.ndim != 2 or x.shape[-1] != weight.shape[1]:
+        raise ValueError("Shared linear requires x[..., K] and weight[N, K]")
+    if x.dtype not in (torch.bfloat16, torch.float32) or weight.dtype != x.dtype:
+        raise ValueError("Shared linear operands must have one common BF16 or FP32 dtype")
+    if x.dtype == torch.float32:
+        import triton
+
+        if triton.knobs.language.fp32_default != "ieee":
+            raise RuntimeError(
+                "FP32 shared linear requires TRITON_F32_DEFAULT=ieee at process startup"
+            )
+    if min(weight.shape) < 1:
+        raise ValueError("Shared linear weight dimensions must be positive")
+    if bias is not None and (bias.shape != (weight.shape[0],) or bias.dtype != x.dtype):
+        raise ValueError("Shared linear bias must be [N] with the input dtype")
+    out = _SharedLinear.apply(x.reshape(-1, x.shape[-1]), weight, bias)
+    return out.reshape(*x.shape[:-1], weight.shape[0])
+
+
+def shared_router(x, weight):
+    """Compute the 512-expert FP32 router from BF16 inputs/parameter storage."""
+    _cuda_tensors(x, weight)
+    if x.dtype != torch.bfloat16 or weight.dtype != torch.bfloat16:
+        raise ValueError("Qwen3-Next router input and stored weight must be BF16")
+    if x.ndim != 2 or weight.shape != (512, x.shape[-1]):
+        raise ValueError("Qwen3-Next router requires x[T, H] and weight[512, H]")
+    return shared_linear(x.float(), weight.float())
+
+
+@dataclass(frozen=True)
+class Routing:
+    """Descending probability routes; equal scores prefer smaller expert IDs."""
+
+    weights: torch.Tensor
+    indices: torch.Tensor
+
+
+def fixed_order_row_sum(values):
+    """Sum the last dimension by a pairwise tree of elementwise adds.
+
+    ``torch.sum`` picks its reduction order from the tensor shape, so the same
+    row summed inside an 8-row and a 32-row tensor can differ by one ulp unless
+    vLLM's batch-invariant aten overrides are installed. Qualification runs
+    execute with ``VLLM_BATCH_INVARIANT=0``, where those overrides are absent.
+    Elementwise adds have no such freedom: every row is summed in one fixed
+    tree whatever the row count. Odd widths are padded with exact zeros.
+    """
+    while values.shape[-1] > 1:
+        if values.shape[-1] % 2:
+            values = torch.cat((values, values.new_zeros((*values.shape[:-1], 1))), dim=-1)
+        values = values[..., 0::2] + values[..., 1::2]
+    return values
+
+
+def fixed_order_softmax(logits):
+    """Row softmax from a max subtraction, exp and one fixed-order row sum."""
+    shifted = logits - torch.amax(logits, dim=-1, keepdim=True)
+    exponentials = torch.exp(shifted)
+    return exponentials / fixed_order_row_sum(exponentials)
+
+
+def stable_top10_routes(router_logits):
+    """Select ten distinct experts and normalize their FP32 probabilities.
+
+    The full 512-way softmax precedes selection, preserving Qwen's normalized
+    top-k arithmetic. Stable sort fixes the otherwise ambiguous tie boundary.
+    Indices are discrete; gradients flow through the selected probabilities.
+    Both row sums use the fixed-order tree, so a token's route weights do not
+    depend on how many other tokens share its forward pass.
+    """
+    _cuda_tensors(router_logits)
+    if router_logits.ndim != 2 or router_logits.shape[1] != 512:
+        raise ValueError("Qwen3-Next routing requires logits[T, 512]")
+    if router_logits.dtype != torch.float32:
+        raise ValueError("Qwen3-Next router logits must be FP32")
+    if not torch.isfinite(router_logits).all():
+        raise ValueError("Qwen3-Next router logits must be finite")
+    probabilities = fixed_order_softmax(router_logits)
+    indices = torch.argsort(router_logits, dim=-1, descending=True, stable=True)[:, :10]
+    selected = probabilities.gather(1, indices)
+    return Routing(selected / fixed_order_row_sum(selected), indices)
+
+
+def combine_routes(expert_outputs, route_weights):
+    """Combine [T, 10, H] BF16 outputs by ten ordered FP32 multiply-adds.
+
+    There are no atomic sums. Each route's product and each addition is a
+    separate eager operation; the single BF16 cast follows the final route.
+    """
+    _cuda_tensors(expert_outputs, route_weights)
+    if expert_outputs.ndim != 3 or expert_outputs.shape[1] != 10:
+        raise ValueError("Qwen3-Next expert outputs must have shape [T, 10, H]")
+    if route_weights.shape != expert_outputs.shape[:2]:
+        raise ValueError("Route weights must have shape [T, 10]")
+    if expert_outputs.dtype != torch.bfloat16 or route_weights.dtype != torch.float32:
+        raise ValueError("Combine requires BF16 expert outputs and FP32 route weights")
+    output = expert_outputs[:, 0].float() * route_weights[:, 0, None]
+    for slot in range(1, 10):
+        output = output + expert_outputs[:, slot].float() * route_weights[:, slot, None]
+    return output.to(torch.bfloat16)
+
+
+def _expert_activation(gate_up_rows):
+    """SwiGLU in FP32 from the BF16 [gate, up] projection; returns its parts too."""
+    gate, up = gate_up_rows.float().chunk(2, dim=-1)
+    silu_gate = F.silu(gate)
+    return silu_gate * up, silu_gate, gate, up
+
+
+class _RoutedExperts(torch.autograd.Function):
+    """Grouped expert forward; per-expert backward into one dense gradient buffer."""
+
+    @staticmethod
+    def forward(ctx, x, gate_up, down, route_indices):
+        ctx.save_for_backward(x, gate_up, down, route_indices)
+        with torch.cuda.device(x.device):
+            projected = grouped_route_linear(x, gate_up, route_indices)
+            activated = _expert_activation(projected)[0]
+            activated = activated.to(x.dtype).reshape(-1, activated.shape[-1])
+            return grouped_route_linear(activated, down, route_indices)
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        x, gate_up, down, route_indices = ctx.saved_tensors
+        dx = torch.zeros_like(x) if ctx.needs_input_grad[0] else None
+        dgate_up = torch.zeros_like(gate_up) if ctx.needs_input_grad[1] else None
+        ddown = torch.zeros_like(down) if ctx.needs_input_grad[2] else None
+        top_k = route_indices.shape[1]
+        slots_by_expert = [[] for _ in range(gate_up.shape[0])]
+        for slot, expert in enumerate(route_indices.flatten().tolist()):
+            slots_by_expert[expert].append(slot)
+        grad_flat = grad_output.reshape(-1, x.shape[1])
+        with torch.cuda.device(x.device):
+            linear = _vllm_linear()
+            projected = grouped_route_linear(x, gate_up, route_indices)
+            projected = projected.reshape(-1, projected.shape[-1])
+            for expert, slots in enumerate(slots_by_expert):
+                if not slots:
+                    continue
+                slots = torch.tensor(slots, dtype=torch.long, device=x.device)
+                tokens = slots // top_k
+                activated, silu_gate, gate32, up32 = _expert_activation(
+                    projected.index_select(0, slots)
+                )
+                activated = activated.to(x.dtype)
+                grad = grad_flat.index_select(0, slots)
+                if ddown is not None:
+                    ddown[expert].copy_(linear(grad.t(), activated.t()))
+                if dx is None and dgate_up is None:
+                    continue
+                rows = x.index_select(0, tokens)
+                dactivated = linear(grad, down[expert].t()).float()
+                sigmoid = gate32.sigmoid()
+                dgate = dactivated * up32 * (sigmoid + gate32 * sigmoid * (1 - sigmoid))
+                dup = dactivated * silu_gate
+                dprojected = torch.cat((dgate, dup), dim=-1).to(x.dtype)
+                if dgate_up is not None:
+                    dgate_up[expert].copy_(linear(dprojected.t(), rows.t()))
+                if dx is not None:
+                    # Top-k has distinct experts, so tokens are unique within this
+                    # launch. Ascending expert order fixes each token's sum order.
+                    partial = linear(dprojected, gate_up[expert].t())
+                    dx.index_copy_(0, tokens, dx.index_select(0, tokens) + partial)
+        return dx, dgate_up, ddown, None
+
+
+def shared_moe(x, router_weight, gate_up_weights, down_weights):
+    """Evaluate TP-local routed experts; caller reduces the returned output.
+
+    Weights are [512, 2*I_local, H] (gate then up) and [512, H, I_local].
+    All experts belong to EP=1. Each (token, route) output row has exactly one
+    writer and the ten routes are combined in route order. Shared-expert and
+    TP-reduction ownership remains with the model adapter. The forward has no
+    host synchronisation; the backward builds per-expert row lists on the host.
+    """
+    _cuda_tensors(x, router_weight, gate_up_weights, down_weights)
+    if x.ndim != 2 or x.dtype != torch.bfloat16:
+        raise ValueError("Qwen3-Next MoE input must be BF16 [T, H]")
+    if gate_up_weights.ndim != 3 or down_weights.ndim != 3:
+        raise ValueError("Qwen3-Next expert weights must have three dimensions")
+    local_intermediate = down_weights.shape[-1]
+    if local_intermediate < 1:
+        raise ValueError("Expert intermediate dimension must be positive")
+    if gate_up_weights.shape != (512, 2 * local_intermediate, x.shape[1]):
+        raise ValueError("gate_up_weights must have shape [512, 2*I_local, H]")
+    if down_weights.shape != (512, x.shape[1], local_intermediate):
+        raise ValueError("down_weights must have shape [512, H, I_local]")
+    if gate_up_weights.dtype != x.dtype or down_weights.dtype != x.dtype:
+        raise ValueError("Qwen3-Next expert weights must remain BF16")
+    routes = stable_top10_routes(shared_router(x, router_weight))
+    outputs = _RoutedExperts.apply(x, gate_up_weights, down_weights, routes.indices)
+    return combine_routes(outputs, routes.weights), routes
diff --git a/rl_engine/integrations/qwen3_next_tp.py b/rl_engine/integrations/qwen3_next_tp.py
new file mode 100644
index 000000000..884a2d043
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_tp.py
@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Differentiable TP4 boundaries and parameter metadata shared by Qwen3-Next blocks.
+
+The forward copy and reduction follow Megatron's column/row-parallel autograd
+pair. Reductions go through ``collective_for_group`` so a strict deployment
+can pin the collective algorithm; nothing here selects one implicitly.
+"""
+
+import torch
+import torch.distributed as dist
+
+
+def _reduce(value, group):
+    from rl_engine.distributed.collectives import collective_for_group
+
+    collective = collective_for_group(group, min_size_bytes=value.numel() * value.element_size())
+    return collective.all_reduce(value.contiguous())
+
+
+class _CopyToTP(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, value, group):
+        ctx.group = group
+        return value
+
+    @staticmethod
+    def backward(ctx, gradient):
+        return _reduce(gradient, ctx.group), None
+
+
+class _ReduceFromTP(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, value, group):
+        return _reduce(value, group)
+
+    @staticmethod
+    def backward(ctx, gradient):
+        return gradient, None
+
+
+def _rank(rank):
+    if isinstance(rank, bool) or not isinstance(rank, int) or rank not in range(4):
+        raise ValueError("TP4 rank must be an integer in [0, 4)")
+
+
+def _weight(value, shape, name):
+    if value.dtype != torch.bfloat16 or value.shape != shape:
+        raise ValueError(f"{name} must be BF16 {shape}")
+
+
+def _tp_group(group):
+    if not dist.is_initialized() or dist.get_world_size(group) != 4:
+        raise ValueError("Qwen3-Next blocks require an initialized four-rank TP group")
+    return dist.group.WORLD if group is None else group
+
+
+def _parallel_parameter(parameter, dimension, *, stride=1, duplicate=False):
+    # MCore uses these attributes when computing the global optimizer gradient
+    # norm. A replicated copy updates normally but must not count twice.
+    parameter.tensor_model_parallel = True
+    parameter.partition_dim = dimension
+    parameter.partition_stride = stride
+    parameter.shared = duplicate
diff --git a/rl_engine/integrations/qwen3_next_tp_blocks.py b/rl_engine/integrations/qwen3_next_tp_blocks.py
new file mode 100644
index 000000000..1c80530d4
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_tp_blocks.py
@@ -0,0 +1,138 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Official Qwen3-Next TP4 MoE block for shared adapters.
+
+The block owns the local arithmetic and differentiable TP boundaries. It does
+not register an engine backend or advertise full-model acceptance.
+"""
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import nn
+
+from rl_engine.integrations.qwen3_next_forward import shared_linear, shared_moe
+from rl_engine.integrations.qwen3_next_tp import (
+    _CopyToTP,
+    _parallel_parameter,
+    _rank,
+    _ReduceFromTP,
+    _tp_group,
+    _weight,
+)
+from rl_engine.testing.tensor_identity import tensor_bitwise_equal
+
+
+def moe_hf_shapes():
+    result = {
+        "gate.weight": (512, 2048),
+        "shared_expert_gate.weight": (1, 2048),
+    }
+    for prefix in ("shared_expert", *(f"experts.{expert}" for expert in range(512))):
+        result[f"{prefix}.gate_proj.weight"] = (512, 2048)
+        result[f"{prefix}.up_proj.weight"] = (512, 2048)
+        result[f"{prefix}.down_proj.weight"] = (2048, 512)
+    return result
+
+
+def shard_moe_weight(name, value, rank):
+    """Shard one official HF tensor without materializing all expert weights."""
+    _rank(rank)
+    shapes = moe_hf_shapes()
+    if name not in shapes:
+        raise ValueError(f"Unknown Qwen3-Next MoE weight: {name}")
+    _weight(value, shapes[name], name)
+    if name in ("gate.weight", "shared_expert_gate.weight"):
+        return value
+    return value.chunk(4, dim=1 if name.endswith("down_proj.weight") else 0)[rank].contiguous()
+
+
+def assemble_moe_weight(name, values):
+    if len(values) != 4:
+        raise ValueError("Exactly four MoE weight shards are required")
+    shapes = moe_hf_shapes()
+    if name not in shapes:
+        raise ValueError(f"Unknown Qwen3-Next MoE weight: {name}")
+    if name in ("gate.weight", "shared_expert_gate.weight"):
+        for value in values:
+            _weight(value, shapes[name], name)
+        if any(not tensor_bitwise_equal(value, values[0]) for value in values[1:]):
+            raise ValueError(f"Replicated MoE weight differs across TP: {name}")
+        return values[0]
+    axis = 1 if name.endswith("down_proj.weight") else 0
+    shape = list(shapes[name])
+    shape[axis] //= 4
+    for value in values:
+        _weight(value, tuple(shape), name)
+    return torch.cat(tuple(values), dim=axis)
+
+
+class TP4MoE(nn.Module):
+    """All 512 experts at EP1 with intermediate width 128 per TP rank."""
+
+    def __init__(self, *, group, device):
+        super().__init__()
+        self.group = _tp_group(group)
+        self.rank = dist.get_rank(self.group)
+        factory = {"device": device, "dtype": torch.bfloat16}
+        self.gate = nn.Linear(2048, 512, bias=False, **factory)
+        self.experts = nn.Module()
+        self.experts.register_parameter(
+            "gate_up", nn.Parameter(torch.empty(512, 256, 2048, **factory))
+        )
+        self.experts.register_parameter(
+            "down", nn.Parameter(torch.empty(512, 2048, 128, **factory))
+        )
+        self.shared_expert = nn.Module()
+        self.shared_expert.gate_proj = nn.Linear(2048, 128, bias=False, **factory)
+        self.shared_expert.up_proj = nn.Linear(2048, 128, bias=False, **factory)
+        self.shared_expert.down_proj = nn.Linear(128, 2048, bias=False, **factory)
+        self.shared_expert_gate = nn.Linear(2048, 1, bias=False, **factory)
+        _parallel_parameter(self.experts.gate_up, 1, stride=2)
+        _parallel_parameter(self.experts.down, 2)
+        _parallel_parameter(self.shared_expert.gate_proj.weight, 0)
+        _parallel_parameter(self.shared_expert.up_proj.weight, 0)
+        _parallel_parameter(self.shared_expert.down_proj.weight, 1)
+
+    def _hf_tensor(self, name):
+        if name.startswith("experts."):
+            _, expert, projection, _ = name.split(".")
+            expert = int(expert)
+            if projection == "down_proj":
+                return self.experts.down[expert]
+            offset = 0 if projection == "gate_proj" else 128
+            return self.experts.gate_up[expert, offset : offset + 128]
+        obj = self
+        for part in name.split("."):
+            obj = getattr(obj, part)
+        return obj
+
+    def load_hf_weights(self, weights):
+        if set(weights) != set(moe_hf_shapes()):
+            raise ValueError("MoE weight names do not match the official HF contract")
+        with torch.no_grad():
+            for name in moe_hf_shapes():
+                self._hf_tensor(name).copy_(shard_moe_weight(name, weights[name], self.rank))
+
+    def export_local_hf_weights(self):
+        return {name: self._hf_tensor(name).detach() for name in moe_hf_shapes()}
+
+    def forward(self, hidden):
+        if hidden.ndim != 2 or hidden.shape[1] != 2048 or hidden.dtype != torch.bfloat16:
+            raise ValueError("MoE hidden must be BF16 [tokens, 2048]")
+        copied = _CopyToTP.apply(hidden, self.group)
+        routed, _ = shared_moe(
+            copied,
+            _CopyToTP.apply(self.gate.weight, self.group),
+            self.experts.gate_up,
+            self.experts.down,
+        )
+        gate = shared_linear(copied, self.shared_expert.gate_proj.weight)
+        up = shared_linear(copied, self.shared_expert.up_proj.weight)
+        activated = (F.silu(gate.float()) * up.float()).to(hidden.dtype)
+        shared = shared_linear(activated, self.shared_expert.down_proj.weight)
+        shared_gate = shared_linear(
+            copied, _CopyToTP.apply(self.shared_expert_gate.weight, self.group)
+        ).sigmoid()
+        return _ReduceFromTP.apply(routed + shared * shared_gate, self.group)
diff --git a/rl_engine/testing/qwen3_next_moe_prior_art.py b/rl_engine/testing/qwen3_next_moe_prior_art.py
new file mode 100644
index 000000000..d8ad92234
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_moe_prior_art.py
@@ -0,0 +1,440 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Prior-art comparison for the Qwen3-Next routed MoE (RFC #428 ``moe_route_combine_contract``).
+
+Every candidate computes the routed half of ``Qwen3NextSparseMoeBlock`` at the
+official TP1 shape (H=2048, 512 experts, top-10, expert width 512): router
+logits, top-10 selection with renormalised weights, the selected SwiGLU
+experts and the weighted combine. The shared expert is left out because every
+candidate evaluates it as the same dense MLP.
+
+For each candidate the report records
+
+* **routing batch invariance** - the expert ids and weights of a fixed block
+  of probe tokens computed alone, and embedded first and last in larger
+  batches, must be bitwise equal;
+* **output batch invariance** - the same for the routed output;
+* **dx / dW batch invariance** (candidates with a backward) - the probe rows'
+  input gradient must not change when unrelated rows join the batch, and the
+  expert weight gradient must not change when rows whose output gradient is
+  zero join it (mathematically neither can);
+* **repeatability** - two identical calls are bitwise equal;
+* **accuracy** - error against an FP64 evaluation of the HF formula with FP64
+  routing, plus the fraction of tokens whose selected expert set differs;
+* **performance** - median CUDA-event latency, candidates interleaved.
+
+A candidate that cannot be imported or launched in the pinned environment is
+recorded as ``unavailable`` with the reason instead of failing the run.
+"""
+
+from __future__ import annotations
+
+import os
+import statistics
+from contextlib import contextmanager
+from typing import Any, Callable
+
+import torch
+import torch.nn.functional as F
+
+HIDDEN = 2048
+EXPERTS = 512
+TOPK = 10
+WIDTH = 512
+PROBE = 8
+BI_SIZES = (16, 64, 256, 1024)
+PERF_TOKENS = (1, 8, 64, 256, 1024, 4096)
+BACKWARD_TOKENS = (64, 1024)
+
+
+# --------------------------------------------------------------------------- #
+# Measurement helpers
+# --------------------------------------------------------------------------- #
+
+
+def _sample_us(fn: Callable[[], Any]) -> float:
+    torch.cuda.synchronize()
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+    start.record()
+    fn()
+    end.record()
+    torch.cuda.synchronize()
+    return start.elapsed_time(end) * 1000.0
+
+
+def time_interleaved(calls: dict[str, Callable[[], Any]], warmup: int = 5, iters: int = 30):
+    """Median latency per candidate; the order alternates so none always runs first."""
+    keys = list(calls)
+    orders = (keys, list(reversed(keys)))
+    for i in range(warmup):
+        for key in orders[i % 2]:
+            calls[key]()
+    samples = {key: [] for key in keys}
+    for i in range(iters):
+        for key in orders[i % 2]:
+            samples[key].append(_sample_us(calls[key]))
+    return {key: statistics.median(values) for key, values in samples.items()}
+
+
+def _bitwise(a: torch.Tensor, b: torch.Tensor) -> bool:
+    return a.shape == b.shape and a.dtype == b.dtype and torch.equal(a, b)
+
+
+def _rel(value: torch.Tensor, reference: torch.Tensor) -> float:
+    reference = reference.double()
+    return float((value.double() - reference).norm() / reference.norm().clamp_min(1e-300))
+
+
+def _guard(key: str, factory: Callable[[], dict[str, Any]]) -> dict[str, Any]:
+    try:
+        return {"key": key, **factory()}
+    except Exception as exc:  # noqa: BLE001 - record and continue
+        return {"key": key, "unavailable": f"{type(exc).__name__}: {str(exc)[:300]}"}
+
+
+@contextmanager
+def _env(name: str, value: str | None):
+    old = os.environ.get(name)
+    if value is None:
+        os.environ.pop(name, None)
+    else:
+        os.environ[name] = value
+    try:
+        yield
+    finally:
+        if old is None:
+            os.environ.pop(name, None)
+        else:
+            os.environ[name] = old
+
+
+# --------------------------------------------------------------------------- #
+# Inputs and the FP64 reference
+# --------------------------------------------------------------------------- #
+
+
+def make_weights(seed: int = 0) -> dict[str, torch.Tensor]:
+    g = torch.Generator(device="cuda").manual_seed(seed)
+
+    def randn(*shape, scale):
+        return (torch.randn(shape, device="cuda", generator=g) * scale).to(torch.bfloat16)
+
+    return {
+        "router": randn(EXPERTS, HIDDEN, scale=0.02),
+        "gate_up": randn(EXPERTS, 2 * WIDTH, HIDDEN, scale=0.02),
+        "down": randn(EXPERTS, HIDDEN, WIDTH, scale=0.02),
+    }
+
+
+def make_tokens(count: int, seed: int) -> torch.Tensor:
+    g = torch.Generator(device="cuda").manual_seed(seed)
+    return torch.randn(count, HIDDEN, device="cuda", generator=g).to(torch.bfloat16)
+
+
+def fp64_reference(x: torch.Tensor, w: dict[str, torch.Tensor]):
+    """HF formula in FP64 with FP64 routing; returns (output, expert ids)."""
+    x64 = x.double()
+    probs = (x64 @ w["router"].double().T).softmax(-1)
+    weights, ids = probs.topk(TOPK, dim=-1)
+    weights = weights / weights.sum(-1, keepdim=True)
+    out = torch.zeros_like(x64)
+    gate_up, down = w["gate_up"], w["down"]
+    for expert in ids.unique().tolist():
+        token, slot = torch.where(ids == expert)
+        gate, up = (x64[token] @ gate_up[expert].double().T).chunk(2, dim=-1)
+        y = (F.silu(gate) * up) @ down[expert].double().T
+        out.index_add_(0, token, y * weights[token, slot, None])
+    return out, ids
+
+
+# --------------------------------------------------------------------------- #
+# Candidates
+#
+# ``route(x) -> (ids [T,10], weights [T,10])`` exposes the routing decision;
+# ``fwd(x) -> [T, H]`` is the routed output; ``graph(x) -> (y, leaves)`` (when
+# the candidate is differentiable) returns the output and [x, gate_up, down].
+# --------------------------------------------------------------------------- #
+
+
+def _rl_kernel(w):
+    from rl_engine.integrations import qwen3_next_forward as provider
+
+    def route(x):
+        routes = provider.stable_top10_routes(provider.shared_router(x, w["router"]))
+        return routes.indices, routes.weights
+
+    def fwd(x):
+        with torch.no_grad():
+            return provider.shared_moe(x, w["router"], w["gate_up"], w["down"])[0]
+
+    gate_up = w["gate_up"].detach().clone().requires_grad_(True)
+    down = w["down"].detach().clone().requires_grad_(True)
+
+    def graph(x):
+        leaf = x.detach().clone().requires_grad_(True)
+        y, _ = provider.shared_moe(leaf, w["router"], gate_up, down)
+        return y, [leaf, gate_up, down]
+
+    return {
+        "name": "rl-kernel shared_moe",
+        "source": "rl_engine.integrations.qwen3_next_forward.shared_moe",
+        "route": route,
+        "fwd": fwd,
+        "graph": graph,
+    }
+
+
+def _hf_modules(w, device="cuda"):
+    from transformers import Qwen3NextConfig
+    from transformers.models.qwen3_next import modeling_qwen3_next as hf
+
+    config = Qwen3NextConfig(
+        hidden_size=HIDDEN,
+        num_experts=EXPERTS,
+        num_experts_per_tok=TOPK,
+        moe_intermediate_size=WIDTH,
+        norm_topk_prob=True,
+    )
+    router = hf.Qwen3NextTopKRouter(config).to(device=device, dtype=torch.bfloat16)
+    experts = hf.Qwen3NextExperts(config).to(device=device, dtype=torch.bfloat16)
+    with torch.no_grad():
+        router.weight.copy_(w["router"])
+        experts.gate_up_proj.copy_(w["gate_up"])
+        experts.down_proj.copy_(w["down"])
+    return router, experts
+
+
+def _hf(w):
+    import transformers
+
+    router, experts = _hf_modules(w)
+
+    def route(x):
+        _, weights, ids = router(x)
+        return ids, weights
+
+    def call(x, mod):
+        _, weights, ids = router(x)
+        return mod(x, ids, weights)
+
+    def fwd(x):
+        with torch.no_grad():
+            return call(x, experts)
+
+    def graph(x):
+        leaf = x.detach().clone().requires_grad_(True)
+        return call(leaf, experts), [leaf, experts.gate_up_proj, experts.down_proj]
+
+    return {
+        "name": "HF transformers Qwen3NextExperts (eager)",
+        "source": f"transformers {transformers.__version__} modeling_qwen3_next",
+        "route": route,
+        "fwd": fwd,
+        "graph": graph,
+    }
+
+
+def _vllm(w, batch_invariant: bool):
+    import vllm
+    from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
+    from vllm.model_executor.layers.fused_moe.router.fused_topk_router import fused_topk
+
+    flag = "1" if batch_invariant else "0"
+    linear = F.linear
+    if batch_invariant:
+        from vllm.model_executor.determinism.batch_invariant import linear_batch_invariant
+
+        linear = linear_batch_invariant
+
+    def route(x):
+        with _env("VLLM_BATCH_INVARIANT", flag):
+            # vLLM's Qwen3-Next gate is a BF16 ReplicatedLinear; batch-invariant
+            # mode replaces its aten GEMM with vLLM's persistent Triton matmul.
+            logits = linear(x, w["router"])
+            weights, ids, _ = fused_topk(x, logits, TOPK, renormalize=True)
+        return ids.long(), weights
+
+    def fwd(x):
+        with torch.no_grad(), _env("VLLM_BATCH_INVARIANT", flag):
+            ids, weights = route(x)
+            return fused_experts(x, w["gate_up"], w["down"], weights, ids.int())
+
+    return {
+        "name": f"vLLM fused_topk + fused_experts (Triton), VLLM_BATCH_INVARIANT={flag}",
+        "source": f"vllm {vllm.__version__} model_executor/layers/fused_moe",
+        "route": route,
+        "fwd": fwd,
+    }
+
+
+def _flashinfer(w):
+    import flashinfer
+    from flashinfer.fused_moe import cutlass_fused_moe
+
+    # CUTLASS SwiGLU takes fc1 as [up; gate] (the opposite half order of HF).
+    gate, up = w["gate_up"].chunk(2, dim=1)
+    fc1 = torch.cat((up, gate), dim=1).contiguous()
+
+    def route(x):
+        probs = F.linear(x, w["router"]).float().softmax(-1)
+        weights, ids = probs.topk(TOPK, dim=-1)
+        return ids, weights / weights.sum(-1, keepdim=True)
+
+    def fwd(x):
+        with torch.no_grad():
+            ids, weights = route(x)
+            out = cutlass_fused_moe(
+                x, ids.int(), weights, fc1, w["down"], torch.bfloat16, quant_scales=[]
+            )
+            return out[0] if isinstance(out, (list, tuple)) else out
+
+    return {
+        "name": "FlashInfer cutlass_fused_moe (routes from torch.topk)",
+        "source": f"flashinfer {flashinfer.__version__} fused_moe.cutlass_fused_moe",
+        "route": route,
+        "fwd": fwd,
+    }
+
+
+def candidates(w) -> list[dict[str, Any]]:
+    return [
+        _guard("rl_kernel_cuda", lambda: _rl_kernel(w)),
+        _guard("hf_transformers", lambda: _hf(w)),
+        _guard("vllm_bi0", lambda: _vllm(w, False)),
+        _guard("vllm_bi1", lambda: _vllm(w, True)),
+        _guard("flashinfer_cutlass", lambda: _flashinfer(w)),
+    ]
+
+
+# --------------------------------------------------------------------------- #
+# Checks
+# --------------------------------------------------------------------------- #
+
+
+def _embedded(x: torch.Tensor, size: int):
+    """Probe rows first in a batch of ``size``, and the same probe rows last."""
+    first = x[:size]
+    last = x[:size].clone()
+    last[size - PROBE :] = x[:PROBE]
+    return first, last
+
+
+def _rows_bi(fn, x) -> dict[str, bool]:
+    alone = fn(x[:PROBE])
+    result = {}
+    for size in BI_SIZES:
+        first, last = _embedded(x, size)
+        a, b = fn(first), fn(last)
+        if isinstance(alone, tuple):
+            ok = all(
+                _bitwise(p[:PROBE], q) and _bitwise(r[size - PROBE :], q)
+                for p, r, q in zip(a, b, alone)
+            )
+        else:
+            ok = _bitwise(a[:PROBE], alone) and _bitwise(b[size - PROBE :], alone)
+        result[str(size)] = bool(ok)
+    return result
+
+
+def _grad_bi(cand, x) -> dict[str, Any]:
+    """dx of the probe rows, and dW with zero-gradient rows appended."""
+    g = torch.Generator(device="cuda").manual_seed(7)
+    dy = torch.randn(x.shape, device="cuda", generator=g).to(torch.bfloat16)
+
+    def grads(rows, dy_rows):
+        y, leaves = cand["graph"](rows)
+        return torch.autograd.grad(y, leaves, dy_rows)
+
+    alone = grads(x[:PROBE], dy[:PROBE])
+    dx, dw = {}, {}
+    for size in BI_SIZES:
+        full = grads(x[:size], dy[:size])
+        dx[str(size)] = bool(_bitwise(full[0][:PROBE], alone[0]))
+        padded = dy[:size].clone()
+        padded[PROBE:] = 0
+        masked = grads(x[:size], padded)
+        dw[str(size)] = bool(all(_bitwise(m, a) for m, a in zip(masked[1:], alone[1:])))
+    return {"dx_rows_bitwise": dx, "dweight_zero_rows_bitwise": dw}
+
+
+def _accuracy(cand, x, w) -> dict[str, Any]:
+    ref, ref_ids = fp64_reference(x, w)
+    ids, _ = cand["route"](x)
+    same = ids.long().sort(-1).values == ref_ids.sort(-1).values
+    out = cand["fwd"](x)
+    return {
+        "rel_l2_vs_fp64": _rel(out, ref),
+        "max_abs_vs_fp64": float((out.double() - ref).abs().max()),
+        "tokens_with_different_expert_set": int((~same.all(-1)).sum()),
+        "tokens": int(x.shape[0]),
+    }
+
+
+def _one(cand, x, w) -> dict[str, Any]:
+    if "unavailable" in cand:
+        return cand
+    out = {"key": cand["key"], "name": cand["name"], "source": cand["source"]}
+    try:
+        out["route_rows_bitwise"] = _rows_bi(cand["route"], x)
+        out["output_rows_bitwise"] = _rows_bi(cand["fwd"], x)
+        out["repeatable"] = bool(_bitwise(cand["fwd"](x[:256]), cand["fwd"](x[:256])))
+        if "graph" in cand:
+            out.update(_grad_bi(cand, x))
+        out["accuracy"] = _accuracy(cand, x[:256], w)
+    except Exception as exc:  # noqa: BLE001 - a crash is a result, not a harness failure
+        out["failed"] = f"{type(exc).__name__}: {str(exc)[:300]}"
+    out["batch_invariant"] = bool(
+        "failed" not in out
+        and all(out["route_rows_bitwise"].values())
+        and all(out["output_rows_bitwise"].values())
+        and all(out.get("dx_rows_bitwise", {"": True}).values())
+        and all(out.get("dweight_zero_rows_bitwise", {"": True}).values())
+    )
+    return out
+
+
+def _latency(cands, tokens) -> dict[str, Any]:
+    ready = [c for c in cands if "unavailable" not in c]
+    forward = {}
+    for count in tokens:
+        x = make_tokens(count, seed=100 + count)
+        calls = {}
+        for cand in ready:
+            try:
+                cand["fwd"](x)
+                calls[cand["key"]] = lambda c=cand: c["fwd"](x)
+            except Exception:  # noqa: BLE001 - recorded by the BI section
+                continue
+        forward[str(count)] = time_interleaved(calls)
+    backward = {}
+    for count in BACKWARD_TOKENS:
+        x = make_tokens(count, seed=200 + count)
+        dy = torch.randn_like(x)
+        calls = {}
+        for cand in ready:
+            if "graph" not in cand:
+                continue
+
+            def step(c=cand):
+                y, leaves = c["graph"](x)
+                torch.autograd.grad(y, leaves, dy)
+
+            calls[cand["key"]] = step
+        backward[str(count)] = time_interleaved(calls, warmup=2, iters=10)
+    return {"forward_us": forward, "forward_plus_backward_us": backward}
+
+
+def moe_report() -> dict[str, Any]:
+    torch.manual_seed(0)
+    w = make_weights()
+    x = make_tokens(max(BI_SIZES), seed=1)
+    cands = candidates(w)
+    return {
+        "op": "qwen3_next_routed_moe",
+        "shape": {"hidden": HIDDEN, "experts": EXPERTS, "top_k": TOPK, "expert_width": WIDTH},
+        "probe_rows": PROBE,
+        "candidates": [_one(c, x, w) for c in cands],
+        "latency": _latency(cands, PERF_TOKENS),
+    }
diff --git a/rl_engine/testing/tensor_identity.py b/rl_engine/testing/tensor_identity.py
new file mode 100644
index 000000000..5d05c0fe9
--- /dev/null
+++ b/rl_engine/testing/tensor_identity.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Finite tensor identity in the original storage dtype, including signed zero."""
+
+import torch
+
+
+def tensor_bitwise_equal(actual: torch.Tensor, expected: torch.Tensor) -> bool:
+    """Compare logical values by bytes without widening or accepting NaN/Inf.
+
+    Strides need not match. A cross-device comparison copies expected bytes to
+    the actual tensor's device; it never converts either tensor's dtype.
+    """
+    if actual.shape != expected.shape or actual.dtype != expected.dtype:
+        return False
+    for value in (actual, expected):
+        if (value.is_floating_point() or value.is_complex()) and not bool(
+            torch.isfinite(value).all()
+        ):
+            return False
+    actual_bytes = actual.detach().contiguous().reshape(-1).view(torch.uint8)
+    expected_bytes = expected.detach().contiguous().reshape(-1).view(torch.uint8)
+    if actual_bytes.device != expected_bytes.device:
+        expected_bytes = expected_bytes.to(actual_bytes.device)
+    return bool(torch.equal(actual_bytes, expected_bytes))
+
+
+def assert_tensor_bitwise_equal(
+    actual: torch.Tensor, expected: torch.Tensor, *, name: str = "tensor"
+) -> None:
+    """Require finite, same-dtype, same-shape raw-bit identity."""
+    if not tensor_bitwise_equal(actual, expected):
+        raise AssertionError(
+            f"{name}: finite raw-bit identity failed; "
+            f"actual={tuple(actual.shape)}/{actual.dtype}, "
+            f"expected={tuple(expected.shape)}/{expected.dtype}"
+        )
diff --git a/scripts/plot_qwen3_next_moe_prior_art.py b/scripts/plot_qwen3_next_moe_prior_art.py
new file mode 100644
index 000000000..17a966b61
--- /dev/null
+++ b/scripts/plot_qwen3_next_moe_prior_art.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Render the routed-MoE prior-art report (``scripts/qwen3_next_moe_prior_art.py``) as a PNG.
+
+Needs only the report JSON and matplotlib, so it runs anywhere:
+
+    python scripts/plot_qwen3_next_moe_prior_art.py \\
+        docs/usage/evidence/qwen3-next-moe-route-b200/report.json
+
+writes ``figure.png`` next to the report. Three panels: which batch-invariance
+checks each implementation passes, accuracy against an FP64 evaluation with
+FP64 routing, and forward latency. RL-Kernel is blue, existing libraries orange.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.colors import ListedColormap  # noqa: E402
+
+RL_KERNEL = "#2a78d6"
+INK = "#2b2b29"
+MUTED = "#6f6e69"
+GRID = "#e4e3dd"
+SURFACE = "#fcfcfb"
+PASS = "#1baf7a"
+FAIL = "#d64545"
+NA = "#e4e3dd"
+EXISTING = ("#eb6834", "#b8501f", "#f29a62", "#8a3a15")
+
+LABELS = {
+    "rl_kernel_cuda": "RL-Kernel shared_moe",
+    "hf_transformers": "HF Qwen3NextExperts",
+    "vllm_bi0": "vLLM fused_moe (BI=0)",
+    "vllm_bi1": "vLLM fused_moe (BI=1)",
+    "flashinfer_cutlass": "FlashInfer cutlass_fused_moe",
+}
+CHECKS = (
+    ("route_rows_bitwise", "routes"),
+    ("output_rows_bitwise", "output"),
+    ("dx_rows_bitwise", "dx"),
+    ("dweight_zero_rows_bitwise", "dW"),
+)
+
+
+def _colors(keys):
+    existing = iter(EXISTING)
+    return {k: RL_KERNEL if k == "rl_kernel_cuda" else next(existing) for k in keys}
+
+
+def _style(ax):
+    ax.set_facecolor(SURFACE)
+    for side in ("top", "right"):
+        ax.spines[side].set_visible(False)
+    ax.tick_params(colors=INK, labelsize=8)
+    ax.grid(color=GRID, linewidth=0.6)
+
+
+def plot(report: dict, out: Path) -> None:
+    cands = [c for c in report["candidates"] if "unavailable" not in c]
+    keys = [c["key"] for c in cands]
+    colors = _colors(keys)
+    sizes = list(next(iter(cands))["output_rows_bitwise"])
+    fig, axes = plt.subplots(1, 3, figsize=(17, 4.6), gridspec_kw={"width_ratios": [1.5, 1, 1.4]})
+    fig.patch.set_facecolor("white")
+
+    # Panel 1: batch-invariance matrix; 1 = pass, 0 = fail, 0.5 = no backward.
+    ax = axes[0]
+    columns = [f"{label} {size}" for _, label in CHECKS for size in sizes]
+    grid = []
+    for cand in cands:
+        row = []
+        for field, _ in CHECKS:
+            values = cand.get(field)
+            row.extend(0.5 if values is None else float(values[s]) for s in sizes)
+        grid.append(row)
+    ax.imshow(grid, cmap=ListedColormap([FAIL, NA, PASS]), vmin=0, vmax=1, aspect="auto")
+    ax.set_xticks(range(len(columns)), columns, fontsize=7, rotation=90)
+    ax.set_yticks(range(len(cands)), [LABELS.get(k, k) for k in keys], fontsize=8)
+    for i, cand in enumerate(cands):
+        mark = "BI" if cand.get("batch_invariant") else "not BI"
+        ax.text(len(columns) - 0.4, i, f"  {mark}", va="center", fontsize=8, color=INK)
+    ax.set_title(
+        f"Batch invariance: {report['probe_rows']} probe tokens alone vs. in a batch\n"
+        "(green bitwise equal, red differs, grey: no backward)",
+        fontsize=9,
+        color=INK,
+    )
+    ax.tick_params(length=0)
+
+    # Panel 2: accuracy.
+    ax = axes[1]
+    _style(ax)
+    rel = [c["accuracy"]["rel_l2_vs_fp64"] if "accuracy" in c else float("nan") for c in cands]
+    ax.barh(range(len(cands)), rel, color=[colors[k] for k in keys])
+    for i, cand in enumerate(cands):
+        acc = cand.get("accuracy")
+        if acc:
+            ax.text(
+                rel[i],
+                i,
+                f"  {rel[i]:.2e}  ({acc['tokens_with_different_expert_set']}/{acc['tokens']}"
+                " tokens route differently)",
+                va="center",
+                fontsize=7,
+                color=MUTED,
+            )
+    ax.set_yticks(range(len(cands)), [LABELS.get(k, k) for k in keys], fontsize=8)
+    ax.invert_yaxis()
+    ax.set_xlim(0, 2.2 * max(r for r in rel if r == r))
+    ax.set_xlabel("relative L2 error vs. FP64 HF formula with FP64 routing", fontsize=8)
+    ax.set_title("Accuracy (256 tokens)", fontsize=9, color=INK)
+
+    # Panel 3: forward latency.
+    ax = axes[2]
+    _style(ax)
+    forward = report["latency"]["forward_us"]
+    tokens = [int(t) for t in forward]
+    for key in keys:
+        ys = [forward[str(t)].get(key, float("nan")) / 1000.0 for t in tokens]
+        ax.plot(tokens, ys, marker="o", ms=3.5, color=colors[key], label=LABELS.get(key, key))
+    ax.set_xscale("log", base=2)
+    ax.set_yscale("log")
+    ax.set_xlabel("tokens", fontsize=8)
+    ax.set_ylabel("forward latency (ms, median)", fontsize=8)
+    ax.set_title("Forward latency\n(H=2048, 512 experts, top-10, width 512)", fontsize=9)
+    ax.legend(fontsize=7, frameon=False)
+
+    env = report["environment"]
+    fig.suptitle(
+        f"Qwen3-Next routed MoE prior art - {env['gpu']}, torch {env['torch']}, "
+        f"commit {report['rl_kernel_commit'][:7]}",
+        fontsize=10,
+        color=INK,
+    )
+    fig.tight_layout()
+    fig.savefig(out, dpi=160)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("report", type=Path)
+    args = parser.parse_args()
+    report = json.loads(args.report.read_text())
+    out = args.report.with_name("figure.png")
+    plot(report, out)
+    print(f"wrote {out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_moe_prior_art.py b/scripts/qwen3_next_moe_prior_art.py
new file mode 100644
index 000000000..398245eee
--- /dev/null
+++ b/scripts/qwen3_next_moe_prior_art.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Write the Qwen3-Next routed-MoE prior-art report (BI + accuracy + latency) as JSON.
+
+Run it on a clean tree with ``TRITON_F32_DEFAULT=ieee`` (the FP32 router needs
+IEEE Triton dots from process start). The report records the commit, whether
+the tracked tree was dirty and the version of every library compared.
+``scripts/plot_qwen3_next_moe_prior_art.py`` turns the report into a figure.
+
+    TRITON_F32_DEFAULT=ieee python scripts/qwen3_next_moe_prior_art.py \\
+        --out docs/usage/evidence/qwen3-next-moe-route-b200/report.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import importlib.metadata as md
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+import torch  # noqa: E402
+
+from rl_engine.testing.qwen3_next_moe_prior_art import moe_report  # noqa: E402
+
+LIBRARIES = (
+    "vllm",
+    "flashinfer-python",
+    "transformers",
+    "triton",
+    "sglang",
+    "megatron-core",
+    "transformer_engine",
+    "vime",
+)
+
+
+def _git(*args: str) -> str:
+    return subprocess.run(
+        ["git", *args], cwd=ROOT, check=True, capture_output=True, text=True
+    ).stdout.strip()
+
+
+def _version(name: str) -> str | None:
+    try:
+        return md.version(name)
+    except md.PackageNotFoundError:
+        return None
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--out", type=Path, required=True)
+    args = parser.parse_args()
+    if not torch.cuda.is_available():
+        raise SystemExit("needs a CUDA device")
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = False
+    report = {
+        "kind": "qwen3_next_prior_art_report",
+        "rfc": 428,
+        "rl_kernel_commit": _git("rev-parse", "HEAD"),
+        "tracked_tree_dirty": bool(_git("status", "--porcelain", "--untracked-files=no")),
+        "environment": {
+            "gpu": torch.cuda.get_device_name(),
+            "torch": torch.__version__,
+            "cuda": torch.version.cuda,
+            "libraries": {name: _version(name) for name in LIBRARIES},
+        },
+        **moe_report(),
+    }
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.out.write_text(json.dumps(report, indent=2) + "\n")
+    print(
+        f"wrote {args.out} (commit {report['rl_kernel_commit'][:7]}, "
+        f"dirty={report['tracked_tree_dirty']})"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_tp_moe_check.py b/scripts/qwen3_next_tp_moe_check.py
new file mode 100644
index 000000000..817c07bcc
--- /dev/null
+++ b/scripts/qwen3_next_tp_moe_check.py
@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""TP4 MoE gate on the official checkpoint's layer-0 weights; four GPUs, torchrun.
+
+    TRITON_F32_DEFAULT=ieee torchrun --nproc-per-node 4 scripts/qwen3_next_tp_moe_check.py \\
+        --checkpoint <Qwen3-Next-80B-A3B-Instruct> --output <dir>
+
+Each rank writes ``rank-<r>.json``. The gate requires, bitwise:
+
+* the HF load -> local shard -> all-gather -> HF export round trip of all 512 experts;
+* identical top-10 routes on every rank (the router is replicated);
+* full batch == concatenated chunks, and reordered tokens == reordered output,
+  for 8, 64, 256 and 1024 tokens;
+* the training forward (autograd on) == the no-grad forward;
+* identical replicated router and shared-expert-gate gradients on every rank,
+  and a finite, nonzero gradient for every parameter.
+"""
+
+import argparse
+import json
+import os
+import subprocess
+from collections.abc import Mapping
+from contextlib import ExitStack
+from datetime import timedelta
+from importlib.metadata import version
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+from safetensors import safe_open
+
+from rl_engine.integrations.qwen3_next_forward import (
+    FORWARD_PROVIDER_ID,
+    shared_router,
+    stable_top10_routes,
+)
+from rl_engine.integrations.qwen3_next_tp_blocks import TP4MoE, assemble_moe_weight, moe_hf_shapes
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+ROOT = Path(__file__).resolve().parents[1]
+TOKEN_COUNTS = (8, 64, 256, 1024)
+
+
+class CheckpointWeights(Mapping):
+    """Keep shard headers open while reading only the requested layer's tensors."""
+
+    def __init__(self, checkpoint, prefix, names, stack):
+        self.root, self.prefix, self.names, self.stack = checkpoint, prefix, tuple(names), stack
+        self.index = json.loads((checkpoint / "model.safetensors.index.json").read_text())[
+            "weight_map"
+        ]
+        self.readers = {}
+
+    def __iter__(self):
+        return iter(self.names)
+
+    def __len__(self):
+        return len(self.names)
+
+    def __getitem__(self, name):
+        if name not in self.names:
+            raise KeyError(name)
+        key = self.prefix + name
+        shard = self.index[key]
+        if shard not in self.readers:
+            self.readers[shard] = self.stack.enter_context(
+                safe_open(self.root / shard, framework="pt", device="cpu")
+            )
+        return self.readers[shard].get_tensor(key)
+
+
+def atomic_json(path, payload):
+    temporary = path.with_suffix(path.suffix + ".partial")
+    temporary.write_text(json.dumps(payload, indent=2) + "\n")
+    temporary.replace(path)
+
+
+def gather(value):
+    peers = [torch.empty_like(value) for _ in range(4)]
+    dist.all_gather(peers, value.contiguous())
+    return peers
+
+
+def check_gradients(module):
+    result = {}
+    for name, parameter in module.named_parameters():
+        grad = parameter.grad
+        if grad is None or not torch.isfinite(grad).all() or not grad.count_nonzero():
+            raise AssertionError(f"Missing finite nonzero gradient: {name}")
+        result[name] = float(grad.float().abs().max())
+    return result
+
+
+def timed(fn):
+    start, end = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
+    start.record()
+    out = fn()
+    end.record()
+    torch.cuda.synchronize()
+    return out, start.elapsed_time(end) * 1000.0
+
+
+def moe_gate(checkpoint, generator):
+    module = TP4MoE(group=dist.group.WORLD, device="cuda")
+    with ExitStack() as stack:
+        weights = CheckpointWeights(checkpoint, "model.layers.0.mlp.", moe_hf_shapes(), stack)
+        module.load_hf_weights(weights)
+        for name, value in module.export_local_hf_weights().items():
+            restored = assemble_moe_weight(name, [peer.cpu() for peer in gather(value)])
+            exact(restored, weights[name], name=f"MoE HF {name}")
+    cases = ["hf-load-export-all512experts"]
+    forward_us = {}
+    with torch.no_grad():
+        for length in TOKEN_COUNTS:
+            hidden = (
+                torch.randn(length, 2048, device="cuda", dtype=torch.bfloat16, generator=generator)
+                * 0.1
+            )
+            # Every rank draws the same tokens from the same generator; verify it.
+            for peer in gather(hidden):
+                exact(peer, hidden, name="replicated input")
+            routes = stable_top10_routes(shared_router(hidden, module.gate.weight))
+            for peer in gather(routes.indices):
+                exact(peer, routes.indices, name=f"routes across ranks {length}")
+            module(hidden)
+            whole, forward_us[str(length)] = timed(lambda: module(hidden))
+            cut = min(65, length - 1)
+            exact(
+                torch.cat((module(hidden[:cut]), module(hidden[cut:]))),
+                whole,
+                name=f"MoE full/chunk {length}",
+            )
+            order = torch.arange(length - 1, -1, -1, device="cuda")
+            exact(module(hidden[order]), whole[order], name=f"MoE reorder {length}")
+            cases.extend((f"routes-replicated-{length}", f"full-chunk-reorder-{length}"))
+    hidden = (
+        torch.randn(16, 2048, device="cuda", dtype=torch.bfloat16, generator=generator) * 0.1
+    ).requires_grad_()
+    with torch.no_grad():
+        expected = module(hidden)
+    output = module(hidden)
+    exact(output, expected, name="MoE independent training forward")
+    output.float().square().mean().backward()
+    if (
+        hidden.grad is None
+        or not torch.isfinite(hidden.grad).all()
+        or not hidden.grad.count_nonzero()
+    ):
+        raise AssertionError("MoE input gradient is missing")
+    gradients = check_gradients(module)
+    for parameter in (module.gate.weight, module.shared_expert_gate.weight):
+        for peer in gather(parameter.grad):
+            exact(peer, parameter.grad, name="MoE replicated router/gate gradient")
+    cases.extend(("training-forward", "all-parameter-gradient", "replicated-router-gradient"))
+    return {"cases": cases, "gradient_max_abs": gradients, "forward_us": forward_us}
+
+
+def _git(*args):
+    return subprocess.run(
+        ["git", *args], cwd=ROOT, check=True, capture_output=True, text=True
+    ).stdout.strip()
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--checkpoint", required=True, type=Path)
+    parser.add_argument("--output", required=True, type=Path)
+    args = parser.parse_args()
+    rank = int(os.environ["LOCAL_RANK"])
+    if int(os.environ["WORLD_SIZE"]) != 4 or torch.cuda.device_count() < 4:
+        raise RuntimeError("The TP4 MoE gate requires exactly four ranks on four GPUs")
+    torch.cuda.set_device(rank)
+    dist.init_process_group(
+        "nccl", timeout=timedelta(minutes=15), device_id=torch.device("cuda", rank)
+    )
+    try:
+        generator = torch.Generator(device="cuda").manual_seed(1234)
+        moe = moe_gate(args.checkpoint, generator)
+        args.output.mkdir(parents=True, exist_ok=True)
+        atomic_json(
+            args.output / f"rank-{rank}.json",
+            {
+                "status": "passed",
+                "scope": "tp4_moe_block_layer0",
+                "rank": rank,
+                "rl_kernel_commit": _git("rev-parse", "HEAD"),
+                "tracked_tree_dirty": bool(_git("status", "--porcelain", "--untracked-files=no")),
+                "provider": FORWARD_PROVIDER_ID,
+                "environment": {
+                    "gpu": torch.cuda.get_device_name(),
+                    "torch": torch.__version__,
+                    "vllm": version("vllm"),
+                    "nccl": ".".join(map(str, torch.cuda.nccl.version())),
+                },
+                "moe": moe,
+            },
+        )
+        dist.barrier()
+    finally:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/check_qwen3_next_forward.py b/tests/check_qwen3_next_forward.py
new file mode 100644
index 000000000..ce4beb1f7
--- /dev/null
+++ b/tests/check_qwen3_next_forward.py
@@ -0,0 +1,238 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Mandatory shared CUDA GEMM and MoE checks; these do not claim model L2."""
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from rl_engine.integrations.qwen3_next_forward import (
+    combine_routes,
+    shared_linear,
+    shared_moe,
+    stable_top10_routes,
+)
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def random(shape, seed=801, *, grad=False, dtype=torch.bfloat16):
+    assert torch.cuda.is_available(), "Mandatory shared-forward CUDA gates cannot skip"
+    gen = torch.Generator(device="cuda").manual_seed(seed)
+    return (
+        torch.randn(shape, generator=gen, device="cuda", dtype=dtype)
+        .mul_(0.02)
+        .requires_grad_(grad)
+    )
+
+
+@pytest.mark.parametrize(
+    "k,n", [(2048, 3072), (2048, 16), (1024, 2048), (2048, 256), (128, 2048), (2048, 37984)]
+)
+@torch.no_grad()
+def test_qwen_linear_shapes_batch_chunk_and_reorder(k, n):
+    x, weight = random((17, k)), random((n, k), seed=802)
+    output = shared_linear(x, weight)
+    exact(
+        torch.cat(
+            [
+                shared_linear(x[:1], weight),
+                shared_linear(x[1:4], weight),
+                shared_linear(x[4:], weight),
+            ]
+        ),
+        output,
+    )
+    order = torch.arange(16, -1, -1, device="cuda")
+    exact(shared_linear(x[order], weight), output[order])
+
+
+def test_shared_linear_vjp_and_empty_tokens():
+    x, weight, bias = (
+        random((4, 2048), grad=True),
+        random((16, 2048), 802, grad=True),
+        random((16,), 803, grad=True),
+    )
+    output = shared_linear(x, weight, bias)
+    grad = random(output.shape, 804)
+    output.backward(grad)
+    refs = [value.detach().double().requires_grad_() for value in (x, weight, bias)]
+    F.linear(*refs).backward(grad.double())
+    for value, ref in zip((x, weight, bias), refs):
+        torch.testing.assert_close(value.grad.float(), ref.grad.float(), atol=0.002, rtol=0.016)
+        assert (value.grad.double() - ref.grad).norm() / ref.grad.norm() < 0.016
+    with torch.no_grad():
+        exact(output, shared_linear(x, weight, bias))
+    empty = random((0, 2048), grad=True)
+    weight.grad = bias.grad = None
+    shared_linear(empty, weight, bias).sum().backward()
+    assert empty.grad.shape == empty.shape
+    assert torch.count_nonzero(weight.grad) == 0 and torch.count_nonzero(bias.grad) == 0
+
+
+def test_stable_route_tie_boundary_and_router_gradient():
+    logits = random((4, 512), dtype=torch.float32)
+    logits.zero_().requires_grad_()
+    routes = stable_top10_routes(logits)
+    assert torch.equal(routes.indices, torch.arange(10, device="cuda").expand(4, 10))
+    exact(routes.weights, torch.full((4, 10), 0.1, device="cuda"))
+    (routes.weights * torch.arange(10, device="cuda")).sum().backward()
+    assert torch.isfinite(logits.grad).all() and logits.grad.abs().max() > 0
+    bad = logits.detach().clone()
+    bad[0, 0] = torch.nan
+    with pytest.raises(ValueError, match="finite"):
+        stable_top10_routes(bad)
+
+
+def test_combine_has_fixed_route_order_and_differentiable_weights():
+    outputs = random((4, 10, 2048), grad=True)
+    weights = random((4, 10), 802, dtype=torch.float32, grad=True)
+    actual = combine_routes(outputs, weights)
+    exact(
+        torch.cat([combine_routes(outputs[i : i + 1], weights[i : i + 1]) for i in range(4)]),
+        actual,
+    )
+    actual.float().square().sum().backward()
+    assert weights.grad.abs().max() > 0 and outputs.grad.abs().max() > 0
+    refs = [value.detach().double().requires_grad_() for value in (outputs, weights)]
+    expected = (refs[0] * refs[1].unsqueeze(-1)).sum(dim=1)
+    expected.backward((2 * actual.float()).double())
+    for value, reference in zip((outputs, weights), refs):
+        torch.testing.assert_close(
+            value.grad.float(), reference.grad.float(), atol=0.002, rtol=0.016
+        )
+
+
+def test_qwen_moe_actual_tp4_shapes_and_independent_training_forward():
+    x = random((4, 2048), grad=True)
+    router = random((512, 2048), 802, grad=True)
+    gate_up = random((512, 256, 2048), 803, grad=True)
+    down = random((512, 2048, 128), 804, grad=True)
+    output, routes = shared_moe(x, router, gate_up, down)
+    with torch.no_grad():
+        row_output, row_routes = shared_moe(x[:1], router, gate_up, down)
+        exact(row_output, output[:1])
+        exact(row_routes.weights, routes.weights[:1])
+        assert torch.equal(row_routes.indices, routes.indices[:1])
+    output.float().square().sum().backward()
+    for value in (x, router, gate_up, down):
+        assert value.grad is not None and torch.isfinite(value.grad).all()
+        assert value.grad.abs().max() > 0
+    active = routes.indices.unique()
+    inactive = torch.ones(512, dtype=torch.bool, device="cuda")
+    inactive[active] = False
+    assert torch.count_nonzero(gate_up.grad[inactive]) == 0
+    assert torch.count_nonzero(down.grad[inactive]) == 0
+
+
+def test_moe_vjp_matches_independent_double_formula():
+    # A small hidden width isolates the derivative; the previous gate covers
+    # the unmodified official TP4 widths and expert count.
+    x = random((2, 32)).mul_(10).requires_grad_()
+    router = random((512, 32), 802).mul_(5).requires_grad_()
+    gate_up = random((512, 16, 32), 803).mul_(10).requires_grad_()
+    down = random((512, 32, 8), 804).mul_(10).requires_grad_()
+    actual, routes = shared_moe(x, router, gate_up, down)
+    grad = random(actual.shape, 805)
+    actual.backward(grad)
+    xr, rr, gu, dr = [v.detach().double().requires_grad_() for v in (x, router, gate_up, down)]
+    probability = (xr @ rr.t()).softmax(-1)
+    selected = probability.gather(1, routes.indices)
+    selected = selected / selected.sum(-1, keepdim=True)
+    rows = []
+    for token in range(2):
+        slots = []
+        for slot, expert in enumerate(routes.indices[token].tolist()):
+            gate, up = F.linear(xr[token], gu[expert]).chunk(2)
+            slots.append(F.linear(F.silu(gate) * up, dr[expert]) * selected[token, slot])
+        rows.append(torch.stack(slots).sum(0))
+    torch.stack(rows).backward(grad.double())
+    for value, reference in zip((x, router, gate_up, down), (xr, rr, gu, dr)):
+        assert torch.isfinite(value.grad).all()
+        # BF16 intermediates are rounded by the actual provider; this compares
+        # derivative correctness, never the raw-bit L1/L2 acceptance criterion.
+        torch.testing.assert_close(
+            value.grad.float(), reference.grad.float(), atol=0.00002, rtol=0.016
+        )
+        assert (value.grad.double() - reference.grad).norm() / reference.grad.norm() < 0.016
+
+
+@torch.no_grad()
+def test_route_weights_are_batch_invariant():
+    logits = random((32, 512), 805, dtype=torch.float32).mul_(100)
+    whole = stable_top10_routes(logits)
+    for count in (1, 8, 17):
+        part = stable_top10_routes(logits[:count])
+        assert torch.equal(part.indices, whole.indices[:count])
+        exact(part.weights, whole.weights[:count])
+    moe_x, router = random((32, 2048), 806), random((512, 2048), 807)
+    gate_up, down = random((512, 256, 2048), 808), random((512, 2048, 128), 809)
+    whole_out, _ = shared_moe(moe_x, router, gate_up, down)
+    part_out, _ = shared_moe(moe_x[:8], router, gate_up, down)
+    exact(part_out, whole_out[:8])
+
+
+class _PerExpertReference(torch.autograd.Function):
+    """The previous provider: one pinned vLLM GEMM pair per expert, in expert order."""
+
+    @staticmethod
+    def forward(ctx, x, gate_up, down, route_indices):
+        from rl_engine.integrations.qwen3_next_forward import _vllm_linear
+
+        output = x.new_empty((x.shape[0] * 10, x.shape[1]))
+        flat = route_indices.flatten()
+        for expert in flat.unique().tolist():
+            slots = (flat == expert).nonzero().squeeze(1)
+            rows = x.index_select(0, slots // 10)
+            gate, up = _vllm_linear()(rows, gate_up[expert]).chunk(2, dim=-1)
+            activated = (F.silu(gate.float()) * up.float()).to(x.dtype)
+            output.index_copy_(0, slots, _vllm_linear()(activated, down[expert]))
+        return output.reshape(x.shape[0], 10, x.shape[1])
+
+
+@torch.no_grad()
+@pytest.mark.parametrize("width", [128, 512])
+@pytest.mark.parametrize("tokens", [1, 7, 13, 300])
+def test_grouped_experts_are_bitwise_the_per_expert_gemms(width, tokens):
+    from rl_engine.integrations.qwen3_next_forward import _RoutedExperts, shared_router
+
+    x, router = random((tokens, 2048), 811), random((512, 2048), 812)
+    gate_up, down = random((512, 2 * width, 2048), 813), random((512, 2048, width), 814)
+    indices = stable_top10_routes(shared_router(x, router)).indices
+    exact(
+        _RoutedExperts.apply(x, gate_up, down, indices),
+        _PerExpertReference.apply(x, gate_up, down, indices),
+    )
+
+
+@torch.no_grad()
+@pytest.mark.parametrize("width", [128, 512])
+def test_grouped_projection_is_bitwise_the_per_expert_gemm(width):
+    # The backward recomputes [gate, up] with the grouped kernel; equality here
+    # leaves every gradient bitwise what the per-expert backward produced.
+    from rl_engine.integrations.qwen3_next_forward import (
+        _vllm_linear,
+        grouped_route_linear,
+        shared_router,
+    )
+
+    x, router = random((300, 2048), 817), random((512, 2048), 818)
+    gate_up = random((512, 2 * width, 2048), 819)
+    indices = stable_top10_routes(shared_router(x, router)).indices
+    grouped = grouped_route_linear(x, gate_up, indices).reshape(-1, 2 * width)
+    flat = indices.flatten()
+    for expert in flat.unique().tolist():
+        slots = (flat == expert).nonzero().squeeze(1)
+        exact(grouped[slots], _vllm_linear()(x[slots // 10], gate_up[expert]), name=f"e{expert}")
+
+
+def test_grouped_experts_fail_closed_on_the_tensor_descriptor_path(monkeypatch):
+    from rl_engine.integrations import qwen3_next_forward as provider
+
+    prepare, dispatch, _ = provider._vllm_grouped()
+    monkeypatch.setattr(provider, "_vllm_grouped", lambda: (prepare, dispatch, lambda: True))
+    x = random((4, 2048), 815)
+    with pytest.raises(RuntimeError, match="VLLM_TRITON_USE_TD"):
+        provider.grouped_route_linear(
+            x, random((512, 256, 2048), 816), torch.zeros(4, 10, device="cuda", dtype=torch.long)
+        )
diff --git a/tests/test_qwen3_next_forward_contract.py b/tests/test_qwen3_next_forward_contract.py
new file mode 100644
index 000000000..76d6df183
--- /dev/null
+++ b/tests/test_qwen3_next_forward_contract.py
@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""CPU checks of the strict shared-forward boundary, without a CPU fallback."""
+
+import pytest
+import torch
+
+from rl_engine.integrations import qwen3_next_forward as provider
+
+
+@pytest.mark.parametrize(
+    "call",
+    [
+        lambda: provider.shared_linear(torch.zeros(1, 8), torch.zeros(8, 8)),
+        lambda: provider.stable_top10_routes(torch.zeros(1, 512)),
+        lambda: provider.combine_routes(torch.zeros(1, 10, 8), torch.zeros(1, 10)),
+    ],
+)
+def test_required_cuda_primitives_reject_cpu(call):
+    with pytest.raises(ValueError, match="no CPU fallback"):
+        call()
+
+
+def test_shared_linear_rejects_an_unpinned_vllm(monkeypatch):
+    provider._vllm_linear.cache_clear()
+    monkeypatch.setattr(provider, "version", lambda package: "0.30.1")
+    with pytest.raises(RuntimeError, match="pinned vLLM 0.30.0"):
+        provider._vllm_linear()
+    provider._vllm_linear.cache_clear()
+
+
+def _rows(count, width, seed):
+    generator = torch.Generator().manual_seed(seed)
+    return torch.randn((count, width), generator=generator, dtype=torch.float32) * 4
+
+
+@pytest.mark.parametrize("width", [1, 2, 7, 10, 512])
+def test_fixed_order_row_sum_matches_sequential_double_sum(width):
+    values = _rows(5, width, 901)
+    actual = provider.fixed_order_row_sum(values)
+    assert actual.shape == (5, 1)
+    reference = values.double().sum(-1, keepdim=True).float()
+    torch.testing.assert_close(actual, reference, rtol=1e-5, atol=1e-4)
+
+
+@pytest.mark.parametrize("width", [10, 512])
+def test_fixed_order_row_sum_is_independent_of_row_count(width):
+    values = _rows(32, width, 902)
+    whole = provider.fixed_order_row_sum(values)
+    for count in (1, 8, 17):
+        assert torch.equal(provider.fixed_order_row_sum(values[:count]), whole[:count])
+
+
+def test_fixed_order_softmax_rows_do_not_depend_on_batch():
+    logits = _rows(32, 512, 903)
+    whole = provider.fixed_order_softmax(logits)
+    torch.testing.assert_close(whole.double().sum(-1), torch.ones(32, dtype=torch.float64))
+    for count in (1, 8):
+        assert torch.equal(provider.fixed_order_softmax(logits[:count]), whole[:count])
+    # The row sum is a fixed sequential chain, so each result is a pure function of
+    # its own row and cannot depend on how many rows share the call.
diff --git a/tests/test_qwen3_next_tp_blocks.py b/tests/test_qwen3_next_tp_blocks.py
new file mode 100644
index 000000000..f2ac9c822
--- /dev/null
+++ b/tests/test_qwen3_next_tp_blocks.py
@@ -0,0 +1,93 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""HF mappings, replica checks and official Qwen3-Next MoE dimensions."""
+
+import pytest
+import torch
+
+from rl_engine.integrations import qwen3_next_tp
+from rl_engine.integrations import qwen3_next_tp_blocks as blocks
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def pattern(shape):
+    return (
+        torch.arange(torch.Size(shape).numel(), dtype=torch.int32)
+        .remainder(251)
+        .to(torch.bfloat16)
+        .reshape(shape)
+    )
+
+
+@pytest.mark.parametrize(
+    "name",
+    [
+        "gate.weight",
+        "shared_expert_gate.weight",
+        "shared_expert.gate_proj.weight",
+        "shared_expert.up_proj.weight",
+        "shared_expert.down_proj.weight",
+        "experts.0.gate_proj.weight",
+        "experts.0.up_proj.weight",
+        "experts.511.down_proj.weight",
+    ],
+)
+def test_moe_hf_weight_roundtrip(name):
+    value = pattern(blocks.moe_hf_shapes()[name])
+    shards = [blocks.shard_moe_weight(name, value, rank) for rank in range(4)]
+    exact(blocks.assemble_moe_weight(name, shards), value, name=name)
+    if name.endswith("down_proj.weight"):
+        assert shards[0].shape == (2048, 128)
+    elif name.endswith(("gate_proj.weight", "up_proj.weight")):
+        assert shards[0].shape == (128, 2048)
+
+
+def test_moe_export_rejects_router_replica_drift():
+    values = [pattern((512, 2048)) for _ in range(4)]
+    values[2][0, 0] = -1
+    with pytest.raises(ValueError, match="Replicated MoE"):
+        blocks.assemble_moe_weight("gate.weight", values)
+
+
+@pytest.mark.parametrize("rank", [-1, 4, True, 0.5])
+def test_invalid_shard_rank_rejected(rank):
+    with pytest.raises(ValueError, match="TP4 rank"):
+        blocks.shard_moe_weight("gate.weight", torch.empty(512, 2048, dtype=torch.bfloat16), rank)
+
+
+def test_official_shared_expert_width_is_512_and_each_expert_has_three_weights():
+    shapes = blocks.moe_hf_shapes()
+    assert len(shapes) == 512 * 3 + 5
+    assert shapes["shared_expert.gate_proj.weight"] == (512, 2048)
+    assert shapes["shared_expert.down_proj.weight"] == (2048, 512)
+
+
+def test_tp4_moe_rejects_a_missing_or_wrong_size_tp_group(monkeypatch):
+    monkeypatch.setattr(qwen3_next_tp.dist, "is_initialized", lambda: False)
+    with pytest.raises(ValueError, match="four-rank TP group"):
+        blocks.TP4MoE(group=None, device="meta")
+    monkeypatch.setattr(qwen3_next_tp.dist, "is_initialized", lambda: True)
+    monkeypatch.setattr(qwen3_next_tp.dist, "get_world_size", lambda group: 8)
+    with pytest.raises(ValueError, match="four-rank TP group"):
+        blocks.TP4MoE(group=None, device="meta")
+
+
+def test_tp4_moe_parameter_ownership(monkeypatch):
+    group = object()
+    monkeypatch.setattr(qwen3_next_tp.dist, "is_initialized", lambda: True)
+    monkeypatch.setattr(qwen3_next_tp.dist, "get_world_size", lambda group: 4)
+    monkeypatch.setattr(blocks.dist, "get_rank", lambda group: 2)
+    module = blocks.TP4MoE(group=group, device="meta")
+    assert module.rank == 2
+    assert module.experts.gate_up.shape == (512, 256, 2048)
+    assert module.experts.down.shape == (512, 2048, 128)
+    assert (
+        module.experts.gate_up.partition_dim == 1 and module.experts.gate_up.partition_stride == 2
+    )
+    assert module.experts.down.partition_dim == 2
+    assert module.shared_expert.down_proj.weight.partition_dim == 1
+    # Router and shared-expert gate are replicated: no TP attributes, full shapes.
+    assert not getattr(module.gate.weight, "tensor_model_parallel", False)
+    assert not getattr(module.shared_expert_gate.weight, "tensor_model_parallel", False)
+    assert module.gate.weight.shape == (512, 2048)
diff --git a/tests/test_tensor_identity.py b/tests/test_tensor_identity.py
new file mode 100644
index 000000000..f85c0c4dd
--- /dev/null
+++ b/tests/test_tensor_identity.py
@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""CPU regressions for the raw-bit identity used by Qwen3-Next acceptance checks."""
+
+import pytest
+import torch
+
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal, tensor_bitwise_equal
+
+
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16, torch.float16])
+def test_signed_zero_is_not_identical(dtype):
+    positive = torch.tensor([0.0], dtype=dtype)
+    negative = torch.tensor([-0.0], dtype=dtype)
+    assert not tensor_bitwise_equal(positive, negative)
+    with pytest.raises(AssertionError, match="raw-bit"):
+        assert_tensor_bitwise_equal(positive, negative)
+
+
+@pytest.mark.parametrize("value", [float("nan"), float("inf"), -float("inf")])
+def test_identical_nonfinite_values_are_rejected(value):
+    tensor = torch.tensor([value])
+    assert not tensor_bitwise_equal(tensor, tensor)
+
+
+def test_same_numbers_in_different_dtypes_are_rejected():
+    assert not tensor_bitwise_equal(torch.ones(2), torch.ones(2, dtype=torch.bfloat16))
+
+
+def test_adjacent_bfloat16_values_are_rejected():
+    value = torch.ones(2, dtype=torch.bfloat16)
+    changed = (value.view(torch.int16) + 1).view(torch.bfloat16)
+    assert not tensor_bitwise_equal(value, changed)
+
+
+@pytest.mark.parametrize(
+    "value", [torch.tensor(1.0), torch.empty(0), torch.arange(12).reshape(3, 4).T]
+)
+def test_logical_identity_does_not_require_matching_strides(value):
+    assert_tensor_bitwise_equal(value, value.contiguous().clone())

From 9e6c4f24ce4b5e8888650e1062922711dbe565e0 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 01:58:09 +0000
Subject: [PATCH 46/70] feat(qwen3-next): TP4 GDN, convolution and D=256
 attention mixers

Prep for RFC #428 full_model_chain; serves the gdn_state_bridge row.

- GDN: packed recurrent prefill/decode through the pinned vLLM 0.30.0
  provider with an explicit state boundary (qwen3_next_gdn.py), the
  depthwise causal convolution with cache handoff (qwen3_next_conv.py),
  their combined shared core (qwen3_next_provider.py) and the TP4 block
  with HF GQA-interleaved weight mapping (qwen3_next_tp_gdn.py), now on the
  shared TP boundaries in qwen3_next_tp.py.
- Attention: the deterministic CUDA attention kernel accepts head dim 256
  (ROCm stays 128) and validates backward inputs; shared_attention and
  TP4FullAttention with the KV head replicated on each rank pair, partial
  RoPE at absolute positions and explicit per-slot KV state.
- Real-weight TP4 gates for GDN (layer 0) and attention (layer 3), sharing
  scripts/qwen3_next_gate_common.py with the MoE gate.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/ci.yml                      |   2 +-
 .github/workflows/qwen3-next-provider-gpu.yml |  11 +-
 .../cuda/attention/deterministic_attention.cu |  25 +-
 rl_engine/integrations/qwen3_next_conv.py     | 133 ++++++++++
 rl_engine/integrations/qwen3_next_forward.py  |  26 ++
 rl_engine/integrations/qwen3_next_gdn.py      | 193 ++++++++++++++
 rl_engine/integrations/qwen3_next_provider.py | 110 ++++++++
 .../integrations/qwen3_next_tp_blocks.py      | 241 +++++++++++++++++-
 rl_engine/integrations/qwen3_next_tp_gdn.py   | 159 ++++++++++++
 .../ops/cuda/attention/deterministic_attn.py  |  12 +-
 scripts/qwen3_next_gate_common.py             | 125 +++++++++
 scripts/qwen3_next_tp_attention_check.py      | 134 ++++++++++
 scripts/qwen3_next_tp_gdn_check.py            |  96 +++++++
 scripts/qwen3_next_tp_moe_check.py            | 127 +--------
 tests/check_qwen3_next_attention.py           | 110 ++++++++
 tests/check_qwen3_next_conv_bridge.py         |  96 +++++++
 tests/check_qwen3_next_core_matrix.py         |  97 +++++++
 tests/check_qwen3_next_gdn_bridge.py          | 112 ++++++++
 tests/check_qwen3_next_gdn_sequence.py        | 102 ++++++++
 tests/check_qwen3_next_shared_core.py         |  83 ++++++
 tests/test_qwen3_next_forward_contract.py     |   1 +
 tests/test_qwen3_next_tp_blocks.py            |  71 +++++-
 tests/test_qwen3_next_tp_gdn.py               |  65 +++++
 23 files changed, 1993 insertions(+), 138 deletions(-)
 create mode 100644 rl_engine/integrations/qwen3_next_conv.py
 create mode 100644 rl_engine/integrations/qwen3_next_gdn.py
 create mode 100644 rl_engine/integrations/qwen3_next_provider.py
 create mode 100644 rl_engine/integrations/qwen3_next_tp_gdn.py
 create mode 100644 scripts/qwen3_next_gate_common.py
 create mode 100644 scripts/qwen3_next_tp_attention_check.py
 create mode 100644 scripts/qwen3_next_tp_gdn_check.py
 create mode 100644 tests/check_qwen3_next_attention.py
 create mode 100644 tests/check_qwen3_next_conv_bridge.py
 create mode 100644 tests/check_qwen3_next_core_matrix.py
 create mode 100644 tests/check_qwen3_next_gdn_bridge.py
 create mode 100644 tests/check_qwen3_next_gdn_sequence.py
 create mode 100644 tests/check_qwen3_next_shared_core.py
 create mode 100644 tests/test_qwen3_next_tp_gdn.py

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 2fb92d641..d2cef82de 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -67,7 +67,7 @@ jobs:
         run: |
           python -m pytest rl_engine/tests/test_dispatch.py -v
           PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 python -m pytest tests/test_attention_correctness.py -q -rs
-          python -m pytest tests/test_forward_invariance.py tests/test_tolerance_contract.py tests/test_ws1_workload.py tests/test_gradient_invariance.py tests/test_elementwise_inventory.py tests/test_four_judgment_matrix.py tests/test_op_checks.py tests/test_operator_inputs.py tests/test_profiler.py tests/test_kv_consistency.py tests/test_ws1_qwen3_dense.py tests/test_ws1_chain_integration.py tests/test_qwen3_next_norm.py tests/test_gdn_state_contract.py tests/test_tensor_identity.py tests/test_qwen3_next_forward_contract.py tests/test_qwen3_next_tp_blocks.py -q
+          python -m pytest tests/test_forward_invariance.py tests/test_tolerance_contract.py tests/test_ws1_workload.py tests/test_gradient_invariance.py tests/test_elementwise_inventory.py tests/test_four_judgment_matrix.py tests/test_op_checks.py tests/test_operator_inputs.py tests/test_profiler.py tests/test_kv_consistency.py tests/test_ws1_qwen3_dense.py tests/test_ws1_chain_integration.py tests/test_qwen3_next_norm.py tests/test_gdn_state_contract.py tests/test_tensor_identity.py tests/test_qwen3_next_forward_contract.py tests/test_qwen3_next_tp_blocks.py tests/test_qwen3_next_tp_gdn.py -q
 
       - name: Run Cross-Configuration Contract Tests (CPU-safe)
         run: |
diff --git a/.github/workflows/qwen3-next-provider-gpu.yml b/.github/workflows/qwen3-next-provider-gpu.yml
index 691489402..c045484bd 100644
--- a/.github/workflows/qwen3-next-provider-gpu.yml
+++ b/.github/workflows/qwen3-next-provider-gpu.yml
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # RFC #428 Qwen3-Next provider comparisons: the C1/C3/C4 norm providers, the C6
-# GDN decode-step goldens and the shared GEMM/MoE route-combine provider, each
-# checked against the real vLLM 0.30.0 provider.
+# GDN decode-step goldens, the shared GEMM/MoE route-combine provider and the TP4
+# GDN/convolution/attention mixers, each checked against the real vLLM 0.30.0 provider.
 #
 # These check_ files import vLLM, so the default pytest collection never runs them
 # (tests/test_framework_operator_integrations.py asserts vLLM is not imported). This
@@ -20,6 +20,7 @@ on:
     branches: [main]
     paths:
       - "rl_engine/kernels/ops/**"
+      - "csrc/cuda/attention/**"
       - "rl_engine/integrations/qwen3_next*"
       - "rl_engine/testing/qwen3_next*"
       - "tests/check_*qwen3_next*.py"
@@ -84,4 +85,8 @@ jobs:
         env:
           TRITON_F32_DEFAULT: ieee
         run: |
-          python3 -m pytest -q tests/check_qwen3_next_norm_providers.py tests/check_gdn_recurrent_golden.py tests/check_qwen3_next_forward.py
+          python3 -m pytest -q tests/check_qwen3_next_norm_providers.py tests/check_gdn_recurrent_golden.py \
+            tests/check_qwen3_next_forward.py tests/check_qwen3_next_attention.py \
+            tests/check_qwen3_next_gdn_bridge.py tests/check_qwen3_next_gdn_sequence.py \
+            tests/check_qwen3_next_conv_bridge.py tests/check_qwen3_next_core_matrix.py \
+            tests/check_qwen3_next_shared_core.py
diff --git a/csrc/cuda/attention/deterministic_attention.cu b/csrc/cuda/attention/deterministic_attention.cu
index 82ec824db..aeae9231c 100644
--- a/csrc/cuda/attention/deterministic_attention.cu
+++ b/csrc/cuda/attention/deterministic_attention.cu
@@ -17,7 +17,6 @@
 
 namespace {
 
-constexpr int64_t kDeterministicAttentionHeadDim = 128;
 constexpr int kSoftmaxThreads = 256;
 
 // ---------------------------------------------------------------------------
@@ -220,15 +219,18 @@ void check_deterministic_attention_inputs(
   const int64_t Hkv = k.size(1);
   const int64_t Skv = k.size(2);
 
-  TORCH_CHECK(D == kDeterministicAttentionHeadDim,
-              "deterministic_attention: head dim D must be ",
-              kDeterministicAttentionHeadDim,
-              ", got ",
-              D);
+#if defined(USE_ROCM)
+  TORCH_CHECK(D == 128, "deterministic_attention: ROCm head dim D must be 128, got ", D);
+#else
+  TORCH_CHECK(D == 128 || D == 256,
+              "deterministic_attention: CUDA head dim D must be 128 or 256, got ", D);
+#endif
   TORCH_CHECK(k.size(0) == B && v.size(0) == B,
               "deterministic_attention: batch size mismatch between q/k/v");
   TORCH_CHECK(v.size(1) == Hkv && v.size(2) == Skv && k.size(3) == D && v.size(3) == D,
               "deterministic_attention: k/v shape mismatch");
+  TORCH_CHECK(B > 0 && Hq > 0 && Hkv > 0,
+              "deterministic_attention: batch size and head counts must be positive");
   TORCH_CHECK(Hq % Hkv == 0,
               "deterministic_attention: Hq (",
               Hq,
@@ -573,6 +575,17 @@ std::vector<torch::Tensor> deterministic_attention_backward(
     double scale,
     torch::optional<torch::Tensor> key_padding_mask) {
 
+  check_deterministic_attention_inputs(q, k, v, key_padding_mask);
+  TORCH_CHECK(grad_output.device() == q.device() && grad_output.scalar_type() == q.scalar_type(),
+              "deterministic_attention_backward: grad_output must match q device and dtype");
+  TORCH_CHECK(grad_output.sizes() == q.sizes(),
+              "deterministic_attention_backward: grad_output must have q shape");
+  TORCH_CHECK(P.device() == q.device() && P.scalar_type() == at::kFloat,
+              "deterministic_attention_backward: P must be FP32 on the input device");
+  TORCH_CHECK(P.dim() == 4 && P.size(0) == q.size(0) && P.size(1) == q.size(1) &&
+              P.size(2) == q.size(2) && P.size(3) == k.size(2),
+              "deterministic_attention_backward: P must have shape [B, Hq, Sq, Skv]");
+
   const at::cuda::OptionalCUDAGuard device_guard(at::device_of(q));
 
   auto dO = grad_output.contiguous();
diff --git a/rl_engine/integrations/qwen3_next_conv.py b/rl_engine/integrations/qwen3_next_conv.py
new file mode 100644
index 000000000..39dd6ad1a
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_conv.py
@@ -0,0 +1,133 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Differentiable, explicit-state bridge to the pinned vLLM causal-conv provider.
+
+Forward is the real vLLM update kernel for both prefill chunks and decode.
+Backward recomputes the reference recurrence from training inputs. This module
+does not install model hooks or advertise backend batch-invariance support.
+"""
+
+from functools import lru_cache
+from importlib.metadata import version
+
+import torch
+from torch.autograd.function import once_differentiable
+
+from rl_engine.kernels.ops.pytorch.linear_attn.causal_conv1d import CausalConv1dUpdateOp
+from rl_engine.kernels.ops.pytorch.linear_attn.gated_delta_rule import _validate_state_indices
+
+
+@lru_cache(maxsize=1)
+def _provider():
+    if version("vllm") != "0.30.0":
+        raise RuntimeError("Qwen3-Next conv bridge requires the audited vLLM 0.30.0 provider")
+    from vllm.model_executor.layers.mamba.ops.causal_conv1d import causal_conv1d_update
+
+    return causal_conv1d_update
+
+
+def _lengths(x, state, weight, indices, cu_seqlens):
+    if x.ndim != 2 or not x.is_cuda or x.dtype != torch.bfloat16 or x.shape[1] == 0:
+        raise ValueError("x must be CUDA BF16 [tokens, dim] with positive dim")
+    if weight.shape != (x.shape[1], 4) or weight.dtype != x.dtype or weight.device != x.device:
+        raise ValueError("weight must be BF16 [dim, 4] on the input device")
+    if (
+        state.ndim != 3
+        or state.shape[1:] != (x.shape[1], 3)
+        or state.device != x.device
+        or state.dtype != x.dtype
+    ):
+        raise ValueError("conv state must be BF16 [blocks, dim, 3] on the input device")
+    if (
+        cu_seqlens.ndim != 1
+        or cu_seqlens.device.type != "cpu"
+        or cu_seqlens.dtype not in (torch.int32, torch.int64)
+        or cu_seqlens.numel() < 1
+    ):
+        raise ValueError("cu_seqlens must be a nonempty CPU integer vector")
+    boundaries = cu_seqlens.tolist()
+    if boundaries[0] != 0 or boundaries[-1] != x.shape[0] or boundaries[-1] >= 2**31:
+        raise ValueError("cu_seqlens must cover all input tokens and fit int32")
+    lengths = [end - start for start, end in zip(boundaries, boundaries[1:])]
+    if any(length < 0 for length in lengths):
+        raise ValueError("cu_seqlens must be nondecreasing")
+    _validate_state_indices(indices, len(lengths), state.shape[0], x.device)
+    if bool((indices <= 0).any().item()):
+        raise ValueError("sequence cache indices must be positive; zero is reserved")
+    return lengths
+
+
+class _CausalConvSequence(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, state, weight, indices, cu_seqlens):
+        lengths = _lengths(x, state, weight, indices, cu_seqlens)
+        ctx.save_for_backward(x, state, weight, indices, cu_seqlens)
+        new_state = state.contiguous().clone()
+        output = torch.zeros_like(x)
+        if x.shape[0]:
+            # The upstream varlen path subtracts (max_len - sequence_len) from
+            # its effective cache length, which is for speculative cache layouts.
+            # A width-1 ordinary cache must instead use the dense sequence path.
+            # Equal-length groups need neither padding nor a speculative cache.
+            boundaries = cu_seqlens.tolist()
+            for length in sorted(set(lengths) - {0}):
+                active = [i for i, size in enumerate(lengths) if size == length]
+                packed = torch.stack(
+                    [x[boundaries[seq] : boundaries[seq + 1]].t() for seq in active]
+                )
+                values = _provider()(
+                    packed,
+                    new_state,
+                    weight.contiguous(),
+                    activation="silu",
+                    conv_state_indices=indices[active].to(torch.int32).contiguous(),
+                    out=torch.empty_like(packed),
+                )
+                for row, seq in enumerate(active):
+                    output[boundaries[seq] : boundaries[seq + 1]] = values[row].t()
+        return output, new_state
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output, grad_state):
+        x, state, weight, indices, cu_seqlens = ctx.saved_tensors
+        with torch.enable_grad():
+            inputs = [tensor.detach().requires_grad_(True) for tensor in (x, state, weight)]
+            x_ref, state_ref, weight_ref = inputs
+            pieces = []
+            boundaries = cu_seqlens.tolist()
+            for seq, (start, end) in enumerate(zip(boundaries, boundaries[1:])):
+                for token in range(start, end):
+                    out, state_ref = CausalConv1dUpdateOp()(
+                        x_ref[token : token + 1], state_ref, weight_ref, indices[seq : seq + 1]
+                    )
+                    pieces.append(out)
+            output = torch.cat(pieces) if pieces else x_ref[:0]
+            grads = torch.autograd.grad(
+                (output, state_ref),
+                inputs,
+                (
+                    torch.zeros_like(output) if grad_output is None else grad_output,
+                    torch.zeros_like(state_ref) if grad_state is None else grad_state,
+                ),
+                allow_unused=True,
+            )
+        return (
+            *(
+                torch.zeros_like(value) if grad is None else grad
+                for value, grad in zip(inputs, grads)
+            ),
+            None,
+            None,
+        )
+
+
+def causal_conv_sequence(x, state, weight, indices, cu_seqlens):
+    """Return packed output and an independent cache; retain cache gradients.
+
+    Qwen3-Next's width-four, bias-free SiLU convolution is the only contract.
+    All tensors except CPU ``cu_seqlens`` live on the input CUDA device. The
+    original cache and inputs are never mutated, including in inference mode.
+    """
+    return _CausalConvSequence.apply(x, state, weight, indices, cu_seqlens)
diff --git a/rl_engine/integrations/qwen3_next_forward.py b/rl_engine/integrations/qwen3_next_forward.py
index b5a1e20e0..18c892261 100644
--- a/rl_engine/integrations/qwen3_next_forward.py
+++ b/rl_engine/integrations/qwen3_next_forward.py
@@ -336,3 +336,29 @@ def shared_moe(x, router_weight, gate_up_weights, down_weights):
     routes = stable_top10_routes(shared_router(x, router_weight))
     outputs = _RoutedExperts.apply(x, gate_up_weights, down_weights, routes.indices)
     return combine_routes(outputs, routes.weights), routes
+
+
+@lru_cache(maxsize=1)
+def _attention_op():
+    from rl_engine.kernels.ops.cuda.attention.deterministic_attn import DeterministicAttentionOp
+
+    return DeterministicAttentionOp()
+
+
+def shared_attention(q, k, v):
+    """Qwen TP4 GQA core: BF16 [B,4,Sq,256] over [B,1,Skv,256].
+
+    Queries must be the final Sq positions of the supplied KV prefix. This
+    covers full prefill, any contiguous prefill chunk, and one-token decode.
+    KV tensors can carry the prompt's gradient graph; no state is detached.
+    """
+    _cuda_tensors(q, k, v)
+    if q.ndim != 4 or k.ndim != 4 or v.ndim != 4:
+        raise ValueError("Qwen3-Next attention requires four-dimensional Q/K/V")
+    if q.shape[1] != 4 or k.shape[1] != 1 or q.shape[-1] != 256:
+        raise ValueError("Qwen3-Next TP4 attention requires Q heads=4, KV heads=1, D=256")
+    if q.dtype != torch.bfloat16 or k.dtype != q.dtype or v.dtype != q.dtype:
+        raise ValueError("Qwen3-Next attention inputs must be BF16")
+    if q.shape[2] > k.shape[2]:
+        raise ValueError("Query tokens must fit within the supplied KV prefix")
+    return _attention_op()(q, k, v, causal=True, scale=1 / 16)
diff --git a/rl_engine/integrations/qwen3_next_gdn.py b/rl_engine/integrations/qwen3_next_gdn.py
new file mode 100644
index 000000000..e7d73c3c4
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_gdn.py
@@ -0,0 +1,193 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Experimental training bridge for the pinned vLLM GDN decode provider.
+
+Forward uses the real packed decode kernel, with a private cache copy. Backward
+recomputes the mathematical recurrence from the training inputs and preserves
+initial-state gradients. This is a single operator bridge, not a model installer,
+prefill/decode equivalence claim, or permission to bypass vLLM's BI guard.
+"""
+
+from functools import lru_cache
+from importlib.metadata import version
+
+import torch
+from torch.autograd.function import once_differentiable
+
+from rl_engine.kernels.ops.pytorch.linear_attn.gated_delta_rule import (
+    GatedDeltaRuleRecurrentStepOp,
+    _validate_state_indices,
+)
+
+
+@lru_cache(maxsize=1)
+def _provider():
+    if version("vllm") != "0.30.0":
+        raise RuntimeError("Qwen3-Next GDN bridge requires the audited vLLM 0.30.0 provider")
+    from vllm.third_party.flash_linear_attention.ops import (
+        fused_recurrent_gated_delta_rule_packed_decode,
+    )
+
+    return fused_recurrent_gated_delta_rule_packed_decode
+
+
+def _validate(qkv, a, b, A_log, dt_bias, state, indices, num_k_heads):
+    if not qkv.is_cuda or qkv.ndim != 2 or qkv.dtype != torch.bfloat16:
+        raise ValueError("qkv must be CUDA BF16 [B, D]")
+    if state.ndim != 4 or state.dtype != torch.float32 or state.shape[-2:] != (128, 128):
+        raise ValueError("state must be FP32 [blocks, HV, 128, 128]")
+    if isinstance(num_k_heads, bool) or not isinstance(num_k_heads, int) or num_k_heads <= 0:
+        raise ValueError("num_k_heads must be a positive integer")
+    batch, hv = qkv.shape[0], state.shape[1]
+    if hv <= 0 or hv % num_k_heads or qkv.shape[1] != 2 * num_k_heads * 128 + hv * 128:
+        raise ValueError("packed qkv width and state heads are inconsistent")
+    for name, tensor, shape, dtype in (
+        ("a", a, (batch, hv), qkv.dtype),
+        ("b", b, (batch, hv), qkv.dtype),
+        ("A_log", A_log, (hv,), torch.float32),
+        ("dt_bias", dt_bias, (hv,), torch.float32),
+    ):
+        if tensor.shape != shape or tensor.dtype != dtype or tensor.device != qkv.device:
+            raise ValueError(f"{name} has an incompatible shape, dtype or device")
+    if state.device != qkv.device:
+        raise ValueError("state must share the qkv device")
+    _validate_state_indices(indices, batch, state.shape[0], qkv.device)
+
+
+class _PackedDecode(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, qkv, a, b, A_log, dt_bias, state, indices, scale, num_k_heads):
+        _validate(qkv, a, b, A_log, dt_bias, state, indices, num_k_heads)
+        ctx.save_for_backward(qkv, a, b, A_log, dt_bias, state, indices)
+        ctx.scale, ctx.num_k_heads = float(scale), num_k_heads
+        new_state = state.contiguous().clone()
+        out = torch.zeros(qkv.shape[0], 1, state.shape[1], 128, device=qkv.device, dtype=qkv.dtype)
+        if qkv.shape[0]:
+            _provider()(
+                qkv.contiguous(),
+                a.contiguous(),
+                b.contiguous(),
+                A_log.contiguous(),
+                dt_bias.contiguous(),
+                float(scale),
+                new_state,
+                out,
+                indices.contiguous(),
+                use_qk_l2norm_in_kernel=True,
+            )
+        return out, new_state
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_out, grad_state):
+        *saved, indices = ctx.saved_tensors
+        with torch.enable_grad():
+            inputs = [tensor.detach().requires_grad_(True) for tensor in saved]
+            out, state = GatedDeltaRuleRecurrentStepOp()(
+                *inputs,
+                indices,
+                scale=ctx.scale,
+                num_k_heads=ctx.num_k_heads,
+            )
+            outputs_and_grads = [
+                (value, torch.zeros_like(value) if grad is None else grad)
+                for value, grad in ((out, grad_out), (state, grad_state))
+                if value.requires_grad
+            ]
+            grads = torch.autograd.grad(
+                tuple(value for value, _ in outputs_and_grads),
+                inputs,
+                tuple(grad for _, grad in outputs_and_grads),
+                allow_unused=True,
+            )
+            grads = tuple(
+                torch.zeros_like(value) if grad is None else grad
+                for value, grad in zip(inputs, grads)
+            )
+
+        return (*grads, None, None, None)
+
+
+def packed_decode_training_step(
+    qkv, a, b, A_log, dt_bias, state, indices, *, num_k_heads, scale=None
+):
+    """One train-side token; pass returned state onward without detaching it.
+
+    Supports first-order gradients only. FP32 recurrent state and Qwen3-Next
+    128-wide heads are mandatory. This function never reads rollout tensors.
+    """
+    return _PackedDecode.apply(
+        qkv,
+        a,
+        b,
+        A_log,
+        dt_bias,
+        state,
+        indices,
+        128**-0.5 if scale is None else float(scale),
+        num_k_heads,
+    )
+
+
+def packed_recurrent_sequence(
+    qkv, a, b, A_log, dt_bias, state, indices, cu_seqlens, *, num_k_heads, scale=None
+):
+    """Apply the decode provider to packed, variable-length sequences.
+
+    ``cu_seqlens`` is CPU int32/int64 metadata delimiting contiguous sequences
+    in ``qkv[T, D]``. ``indices[B]`` maps each sequence to a distinct positive
+    cache slot; slot zero remains reserved by the decode provider. Returning
+    the entire FP32 state bank makes chunk continuation and sequence reordering
+    explicit. Callers must pass that state onward without detaching it for
+    training. No rollout cache is read and no batch-invariance capability is
+    registered. This intentionally slow recurrence is an integration reference,
+    not equivalence evidence for the upstream chunk-prefill kernel.
+    """
+    if (
+        not isinstance(cu_seqlens, torch.Tensor)
+        or cu_seqlens.device.type != "cpu"
+        or cu_seqlens.dtype not in (torch.int32, torch.int64)
+        or cu_seqlens.ndim != 1
+        or cu_seqlens.numel() < 1
+    ):
+        raise ValueError("cu_seqlens must be a nonempty CPU int32/int64 vector")
+    boundaries = cu_seqlens.tolist()
+    if qkv.ndim != 2 or boundaries[0] != 0 or boundaries[-1] != qkv.shape[0]:
+        raise ValueError("cu_seqlens must span all packed qkv tokens, starting at zero")
+    lengths = [end - start for start, end in zip(boundaries, boundaries[1:])]
+    if any(length < 0 for length in lengths):
+        raise ValueError("cu_seqlens must be nondecreasing")
+    if a.ndim != 2 or b.ndim != 2 or a.shape[0] != qkv.shape[0] or b.shape[0] != qkv.shape[0]:
+        raise ValueError("a and b must have one row per packed token")
+    _validate(qkv[:0], a[:0], b[:0], A_log, dt_bias, state, indices[:0], num_k_heads)
+    _validate_state_indices(indices, len(lengths), state.shape[0], qkv.device)
+    if bool((indices <= 0).any().item()):
+        raise ValueError("sequence cache indices must be positive; zero is reserved")
+    outputs, positions = [], []
+    for position in range(max(lengths, default=0)):
+        active = [seq for seq, length in enumerate(lengths) if position < length]
+        rows = [boundaries[seq] + position for seq in active]
+        row_ids = torch.tensor(rows, device=qkv.device, dtype=torch.int64)
+        seq_ids = torch.tensor(active, device=qkv.device, dtype=torch.int64)
+        out, state = packed_decode_training_step(
+            qkv.index_select(0, row_ids),
+            a.index_select(0, row_ids),
+            b.index_select(0, row_ids),
+            A_log,
+            dt_bias,
+            state,
+            indices.index_select(0, seq_ids),
+            num_k_heads=num_k_heads,
+            scale=scale,
+        )
+        outputs.append(out[:, 0])
+        positions.extend(rows)
+    if not outputs:
+        return qkv.new_empty((0, state.shape[1], 128)), state
+    # Restore packed sequence order after the time-major recurrent traversal.
+    inverse = [0] * len(positions)
+    for source, destination in enumerate(positions):
+        inverse[destination] = source
+    order = torch.tensor(inverse, device=qkv.device, dtype=torch.int64)
+    return torch.cat(outputs).index_select(0, order), state
diff --git a/rl_engine/integrations/qwen3_next_provider.py b/rl_engine/integrations/qwen3_next_provider.py
new file mode 100644
index 000000000..ea568a020
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_provider.py
@@ -0,0 +1,110 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Shared Qwen3-Next GDN core with explicit provider and state identities.
+
+This is the convolution/recurrence/gated-norm boundary, after the projections
+and before the output projection. It is not a full attention or model adapter.
+The same call can be made under inference_mode or with training autograd.
+"""
+
+from dataclasses import asdict, dataclass
+
+import torch
+
+from rl_engine.integrations.qwen3_next_conv import causal_conv_sequence
+from rl_engine.integrations.qwen3_next_gdn import packed_recurrent_sequence
+
+
+@dataclass(frozen=True)
+class GDNProviderConfig:
+    """The only implemented profile; other providers cannot silently substitute."""
+
+    version: str = "qwen3-next-gdn-core-v1"
+    convolution: str = "vllm-0.30.0-causal-conv-update-equal-length-groups"
+    recurrence: str = "vllm-0.30.0-packed-recurrent-decode"
+    gated_norm: str = "rl-engine-cuda-rmsnorm-gated"
+    recurrent_dtype: str = "float32"
+    activation_dtype: str = "bfloat16"
+    head_dim: int = 128
+
+    def validate(self):
+        if self != GDNProviderConfig():
+            raise ValueError("Unsupported GDN provider configuration")
+
+    def identity(self):
+        self.validate()
+        return asdict(self)
+
+
+@dataclass(frozen=True)
+class GDNState:
+    convolution: torch.Tensor
+    recurrent: torch.Tensor
+
+
+def shared_gdn_core(
+    qkv,
+    a,
+    b,
+    z,
+    A_log,
+    dt_bias,
+    conv_weight,
+    norm_weight,
+    state,
+    indices,
+    cu_seqlens,
+    *,
+    config,
+    num_k_heads,
+    eps=1e-6,
+):
+    """Independently recompute packed training inputs or execute rollout inputs.
+
+    qkv is laid out as contiguous Q, K, V groups. z is [tokens, value_heads, 128].
+    Both callers must explicitly supply the same GDNProviderConfig. State is
+    returned without detachment so a response loss can differentiate through
+    the prompt. No global vLLM batch-invariance capability is changed.
+    """
+    if not isinstance(config, GDNProviderConfig):
+        raise ValueError("An explicit GDNProviderConfig is required")
+    config.validate()
+    if not isinstance(state, GDNState):
+        raise ValueError("An explicit convolution and recurrent GDNState is required")
+    if state.recurrent.ndim != 4:
+        raise ValueError("Recurrent state must have four dimensions")
+    if (
+        z.shape != (qkv.shape[0], state.recurrent.shape[1], 128)
+        or z.dtype != torch.bfloat16
+        or z.device != qkv.device
+    ):
+        raise ValueError("z must be BF16 [tokens, value_heads, 128] on the input device")
+    if (
+        norm_weight.shape != (128,)
+        or norm_weight.dtype != torch.bfloat16
+        or norm_weight.device != qkv.device
+    ):
+        raise ValueError("Gated norm weight must be BF16 [128] on the input device")
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp
+
+    norm = Qwen3NextRMSNormGatedCudaOp()
+
+    convolved, conv_state = causal_conv_sequence(
+        qkv, state.convolution, conv_weight, indices, cu_seqlens
+    )
+    recurrent, recurrent_state = packed_recurrent_sequence(
+        convolved,
+        a,
+        b,
+        A_log,
+        dt_bias,
+        state.recurrent,
+        indices,
+        cu_seqlens,
+        num_k_heads=num_k_heads,
+    )
+    normed = norm(recurrent.reshape(-1, 128), norm_weight, z.reshape(-1, 128), eps=eps).reshape_as(
+        z
+    )
+    return normed, GDNState(conv_state, recurrent_state)
diff --git a/rl_engine/integrations/qwen3_next_tp_blocks.py b/rl_engine/integrations/qwen3_next_tp_blocks.py
index 1c80530d4..7a45c1ad2 100644
--- a/rl_engine/integrations/qwen3_next_tp_blocks.py
+++ b/rl_engine/integrations/qwen3_next_tp_blocks.py
@@ -1,18 +1,22 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-"""Official Qwen3-Next TP4 MoE block for shared adapters.
+"""Official Qwen3-Next TP4 full-attention and MoE blocks for shared adapters.
 
-The block owns the local arithmetic and differentiable TP boundaries. It does
-not register an engine backend or advertise full-model acceptance.
+These blocks own the local arithmetic and differentiable TP boundaries. They
+do not register an engine backend or advertise full-model acceptance.
 """
 
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
+from functools import lru_cache
+
 import torch
 import torch.distributed as dist
 import torch.nn.functional as F
 from torch import nn
 
-from rl_engine.integrations.qwen3_next_forward import shared_linear, shared_moe
+from rl_engine.integrations.qwen3_next_forward import shared_attention, shared_linear, shared_moe
 from rl_engine.integrations.qwen3_next_tp import (
     _CopyToTP,
     _parallel_parameter,
@@ -23,6 +27,235 @@
 )
 from rl_engine.testing.tensor_identity import tensor_bitwise_equal
 
+ATTENTION_HF_SHAPES = {
+    "q_proj.weight": (8192, 2048),
+    "k_proj.weight": (512, 2048),
+    "v_proj.weight": (512, 2048),
+    "o_proj.weight": (2048, 4096),
+    "q_norm.weight": (256,),
+    "k_norm.weight": (256,),
+}
+
+
+def shard_attention_weights(weights: Mapping[str, torch.Tensor], rank: int):
+    """Preserve Q/gate interleaving and replicate each KV head on a rank pair."""
+    _rank(rank)
+    if set(weights) != set(ATTENTION_HF_SHAPES):
+        raise ValueError("Attention weight names do not match the official HF contract")
+    result = {}
+    for name, shape in ATTENTION_HF_SHAPES.items():
+        value = weights[name]
+        _weight(value, shape, name)
+        if name.startswith(("q_norm.", "k_norm.")):
+            part = value
+        elif name in ("k_proj.weight", "v_proj.weight"):
+            part = value.chunk(2, dim=0)[rank // 2]
+        else:
+            part = value.chunk(4, dim=1 if name == "o_proj.weight" else 0)[rank]
+        result[name] = part.contiguous()
+    return result
+
+
+def assemble_attention_weights(shards: Sequence[Mapping[str, torch.Tensor]]):
+    """Invert four local HF mappings and reject divergent replicated storage."""
+    if len(shards) != 4 or any(set(shard) != set(ATTENTION_HF_SHAPES) for shard in shards):
+        raise ValueError("Exactly four complete attention HF shards are required")
+    result = {}
+    for name, shape in ATTENTION_HF_SHAPES.items():
+        values = [shard[name] for shard in shards]
+        if name.startswith(("q_norm.", "k_norm.")):
+            for value in values:
+                _weight(value, shape, name)
+            if any(not tensor_bitwise_equal(value, values[0]) for value in values[1:]):
+                raise ValueError(f"Replicated attention norm differs: {name}")
+            result[name] = values[0]
+        elif name in ("k_proj.weight", "v_proj.weight"):
+            for value in values:
+                _weight(value, (256, 2048), name)
+            if not tensor_bitwise_equal(values[0], values[1]) or not tensor_bitwise_equal(
+                values[2], values[3]
+            ):
+                raise ValueError(f"Replicated KV head differs within its rank pair: {name}")
+            result[name] = torch.cat((values[0], values[2]), dim=0)
+        else:
+            axis = 1 if name == "o_proj.weight" else 0
+            local_shape = list(shape)
+            local_shape[axis] //= 4
+            for value in values:
+                _weight(value, tuple(local_shape), name)
+            result[name] = torch.cat(values, dim=axis)
+    return result
+
+
+@lru_cache(maxsize=4)
+def _kv_replica_groups(group):
+    ranks = dist.get_process_group_ranks(group)
+    # Every TP rank creates both groups in the same order. Only members use a
+    # group's collective, and the rank pairs follow the official KV layout.
+    return tuple(
+        dist.new_group(ranks[start : start + 2], use_local_synchronization=True) for start in (0, 2)
+    )
+
+
+def _norm_module(device):
+    module = nn.Module()
+    module.register_parameter(
+        "weight", nn.Parameter(torch.zeros(256, device=device, dtype=torch.bfloat16))
+    )
+    return module
+
+
+@dataclass(frozen=True)
+class KVState:
+    """Per-slot [1,1,length,256] KV tensors; length is the next absolute position."""
+
+    keys: tuple[torch.Tensor, ...]
+    values: tuple[torch.Tensor, ...]
+
+
+def _packed_sequences(hidden, state, indices, cu_seqlens):
+    if (
+        hidden.ndim != 2
+        or hidden.shape[1] != 2048
+        or hidden.dtype != torch.bfloat16
+        or not hidden.is_cuda
+    ):
+        raise ValueError("Attention hidden must be CUDA BF16 [tokens, 2048]")
+    if hidden.shape[0] < 1:
+        raise ValueError("Attention requires at least one active token")
+    if (
+        not isinstance(state, KVState)
+        or len(state.keys) < 2
+        or len(state.keys) != len(state.values)
+    ):
+        raise ValueError("KVState requires a reserved slot and matching key/value slots")
+    if (
+        indices.ndim != 1
+        or indices.dtype not in (torch.int32, torch.int64)
+        or indices.device != hidden.device
+    ):
+        raise ValueError("State indices must be an integer vector on the input device")
+    if (
+        cu_seqlens.device.type != "cpu"
+        or cu_seqlens.ndim != 1
+        or cu_seqlens.dtype not in (torch.int32, torch.int64)
+    ):
+        raise ValueError("cu_seqlens must be a CPU integer vector")
+    slots, ends = indices.tolist(), cu_seqlens.tolist()
+    if len(ends) != len(slots) + 1 or ends[0] != 0 or ends[-1] != hidden.shape[0]:
+        raise ValueError("cu_seqlens must describe all packed tokens")
+    if any(left > right for left, right in zip(ends, ends[1:])):
+        raise ValueError("cu_seqlens must be nondecreasing")
+    if len(set(slots)) != len(slots) or any(slot <= 0 or slot >= len(state.keys) for slot in slots):
+        raise ValueError("Active state slots must be unique and exclude reserved slot zero")
+    for key, value in zip(state.keys, state.values):
+        if (
+            key.ndim != 4
+            or key.shape[:2] != (1, 1)
+            or key.shape[-1] != 256
+            or key.shape != value.shape
+        ):
+            raise ValueError("KV slots must have matching [1,1,length,256] tensors")
+        if (
+            key.dtype != hidden.dtype
+            or value.dtype != hidden.dtype
+            or key.device != hidden.device
+            or value.device != hidden.device
+        ):
+            raise ValueError("KV slots must have the input device and BF16 dtype")
+    return slots, ends
+
+
+class TP4FullAttention(nn.Module):
+    """Four local query heads and one pair-replicated KV head, with explicit state."""
+
+    def __init__(self, *, group, device, eps=1e-6, rope_theta=10000000):
+        super().__init__()
+        self.group = _tp_group(group)
+        self.rank = dist.get_rank(self.group)
+        self.kv_group = _kv_replica_groups(self.group)[self.rank // 2]
+        if eps != 1e-6 or rope_theta != 10000000:
+            raise ValueError("Full attention requires official epsilon and RoPE theta")
+        self.eps = eps
+        factory = {"device": device, "dtype": torch.bfloat16}
+        self.q_proj = nn.Linear(2048, 2048, bias=False, **factory)
+        self.k_proj = nn.Linear(2048, 256, bias=False, **factory)
+        self.v_proj = nn.Linear(2048, 256, bias=False, **factory)
+        self.o_proj = nn.Linear(1024, 2048, bias=False, **factory)
+        self.q_norm, self.k_norm = _norm_module(device), _norm_module(device)
+        _parallel_parameter(self.q_proj.weight, 0)
+        _parallel_parameter(self.o_proj.weight, 1)
+        _parallel_parameter(self.k_proj.weight, 0, duplicate=self.rank % 2 == 1)
+        _parallel_parameter(self.v_proj.weight, 0, duplicate=self.rank % 2 == 1)
+        # Kept as a Python float, not a buffer: Megatron's Float16Module casts every
+        # floating-point buffer of the actor to BF16, and BF16-rounded inverse
+        # frequencies rotate keys differently from the rollout engine's FP32 ones.
+        self.rope_theta = float(rope_theta)
+
+    def load_hf_weights(self, weights):
+        self.load_state_dict(shard_attention_weights(weights, self.rank), strict=True)
+
+    def export_local_hf_weights(self):
+        return self.state_dict()
+
+    def initial_state(self, blocks):
+        if isinstance(blocks, bool) or not isinstance(blocks, int) or blocks < 2:
+            raise ValueError("KV state requires a reserved slot and an active slot")
+        return KVState(
+            tuple(self.q_proj.weight.new_empty((1, 1, 0, 256)) for _ in range(blocks)),
+            tuple(self.q_proj.weight.new_empty((1, 1, 0, 256)) for _ in range(blocks)),
+        )
+
+    @staticmethod
+    def inverse_frequencies(rope_theta, device):
+        """FP32 inverse frequencies of the 64-wide rotary quarter, computed on demand."""
+        exponents = torch.arange(0, 64, 2, dtype=torch.float32, device=device) / 64
+        return 1.0 / (float(rope_theta) ** exponents)
+
+    def _rotate(self, value, positions):
+        inv_freq = TP4FullAttention.inverse_frequencies(self.rope_theta, value.device)
+        angles = positions.float()[:, None] * inv_freq[None, :]
+        angles = torch.cat((angles, angles), dim=-1)
+        cos, sin = angles.cos().to(value.dtype)[:, None], angles.sin().to(value.dtype)[:, None]
+        rotary, tail = value[..., :64], value[..., 64:]
+        rotated = torch.cat((-rotary[..., 32:], rotary[..., :32]), dim=-1)
+        return torch.cat((rotary * cos + rotated * sin, tail), dim=-1)
+
+    def forward(self, hidden, state, indices, cu_seqlens):
+        from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp
+
+        slots, ends = _packed_sequences(hidden, state, indices, cu_seqlens)
+        copied = _CopyToTP.apply(hidden, self.group)
+        query, gate = shared_linear(copied, self.q_proj.weight).reshape(-1, 4, 512).chunk(2, dim=-1)
+        key = shared_linear(copied, _CopyToTP.apply(self.k_proj.weight, self.kv_group)).reshape(
+            -1, 1, 256
+        )
+        value = shared_linear(copied, _CopyToTP.apply(self.v_proj.weight, self.kv_group)).reshape(
+            -1, 1, 256
+        )
+        norm = Qwen3NextRMSNormCudaOp()
+        query = norm(query, _CopyToTP.apply(self.q_norm.weight, self.group), eps=self.eps)
+        key = norm(key, _CopyToTP.apply(self.k_norm.weight, self.group), eps=self.eps)
+        next_keys, next_values = list(state.keys), list(state.values)
+        outputs = []
+        for slot, begin, end in zip(slots, ends[:-1], ends[1:]):
+            if begin == end:
+                continue
+            offset = state.keys[slot].shape[2]
+            if offset + end - begin > 262144:
+                raise ValueError("KV continuation exceeds the official maximum position")
+            positions = torch.arange(offset, offset + end - begin, device=hidden.device)
+            q = self._rotate(query[begin:end], positions).transpose(0, 1).unsqueeze(0)
+            k = self._rotate(key[begin:end], positions).transpose(0, 1).unsqueeze(0)
+            v = value[begin:end].transpose(0, 1).unsqueeze(0)
+            next_keys[slot] = torch.cat((state.keys[slot], k), dim=2)
+            next_values[slot] = torch.cat((state.values[slot], v), dim=2)
+            out = shared_attention(q, next_keys[slot], next_values[slot])
+            outputs.append(out.squeeze(0).transpose(0, 1))
+        attended = torch.cat(outputs, dim=0) * gate.sigmoid()
+        local = shared_linear(attended.reshape(-1, 1024), self.o_proj.weight)
+        return _ReduceFromTP.apply(local, self.group), KVState(tuple(next_keys), tuple(next_values))
+
 
 def moe_hf_shapes():
     result = {
diff --git a/rl_engine/integrations/qwen3_next_tp_gdn.py b/rl_engine/integrations/qwen3_next_tp_gdn.py
new file mode 100644
index 000000000..a5b883d2b
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_tp_gdn.py
@@ -0,0 +1,159 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""TP4 Qwen3-Next GDN projections, HF weight mapping and explicit recurrent state.
+
+The module is shared by engine adapters. It does not install a vLLM backend or
+claim model-level acceptance. The process group must contain exactly four ranks.
+"""
+
+from collections.abc import Mapping, Sequence
+
+import torch
+import torch.distributed as dist
+from torch import nn
+
+from rl_engine.integrations.qwen3_next_provider import GDNProviderConfig, GDNState, shared_gdn_core
+from rl_engine.integrations.qwen3_next_tp import _CopyToTP, _rank, _ReduceFromTP
+from rl_engine.testing.tensor_identity import tensor_bitwise_equal
+
+_GLOBAL_SHAPES = {
+    "in_proj_qkvz.weight": (12288, 2048),
+    "in_proj_ba.weight": (64, 2048),
+    "conv1d.weight": (8192, 1, 4),
+    "A_log": (32,),
+    "dt_bias": (32,),
+    "norm.weight": (128,),
+    "out_proj.weight": (2048, 4096),
+}
+_LOCAL_SHAPES = {
+    "in_proj_qkvz.weight": (3072, 2048),
+    "in_proj_ba.weight": (16, 2048),
+    "conv1d.weight": (2048, 1, 4),
+    "A_log": (8,),
+    "dt_bias": (8,),
+    "norm.weight": (128,),
+    "out_proj.weight": (2048, 1024),
+}
+
+
+def _validate_weights(weights, shapes):
+    if set(weights) != set(shapes):
+        raise ValueError("GDN weight names do not match the explicit HF contract")
+    device = weights["A_log"].device
+    for name, shape in shapes.items():
+        value = weights[name]
+        if value.shape != shape or value.dtype != torch.bfloat16 or value.device != device:
+            raise ValueError(f"{name} must be BF16 {shape} on {device}")
+
+
+def shard_gdn_weights(weights: Mapping[str, torch.Tensor], rank: int) -> dict[str, torch.Tensor]:
+    """Shard official HF GQA-interleaved projections and Q/K/V-segmented conv."""
+    _rank(rank)
+    _validate_weights(weights, _GLOBAL_SHAPES)
+    result = {}
+    for name, value in weights.items():
+        if name == "norm.weight":
+            shard = value
+        elif name == "out_proj.weight":
+            shard = value[:, rank * 1024 : (rank + 1) * 1024]
+        elif name == "conv1d.weight":
+            q, k, v = value.split((2048, 2048, 4096), dim=0)
+            shard = torch.cat([part.chunk(4, dim=0)[rank] for part in (q, k, v)])
+        else:
+            shard = value.chunk(4, dim=0)[rank]
+        result[name] = shard.contiguous()
+    return result
+
+
+def assemble_gdn_weights(shards: Sequence[Mapping[str, torch.Tensor]]) -> dict[str, torch.Tensor]:
+    """Inverse mapping; reject divergent replicated norm weights."""
+    if len(shards) != 4:
+        raise ValueError("Exactly four ordered TP shards are required")
+    for shard in shards:
+        _validate_weights(shard, _LOCAL_SHAPES)
+    result = {}
+    for name in _GLOBAL_SHAPES:
+        values = [shard[name] for shard in shards]
+        if name == "norm.weight":
+            if any(not tensor_bitwise_equal(value, values[0]) for value in values[1:]):
+                raise ValueError("Replicated GDN norm weights differ across TP ranks")
+            result[name] = values[0]
+        elif name == "conv1d.weight":
+            segments = [value.split((512, 512, 1024), dim=0) for value in values]
+            result[name] = torch.cat(
+                [segments[rank][part] for part in range(3) for rank in range(4)]
+            )
+        else:
+            result[name] = torch.cat(values, dim=1 if name == "out_proj.weight" else 0)
+    return result
+
+
+class TP4GDN(nn.Module):
+    """Complete GDN projection/core/output block with differentiable TP semantics."""
+
+    def __init__(self, *, group, device, config: GDNProviderConfig, eps: float = 1e-6):
+        super().__init__()
+        if not dist.is_initialized() or dist.get_world_size(group) != 4:
+            raise ValueError("TP4GDN requires an initialized four-rank process group")
+        config.validate()
+        self.group = dist.group.WORLD if group is None else group
+        self.provider_config, self.eps = config, eps
+        self.rank = dist.get_rank(group)
+        factory = {"device": device, "dtype": torch.bfloat16}
+        self.in_proj_qkvz = nn.Linear(2048, 3072, bias=False, **factory)
+        self.in_proj_ba = nn.Linear(2048, 16, bias=False, **factory)
+        self.conv1d = nn.Conv1d(2048, 2048, 4, groups=2048, bias=False, **factory)
+        self.A_log = nn.Parameter(torch.zeros(8, **factory))
+        self.dt_bias = nn.Parameter(torch.zeros(8, **factory))
+        self.norm = nn.Module()
+        self.norm.register_parameter("weight", nn.Parameter(torch.ones(128, **factory)))
+        self.out_proj = nn.Linear(1024, 2048, bias=False, **factory)
+
+    def load_hf_weights(self, weights: Mapping[str, torch.Tensor]):
+        self.load_state_dict(shard_gdn_weights(weights, self.rank), strict=True)
+
+    def initial_state(self, blocks: int) -> GDNState:
+        if blocks < 2:
+            raise ValueError("State must include a reserved slot and at least one active slot")
+        return GDNState(
+            self.A_log.new_zeros((blocks, 2048, 3)),
+            torch.zeros(blocks, 8, 128, 128, dtype=torch.float32, device=self.A_log.device),
+        )
+
+    def forward(self, hidden, state, indices, cu_seqlens):
+        from rl_engine.integrations.qwen3_next_forward import shared_linear
+
+        if hidden.ndim != 2 or hidden.shape[1] != 2048 or hidden.dtype != torch.bfloat16:
+            raise ValueError("GDN hidden states must be BF16 [tokens, 2048]")
+        copied = _CopyToTP.apply(hidden, self.group)
+        qkvz = shared_linear(copied, self.in_proj_qkvz.weight).reshape(-1, 4, 768)
+        ba = shared_linear(copied, self.in_proj_ba.weight).reshape(-1, 4, 4)
+        q, k, v, z = qkvz.split((128, 128, 256, 256), dim=-1)
+        b, a = ba.split(2, dim=-1)
+        tokens = hidden.shape[0]
+        packed = torch.cat(
+            [value.reshape(tokens, width) for value, width in ((q, 512), (k, 512), (v, 1024))],
+            dim=-1,
+        )
+        # The norm weight is shared by heads on all four ranks; its gradient
+        # sums contributions from all local head partitions exactly once.
+        norm_weight = _CopyToTP.apply(self.norm.weight, self.group)
+        output, next_state = shared_gdn_core(
+            packed,
+            a.reshape(tokens, 8),
+            b.reshape(tokens, 8),
+            z.reshape(tokens, 8, 128),
+            self.A_log.float(),
+            self.dt_bias.float(),
+            self.conv1d.weight[:, 0],
+            norm_weight,
+            state,
+            indices,
+            cu_seqlens,
+            config=self.provider_config,
+            num_k_heads=4,
+            eps=self.eps,
+        )
+        local = shared_linear(output.reshape(tokens, 1024), self.out_proj.weight)
+        return _ReduceFromTP.apply(local, self.group), next_state
diff --git a/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py b/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py
index 3a9084b18..5b5e636a4 100644
--- a/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py
+++ b/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py
@@ -27,8 +27,8 @@
 from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
 from rl_engine.utils.logger import logger
 
-_HEAD_DIM = 128
 _IS_ROCM = torch.version.hip is not None
+_HEAD_DIMS = (128,) if _IS_ROCM else (128, 256)
 _GPU_PLATFORM = "ROCm" if _IS_ROCM else "CUDA"
 
 
@@ -201,8 +201,10 @@ def _validate_inputs(
                 f"k/v shape mismatch: k={tuple(k.shape)}, v={tuple(v.shape)}, "
                 f"expected k/v [B={b}, Hkv, Skv, D={d}]"
             )
-        if d != _HEAD_DIM:
-            raise ValueError(f"head dim D must be {_HEAD_DIM}, got {d}")
+        if d not in _HEAD_DIMS:
+            raise ValueError(f"head dim D must be one of {_HEAD_DIMS}, got {d}")
+        if b < 1 or hq < 1 or hkv < 1:
+            raise ValueError("batch size and query/key head counts must be positive")
         if hq % hkv != 0:
             raise ValueError(f"Hq={hq} not divisible by Hkv={hkv} (GQA group)")
         if q.dtype not in (torch.float16, torch.bfloat16):
@@ -211,7 +213,11 @@ def _validate_inputs(
             raise ValueError("q, k, v must share the same dtype")
         if not (q.is_cuda and k.is_cuda and v.is_cuda):
             raise ValueError("q, k, v must be GPU tensors")
+        if not (q.device == k.device == v.device):
+            raise ValueError("q, k, v must be on the same device")
         if key_padding_mask is not None:
+            if key_padding_mask.device != q.device:
+                raise ValueError("key_padding_mask must be on the input device")
             if key_padding_mask.dtype != torch.bool:
                 raise ValueError("key_padding_mask must be bool")
             if key_padding_mask.shape != (b, skv):
diff --git a/scripts/qwen3_next_gate_common.py b/scripts/qwen3_next_gate_common.py
new file mode 100644
index 000000000..00642c8de
--- /dev/null
+++ b/scripts/qwen3_next_gate_common.py
@@ -0,0 +1,125 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Shared pieces of the Qwen3-Next TP4 real-weight gates (``scripts/qwen3_next_tp_*_check.py``).
+
+Each gate is launched as ``torchrun --nproc-per-node 4 scripts/<gate>.py
+--checkpoint <dir> --output <dir>`` and writes one ``rank-<r>.json``.
+"""
+
+import argparse
+import json
+import os
+import subprocess
+from collections.abc import Mapping
+from datetime import timedelta
+from importlib.metadata import version
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+from safetensors import safe_open
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+class CheckpointWeights(Mapping):
+    """Keep shard headers open while reading only the requested layer's tensors."""
+
+    def __init__(self, checkpoint, prefix, names, stack):
+        self.root, self.prefix, self.names, self.stack = checkpoint, prefix, tuple(names), stack
+        self.index = json.loads((checkpoint / "model.safetensors.index.json").read_text())[
+            "weight_map"
+        ]
+        self.readers = {}
+
+    def __iter__(self):
+        return iter(self.names)
+
+    def __len__(self):
+        return len(self.names)
+
+    def __getitem__(self, name):
+        if name not in self.names:
+            raise KeyError(name)
+        key = self.prefix + name
+        shard = self.index[key]
+        if shard not in self.readers:
+            self.readers[shard] = self.stack.enter_context(
+                safe_open(self.root / shard, framework="pt", device="cpu")
+            )
+        return self.readers[shard].get_tensor(key)
+
+
+def gather(value):
+    peers = [torch.empty_like(value) for _ in range(4)]
+    dist.all_gather(peers, value.contiguous())
+    return peers
+
+
+def check_gradients(module):
+    result = {}
+    for name, parameter in module.named_parameters():
+        grad = parameter.grad
+        if grad is None or not torch.isfinite(grad).all() or not grad.count_nonzero():
+            raise AssertionError(f"Missing finite nonzero gradient: {name}")
+        result[name] = float(grad.float().abs().max())
+    return result
+
+
+def timed(fn):
+    start, end = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
+    start.record()
+    out = fn()
+    end.record()
+    torch.cuda.synchronize()
+    return out, start.elapsed_time(end) * 1000.0
+
+
+def _git(*args):
+    return subprocess.run(
+        ["git", *args], cwd=ROOT, check=True, capture_output=True, text=True
+    ).stdout.strip()
+
+
+def provenance():
+    from rl_engine.integrations.qwen3_next_forward import FORWARD_PROVIDER_ID
+
+    return {
+        "rl_kernel_commit": _git("rev-parse", "HEAD"),
+        "tracked_tree_dirty": bool(_git("status", "--porcelain", "--untracked-files=no")),
+        "provider": FORWARD_PROVIDER_ID,
+        "environment": {
+            "gpu": torch.cuda.get_device_name(),
+            "torch": torch.__version__,
+            "vllm": version("vllm"),
+            "nccl": ".".join(map(str, torch.cuda.nccl.version())),
+        },
+    }
+
+
+def run_gate(description, scope, gate):
+    """Parse ``--checkpoint/--output``, run ``gate(checkpoint, generator)`` on four ranks."""
+    parser = argparse.ArgumentParser(description=description)
+    parser.add_argument("--checkpoint", required=True, type=Path)
+    parser.add_argument("--output", required=True, type=Path)
+    args = parser.parse_args()
+    rank = int(os.environ["LOCAL_RANK"])
+    if int(os.environ["WORLD_SIZE"]) != 4 or torch.cuda.device_count() < 4:
+        raise RuntimeError(f"The {scope} gate requires exactly four ranks on four GPUs")
+    torch.cuda.set_device(rank)
+    dist.init_process_group(
+        "nccl", timeout=timedelta(minutes=15), device_id=torch.device("cuda", rank)
+    )
+    try:
+        generator = torch.Generator(device="cuda").manual_seed(1234)
+        result = gate(args.checkpoint, generator)
+        args.output.mkdir(parents=True, exist_ok=True)
+        path = args.output / f"rank-{rank}.json"
+        partial = path.with_suffix(".json.partial")
+        payload = {"status": "passed", "scope": scope, "rank": rank, **provenance(), **result}
+        partial.write_text(json.dumps(payload, indent=2) + "\n")
+        partial.replace(path)
+        dist.barrier()
+    finally:
+        dist.destroy_process_group()
diff --git a/scripts/qwen3_next_tp_attention_check.py b/scripts/qwen3_next_tp_attention_check.py
new file mode 100644
index 000000000..00ded482b
--- /dev/null
+++ b/scripts/qwen3_next_tp_attention_check.py
@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""TP4 full-attention gate on the official checkpoint's layer-3 weights; four GPUs, torchrun.
+
+    torchrun --nproc-per-node 4 scripts/qwen3_next_tp_attention_check.py \\
+        --checkpoint <Qwen3-Next-80B-A3B-Instruct> --output <dir>
+
+Requires, bitwise: the HF load/export round trip with the KV head replicated on
+each rank pair; full prefill == chunked prefill (output and KV continuation) for
+8-1024 tokens; a variable-length batch of four == each sequence alone, in any
+order; a sixteen-token decode == the prefill rows; the training recompute == the
+no-grad forward; and identical KV-pair and QK-norm gradients across replicas.
+"""
+
+from contextlib import ExitStack
+
+import torch
+import torch.distributed as dist
+from qwen3_next_gate_common import CheckpointWeights, check_gradients, gather, run_gate
+
+from rl_engine.integrations.qwen3_next_tp_blocks import (
+    ATTENTION_HF_SHAPES,
+    TP4FullAttention,
+    assemble_attention_weights,
+)
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def attention_gate(checkpoint, generator):
+    module = TP4FullAttention(group=dist.group.WORLD, device="cuda")
+    with ExitStack() as stack:
+        weights = CheckpointWeights(
+            checkpoint, "model.layers.3.self_attn.", ATTENTION_HF_SHAPES, stack
+        )
+        module.load_hf_weights(weights)
+        shards = [dict() for _ in range(4)]
+        for name, tensor in module.export_local_hf_weights().items():
+            for rank, value in enumerate(gather(tensor)):
+                shards[rank][name] = value.cpu()
+        for name, tensor in assemble_attention_weights(shards).items():
+            exact(tensor, weights[name], name=f"attention HF {name}")
+    del shards, weights
+    cases = ["hf-load-export"]
+    slots = torch.tensor([1], device="cuda", dtype=torch.int32)
+    with torch.no_grad():
+        for length in (8, 64, 256, 1024):
+            hidden = (
+                torch.randn(length, 2048, device="cuda", dtype=torch.bfloat16, generator=generator)
+                * 0.1
+            )
+            state = module.initial_state(2)
+            whole, expected = module(hidden, state, slots, torch.tensor([0, length]))
+            cut = min(65, length - 1)
+            first, state = module(hidden[:cut], state, slots, torch.tensor([0, cut]))
+            last, state = module(hidden[cut:], state, slots, torch.tensor([0, length - cut]))
+            exact(torch.cat((first, last)), whole, name=f"attention full/chunk {length}")
+            exact(state.keys[1], expected.keys[1], name="attention key continuation")
+            exact(state.values[1], expected.values[1], name="attention value continuation")
+            cases.append(f"full-chunk-{length}")
+        hidden = (
+            torch.randn(88, 2048, device="cuda", dtype=torch.bfloat16, generator=generator) * 0.1
+        )
+        packed, state = module(
+            hidden,
+            module.initial_state(5),
+            torch.tensor([1, 2, 3, 4], device="cuda"),
+            torch.tensor([0, 8, 24, 48, 88]),
+        )
+        order = [3, 1, 0, 2]
+        segments = [hidden[:8], hidden[8:24], hidden[24:48], hidden[48:]]
+        ends = [0]
+        for index in order:
+            ends.append(ends[-1] + segments[index].shape[0])
+        reordered, _ = module(
+            torch.cat([segments[i] for i in order]),
+            module.initial_state(5),
+            torch.tensor([i + 1 for i in order], device="cuda"),
+            torch.tensor(ends),
+        )
+        expected = packed.split((8, 16, 24, 40))
+        exact(
+            reordered,
+            torch.cat([expected[i] for i in order]),
+            name="attention variable-length batch4 reorder",
+        )
+        for index, segment in enumerate(segments):
+            single, _ = module(
+                segment, module.initial_state(2), slots, torch.tensor([0, segment.shape[0]])
+            )
+            exact(single, expected[index], name="attention batch4/single")
+        cases.append("batch4-varlen-reorder")
+    hidden = (
+        torch.randn(80, 2048, device="cuda", dtype=torch.bfloat16, generator=generator) * 0.1
+    ).requires_grad_()
+    with torch.no_grad():
+        expected, _ = module(hidden, module.initial_state(2), slots, torch.tensor([0, 80]))
+        _, decode_state = module(hidden[:64], module.initial_state(2), slots, torch.tensor([0, 64]))
+        decoded = []
+        for token in hidden[64:].split(1):
+            value, decode_state = module(token, decode_state, slots, torch.tensor([0, 1]))
+            decoded.append(value)
+        exact(torch.cat(decoded), expected[64:], name="attention sixteen-token decode")
+    _, state = module(hidden[:64], module.initial_state(2), slots, torch.tensor([0, 64]))
+    output, _ = module(hidden[64:], state, slots, torch.tensor([0, 16]))
+    exact(output, expected[64:], name="attention independent response recompute")
+    output.float().square().mean().backward()
+    if (
+        hidden.grad is None
+        or not torch.isfinite(hidden.grad).all()
+        or not hidden.grad[:64].count_nonzero()
+    ):
+        raise AssertionError("Attention response gradient did not reach the prompt")
+    gradients = check_gradients(module)
+    for projection in (module.k_proj, module.v_proj):
+        peers = gather(projection.weight.grad)
+        exact(peers[0], peers[1], name="KV replica gradient pair0")
+        exact(peers[2], peers[3], name="KV replica gradient pair1")
+    for norm in (module.q_norm, module.k_norm):
+        for peer in gather(norm.weight.grad):
+            exact(peer, norm.weight.grad, name="QK norm replicated gradient")
+    cases.extend(
+        ("sixteen-token-decode", "prompt-gradient", "kv-pair-gradient", "qk-norm-gradient")
+    )
+    return {"cases": cases, "gradient_max_abs": gradients}
+
+
+if __name__ == "__main__":
+    run_gate(
+        __doc__,
+        "tp4_full_attention_layer3",
+        lambda checkpoint, generator: {"attention": attention_gate(checkpoint, generator)},
+    )
diff --git a/scripts/qwen3_next_tp_gdn_check.py b/scripts/qwen3_next_tp_gdn_check.py
new file mode 100644
index 000000000..e3aae3692
--- /dev/null
+++ b/scripts/qwen3_next_tp_gdn_check.py
@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""TP4 GDN gate on the official checkpoint's layer-0 weights; four GPUs, torchrun.
+
+    torchrun --nproc-per-node 4 scripts/qwen3_next_tp_gdn_check.py \\
+        --checkpoint <Qwen3-Next-80B-A3B-Instruct> --output <dir>
+
+A one-layer integration gate, not full-model evidence. Requires, bitwise: the
+HF load/export round trip of the actual parameter storage; full prefill ==
+chunked prefill, output and convolution/recurrent state handoff, for 8-1024
+tokens; the training response recompute == the no-grad forward; a gradient that
+reaches the prompt through the state; identical replicated norm gradients.
+"""
+
+import json
+
+import torch
+import torch.distributed as dist
+from qwen3_next_gate_common import check_gradients, run_gate
+from safetensors import safe_open
+
+from rl_engine.integrations.qwen3_next_provider import GDNProviderConfig
+from rl_engine.integrations.qwen3_next_tp_gdn import _GLOBAL_SHAPES, TP4GDN, assemble_gdn_weights
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def load_first_gdn(checkpoint):
+    index = json.loads((checkpoint / "model.safetensors.index.json").read_text())["weight_map"]
+    result = {}
+    for name in _GLOBAL_SHAPES:
+        key = f"model.layers.0.linear_attn.{name}"
+        with safe_open(checkpoint / index[key], framework="pt", device="cpu") as reader:
+            result[name] = reader.get_tensor(key)
+    return result
+
+
+def gdn_gate(checkpoint, generator):
+    weights = load_first_gdn(checkpoint)
+    module = TP4GDN(group=dist.group.WORLD, device="cuda", config=GDNProviderConfig())
+    module.load_hf_weights(weights)
+    # Validate actual parameter storage, not just the pure conversion helper.
+    shards = [dict() for _ in range(4)]
+    for name, parameter in module.named_parameters():
+        gathered = [torch.empty_like(parameter) for _ in range(4)]
+        dist.all_gather(gathered, parameter.detach())
+        for peer, value in enumerate(gathered):
+            shards[peer][name] = value.cpu()
+    for name, restored in assemble_gdn_weights(shards).items():
+        exact(restored, weights[name], name=f"loaded weight {name}")
+    del shards, weights
+    cases = []
+    indices = torch.tensor([1], device="cuda", dtype=torch.int32)
+    with torch.no_grad():
+        for length in (8, 64, 256, 1024):
+            hidden = (
+                torch.randn(length, 2048, device="cuda", dtype=torch.bfloat16, generator=generator)
+                * 0.1
+            )
+            initial = module.initial_state(2)
+            whole, expected_state = module(hidden, initial, indices, torch.tensor([0, length]))
+            cut = min(65, length - 1)
+            first, state = module(hidden[:cut], initial, indices, torch.tensor([0, cut]))
+            last, state = module(hidden[cut:], state, indices, torch.tensor([0, length - cut]))
+            exact(torch.cat((first, last)), whole, name=f"TP4 layer length={length}")
+            exact(state.convolution, expected_state.convolution, name="convolution handoff")
+            exact(state.recurrent, expected_state.recurrent, name="recurrent handoff")
+            cases.append(f"full-chunk-{length}")
+    hidden = (
+        torch.randn(80, 2048, device="cuda", dtype=torch.bfloat16, generator=generator) * 0.1
+    ).requires_grad_()
+    initial = module.initial_state(2)
+    with torch.no_grad():
+        expected, _ = module(hidden, initial, indices, torch.tensor([0, 80]))
+    _, prompt_state = module(hidden[:64], initial, indices, torch.tensor([0, 64]))
+    actual, _ = module(hidden[64:], prompt_state, indices, torch.tensor([0, 16]))
+    exact(actual, expected[64:], name="independent training response")
+    actual.float().square().mean().backward()
+    if (
+        hidden.grad is None
+        or not torch.isfinite(hidden.grad).all()
+        or not hidden.grad[:64].count_nonzero()
+    ):
+        raise AssertionError("Response gradient did not reach prompt input")
+    grad_stats = check_gradients(module)
+    norm_grads = [torch.empty_like(module.norm.weight.grad) for _ in range(4)]
+    dist.all_gather(norm_grads, module.norm.weight.grad)
+    for other in norm_grads:
+        exact(other, module.norm.weight.grad, name="replicated norm gradient")
+    cases.extend(("prompt-gradient", "replicated-norm-gradient", "hf-load-export"))
+    return {"cases": cases, "gradient_max_abs": grad_stats}
+
+
+if __name__ == "__main__":
+    run_gate(__doc__, "tp4_gdn_layer0", gdn_gate)
diff --git a/scripts/qwen3_next_tp_moe_check.py b/scripts/qwen3_next_tp_moe_check.py
index 817c07bcc..450f7fd51 100644
--- a/scripts/qwen3_next_tp_moe_check.py
+++ b/scripts/qwen3_next_tp_moe_check.py
@@ -18,91 +18,19 @@
   and a finite, nonzero gradient for every parameter.
 """
 
-import argparse
-import json
-import os
-import subprocess
-from collections.abc import Mapping
 from contextlib import ExitStack
-from datetime import timedelta
-from importlib.metadata import version
-from pathlib import Path
 
 import torch
 import torch.distributed as dist
-from safetensors import safe_open
+from qwen3_next_gate_common import CheckpointWeights, check_gradients, gather, run_gate, timed
 
-from rl_engine.integrations.qwen3_next_forward import (
-    FORWARD_PROVIDER_ID,
-    shared_router,
-    stable_top10_routes,
-)
+from rl_engine.integrations.qwen3_next_forward import shared_router, stable_top10_routes
 from rl_engine.integrations.qwen3_next_tp_blocks import TP4MoE, assemble_moe_weight, moe_hf_shapes
 from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
 
-ROOT = Path(__file__).resolve().parents[1]
 TOKEN_COUNTS = (8, 64, 256, 1024)
 
 
-class CheckpointWeights(Mapping):
-    """Keep shard headers open while reading only the requested layer's tensors."""
-
-    def __init__(self, checkpoint, prefix, names, stack):
-        self.root, self.prefix, self.names, self.stack = checkpoint, prefix, tuple(names), stack
-        self.index = json.loads((checkpoint / "model.safetensors.index.json").read_text())[
-            "weight_map"
-        ]
-        self.readers = {}
-
-    def __iter__(self):
-        return iter(self.names)
-
-    def __len__(self):
-        return len(self.names)
-
-    def __getitem__(self, name):
-        if name not in self.names:
-            raise KeyError(name)
-        key = self.prefix + name
-        shard = self.index[key]
-        if shard not in self.readers:
-            self.readers[shard] = self.stack.enter_context(
-                safe_open(self.root / shard, framework="pt", device="cpu")
-            )
-        return self.readers[shard].get_tensor(key)
-
-
-def atomic_json(path, payload):
-    temporary = path.with_suffix(path.suffix + ".partial")
-    temporary.write_text(json.dumps(payload, indent=2) + "\n")
-    temporary.replace(path)
-
-
-def gather(value):
-    peers = [torch.empty_like(value) for _ in range(4)]
-    dist.all_gather(peers, value.contiguous())
-    return peers
-
-
-def check_gradients(module):
-    result = {}
-    for name, parameter in module.named_parameters():
-        grad = parameter.grad
-        if grad is None or not torch.isfinite(grad).all() or not grad.count_nonzero():
-            raise AssertionError(f"Missing finite nonzero gradient: {name}")
-        result[name] = float(grad.float().abs().max())
-    return result
-
-
-def timed(fn):
-    start, end = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
-    start.record()
-    out = fn()
-    end.record()
-    torch.cuda.synchronize()
-    return out, start.elapsed_time(end) * 1000.0
-
-
 def moe_gate(checkpoint, generator):
     module = TP4MoE(group=dist.group.WORLD, device="cuda")
     with ExitStack() as stack:
@@ -158,50 +86,9 @@ def moe_gate(checkpoint, generator):
     return {"cases": cases, "gradient_max_abs": gradients, "forward_us": forward_us}
 
 
-def _git(*args):
-    return subprocess.run(
-        ["git", *args], cwd=ROOT, check=True, capture_output=True, text=True
-    ).stdout.strip()
-
-
-def main():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--checkpoint", required=True, type=Path)
-    parser.add_argument("--output", required=True, type=Path)
-    args = parser.parse_args()
-    rank = int(os.environ["LOCAL_RANK"])
-    if int(os.environ["WORLD_SIZE"]) != 4 or torch.cuda.device_count() < 4:
-        raise RuntimeError("The TP4 MoE gate requires exactly four ranks on four GPUs")
-    torch.cuda.set_device(rank)
-    dist.init_process_group(
-        "nccl", timeout=timedelta(minutes=15), device_id=torch.device("cuda", rank)
-    )
-    try:
-        generator = torch.Generator(device="cuda").manual_seed(1234)
-        moe = moe_gate(args.checkpoint, generator)
-        args.output.mkdir(parents=True, exist_ok=True)
-        atomic_json(
-            args.output / f"rank-{rank}.json",
-            {
-                "status": "passed",
-                "scope": "tp4_moe_block_layer0",
-                "rank": rank,
-                "rl_kernel_commit": _git("rev-parse", "HEAD"),
-                "tracked_tree_dirty": bool(_git("status", "--porcelain", "--untracked-files=no")),
-                "provider": FORWARD_PROVIDER_ID,
-                "environment": {
-                    "gpu": torch.cuda.get_device_name(),
-                    "torch": torch.__version__,
-                    "vllm": version("vllm"),
-                    "nccl": ".".join(map(str, torch.cuda.nccl.version())),
-                },
-                "moe": moe,
-            },
-        )
-        dist.barrier()
-    finally:
-        dist.destroy_process_group()
-
-
 if __name__ == "__main__":
-    main()
+    run_gate(
+        __doc__,
+        "tp4_moe_block_layer0",
+        lambda checkpoint, generator: {"moe": moe_gate(checkpoint, generator)},
+    )
diff --git a/tests/check_qwen3_next_attention.py b/tests/check_qwen3_next_attention.py
new file mode 100644
index 000000000..d184d5735
--- /dev/null
+++ b/tests/check_qwen3_next_attention.py
@@ -0,0 +1,110 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Required Qwen3-Next CUDA attention gates; unavailable CUDA is a failure."""
+
+import pytest
+import torch
+
+from rl_engine.kernels.ops.base import _C
+from rl_engine.kernels.ops.cuda.attention.deterministic_attn import DeterministicAttentionOp
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def inputs(tokens, *, batch=1, query_tokens=None, device="cuda", grad=False):
+    assert torch.cuda.is_available(), "Mandatory attention CUDA gate cannot skip"
+    gen = torch.Generator(device=device).manual_seed(920)
+    query_tokens = tokens if query_tokens is None else query_tokens
+    return tuple(
+        torch.randn(batch, heads, length, 256, device=device, dtype=torch.bfloat16, generator=gen)
+        .mul_(0.25)
+        .requires_grad_(grad)
+        for heads, length in ((4, query_tokens), (1, tokens), (1, tokens))
+    )
+
+
+@pytest.mark.parametrize("tokens", [8, 64, 256, 1024])
+@torch.no_grad()
+def test_d256_full_chunk_decode_and_batch_are_raw_bit_equal(tokens):
+    q, k, v = inputs(tokens, batch=4)
+    op = DeterministicAttentionOp()
+    whole, lse = op.forward_with_lse(q, k, v)
+    order = torch.tensor([3, 1, 0, 2], device=q.device)
+    exact(op(q[order], k[order], v[order]), whole[order])
+    for row in range(4):
+        exact(op(q[row : row + 1], k[row : row + 1], v[row : row + 1]), whole[row : row + 1])
+    starts = [0, 1, 7, tokens - 1, tokens]
+    starts = sorted(set(starts))
+    for start, end in zip(starts[:-1], starts[1:]):
+        out, part_lse = op.forward_with_lse(q[:, :, start:end], k[:, :, :end], v[:, :, :end])
+        exact(out, whole[:, :, start:end])
+        exact(part_lse, lse[:, :, start:end])
+
+
+def test_d256_backward_with_cached_prefix_matches_float64_formula():
+    q, k, v = inputs(19, query_tokens=3, grad=True)
+    op = DeterministicAttentionOp()
+    out = op(q, k, v)
+    grad = torch.linspace(-1, 1, out.numel(), device=q.device).reshape_as(out).to(out.dtype)
+    out.backward(grad)
+    qr, kr, vr = (x.detach().double().requires_grad_() for x in (q, k, v))
+    scores = qr @ kr.repeat_interleave(4, dim=1).transpose(-1, -2) / 16
+    causal = (
+        torch.arange(19, device=q.device)[None, :] <= torch.arange(16, 19, device=q.device)[:, None]
+    )
+    expected = scores.masked_fill(~causal, -torch.inf).softmax(-1) @ vr.repeat_interleave(4, dim=1)
+    expected.backward(grad.double())
+    # Formula accuracy is separate from the raw-bit provider comparisons above.
+    for actual, reference in zip((q, k, v), (qr, kr, vr)):
+        assert torch.isfinite(actual.grad).all() and actual.grad.abs().max() > 0
+        torch.testing.assert_close(
+            actual.grad.float(), reference.grad.float(), atol=0.002, rtol=0.016
+        )
+    assert k.grad[:, :, :16].abs().max() > 0
+    assert v.grad[:, :, :16].abs().max() > 0
+
+
+def test_native_backward_rejects_malformed_probability_and_gradient_buffers():
+    q, k, v = inputs(8)
+    out, _, probabilities = _C.deterministic_attention_forward(q, k, v, True, 1 / 16, None)
+    with pytest.raises(RuntimeError, match="P must be FP32"):
+        _C.deterministic_attention_backward(
+            out, q, k, v, probabilities.to(q.dtype), True, 1 / 16, None
+        )
+    with pytest.raises(RuntimeError, match="grad_output must have q shape"):
+        _C.deterministic_attention_backward(
+            out[:, :, :1], q, k, v, probabilities, True, 1 / 16, None
+        )
+    with pytest.raises(RuntimeError, match="P must have shape"):
+        _C.deterministic_attention_backward(
+            out, q, k, v, probabilities[:, :, :1], True, 1 / 16, None
+        )
+
+
+def test_d256_input_device_guard_and_nondefault_stream():
+    assert torch.cuda.device_count() >= 2, "Mandatory cross-device gate requires allocated GPUs"
+    previous = torch.cuda.current_device()
+    try:
+        q, k, v = inputs(8, device="cuda:1", grad=True)
+        op = DeterministicAttentionOp()
+        torch.cuda.set_device(1)
+        expected = op(q, k, v)
+        expected.sum().backward()
+        gradients = [x.grad.clone() for x in (q, k, v)]
+        for x in (q, k, v):
+            x.grad = None
+        torch.cuda.synchronize(1)
+        stream = torch.cuda.Stream(device=1)
+        with torch.cuda.stream(stream):
+            torch.cuda.set_device(0)
+            actual = op(q, k, v)
+            actual.sum().backward()
+            assert torch.cuda.current_device() == 0
+        stream.synchronize()
+        exact(actual, expected)
+        for x, expected_grad in zip((q, k, v), gradients):
+            exact(x.grad, expected_grad)
+        with pytest.raises(ValueError, match="same device"):
+            op(q, k.to("cuda:0"), v)
+    finally:
+        torch.cuda.set_device(previous)
diff --git a/tests/check_qwen3_next_conv_bridge.py b/tests/check_qwen3_next_conv_bridge.py
new file mode 100644
index 000000000..fb6778a71
--- /dev/null
+++ b/tests/check_qwen3_next_conv_bridge.py
@@ -0,0 +1,96 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""The convolution provider must agree across batch, chunk and cache layouts."""
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_conv import _provider, causal_conv_sequence
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def inputs(lengths):
+    assert torch.cuda.is_available(), "Required provider tests cannot skip CUDA"
+    gen = torch.Generator(device="cuda").manual_seed(17)
+
+    def rand(*shape):
+        return torch.randn(*shape, device="cuda", dtype=torch.bfloat16, generator=gen)
+
+    return (
+        rand(sum(lengths), 2048),
+        rand(len(lengths) + 1, 2048, 3),
+        rand(2048, 4),
+        torch.arange(1, len(lengths) + 1, device="cuda", dtype=torch.int32),
+        torch.tensor([0, *torch.tensor(lengths).cumsum(0).tolist()]),
+    )
+
+
+@torch.no_grad()
+@pytest.mark.parametrize("lengths", [(1,), (64, 8, 0, 16), (1024, 256, 64, 8)])
+def test_varlen_matches_repeated_decode_and_preserves_inputs(lengths):
+    x, state, weight, indices, cu = inputs(lengths)
+    saved = [tensor.clone() for tensor in (x, state, weight)]
+    output, final_state = causal_conv_sequence(x, state, weight, indices, cu)
+    reference_state = state.clone()
+    reference_rows = []
+    for seq in range(len(lengths)):
+        for token in range(cu[seq], cu[seq + 1]):
+            value = x[token : token + 1].clone()
+            reference_rows.append(
+                _provider()(
+                    value,
+                    reference_state,
+                    weight,
+                    activation="silu",
+                    conv_state_indices=indices[seq : seq + 1],
+                )
+            )
+    exact(output, torch.cat(reference_rows))
+    exact(final_state, reference_state)
+    for original, before in zip((x, state, weight), saved):
+        exact(original, before)
+
+
+@torch.no_grad()
+def test_chunked_and_reordered_sequences_match():
+    x, state, weight, indices, cu = inputs((1024, 64))
+    whole, whole_state = causal_conv_sequence(x, state, weight, indices, cu)
+    # Continue both sequences after 32 tokens, in the opposite batch order.
+    prompt = torch.cat((x[:32], x[1024:1056]))
+    first, next_state = causal_conv_sequence(
+        prompt, state, weight, indices, torch.tensor([0, 32, 64])
+    )
+    response = torch.cat((x[1056:], x[32:1024]))
+    second, next_state = causal_conv_sequence(
+        response, next_state, weight, indices.flip(0), torch.tensor([0, 32, 1024])
+    )
+    restored = torch.cat((first[:32], second[32:], first[32:], second[:32]))
+    exact(restored, whole)
+    exact(next_state, whole_state)
+
+
+def test_response_gradient_reaches_prompt_and_convolution_weight():
+    x, state, weight, indices, _ = inputs((8,))
+    x.requires_grad_()
+    state.requires_grad_()
+    weight.requires_grad_()
+    _, prompt_state = causal_conv_sequence(x[:4], state, weight, indices, torch.tensor([0, 4]))
+    out, final_state = causal_conv_sequence(
+        x[4:], prompt_state, weight, indices, torch.tensor([0, 4])
+    )
+    (out.float().square().sum() + final_state.float().square().sum() * 0.01).backward()
+    for tensor in (x, weight):
+        assert tensor.grad is not None and torch.isfinite(tensor.grad).all()
+        assert tensor.grad.abs().max() > 0
+    assert x.grad[:4].abs().max() > 0
+
+
+def test_empty_sequence_preserves_cache_and_gradients():
+    x, state, weight, indices, cu = inputs((0,))
+    state.requires_grad_()
+    out, next_state = causal_conv_sequence(x, state, weight, indices, cu)
+    assert out.shape == x.shape
+    exact(next_state, state)
+    next_state.float().sum().backward()
+    exact(state.grad, torch.ones_like(state))
diff --git a/tests/check_qwen3_next_core_matrix.py b/tests/check_qwen3_next_core_matrix.py
new file mode 100644
index 000000000..8c299a87f
--- /dev/null
+++ b/tests/check_qwen3_next_core_matrix.py
@@ -0,0 +1,97 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Mandatory TP4-shape GDN state tests; no full-model claim or optional skips."""
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_provider import GDNProviderConfig, GDNState, shared_gdn_core
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def _inputs(lengths):
+    assert torch.cuda.is_available(), "Required shared-provider CUDA tests cannot skip"
+    generator = torch.Generator(device="cuda").manual_seed(20261002)
+
+    def rand(*shape, dtype=torch.bfloat16):
+        return torch.randn(*shape, device="cuda", dtype=dtype, generator=generator) * 0.1
+
+    tokens = sum(lengths)
+    values = [rand(tokens, 2048), rand(tokens, 8), rand(tokens, 8), rand(tokens, 8, 128)]
+    weights = [rand(8, dtype=torch.float32), rand(8, dtype=torch.float32), rand(2048, 4), rand(128)]
+    state = GDNState(
+        rand(len(lengths) + 2, 2048, 3),
+        rand(len(lengths) + 2, 8, 128, 128, dtype=torch.float32),
+    )
+    indices = torch.arange(1, len(lengths) + 1, device="cuda", dtype=torch.int32)
+    return values, weights, state, indices
+
+
+def _run(values, weights, state, indices, lengths):
+    boundaries = torch.tensor([0, *torch.tensor(lengths).cumsum(0).tolist()], dtype=torch.int64)
+    return shared_gdn_core(
+        *values, *weights, state, indices, boundaries, config=GDNProviderConfig(), num_k_heads=4
+    )
+
+
+@pytest.mark.parametrize("batch", [1, 4])
+@pytest.mark.parametrize("length", [8, 64, 256, 1024])
+@torch.no_grad()
+def test_full_chunked_reordered_shared_core(batch, length):
+    lengths = [length] * batch
+    values, weights, initial, indices = _inputs(lengths)
+    whole, final = _run(values, weights, initial, indices, lengths)
+    for cut in (1, 63, 64, 65, 100, 528, 576):
+        cut = min(cut, length)
+        state = initial
+        pieces = [[] for _ in lengths]
+        for start, end in ((0, cut), (cut, length)):
+            # Reorder the response-bearing step, preserving sequence/cache identity.
+            order = list(range(batch)) if start == 0 else list(reversed(range(batch)))
+            rows = [row for seq in order for row in range(seq * length + start, seq * length + end)]
+            row_ids = torch.tensor(rows, device="cuda", dtype=torch.int64)
+            chunk = [value.index_select(0, row_ids) for value in values]
+            part, state = _run(chunk, weights, state, indices[order], [end - start] * batch)
+            for position, seq in enumerate(order):
+                pieces[seq].append(part[position * (end - start) : (position + 1) * (end - start)])
+        exact(torch.cat([torch.cat(seq) for seq in pieces]), whole, name=f"output cut={cut}")
+        exact(state.convolution, final.convolution, name=f"conv state cut={cut}")
+        exact(state.recurrent, final.recurrent, name=f"recurrent state cut={cut}")
+    exact(final.convolution[0], initial.convolution[0], name="reserved conv slot")
+    exact(final.recurrent[0], initial.recurrent[0], name="reserved recurrent slot")
+
+
+@torch.no_grad()
+def test_variable_lengths_empty_and_mixed_step_match_independent_sequences():
+    lengths = [65, 1, 0, 17]
+    values, weights, initial, indices = _inputs(lengths)
+    mixed, mixed_state = _run(values, weights, initial, indices, lengths)
+    start = 0
+    for seq, length in enumerate(lengths):
+        end = start + length
+        output, state = _run(
+            [value[start:end] for value in values],
+            weights,
+            initial,
+            indices[seq : seq + 1],
+            [length],
+        )
+        exact(output, mixed[start:end], name=f"sequence {seq}")
+        exact(state.convolution[seq + 1], mixed_state.convolution[seq + 1], name="conv state")
+        exact(state.recurrent[seq + 1], mixed_state.recurrent[seq + 1], name="recurrent state")
+        start = end
+    exact(mixed_state.recurrent[3], initial.recurrent[3], name="empty sequence state")
+
+
+@pytest.mark.parametrize("batch", [1, 4])
+@torch.no_grad()
+def test_shared_core_one_hundred_repetitions(batch):
+    lengths = [8] * batch
+    values, weights, initial, indices = _inputs(lengths)
+    expected, expected_state = _run(values, weights, initial, indices, lengths)
+    for repeat in range(100):
+        output, state = _run(values, weights, initial, indices, lengths)
+        exact(output, expected, name=f"output repeat={repeat}")
+        exact(state.convolution, expected_state.convolution, name="conv state")
+        exact(state.recurrent, expected_state.recurrent, name="recurrent state")
diff --git a/tests/check_qwen3_next_gdn_bridge.py b/tests/check_qwen3_next_gdn_bridge.py
new file mode 100644
index 000000000..9e44cc9e0
--- /dev/null
+++ b/tests/check_qwen3_next_gdn_bridge.py
@@ -0,0 +1,112 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Explicit subprocess provider tests; never import vLLM into ordinary pytest."""
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_gdn import _provider, packed_decode_training_step
+from rl_engine.kernels.ops.pytorch.linear_attn import GatedDeltaRuleRecurrentStepOp
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal, tensor_bitwise_equal
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
+
+
+def _inputs(seed=1):
+    gen = torch.Generator(device="cuda").manual_seed(seed)
+
+    def rand(*shape, dtype=torch.bfloat16):
+        return torch.randn(*shape, device="cuda", dtype=dtype, generator=gen)
+
+    return [
+        rand(2, 512),
+        rand(2, 2),
+        rand(2, 2),
+        rand(2, dtype=torch.float32),
+        rand(2, dtype=torch.float32),
+        rand(3, 2, 128, 128, dtype=torch.float32) * 0.01,
+    ]
+
+
+def test_bridge_forward_is_provider_exact_and_does_not_mutate_inputs():
+    inputs = _inputs()
+    state_before = inputs[-1].clone()
+    indices = torch.tensor([1, 2], device="cuda", dtype=torch.int32)
+    out, state = packed_decode_training_step(*inputs, indices, num_k_heads=1)
+    expected = torch.empty_like(out)
+    expected_state = state_before.clone()
+    _provider()(
+        *inputs[:-1], 128**-0.5, expected_state, expected, indices, use_qk_l2norm_in_kernel=True
+    )
+    assert_tensor_bitwise_equal(out, expected)
+    assert_tensor_bitwise_equal(state, expected_state)
+    assert tensor_bitwise_equal(inputs[-1], state_before)
+
+
+def test_bridge_keeps_prompt_state_gradient_through_response():
+    inputs = [x.requires_grad_() for x in _inputs()]
+    indices = torch.tensor([1, 2], device="cuda", dtype=torch.int32)
+    _, prompt_state = packed_decode_training_step(*inputs, indices, num_k_heads=1)
+    prompt_state.retain_grad()
+    response = _inputs(seed=2)[0].requires_grad_()
+    out, final_state = packed_decode_training_step(
+        response, *inputs[1:5], prompt_state, indices, num_k_heads=1
+    )
+    (out.float().square().sum() + final_state.square().sum() * 0.01).backward()
+    assert prompt_state.grad is not None and prompt_state.grad.abs().max() > 0
+    for tensor in [*inputs, response]:
+        assert tensor.grad is not None
+        assert torch.isfinite(tensor.grad).all()
+        assert tensor.grad.abs().max() > 0
+
+
+def test_bridge_backward_matches_recomputed_recurrence_vjp():
+    inputs = [x.requires_grad_() for x in _inputs()]
+    indices = torch.tensor([1, 2], device="cuda", dtype=torch.int32)
+    out, state = packed_decode_training_step(*inputs, indices, num_k_heads=1)
+    grad_out = torch.ones_like(out) * 0.125
+    grad_state = torch.ones_like(state) * 0.001
+    torch.autograd.backward((out, state), (grad_out, grad_state))
+    refs = [x.detach().clone().requires_grad_() for x in inputs]
+    ref_out, ref_state = GatedDeltaRuleRecurrentStepOp()(
+        *refs, indices, scale=128**-0.5, num_k_heads=1
+    )
+    torch.autograd.backward((ref_out, ref_state), (grad_out, grad_state))
+    for actual, ref in zip(inputs, refs):
+        assert tensor_bitwise_equal(actual.grad, ref.grad)
+
+
+def test_initial_state_vjp_matches_provider_directional_difference():
+    inputs = _inputs()
+    inputs[-1].requires_grad_()
+    indices = torch.tensor([1, 2], device="cuda", dtype=torch.int32)
+    _, output = packed_decode_training_step(*inputs, indices, num_k_heads=1)
+    direction = torch.ones_like(inputs[-1]) * 0.03125
+    probe = torch.linspace(-0.01, 0.01, output.numel(), device="cuda").reshape_as(output)
+    (output.double() * probe.double()).sum().backward()
+    analytic = (inputs[-1].grad.double() * direction.double()).sum()
+    eps = 0.01
+    with torch.no_grad():
+        _, plus = packed_decode_training_step(
+            *inputs[:-1], inputs[-1] + eps * direction, indices, num_k_heads=1
+        )
+        _, minus = packed_decode_training_step(
+            *inputs[:-1], inputs[-1] - eps * direction, indices, num_k_heads=1
+        )
+        numerical = ((plus.double() - minus.double()) * probe.double()).sum() / (2 * eps)
+    torch.testing.assert_close(analytic, numerical, rtol=1e-3, atol=1e-4)
+
+
+@pytest.mark.parametrize("empty", [False, True])
+def test_inactive_state_is_identity_in_backward(empty):
+    inputs = _inputs()
+    if empty:
+        inputs[:3] = [x[:0] for x in inputs[:3]]
+    inputs = [x.requires_grad_() for x in inputs]
+    indices = torch.zeros(inputs[0].shape[0], device="cuda", dtype=torch.int32)
+    out, state = packed_decode_training_step(*inputs, indices, num_k_heads=1)
+    (out.float().sum() + state.sum()).backward()
+    assert tensor_bitwise_equal(inputs[-1].grad, torch.ones_like(state))
+    for tensor in inputs[:-1]:
+        assert torch.count_nonzero(tensor.grad) == 0
diff --git a/tests/check_qwen3_next_gdn_sequence.py b/tests/check_qwen3_next_gdn_sequence.py
new file mode 100644
index 000000000..fef317872
--- /dev/null
+++ b/tests/check_qwen3_next_gdn_sequence.py
@@ -0,0 +1,102 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Packed decode recurrence boundary checks; no complete-model L2 claim."""
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_gdn import packed_recurrent_sequence
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def inputs(lengths, seed=7):
+    assert torch.cuda.is_available(), "CUDA is required; this acceptance suite cannot skip"
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+
+    def rand(*shape, dtype=torch.bfloat16):
+        return torch.randn(*shape, device="cuda", dtype=dtype, generator=generator)
+
+    tokens = sum(lengths)
+    # Actual TP4 shard: 16 / 4 key heads, 32 / 4 value heads, K=V=128.
+    tensors = [
+        rand(tokens, 2048),
+        rand(tokens, 8),
+        rand(tokens, 8),
+        rand(8, dtype=torch.float32),
+        rand(8, dtype=torch.float32),
+        rand(len(lengths) + 1, 8, 128, 128, dtype=torch.float32) * 0.01,
+    ]
+    indices = torch.arange(1, len(lengths) + 1, device="cuda", dtype=torch.int32)
+    cu = torch.tensor([0, *torch.tensor(lengths).cumsum(0).tolist()], dtype=torch.int64)
+    return tensors, indices, cu
+
+
+def run(tensors, indices, cu):
+    return packed_recurrent_sequence(*tensors, indices, cu, num_k_heads=4)
+
+
+@torch.no_grad()
+@pytest.mark.parametrize("lengths", [(8,), (64, 8, 0, 16), (1024, 256, 64, 8)])
+def test_packed_matches_independent_sequences_and_reordering(lengths):
+    tensors, indices, cu = inputs(lengths)
+    original_state = tensors[-1].clone()
+    output, state = run(tensors, indices, cu)
+    for seq, length in enumerate(lengths):
+        start, end = cu[seq : seq + 2].tolist()
+        one = [*(value[start:end] for value in tensors[:3]), *tensors[3:]]
+        expected, expected_state = run(one, indices[seq : seq + 1], torch.tensor([0, length]))
+        exact(output[start:end], expected)
+        exact(state[seq + 1], expected_state[seq + 1])
+    permutation = list(reversed(range(len(lengths))))
+    rows = torch.cat([torch.arange(cu[seq], cu[seq + 1], device="cuda") for seq in permutation])
+    changed = [*(value.index_select(0, rows) for value in tensors[:3]), *tensors[3:]]
+    reordered_cu = torch.tensor([0, *torch.tensor([lengths[i] for i in permutation]).cumsum(0)])
+    reordered, reordered_state = run(changed, indices[permutation], reordered_cu)
+    exact(reordered, output.index_select(0, rows))
+    exact(reordered_state, state)
+    exact(tensors[-1], original_state)
+
+
+@torch.no_grad()
+def test_1024_token_chunk_continuation_is_bitwise_exact():
+    tensors, indices, cu = inputs((1024,))
+    whole, whole_state = run(tensors, indices, cu)
+    state, pieces = tensors[-1], []
+    cuts = (0, 1, 8, 64, 256, 513, 1024)
+    for start, end in zip(cuts, cuts[1:]):
+        chunk = [*(value[start:end] for value in tensors[:3]), *tensors[3:5], state]
+        output, state = run(chunk, indices, torch.tensor([0, end - start]))
+        pieces.append(output)
+    exact(torch.cat(pieces), whole)
+    exact(state, whole_state)
+
+
+def test_chunk_boundary_retains_prompt_and_initial_state_gradients():
+    tensors, indices, cu = inputs((8,))
+    tensors = [tensor.requires_grad_() for tensor in tensors]
+    prompt = [*(value[:4] for value in tensors[:3]), *tensors[3:]]
+    _, prompt_state = run(prompt, indices, torch.tensor([0, 4]))
+    prompt_state.retain_grad()
+    response = [*(value[4:] for value in tensors[:3]), *tensors[3:5], prompt_state]
+    out, state = run(response, indices, torch.tensor([0, 4]))
+    (out.float().square().sum() + state.square().sum() * 0.01).backward()
+    assert prompt_state.grad is not None and prompt_state.grad.abs().max() > 0
+    for value in tensors:
+        assert value.grad is not None and torch.isfinite(value.grad).all()
+        assert value.grad.abs().max() > 0
+    assert tensors[0].grad[:4].abs().max() > 0
+
+
+@pytest.mark.parametrize("bad", [[1, 8], [0, 9], [0, 6, 5, 8]])
+def test_bad_packing_is_rejected(bad):
+    tensors, indices, _ = inputs((8,))
+    with pytest.raises(ValueError, match="cu_seqlens"):
+        run(tensors, indices, torch.tensor(bad))
+
+
+def test_reserved_and_duplicate_slots_are_rejected():
+    tensors, indices, cu = inputs((4, 4))
+    for bad in ([0, 1], [1, 1]):
+        with pytest.raises(ValueError):
+            run(tensors, torch.tensor(bad, device="cuda", dtype=torch.int32), cu)
diff --git a/tests/check_qwen3_next_shared_core.py b/tests/check_qwen3_next_shared_core.py
new file mode 100644
index 000000000..e65158d07
--- /dev/null
+++ b/tests/check_qwen3_next_shared_core.py
@@ -0,0 +1,83 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Combined provider contract; does not instantiate a complete Qwen model."""
+
+from dataclasses import replace
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_provider import GDNProviderConfig, GDNState, shared_gdn_core
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def inputs(tokens, grad=False):
+    assert torch.cuda.is_available(), "Required CUDA provider suite cannot skip"
+    gen = torch.Generator(device="cuda").manual_seed(73)
+
+    def rand(*shape, dtype=torch.bfloat16):
+        return torch.randn(*shape, device="cuda", dtype=dtype, generator=gen).requires_grad_(grad)
+
+    args = [
+        rand(tokens, 2048),
+        rand(tokens, 8),
+        rand(tokens, 8),
+        rand(tokens, 8, 128),
+        rand(8, dtype=torch.float32),
+        rand(8, dtype=torch.float32),
+        rand(2048, 4),
+        rand(128),
+        GDNState(rand(2, 2048, 3), rand(2, 8, 128, 128, dtype=torch.float32)),
+    ]
+    return args, torch.tensor([1], device="cuda", dtype=torch.int32)
+
+
+@pytest.mark.parametrize("tokens", [8, 1024])
+@torch.no_grad()
+def test_shared_core_chunk_and_decode_state_match(tokens):
+    args, indices = inputs(tokens)
+    config = GDNProviderConfig()
+    output, state = shared_gdn_core(
+        *args, indices, torch.tensor([0, tokens]), config=config, num_k_heads=4
+    )
+    pieces, next_state = [], args[-1]
+    for start, end in ((0, 3), (3, tokens - 1), (tokens - 1, tokens)):
+        chunk = [*(value[start:end] for value in args[:4]), *args[4:8], next_state]
+        part, next_state = shared_gdn_core(
+            *chunk, indices, torch.tensor([0, end - start]), config=config, num_k_heads=4
+        )
+        pieces.append(part)
+    exact(torch.cat(pieces), output)
+    exact(next_state.convolution, state.convolution)
+    exact(next_state.recurrent, state.recurrent)
+
+
+def test_independent_training_forward_matches_inference_and_retains_gradients():
+    args, indices = inputs(8, grad=True)
+    config = GDNProviderConfig()
+    with torch.no_grad():
+        reference, _ = shared_gdn_core(
+            *args, indices, torch.tensor([0, 8]), config=config, num_k_heads=4
+        )
+    prompt = [*(value[:4] for value in args[:4]), *args[4:]]
+    _, prompt_state = shared_gdn_core(
+        *prompt, indices, torch.tensor([0, 4]), config=config, num_k_heads=4
+    )
+    prompt_state.recurrent.retain_grad()
+    response = [*(value[4:] for value in args[:4]), *args[4:8], prompt_state]
+    actual, _ = shared_gdn_core(
+        *response, indices, torch.tensor([0, 4]), config=config, num_k_heads=4
+    )
+    exact(actual, reference[4:])
+    actual.float().square().sum().backward()
+    for value in args[:8]:
+        assert value.grad is not None and torch.isfinite(value.grad).all()
+        assert value.grad.abs().max() > 0
+    assert args[0].grad[:4].abs().max() > 0
+    assert prompt_state.recurrent.grad.abs().max() > 0
+
+
+def test_unimplemented_provider_profile_is_rejected():
+    with pytest.raises(ValueError, match="Unsupported"):
+        replace(GDNProviderConfig(), recurrence="unverified-prefill").validate()
diff --git a/tests/test_qwen3_next_forward_contract.py b/tests/test_qwen3_next_forward_contract.py
index 76d6df183..56d681b21 100644
--- a/tests/test_qwen3_next_forward_contract.py
+++ b/tests/test_qwen3_next_forward_contract.py
@@ -15,6 +15,7 @@
         lambda: provider.shared_linear(torch.zeros(1, 8), torch.zeros(8, 8)),
         lambda: provider.stable_top10_routes(torch.zeros(1, 512)),
         lambda: provider.combine_routes(torch.zeros(1, 10, 8), torch.zeros(1, 10)),
+        lambda: provider.shared_attention(*(torch.zeros(1, 4, 8, 256) for _ in range(3))),
     ],
 )
 def test_required_cuda_primitives_reject_cpu(call):
diff --git a/tests/test_qwen3_next_tp_blocks.py b/tests/test_qwen3_next_tp_blocks.py
index f2ac9c822..f97ad07a2 100644
--- a/tests/test_qwen3_next_tp_blocks.py
+++ b/tests/test_qwen3_next_tp_blocks.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-"""HF mappings, replica checks and official Qwen3-Next MoE dimensions."""
+"""HF mappings, KV replica ownership and official Qwen3-Next attention/MoE dimensions."""
 
 import pytest
 import torch
@@ -20,6 +20,32 @@ def pattern(shape):
     )
 
 
+@pytest.fixture(scope="module")
+def attention_weights():
+    return {name: pattern(shape) for name, shape in blocks.ATTENTION_HF_SHAPES.items()}
+
+
+def test_attention_hf_roundtrip_and_kv_pair_layout(attention_weights):
+    shards = [blocks.shard_attention_weights(attention_weights, rank) for rank in range(4)]
+    for name, value in blocks.assemble_attention_weights(shards).items():
+        exact(value, attention_weights[name], name=name)
+    for rank, shard in enumerate(shards):
+        exact(
+            shard["q_proj.weight"],
+            attention_weights["q_proj.weight"][rank * 2048 : (rank + 1) * 2048],
+        )
+        begin = rank // 2 * 256
+        exact(shard["k_proj.weight"], attention_weights["k_proj.weight"][begin : begin + 256])
+
+
+def test_attention_export_rejects_disagreeing_kv_replica(attention_weights):
+    shards = [blocks.shard_attention_weights(attention_weights, rank) for rank in range(4)]
+    shards[1]["k_proj.weight"] = shards[1]["k_proj.weight"].clone()
+    shards[1]["k_proj.weight"][0, 0] = -1
+    with pytest.raises(ValueError, match="Replicated KV"):
+        blocks.assemble_attention_weights(shards)
+
+
 @pytest.mark.parametrize(
     "name",
     [
@@ -91,3 +117,46 @@ def test_tp4_moe_parameter_ownership(monkeypatch):
     assert not getattr(module.gate.weight, "tensor_model_parallel", False)
     assert not getattr(module.shared_expert_gate.weight, "tensor_model_parallel", False)
     assert module.gate.weight.shape == (512, 2048)
+
+
+@pytest.fixture
+def fake_tp(monkeypatch):
+    group = object()
+    monkeypatch.setattr(blocks.dist, "is_initialized", lambda: True)
+    monkeypatch.setattr(blocks.dist, "get_world_size", lambda group: 4)
+    monkeypatch.setattr(blocks, "_kv_replica_groups", lambda group: (object(), object()))
+    return group
+
+
+@pytest.mark.parametrize("rank", [0, 1, 2, 3])
+def test_attention_parameter_ownership_and_actual_hf_storage(
+    fake_tp, monkeypatch, rank, attention_weights
+):
+    monkeypatch.setattr(blocks.dist, "get_rank", lambda group: rank)
+    module = blocks.TP4FullAttention(group=fake_tp, device="cpu")
+    module.load_hf_weights(attention_weights)
+    expected = blocks.shard_attention_weights(attention_weights, rank)
+    for name, value in module.export_local_hf_weights().items():
+        exact(value, expected[name], name=name)
+    assert module.q_proj.weight.tensor_model_parallel
+    assert module.o_proj.weight.partition_dim == 1
+    assert module.k_proj.weight.shared == (rank % 2 == 1)
+    assert module.v_proj.weight.shared == (rank % 2 == 1)
+    assert not getattr(module.q_norm.weight, "tensor_model_parallel", False)
+    state = module.initial_state(3)
+    assert len(state.keys) == len(state.values) == 3
+    assert all(value.shape == (1, 1, 0, 256) for value in state.keys)
+
+
+def test_rope_uses_absolute_positions_and_preserves_unrotated_tail(fake_tp, monkeypatch):
+    monkeypatch.setattr(blocks.dist, "get_rank", lambda group: 0)
+    module = blocks.TP4FullAttention(group=fake_tp, device="cpu")
+    x = torch.randn(8, 4, 256, dtype=torch.bfloat16)
+    positions = torch.arange(61, 69)
+    whole = module._rotate(x, positions)
+    exact(whole[..., 64:], x[..., 64:])
+    exact(
+        torch.cat([module._rotate(x[:3], positions[:3]), module._rotate(x[3:], positions[3:])]),
+        whole,
+    )
+    assert not torch.equal(module._rotate(x, torch.arange(8)), whole)
diff --git a/tests/test_qwen3_next_tp_gdn.py b/tests/test_qwen3_next_tp_gdn.py
new file mode 100644
index 000000000..69d047983
--- /dev/null
+++ b/tests/test_qwen3_next_tp_gdn.py
@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Official HF interleaving and TP shard round trips, without a GPU dependency."""
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_tp_gdn import (
+    _GLOBAL_SHAPES,
+    assemble_gdn_weights,
+    shard_gdn_weights,
+)
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal
+
+
+@pytest.fixture(scope="module")
+def weights():
+    return {
+        name: torch.arange(torch.Size(shape).numel(), dtype=torch.int32)
+        .remainder(251)
+        .to(torch.bfloat16)
+        .reshape(shape)
+        for name, shape in _GLOBAL_SHAPES.items()
+    }
+
+
+def test_official_interleaving_survives_tp4_round_trip(weights):
+    shards = [shard_gdn_weights(weights, rank) for rank in range(4)]
+    restored = assemble_gdn_weights(shards)
+    for name, tensor in weights.items():
+        assert_tensor_bitwise_equal(restored[name], tensor, name=name)
+    for rank, shard in enumerate(shards):
+        # Conv is Q/K/V segmented, unlike the GQA-interleaved qkvz projection.
+        assert_tensor_bitwise_equal(
+            shard["conv1d.weight"][:512], weights["conv1d.weight"][rank * 512 : (rank + 1) * 512]
+        )
+        assert_tensor_bitwise_equal(
+            shard["conv1d.weight"][512:1024],
+            weights["conv1d.weight"][2048 + rank * 512 : 2048 + (rank + 1) * 512],
+        )
+        assert_tensor_bitwise_equal(
+            shard["in_proj_qkvz.weight"],
+            weights["in_proj_qkvz.weight"][rank * 3072 : (rank + 1) * 3072],
+        )
+
+
+@pytest.mark.parametrize("rank", [-1, 4, True, 1.0])
+def test_invalid_tp_rank_is_rejected(weights, rank):
+    with pytest.raises(ValueError, match="TP4 rank"):
+        shard_gdn_weights(weights, rank)
+
+
+def test_divergent_replicated_norm_is_not_silently_exported(weights):
+    shards = [shard_gdn_weights(weights, rank) for rank in range(4)]
+    shards[1]["norm.weight"] = shards[1]["norm.weight"].clone()
+    shards[1]["norm.weight"][0] = -1
+    with pytest.raises(ValueError, match="Replicated"):
+        assemble_gdn_weights(shards)
+
+
+def test_missing_hf_weight_is_rejected(weights):
+    incomplete = {name: value for name, value in weights.items() if name != "dt_bias"}
+    with pytest.raises(ValueError, match="weight names"):
+        shard_gdn_weights(incomplete, 0)

From 3fe632d0a16af4bda824efb95eb13002ae451e80 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 02:12:10 +0000
Subject: [PATCH 47/70] docs(qwen3-next): MoE route/combine contract, prior-art
 and TP4 evidence (B200, 2e950f3)

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/operators/README.md                      |   1 +
 .../operators/qwen3-next-moe-route-combine.md | 137 ++++++++++
 .../qwen3-next-moe-route-b200/README.md       |  34 +++
 .../qwen3-next-moe-route-b200/figure.png      | Bin 0 -> 232121 bytes
 .../qwen3-next-moe-route-b200/report.json     | 236 ++++++++++++++++++
 .../tp4-moe/rank-0.json                       |  45 ++++
 .../tp4-moe/rank-1.json                       |  45 ++++
 .../tp4-moe/rank-2.json                       |  45 ++++
 .../tp4-moe/rank-3.json                       |  45 ++++
 9 files changed, 588 insertions(+)
 create mode 100644 docs/operators/qwen3-next-moe-route-combine.md
 create mode 100644 docs/usage/evidence/qwen3-next-moe-route-b200/README.md
 create mode 100644 docs/usage/evidence/qwen3-next-moe-route-b200/figure.png
 create mode 100644 docs/usage/evidence/qwen3-next-moe-route-b200/report.json
 create mode 100644 docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-0.json
 create mode 100644 docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-1.json
 create mode 100644 docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-2.json
 create mode 100644 docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-3.json

diff --git a/docs/operators/README.md b/docs/operators/README.md
index 0b93b3d90..be74cb6d0 100644
--- a/docs/operators/README.md
+++ b/docs/operators/README.md
@@ -34,4 +34,5 @@ Every operator page should include:
 - [Token Embedding](embedding.md)
 - [Qwen3-Next RMSNorm (zero-centred)](qwen3-next-rms-norm.md)
 - [Qwen3-Next Gated RMSNorm](qwen3-next-rms-norm-gated.md)
+- [Qwen3-Next MoE route / combine](qwen3-next-moe-route-combine.md)
 - [Operator Doc Template](../contributing/operator-doc-template.md)
diff --git a/docs/operators/qwen3-next-moe-route-combine.md b/docs/operators/qwen3-next-moe-route-combine.md
new file mode 100644
index 000000000..ed11301a0
--- /dev/null
+++ b/docs/operators/qwen3-next-moe-route-combine.md
@@ -0,0 +1,137 @@
+# Qwen3-Next MoE route / combine contract
+
+## Summary
+
+Qwen3-Next's sparse MoE block routes every token to 10 of 512 experts (expert
+width 512, TP4-local width 128) and adds a gated shared expert. RFC #428 row
+`moe_route_combine_contract` asks for a routed MoE whose output for a token is a
+pure function of that token: the same bits whatever else is in the batch, in
+training replay and in rollout. This page states the contract and how
+`rl_engine.integrations.qwen3_next_forward.shared_moe` meets it.
+
+Upstream references: `transformers` `Qwen3NextSparseMoeBlock` (5.17.0) and vLLM
+0.30.0 `fused_topk` + `fused_experts`.
+
+## Entry Point
+
+```python
+from rl_engine.integrations.qwen3_next_forward import shared_moe, stable_top10_routes
+from rl_engine.integrations.qwen3_next_tp_blocks import TP4MoE
+
+routed, routes = shared_moe(x, router_weight, gate_up, down)  # TP-local, not reduced
+block = TP4MoE(group=tp_group, device="cuda")                  # routed + shared, reduced
+block.load_hf_weights(hf_layer_mlp_weights)
+y = block(hidden)
+```
+
+`TRITON_F32_DEFAULT=ieee` must be set before the process starts: the FP32 router
+GEMM refuses to run on TF32 Triton dots.
+
+## Contract
+
+| Stage | Order and dtype | Atomics |
+| --- | --- | --- |
+| Router | BF16 `x`, `W` upcast to FP32; one pinned vLLM batch-invariant GEMM (IEEE FP32) | none |
+| Softmax | max subtraction, `exp`, row sum by a fixed pairwise tree (`fixed_order_row_sum`) | none |
+| Selection | `argsort(logits, descending, stable)[:, :10]`: score descending, equal scores to the lower expert id | none |
+| Weights | the ten selected FP32 probabilities divided by their fixed-tree sum | none |
+| Experts | per (token, route) row: `gate_up` GEMM (BF16 in, FP32 accumulate, BF16 out), SwiGLU in FP32, BF16 cast, `down` GEMM | none: one writer per row |
+| Combine | `out = e0*w0`, then `out += e_k*w_k` for k = 1..9 in route order, FP32; one BF16 cast | none |
+| Shared expert (TP4 block) | `routed + shared * sigmoid(gate)` in BF16, then one TP all-reduce | collective as configured |
+
+Payload dtype between stages: BF16 expert outputs, FP32 route weights, BF16
+block output. The expert GEMMs are vLLM 0.30.0's `fused_moe` Triton kernel with
+the tile vLLM itself uses under `VLLM_BATCH_INVARIANT=1`
+(`BLOCK_SIZE_M/N/K = 64/64/32`, `GROUP_SIZE_M = 8`, `SPLIT_K = 1`), passed
+explicitly so no environment variable, tuned-config file or token count can
+change it. The routed weight is *not* multiplied inside the kernel and the
+kernel does not sum over routes; that stays in the combine above.
+
+Fail-closed checks: CUDA tensors only (no CPU fallback, no ROCm), BF16 weights of
+the exact `[512, 2*I, H]` / `[512, H, I]` shapes, finite FP32 router logits,
+vLLM exactly 0.30.0, `VLLM_TRITON_USE_TD` off (the tensor-descriptor path is a
+different instruction stream), and IEEE Triton FP32.
+
+### Backward
+
+`_RoutedExperts.backward` recomputes the `[gate, up]` projection with the same
+grouped kernel, then visits experts in ascending id. Each expert's `dgate_up`
+and `ddown` slice is one pinned GEMM written once into a dense buffer; `dx` is
+accumulated per token in ascending expert order (a token's ten experts are
+distinct, so each launch has unique rows). Router-weight gradients flow through
+the selected probabilities; the indices are discrete.
+
+## Reuse decision
+
+| Implementation | Routes BI | Output BI | Backward | Why not reused as is |
+| --- | --- | --- | --- | --- |
+| vLLM `fused_topk` + `fused_experts`, `VLLM_BATCH_INVARIANT=0` | no | no | none | tuned configs and the BF16 router GEMM depend on the token count |
+| vLLM, `VLLM_BATCH_INVARIANT=1` | yes | yes | none | no VJP for training; BF16 router logits (Qwen3-Next and VIME route in FP32); routed weight applied in the expert GEMM epilogue on the BF16 output, then routes summed by `moe_sum` |
+| HF `Qwen3NextExperts` (eager) | yes | yes | autograd | `dx`/`dW` change with batch size (cuBLAS shape heuristics); BF16 router; `index_add_` combine in BF16 |
+| FlashInfer `cutlass_fused_moe` | (torch routing) | no | none | output changes with batch size; no VJP |
+| SGLang, Megatron-core, Transformer Engine | not measured | not measured | - | not installable next to the pinned vLLM 0.30.0 / torch 2.13 runtime |
+
+What RL-Kernel reuses: vLLM's batch-invariant GEMM for the router and the
+backward, and vLLM's `fused_moe` Triton kernel for the forward expert GEMMs,
+which is batch-invariant per route at the fixed tile. What it implements: the
+FP32 fixed-order routing, the stable tie rule, the atomic-free FP32 combine, the
+deterministic backward and the TP4 boundaries. The grouped kernel's per-route
+output is bitwise equal to the per-expert pinned GEMM loop it replaced
+(`tests/check_qwen3_next_forward.py::test_grouped_*`), so this is a speed change
+with no change in bits.
+
+## Results
+
+Measured on B200 with `scripts/qwen3_next_moe_prior_art.py` and the TP4 gate
+`scripts/qwen3_next_tp_moe_check.py`; the reports, the figure and the exact
+commands are in
+[`docs/usage/evidence/qwen3-next-moe-route-b200/`](../usage/evidence/qwen3-next-moe-route-b200/README.md).
+
+![Qwen3-Next routed MoE prior art](../usage/evidence/qwen3-next-moe-route-b200/figure.png)
+
+TP1 shape (H=2048, 512 experts, top-10, width 512), random weights, BF16:
+
+| | RL-Kernel `shared_moe` | vLLM BI=1 | vLLM BI=0 | FlashInfer CUTLASS | HF eager |
+| --- | --- | --- | --- | --- | --- |
+| routes / output BI (16-1024 tokens) | **yes / yes** | yes / yes | no / no (256+) | no / no (256+) | yes / yes |
+| dx / dW BI | **yes / yes** | no backward | no backward | no backward | no / no (1024) |
+| rel. L2 error vs FP64 | **3.9e-3** | 7.0e-2 | 7.0e-2 | 7.0e-2 | 7.0e-2 |
+| tokens routed unlike FP64 (of 256) | **0** | 11 | 11 | 11 | 11 |
+| forward, 1 / 64 / 1024 / 4096 tokens (ms) | 1.38 / 1.71 / 2.05 / 3.05 | 0.37 / 0.66 / 0.88 / 1.06 | 0.36 / 0.65 / 0.87 / 1.06 | 0.34 / 0.67 / 0.85 / 1.08 | 2.0 / 61 / 89 / 89 |
+| forward + backward, 64 / 1024 tokens (ms) | 112 / 166 | - | - | - | 878 / 1283 |
+
+* The accuracy gap is the router: every other candidate computes BF16 router
+  logits, and 11 of 256 tokens then select a different expert set than FP64
+  routing does. RL-Kernel routes in FP32 and selects the same experts.
+* RL-Kernel's forward is 2-4x slower than vLLM's. About 0.5 ms of it, at any
+  token count, is the FP32 IEEE router GEMM, which is part of the contract.
+  The expert GEMMs themselves run in vLLM's kernel. The per-expert GEMM loop
+  they replace (same bits) took 47 / 66 / 71 ms at 64 / 1024 / 4096 tokens in
+  a local B200 run of the same runner; that run is not part of this evidence.
+* The backward is still a per-expert loop of pinned GEMMs (512 experts x 4
+  GEMMs) and dominates a training step's MoE time.
+
+TP4 gate on the real layer-0 weights (`tp4-moe/rank-*.json`): all twelve cases
+pass on all four ranks, including the round trip of all 512 experts, identical
+routes on every rank, full == chunked and reordered batches at 8-1024 tokens,
+and identical replicated router gradients. The TP4 block forward (routed +
+shared expert + all-reduce) takes 1.9-2.4 ms at 8-1024 tokens.
+
+## Tests
+
+| File | Device | Covers |
+| --- | --- | --- |
+| `tests/test_qwen3_next_forward_contract.py` | CPU | CPU rejection, vLLM pin, fixed-order sum/softmax row independence |
+| `tests/test_qwen3_next_tp_blocks.py` | CPU | HF shard/assemble round trips, replica drift rejection, TP4 parameter ownership |
+| `tests/test_tensor_identity.py` | CPU | raw-bit identity (signed zero, NaN/Inf, dtype) |
+| `tests/check_qwen3_next_forward.py` | CUDA + vLLM | GEMM batch/chunk/reorder, route ties, combine order, MoE VJP vs FP64, route and output batch invariance, grouped == per-expert bitwise, fail-closed TD path |
+| `scripts/qwen3_next_tp_moe_check.py` | 4 x CUDA | real layer-0 weights: HF round trip, replicated routes, chunk/reorder, training forward, gradients |
+
+The `check_` file imports vLLM, so it runs in the `Qwen3-Next-provider-GPU`
+workflow rather than the default collection.
+
+## Scope
+
+TP4 with EP1 (all 512 experts on every rank) on CUDA B200. ROCm, EP > 1 and
+other TP sizes are separate claims. Model-level parity uses this block but is
+the `full_model_chain` row.
diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/README.md b/docs/usage/evidence/qwen3-next-moe-route-b200/README.md
new file mode 100644
index 000000000..52f528486
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/README.md
@@ -0,0 +1,34 @@
+# Qwen3-Next routed MoE: prior art and TP4 gate (B200)
+
+Measured at commit `2e950f3` from a clean clone (`tracked_tree_dirty: false` in
+every file), with that clone's own `rl_engine._C`, on one node with four B200
+GPUs (driver 580.126.20), torch 2.13.0+cu130, vLLM 0.30.0, Triton 3.7.1,
+transformers 5.17.0, FlashInfer 0.6.18.post1, NCCL 2.29.7.
+
+| File | Produced by |
+| --- | --- |
+| `report.json` | `TRITON_F32_DEFAULT=ieee python scripts/qwen3_next_moe_prior_art.py --out report.json` (one GPU) |
+| `figure.png` | `python scripts/plot_qwen3_next_moe_prior_art.py report.json` |
+| `tp4-moe/rank-{0..3}.json` | `TRITON_F32_DEFAULT=ieee torchrun --nproc-per-node 4 scripts/qwen3_next_tp_moe_check.py --checkpoint <Qwen3-Next-80B-A3B-Instruct> --output tp4-moe` |
+
+The checkpoint is the official `Qwen/Qwen3-Next-80B-A3B-Instruct` revision
+`9c7f2fbe84465e40164a94cc16cd30b6999b0cc7`; the TP4 gate reads layer 0's MoE.
+
+In the same job, `TRITON_F32_DEFAULT=ieee python -m pytest -q
+tests/check_qwen3_next_forward.py tests/test_tensor_identity.py
+tests/test_qwen3_next_forward_contract.py tests/test_qwen3_next_tp_blocks.py`
+passed (65 tests). `tests/test_framework_operator_integrations.py` was collected
+into that same process and failed, as it must once a `check_` file has imported
+vLLM; run in its own process, as CI does, it passes.
+
+## Reading the report
+
+* Batch invariance: 8 probe tokens computed alone, and placed first and last in
+  batches of 16, 64, 256 and 1024 tokens; `true` means bitwise equal everywhere.
+  `dweight_zero_rows_bitwise` appends rows whose output gradient is zero.
+* Accuracy: 256 tokens against the HF formula evaluated in FP64 with FP64
+  routing; `tokens_with_different_expert_set` counts tokens whose ten selected
+  experts differ from the FP64 selection.
+* Latency: CUDA-event medians, candidates interleaved with the order reversed
+  every iteration. Weights are random (scale 0.02), shape H=2048, 512 experts,
+  top-10, expert width 512 (TP1).
diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/figure.png b/docs/usage/evidence/qwen3-next-moe-route-b200/figure.png
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diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/report.json b/docs/usage/evidence/qwen3-next-moe-route-b200/report.json
new file mode 100644
index 000000000..271e69c8a
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/report.json
@@ -0,0 +1,236 @@
+{
+  "kind": "qwen3_next_prior_art_report",
+  "rfc": 428,
+  "rl_kernel_commit": "2e950f386bd3c4c2c07f41472f48878be94ea3b8",
+  "tracked_tree_dirty": false,
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "cuda": "13.0",
+    "libraries": {
+      "vllm": "0.30.0",
+      "flashinfer-python": "0.6.18.post1",
+      "transformers": "5.17.0",
+      "triton": "3.7.1",
+      "sglang": null,
+      "megatron-core": null,
+      "transformer_engine": null,
+      "vime": null
+    }
+  },
+  "op": "qwen3_next_routed_moe",
+  "shape": {
+    "hidden": 2048,
+    "experts": 512,
+    "top_k": 10,
+    "expert_width": 512
+  },
+  "probe_rows": 8,
+  "candidates": [
+    {
+      "key": "rl_kernel_cuda",
+      "name": "rl-kernel shared_moe",
+      "source": "rl_engine.integrations.qwen3_next_forward.shared_moe",
+      "route_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "output_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "repeatable": true,
+      "dx_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "dweight_zero_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.0038748319159183277,
+        "max_abs_vs_fp64": 0.0021274995396960983,
+        "tokens_with_different_expert_set": 0,
+        "tokens": 256
+      },
+      "batch_invariant": true
+    },
+    {
+      "key": "hf_transformers",
+      "name": "HF transformers Qwen3NextExperts (eager)",
+      "source": "transformers 5.17.0 modeling_qwen3_next",
+      "route_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "output_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "repeatable": true,
+      "dx_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": false
+      },
+      "dweight_zero_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": false
+      },
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.0700071926559018,
+        "max_abs_vs_fp64": 0.10691143625663532,
+        "tokens_with_different_expert_set": 11,
+        "tokens": 256
+      },
+      "batch_invariant": false
+    },
+    {
+      "key": "vllm_bi0",
+      "name": "vLLM fused_topk + fused_experts (Triton), VLLM_BATCH_INVARIANT=0",
+      "source": "vllm 0.30.0 model_executor/layers/fused_moe",
+      "route_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": false,
+        "1024": false
+      },
+      "output_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": false,
+        "1024": false
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.06987086350006304,
+        "max_abs_vs_fp64": 0.10691143625663532,
+        "tokens_with_different_expert_set": 11,
+        "tokens": 256
+      },
+      "batch_invariant": false
+    },
+    {
+      "key": "vllm_bi1",
+      "name": "vLLM fused_topk + fused_experts (Triton), VLLM_BATCH_INVARIANT=1",
+      "source": "vllm 0.30.0 model_executor/layers/fused_moe",
+      "route_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "output_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.06987086350006304,
+        "max_abs_vs_fp64": 0.10691143625663532,
+        "tokens_with_different_expert_set": 11,
+        "tokens": 256
+      },
+      "batch_invariant": true
+    },
+    {
+      "key": "flashinfer_cutlass",
+      "name": "FlashInfer cutlass_fused_moe (routes from torch.topk)",
+      "source": "flashinfer 0.6.18.post1 fused_moe.cutlass_fused_moe",
+      "route_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": false,
+        "1024": false
+      },
+      "output_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": false,
+        "1024": false
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.06984271064260991,
+        "max_abs_vs_fp64": 0.10739971750663532,
+        "tokens_with_different_expert_set": 11,
+        "tokens": 256
+      },
+      "batch_invariant": false
+    }
+  ],
+  "latency": {
+    "forward_us": {
+      "1": {
+        "rl_kernel_cuda": 1376.3200044631958,
+        "hf_transformers": 2020.9439992904663,
+        "vllm_bi0": 356.7200005054474,
+        "vllm_bi1": 367.8240031003952,
+        "flashinfer_cutlass": 341.74399077892303
+      },
+      "8": {
+        "rl_kernel_cuda": 1379.8879981040955,
+        "hf_transformers": 12903.615951538086,
+        "vllm_bi0": 364.6879941225052,
+        "vllm_bi1": 366.5599972009659,
+        "flashinfer_cutlass": 385.2960020303726
+      },
+      "64": {
+        "rl_kernel_cuda": 1706.6399455070496,
+        "hf_transformers": 60610.20851135254,
+        "vllm_bi0": 653.984010219574,
+        "vllm_bi1": 660.3359878063202,
+        "flashinfer_cutlass": 672.1920073032379
+      },
+      "256": {
+        "rl_kernel_cuda": 1892.9439783096313,
+        "hf_transformers": 85160.57586669922,
+        "vllm_bi0": 778.6239981651306,
+        "vllm_bi1": 785.8560085296631,
+        "flashinfer_cutlass": 831.5519988536835
+      },
+      "1024": {
+        "rl_kernel_cuda": 2048.975944519043,
+        "hf_transformers": 88626.1100769043,
+        "vllm_bi0": 872.2079992294312,
+        "vllm_bi1": 881.5999925136566,
+        "flashinfer_cutlass": 850.0800132751465
+      },
+      "4096": {
+        "rl_kernel_cuda": 3047.216057777405,
+        "hf_transformers": 88805.53817749023,
+        "vllm_bi0": 1060.5120062828064,
+        "vllm_bi1": 1061.1519813537598,
+        "flashinfer_cutlass": 1079.200029373169
+      }
+    },
+    "forward_plus_backward_us": {
+      "64": {
+        "rl_kernel_cuda": 111661.79275512695,
+        "hf_transformers": 877700.2563476562
+      },
+      "1024": {
+        "rl_kernel_cuda": 166025.42114257812,
+        "hf_transformers": 1282811.6455078125
+      }
+    }
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-0.json b/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-0.json
new file mode 100644
index 000000000..188c00b77
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-0.json
@@ -0,0 +1,45 @@
+{
+  "status": "passed",
+  "scope": "tp4_moe_block_layer0",
+  "rank": 0,
+  "rl_kernel_commit": "2e950f386bd3c4c2c07f41472f48878be94ea3b8",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "moe": {
+    "cases": [
+      "hf-load-export-all512experts",
+      "routes-replicated-8",
+      "full-chunk-reorder-8",
+      "routes-replicated-64",
+      "full-chunk-reorder-64",
+      "routes-replicated-256",
+      "full-chunk-reorder-256",
+      "routes-replicated-1024",
+      "full-chunk-reorder-1024",
+      "training-forward",
+      "all-parameter-gradient",
+      "replicated-router-gradient"
+    ],
+    "gradient_max_abs": {
+      "gate.weight": 1.9354047253727913e-09,
+      "experts.gate_up": 4.598405212163925e-09,
+      "experts.down": 7.8580342233181e-09,
+      "shared_expert.gate_proj.weight": 2.5890767574310303e-07,
+      "shared_expert.up_proj.weight": 4.3585896492004395e-07,
+      "shared_expert.down_proj.weight": 2.9243528842926025e-07,
+      "shared_expert_gate.weight": 5.513429641723633e-07
+    },
+    "forward_us": {
+      "8": 2185.663938522339,
+      "64": 2399.2319107055664,
+      "256": 1903.6799669265747,
+      "1024": 1860.6079816818237
+    }
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-1.json b/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-1.json
new file mode 100644
index 000000000..755bed167
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-1.json
@@ -0,0 +1,45 @@
+{
+  "status": "passed",
+  "scope": "tp4_moe_block_layer0",
+  "rank": 1,
+  "rl_kernel_commit": "2e950f386bd3c4c2c07f41472f48878be94ea3b8",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "moe": {
+    "cases": [
+      "hf-load-export-all512experts",
+      "routes-replicated-8",
+      "full-chunk-reorder-8",
+      "routes-replicated-64",
+      "full-chunk-reorder-64",
+      "routes-replicated-256",
+      "full-chunk-reorder-256",
+      "routes-replicated-1024",
+      "full-chunk-reorder-1024",
+      "training-forward",
+      "all-parameter-gradient",
+      "replicated-router-gradient"
+    ],
+    "gradient_max_abs": {
+      "gate.weight": 1.9354047253727913e-09,
+      "experts.gate_up": 2.240994945168495e-09,
+      "experts.down": 5.558831617236137e-09,
+      "shared_expert.gate_proj.weight": 3.129243850708008e-07,
+      "shared_expert.up_proj.weight": 3.725290298461914e-07,
+      "shared_expert.down_proj.weight": 4.6938657760620117e-07,
+      "shared_expert_gate.weight": 5.513429641723633e-07
+    },
+    "forward_us": {
+      "8": 2204.67209815979,
+      "64": 2400.2881050109863,
+      "256": 1920.4479455947876,
+      "1024": 1854.464054107666
+    }
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-2.json b/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-2.json
new file mode 100644
index 000000000..72343dc29
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-2.json
@@ -0,0 +1,45 @@
+{
+  "status": "passed",
+  "scope": "tp4_moe_block_layer0",
+  "rank": 2,
+  "rl_kernel_commit": "2e950f386bd3c4c2c07f41472f48878be94ea3b8",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "moe": {
+    "cases": [
+      "hf-load-export-all512experts",
+      "routes-replicated-8",
+      "full-chunk-reorder-8",
+      "routes-replicated-64",
+      "full-chunk-reorder-64",
+      "routes-replicated-256",
+      "full-chunk-reorder-256",
+      "routes-replicated-1024",
+      "full-chunk-reorder-1024",
+      "training-forward",
+      "all-parameter-gradient",
+      "replicated-router-gradient"
+    ],
+    "gradient_max_abs": {
+      "gate.weight": 1.9354047253727913e-09,
+      "experts.gate_up": 2.0227162167429924e-09,
+      "experts.down": 5.122274160385132e-09,
+      "shared_expert.gate_proj.weight": 2.337619662284851e-07,
+      "shared_expert.up_proj.weight": 3.9674341678619385e-07,
+      "shared_expert.down_proj.weight": 2.384185791015625e-07,
+      "shared_expert_gate.weight": 5.513429641723633e-07
+    },
+    "forward_us": {
+      "8": 2204.67209815979,
+      "64": 2398.303985595703,
+      "256": 1881.0559511184692,
+      "1024": 1856.511950492859
+    }
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-3.json b/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-3.json
new file mode 100644
index 000000000..2c4d8f376
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/tp4-moe/rank-3.json
@@ -0,0 +1,45 @@
+{
+  "status": "passed",
+  "scope": "tp4_moe_block_layer0",
+  "rank": 3,
+  "rl_kernel_commit": "2e950f386bd3c4c2c07f41472f48878be94ea3b8",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "moe": {
+    "cases": [
+      "hf-load-export-all512experts",
+      "routes-replicated-8",
+      "full-chunk-reorder-8",
+      "routes-replicated-64",
+      "full-chunk-reorder-64",
+      "routes-replicated-256",
+      "full-chunk-reorder-256",
+      "routes-replicated-1024",
+      "full-chunk-reorder-1024",
+      "training-forward",
+      "all-parameter-gradient",
+      "replicated-router-gradient"
+    ],
+    "gradient_max_abs": {
+      "gate.weight": 1.9354047253727913e-09,
+      "experts.gate_up": 2.546585164964199e-09,
+      "experts.down": 5.296897143125534e-09,
+      "shared_expert.gate_proj.weight": 1.2665987014770508e-07,
+      "shared_expert.up_proj.weight": 2.644956111907959e-07,
+      "shared_expert.down_proj.weight": 2.998858690261841e-07,
+      "shared_expert_gate.weight": 5.513429641723633e-07
+    },
+    "forward_us": {
+      "8": 2206.4640522003174,
+      "64": 2399.104118347168,
+      "256": 1923.0719804763794,
+      "1024": 1855.455994606018
+    }
+  }
+}

From 65170777576cb957fb5557ef13bb46ce695a9f1a Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 02:19:35 +0000
Subject: [PATCH 48/70] test(qwen3-next): prior-art runner for the TP4 D=256
 attention core

Compares RL-Kernel's deterministic attention with torch SDPA, vLLM FlashAttention-2 (num_splits auto and 1), vLLM's Triton unified attention and FlashInfer's ragged prefill: batch, chunked-prefill and decode invariance, backward repeatability, accuracy against FP64 and latency.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../testing/qwen3_next_attention_prior_art.py | 404 ++++++++++++++++++
 .../plot_qwen3_next_attention_prior_art.py    | 138 ++++++
 scripts/qwen3_next_attention_prior_art.py     |  87 ++++
 3 files changed, 629 insertions(+)
 create mode 100644 rl_engine/testing/qwen3_next_attention_prior_art.py
 create mode 100644 scripts/plot_qwen3_next_attention_prior_art.py
 create mode 100644 scripts/qwen3_next_attention_prior_art.py

diff --git a/rl_engine/testing/qwen3_next_attention_prior_art.py b/rl_engine/testing/qwen3_next_attention_prior_art.py
new file mode 100644
index 000000000..8a089b84a
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_attention_prior_art.py
@@ -0,0 +1,404 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Prior-art comparison for the Qwen3-Next TP4 full-attention core (D=256, GQA 4:1).
+
+Every candidate computes causal attention for one rank of Qwen3-Next's TP4
+layout: 4 query heads over 1 KV head, head dim 256, scale 1/16, queries aligned
+to the end of the KV sequence. For each candidate the report records
+
+* **batch invariance** - a target sequence's output computed alone, and inside
+  a batch with three other sequences (placed first and last), must be bitwise
+  equal; for candidates with a backward, the same for its dq/dk/dv;
+* **prefill/decode invariance** - the last 64 query rows, and the last single
+  row, computed against the full KV must equal the same rows of the full
+  prefill. This is the replay-vs-rollout boundary of RFC #428;
+* **repeatability** - two identical calls are bitwise equal;
+* **accuracy** - error against an FP64 evaluation;
+* **performance** - median CUDA-event latency of prefill, decode and backward.
+
+A candidate that cannot be imported or launched in the pinned environment is
+recorded as ``unavailable`` with the reason instead of failing the run.
+"""
+
+from __future__ import annotations
+
+import statistics
+from typing import Any, Callable
+
+import torch
+import torch.nn.functional as F
+
+HQ, HKV, D = 4, 1, 256
+SCALE = 1.0 / 16
+TARGET = 1000
+COMPANIONS = (777, 1500, 64)
+CHUNK = 64
+PREFILL_TOKENS = (512, 2048, 8192)
+DECODE_KV = 8192
+BACKWARD_TOKENS = 2048
+
+
+# --------------------------------------------------------------------------- #
+# Measurement helpers
+# --------------------------------------------------------------------------- #
+
+
+def _sample_us(fn: Callable[[], Any]) -> float:
+    torch.cuda.synchronize()
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+    start.record()
+    fn()
+    end.record()
+    torch.cuda.synchronize()
+    return start.elapsed_time(end) * 1000.0
+
+
+def time_interleaved(calls: dict[str, Callable[[], Any]], warmup: int = 5, iters: int = 30):
+    keys = list(calls)
+    orders = (keys, list(reversed(keys)))
+    for i in range(warmup):
+        for key in orders[i % 2]:
+            calls[key]()
+    samples = {key: [] for key in keys}
+    for i in range(iters):
+        for key in orders[i % 2]:
+            samples[key].append(_sample_us(calls[key]))
+    return {key: statistics.median(values) for key, values in samples.items()}
+
+
+def _bitwise(a: torch.Tensor, b: torch.Tensor) -> bool:
+    return a.shape == b.shape and a.dtype == b.dtype and torch.equal(a, b)
+
+
+def _guard(key: str, factory: Callable[[], dict[str, Any]]) -> dict[str, Any]:
+    try:
+        return {"key": key, **factory()}
+    except Exception as exc:  # noqa: BLE001 - record and continue
+        return {"key": key, "unavailable": f"{type(exc).__name__}: {str(exc)[:300]}"}
+
+
+def make_sequence(length: int, seed: int, *, kv_length: int | None = None):
+    """q [Lq, HQ, D], k/v [Lk, HKV, D] in BF16 (token-major, as engines store them)."""
+    kv_length = length if kv_length is None else kv_length
+    g = torch.Generator(device="cuda").manual_seed(seed)
+
+    def randn(*shape):
+        return torch.randn(shape, device="cuda", generator=g).to(torch.bfloat16)
+
+    return randn(length, HQ, D), randn(kv_length, HKV, D), randn(kv_length, HKV, D)
+
+
+def fp64_reference(q, k, v):
+    lq, lk = q.shape[0], k.shape[0]
+    qd, kd, vd = (t.double().transpose(0, 1) for t in (q, k, v))
+    kd, vd = kd.expand(HQ, -1, -1), vd.expand(HQ, -1, -1)
+    scores = qd @ kd.transpose(1, 2) * SCALE
+    rows = torch.arange(lq, device=q.device)[:, None] + (lk - lq)
+    scores = scores.masked_fill(torch.arange(lk, device=q.device)[None, :] > rows, float("-inf"))
+    return (scores.softmax(-1) @ vd).transpose(0, 1)
+
+
+# --------------------------------------------------------------------------- #
+# Candidates
+#
+# ``run(seqs) -> outputs``: ``seqs`` is a list of (q, k, v) with queries aligned
+# to the end of each KV sequence; every candidate computes the whole list in the
+# way its engine batches it (one varlen launch, or one launch per sequence).
+# ``grad(q, k, v, dy) -> (dq, dk, dv)`` when the candidate has a backward.
+# --------------------------------------------------------------------------- #
+
+
+def _rl_kernel():
+    from rl_engine.integrations.qwen3_next_forward import shared_attention
+
+    def one(q, k, v):
+        out = shared_attention(*(t.transpose(0, 1).unsqueeze(0) for t in (q, k, v)))
+        return out.squeeze(0).transpose(0, 1)
+
+    def run(seqs):
+        with torch.no_grad():
+            return [one(*s) for s in seqs]
+
+    def grad(q, k, v, dy):
+        leaves = [t.detach().clone().requires_grad_(True) for t in (q, k, v)]
+        return torch.autograd.grad(one(*leaves), leaves, dy)
+
+    return {
+        "name": "RL-Kernel deterministic attention (one launch per sequence)",
+        "source": "rl_engine.integrations.qwen3_next_forward.shared_attention",
+        "run": run,
+        "grad": grad,
+    }
+
+
+def _sdpa():
+    def one(q, k, v):
+        lq, lk = q.shape[0], k.shape[0]
+        qh, kh, vh = (t.transpose(0, 1).unsqueeze(0) for t in (q, k, v))
+        if lq == lk:
+            out = F.scaled_dot_product_attention(
+                qh, kh, vh, is_causal=True, scale=SCALE, enable_gqa=True
+            )
+        else:
+            rows = torch.arange(lq, device=q.device)[:, None] + (lk - lq)
+            mask = torch.arange(lk, device=q.device)[None, :] <= rows
+            out = F.scaled_dot_product_attention(
+                qh, kh, vh, attn_mask=mask, scale=SCALE, enable_gqa=True
+            )
+        return out.squeeze(0).transpose(0, 1)
+
+    def run(seqs):
+        with torch.no_grad():
+            return [one(*s) for s in seqs]
+
+    def grad(q, k, v, dy):
+        leaves = [t.detach().clone().requires_grad_(True) for t in (q, k, v)]
+        return torch.autograd.grad(one(*leaves), leaves, dy)
+
+    return {
+        "name": "torch SDPA (default backend, one launch per sequence)",
+        "source": f"torch {torch.__version__} scaled_dot_product_attention",
+        "run": run,
+        "grad": grad,
+    }
+
+
+def _cu(lengths):
+    out = [0]
+    for n in lengths:
+        out.append(out[-1] + n)
+    return torch.tensor(out, device="cuda", dtype=torch.int32)
+
+
+def _split(out, lengths):
+    return list(out.split(list(lengths)))
+
+
+def _vllm_fa(num_splits: int):
+    import vllm
+    from vllm.vllm_flash_attn import flash_attn_varlen_func
+
+    def run(seqs):
+        lq = [s[0].shape[0] for s in seqs]
+        lk = [s[1].shape[0] for s in seqs]
+        q, k, v = (torch.cat([s[i] for s in seqs]) for i in range(3))
+        out = flash_attn_varlen_func(
+            q,
+            k,
+            v,
+            max_seqlen_q=max(lq),
+            cu_seqlens_q=_cu(lq),
+            max_seqlen_k=max(lk),
+            cu_seqlens_k=_cu(lk),
+            softmax_scale=SCALE,
+            causal=True,
+            num_splits=num_splits,
+            fa_version=2,
+        )
+        return _split(out, lq)
+
+    return {
+        "name": f"vLLM FlashAttention-2 varlen, num_splits={num_splits}",
+        "source": f"vllm {vllm.__version__} vllm_flash_attn.flash_attn_varlen_func",
+        "run": run,
+    }
+
+
+def _vllm_triton():
+    import vllm
+    from vllm.v1.attention.ops.triton_unified_attention import unified_attention
+
+    block = 16
+
+    def run(seqs):
+        lq = [s[0].shape[0] for s in seqs]
+        lk = [s[1].shape[0] for s in seqs]
+        blocks = [(n + block - 1) // block for n in lk]
+        k_cache = torch.zeros(sum(blocks), block, HKV, D, device="cuda", dtype=torch.bfloat16)
+        v_cache = torch.zeros_like(k_cache)
+        table = torch.zeros(len(seqs), max(blocks), device="cuda", dtype=torch.int32)
+        first = 0
+        for i, (s, n) in enumerate(zip(seqs, blocks)):
+            pad = n * block - s[1].shape[0]
+            k_cache[first : first + n] = F.pad(s[1], (0, 0, 0, 0, 0, pad)).view(n, block, HKV, D)
+            v_cache[first : first + n] = F.pad(s[2], (0, 0, 0, 0, 0, pad)).view(n, block, HKV, D)
+            table[i, :n] = torch.arange(first, first + n, device="cuda")
+            first += n
+        q = torch.cat([s[0] for s in seqs])
+        out = torch.empty_like(q)
+        unified_attention(
+            q,
+            k_cache,
+            v_cache,
+            out,
+            cu_seqlens_q=_cu(lq),
+            max_seqlen_q=max(lq),
+            seqused_k=torch.tensor(lk, device="cuda", dtype=torch.int32),
+            max_seqlen_k=max(lk),
+            softmax_scale=SCALE,
+            causal=True,
+            window_size=(-1, -1),
+            block_table=table,
+            softcap=0,
+            q_descale=None,
+            k_descale=None,
+            v_descale=None,
+        )
+        return _split(out, lq)
+
+    return {
+        # Without split-softmax buffers the 2D kernel runs for every batch, which
+        # is also the only kernel vLLM's batch-invariant mode allows.
+        "name": "vLLM Triton unified attention (2D kernel)",
+        "source": f"vllm {vllm.__version__} v1/attention/ops/triton_unified_attention",
+        "run": run,
+    }
+
+
+def _flashinfer():
+    import flashinfer
+
+    workspace = torch.empty(256 << 20, device="cuda", dtype=torch.uint8)
+    wrapper = flashinfer.BatchPrefillWithRaggedKVCacheWrapper(workspace, "NHD")
+
+    def run(seqs):
+        lq = [s[0].shape[0] for s in seqs]
+        lk = [s[1].shape[0] for s in seqs]
+        wrapper.plan(
+            _cu(lq),
+            _cu(lk),
+            HQ,
+            HKV,
+            D,
+            causal=True,
+            sm_scale=SCALE,
+            q_data_type=torch.bfloat16,
+        )
+        q, k, v = (torch.cat([s[i] for s in seqs]) for i in range(3))
+        return _split(wrapper.run(q, k, v), lq)
+
+    return {
+        "name": "FlashInfer BatchPrefillWithRaggedKVCache",
+        "source": f"flashinfer {flashinfer.__version__}",
+        "run": run,
+    }
+
+
+def candidates() -> list[dict[str, Any]]:
+    return [
+        _guard("rl_kernel_cuda", _rl_kernel),
+        _guard("torch_sdpa", _sdpa),
+        _guard("vllm_fa2_auto", lambda: _vllm_fa(0)),
+        _guard("vllm_fa2_split1", lambda: _vllm_fa(1)),
+        _guard("vllm_triton_2d", _vllm_triton),
+        _guard("flashinfer", _flashinfer),
+    ]
+
+
+# --------------------------------------------------------------------------- #
+# Checks
+# --------------------------------------------------------------------------- #
+
+
+def _batch_bi(cand, target, others) -> dict[str, bool]:
+    alone = cand["run"]([target])[0]
+    first = cand["run"]([target, *others])[0]
+    last = cand["run"]([*others, target])[-1]
+    return {"first": _bitwise(first, alone), "last": _bitwise(last, alone)}
+
+
+def _prefill_decode(cand, target) -> dict[str, bool]:
+    q, k, v = target
+    full = cand["run"]([target])[0]
+    result = {}
+    for rows in (CHUNK, 1):
+        part = cand["run"]([(q[-rows:], k, v)])[0]
+        result[f"last_{rows}"] = _bitwise(part, full[-rows:])
+    return result
+
+
+def _grad_bi(cand, target, others) -> dict[str, bool]:
+    g = torch.Generator(device="cuda").manual_seed(5)
+    dy = torch.randn(target[0].shape, device="cuda", generator=g).to(torch.bfloat16)
+    alone = cand["grad"](*target, dy)
+    # A backward is per sequence in every engine here; what can change is the
+    # kernel the shape selects, so compare against a second target placed after
+    # other sequences have been differentiated.
+    for other in others:
+        cand["grad"](*other, torch.ones_like(other[0]))
+    again = cand["grad"](*target, dy)
+    return {name: _bitwise(a, b) for name, a, b in zip(("dq", "dk", "dv"), again, alone)}
+
+
+def _one(cand, target, others) -> dict[str, Any]:
+    if "unavailable" in cand:
+        return cand
+    out = {"key": cand["key"], "name": cand["name"], "source": cand["source"]}
+    try:
+        out["batch_bitwise"] = _batch_bi(cand, target, others)
+        out["prefill_decode_bitwise"] = _prefill_decode(cand, target)
+        out["repeatable"] = _bitwise(cand["run"]([target])[0], cand["run"]([target])[0])
+        if "grad" in cand:
+            out["backward_repeat_bitwise"] = _grad_bi(cand, target, others)
+        ref = fp64_reference(*target)
+        got = cand["run"]([target])[0].double()
+        out["accuracy"] = {
+            "rel_l2_vs_fp64": float((got - ref).norm() / ref.norm()),
+            "max_abs_vs_fp64": float((got - ref).abs().max()),
+        }
+    except Exception as exc:  # noqa: BLE001 - a crash is a result, not a harness failure
+        out["failed"] = f"{type(exc).__name__}: {str(exc)[:300]}"
+    out["batch_invariant"] = bool(
+        "failed" not in out
+        and all(out["batch_bitwise"].values())
+        and all(out["prefill_decode_bitwise"].values())
+        and all(out.get("backward_repeat_bitwise", {"": True}).values())
+    )
+    return out
+
+
+def _latency(cands) -> dict[str, Any]:
+    ready = [c for c in cands if "unavailable" not in c]
+
+    def calls_for(seqs):
+        calls = {}
+        for cand in ready:
+            try:
+                cand["run"](seqs)
+            except Exception:  # noqa: BLE001 - recorded by the BI section
+                continue
+            calls[cand["key"]] = lambda c=cand: c["run"](seqs)
+        return calls
+
+    prefill = {
+        str(n): time_interleaved(calls_for([make_sequence(n, 300 + n)])) for n in PREFILL_TOKENS
+    }
+    decode = time_interleaved(calls_for([make_sequence(1, 401, kv_length=DECODE_KV)]))
+    q, k, v = make_sequence(BACKWARD_TOKENS, 402)
+    dy = torch.randn_like(q)
+    backward = time_interleaved(
+        {c["key"]: (lambda c=c: c["grad"](q, k, v, dy)) for c in ready if "grad" in c},
+        warmup=3,
+        iters=15,
+    )
+    return {
+        "prefill_us": prefill,
+        f"decode_us_kv{DECODE_KV}": decode,
+        f"forward_plus_backward_us_{BACKWARD_TOKENS}": backward,
+    }
+
+
+def attention_report() -> dict[str, Any]:
+    target = make_sequence(TARGET, 1)
+    others = [make_sequence(n, 10 + i) for i, n in enumerate(COMPANIONS)]
+    cands = candidates()
+    return {
+        "op": "qwen3_next_tp4_attention",
+        "shape": {"q_heads": HQ, "kv_heads": HKV, "head_dim": D, "scale": SCALE},
+        "target_tokens": TARGET,
+        "companion_tokens": list(COMPANIONS),
+        "candidates": [_one(c, target, others) for c in cands],
+        "latency": _latency(cands),
+    }
diff --git a/scripts/plot_qwen3_next_attention_prior_art.py b/scripts/plot_qwen3_next_attention_prior_art.py
new file mode 100644
index 000000000..8343c5851
--- /dev/null
+++ b/scripts/plot_qwen3_next_attention_prior_art.py
@@ -0,0 +1,138 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Render the TP4 attention prior-art report (``scripts/qwen3_next_attention_prior_art.py``).
+
+    python scripts/plot_qwen3_next_attention_prior_art.py \\
+        docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/report.json
+
+writes ``figure.png`` next to the report: which invariance checks each
+implementation passes, accuracy against FP64, and prefill/decode latency.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.colors import ListedColormap  # noqa: E402
+
+RL_KERNEL = "#2a78d6"
+INK = "#2b2b29"
+MUTED = "#6f6e69"
+GRID = "#e4e3dd"
+SURFACE = "#fcfcfb"
+PASS = "#1baf7a"
+FAIL = "#d64545"
+NA = "#e4e3dd"
+EXISTING = ("#eb6834", "#b8501f", "#f29a62", "#8a3a15", "#f6bd94", "#d9773f")
+
+LABELS = {
+    "rl_kernel_cuda": "RL-Kernel deterministic",
+    "torch_sdpa": "torch SDPA",
+    "vllm_fa2_auto": "vLLM FA2 (num_splits auto)",
+    "vllm_fa2_split1": "vLLM FA2 (num_splits=1)",
+    "vllm_triton_2d": "vLLM Triton unified (2D)",
+    "flashinfer": "FlashInfer ragged prefill",
+}
+COLUMNS = (
+    ("batch_bitwise", "first", "batch: first"),
+    ("batch_bitwise", "last", "batch: last"),
+    ("prefill_decode_bitwise", "last_64", "chunk: last 64"),
+    ("prefill_decode_bitwise", "last_1", "decode: last 1"),
+    ("backward_repeat_bitwise", "dq", "bwd dq"),
+    ("backward_repeat_bitwise", "dk", "bwd dk"),
+    ("backward_repeat_bitwise", "dv", "bwd dv"),
+)
+
+
+def _style(ax):
+    ax.set_facecolor(SURFACE)
+    for side in ("top", "right"):
+        ax.spines[side].set_visible(False)
+    ax.tick_params(colors=INK, labelsize=8)
+    ax.grid(color=GRID, linewidth=0.6)
+
+
+def plot(report: dict, out: Path) -> None:
+    cands = [c for c in report["candidates"] if "unavailable" not in c and "failed" not in c]
+    keys = [c["key"] for c in cands]
+    existing = iter(EXISTING)
+    colors = {k: RL_KERNEL if k == "rl_kernel_cuda" else next(existing) for k in keys}
+    labels = [LABELS.get(k, k) for k in keys]
+    fig, axes = plt.subplots(1, 3, figsize=(17, 4.6), gridspec_kw={"width_ratios": [1.3, 1, 1.4]})
+
+    ax = axes[0]
+    grid = [
+        [0.5 if c.get(field) is None else float(c[field][item]) for field, item, _ in COLUMNS]
+        for c in cands
+    ]
+    ax.imshow(grid, cmap=ListedColormap([FAIL, NA, PASS]), vmin=0, vmax=1, aspect="auto")
+    ax.set_xticks(range(len(COLUMNS)), [c[2] for c in COLUMNS], fontsize=7, rotation=90)
+    ax.set_yticks(range(len(cands)), labels, fontsize=8)
+    for i, cand in enumerate(cands):
+        mark = "BI" if cand.get("batch_invariant") else "not BI"
+        ax.text(len(COLUMNS) - 0.4, i, f"  {mark}", va="center", fontsize=8, color=INK)
+    ax.set_title(
+        f"Invariance: {report['target_tokens']}-token target alone vs. batched / chunked\n"
+        "(green bitwise equal, red differs, grey: no backward)",
+        fontsize=9,
+        color=INK,
+    )
+    ax.tick_params(length=0)
+
+    ax = axes[1]
+    _style(ax)
+    rel = [c["accuracy"]["rel_l2_vs_fp64"] for c in cands]
+    ax.barh(range(len(cands)), rel, color=[colors[k] for k in keys])
+    for i, value in enumerate(rel):
+        ax.text(value, i, f"  {value:.2e}", va="center", fontsize=7, color=MUTED)
+    ax.set_yticks(range(len(cands)), labels, fontsize=8)
+    ax.invert_yaxis()
+    ax.set_xlim(0, 1.35 * max(rel))
+    ax.set_xlabel("relative L2 error vs. FP64", fontsize=8)
+    ax.set_title("Accuracy", fontsize=9, color=INK)
+
+    ax = axes[2]
+    _style(ax)
+    prefill = report["latency"]["prefill_us"]
+    tokens = [int(t) for t in prefill]
+    for key in keys:
+        ys = [prefill[str(t)].get(key, float("nan")) / 1000.0 for t in tokens]
+        ax.plot(tokens, ys, marker="o", ms=3.5, color=colors[key], label=LABELS.get(key, key))
+    ax.set_xscale("log", base=2)
+    ax.set_yscale("log")
+    ax.set_xlabel("prefill tokens", fontsize=8)
+    ax.set_ylabel("forward latency (ms, median)", fontsize=8)
+    ax.set_title("Prefill forward latency\n(4 Q heads, 1 KV head, D=256, causal)", fontsize=9)
+    ax.legend(fontsize=7, frameon=False)
+
+    env = report["environment"]
+    fig.suptitle(
+        f"Qwen3-Next TP4 attention prior art - {env['gpu']}, torch {env['torch']}, "
+        f"commit {report['rl_kernel_commit'][:7]}",
+        fontsize=10,
+        color=INK,
+    )
+    fig.tight_layout()
+    fig.savefig(out, dpi=160)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("report", type=Path)
+    args = parser.parse_args()
+    report = json.loads(args.report.read_text())
+    out = args.report.with_name("figure.png")
+    plot(report, out)
+    print(f"wrote {out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_attention_prior_art.py b/scripts/qwen3_next_attention_prior_art.py
new file mode 100644
index 000000000..2ded9d0ca
--- /dev/null
+++ b/scripts/qwen3_next_attention_prior_art.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Write the Qwen3-Next TP4 attention prior-art report (BI + accuracy + latency) as JSON.
+
+Run it on a clean tree with ``rl_engine._C`` built in place. The report records
+the commit, whether the tracked tree was dirty and the version of every library
+compared.
+``scripts/plot_qwen3_next_attention_prior_art.py`` turns the report into a figure.
+
+    python scripts/qwen3_next_attention_prior_art.py \\
+        --out docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/report.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import importlib.metadata as md
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+import torch  # noqa: E402
+
+from rl_engine.testing.qwen3_next_attention_prior_art import attention_report  # noqa: E402
+
+LIBRARIES = (
+    "vllm",
+    "flashinfer-python",
+    "transformers",
+    "triton",
+    "sglang",
+    "megatron-core",
+    "transformer_engine",
+    "vime",
+)
+
+
+def _git(*args: str) -> str:
+    return subprocess.run(
+        ["git", *args], cwd=ROOT, check=True, capture_output=True, text=True
+    ).stdout.strip()
+
+
+def _version(name: str) -> str | None:
+    try:
+        return md.version(name)
+    except md.PackageNotFoundError:
+        return None
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--out", type=Path, required=True)
+    args = parser.parse_args()
+    if not torch.cuda.is_available():
+        raise SystemExit("needs a CUDA device")
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = False
+    report = {
+        "kind": "qwen3_next_prior_art_report",
+        "rfc": 428,
+        "rl_kernel_commit": _git("rev-parse", "HEAD"),
+        "tracked_tree_dirty": bool(_git("status", "--porcelain", "--untracked-files=no")),
+        "environment": {
+            "gpu": torch.cuda.get_device_name(),
+            "torch": torch.__version__,
+            "cuda": torch.version.cuda,
+            "libraries": {name: _version(name) for name in LIBRARIES},
+        },
+        **attention_report(),
+    }
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.out.write_text(json.dumps(report, indent=2) + "\n")
+    print(
+        f"wrote {args.out} (commit {report['rl_kernel_commit'][:7]}, "
+        f"dirty={report['tracked_tree_dirty']})"
+    )
+
+
+if __name__ == "__main__":
+    main()

From b6fddb358e5d73806f8afee1891828b1ce4e29c6 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 02:31:53 +0000
Subject: [PATCH 49/70] docs(qwen3-next): TP4 mixer gates and D=256 attention
 prior-art evidence (B200, 9e6c4f2/6517077)

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/operators/attention.md                   |   2 +-
 .../qwen3-next-tp4-mixers-b200/README.md      |  36 ++++
 .../attention/figure.png                      | Bin 0 -> 204631 bytes
 .../attention/report.json                     | 200 ++++++++++++++++++
 .../tp4-attention/rank-0.json                 |  36 ++++
 .../tp4-attention/rank-1.json                 |  36 ++++
 .../tp4-attention/rank-2.json                 |  36 ++++
 .../tp4-attention/rank-3.json                 |  36 ++++
 .../tp4-gdn/rank-0.json                       |  32 +++
 .../tp4-gdn/rank-1.json                       |  32 +++
 .../tp4-gdn/rank-2.json                       |  32 +++
 .../tp4-gdn/rank-3.json                       |  32 +++
 .../tp4-moe/rank-0.json                       |  45 ++++
 .../tp4-moe/rank-1.json                       |  45 ++++
 .../tp4-moe/rank-2.json                       |  45 ++++
 .../tp4-moe/rank-3.json                       |  45 ++++
 16 files changed, 689 insertions(+), 1 deletion(-)
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/README.md
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/figure.png
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/report.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-0.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-1.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-2.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-3.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-0.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-1.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-2.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-3.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-0.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-1.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-2.json
 create mode 100644 docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-3.json

diff --git a/docs/operators/attention.md b/docs/operators/attention.md
index 074bf9046..95537707f 100644
--- a/docs/operators/attention.md
+++ b/docs/operators/attention.md
@@ -265,7 +265,7 @@ for measured peak memory at representative shapes.
 
 ## Known Limitations
 
-- First version: `D=128` only (Qwen3-8B alignment).
+- Head dim: `D=128` (Qwen3-8B) on CUDA and ROCm; `D=256` (Qwen3-Next full attention) on CUDA only.
 - Supported dtypes: BF16, FP16.
 - Full materialization of scores/P limits practical sequence length.
 - `Hq` must be divisible by `Hkv` (raises `ValueError` otherwise).
diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/README.md b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/README.md
new file mode 100644
index 000000000..40d824d59
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/README.md
@@ -0,0 +1,36 @@
+# Qwen3-Next TP4 mixers: real-weight gates and attention prior art (B200)
+
+| Directory | Measured at | Produced by |
+| --- | --- | --- |
+| `tp4-gdn/` | `9e6c4f2` | `torchrun --nproc-per-node 4 scripts/qwen3_next_tp_gdn_check.py --checkpoint <ckpt> --output tp4-gdn` |
+| `tp4-attention/` | `9e6c4f2` | `torchrun --nproc-per-node 4 scripts/qwen3_next_tp_attention_check.py --checkpoint <ckpt> --output tp4-attention` |
+| `tp4-moe/` | `9e6c4f2` | `TRITON_F32_DEFAULT=ieee torchrun --nproc-per-node 4 scripts/qwen3_next_tp_moe_check.py --checkpoint <ckpt> --output tp4-moe` |
+| `attention/report.json`, `attention/figure.png` | `6517077` | `python scripts/qwen3_next_attention_prior_art.py --out attention/report.json`, then `scripts/plot_qwen3_next_attention_prior_art.py` |
+
+Every file records its commit and `tracked_tree_dirty: false`; each run used a
+clean clone with its own `rl_engine._C` built in place, on B200 GPUs (driver
+580.126.20), torch 2.13.0+cu130, vLLM 0.30.0, NCCL 2.29.7. `<ckpt>` is the
+official `Qwen/Qwen3-Next-80B-A3B-Instruct` revision
+`9c7f2fbe84465e40164a94cc16cd30b6999b0cc7`: the GDN and MoE gates read layer 0,
+the attention gate layer 3. `6517077` adds only the attention prior-art runner
+and plot on top of `9e6c4f2` (and the MoE evidence merge).
+
+In the job that ran the gates, the mixer GPU checks
+(`tests/check_qwen3_next_{attention,gdn_bridge,gdn_sequence,conv_bridge,core_matrix,shared_core,forward}.py`)
+and the CPU tests passed together (123 tests); `tests/test_framework_operator_integrations.py`,
+collected into the same process after vLLM was imported, failed as it must and
+passes in its own process. The existing attention suites
+(`test_deterministic_attention_cuda`, `test_attention`, `test_attention_correctness`,
+`test_kv_cache_attention`, `test_attention_contract`, `test_attention_dispatch`)
+gave 766 passed and the two `test_attention_dispatch` failures that `main` also has.
+
+## Attention prior-art report
+
+One TP4 rank of Qwen3-Next full attention: 4 query heads, 1 KV head, D=256,
+scale 1/16, causal. A 1000-token target sequence is computed alone and in a
+batch with sequences of 777, 1500 and 64 tokens (first and last);
+`prefill_decode_bitwise` compares the last 64 rows and the last row computed
+against the full KV with the same rows of the full prefill;
+`backward_repeat_bitwise` repeats the target's backward after the companions'
+backwards ran. Accuracy is against FP64; latencies are CUDA-event medians with
+the candidate order reversed every iteration.
diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/figure.png b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/figure.png
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new file mode 100644
index 000000000..4a95898a2
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/report.json
@@ -0,0 +1,200 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-0.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-0.json
new file mode 100644
index 000000000..457309564
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-0.json
@@ -0,0 +1,36 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-1.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-1.json
new file mode 100644
index 000000000..c8b9c07a5
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-1.json
@@ -0,0 +1,36 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-2.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-2.json
new file mode 100644
index 000000000..db5ec61cb
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-2.json
@@ -0,0 +1,36 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-3.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-3.json
new file mode 100644
index 000000000..5ee714ab1
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-attention/rank-3.json
@@ -0,0 +1,36 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-0.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-0.json
new file mode 100644
index 000000000..2e2501dfb
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-0.json
@@ -0,0 +1,32 @@
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diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-1.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-1.json
new file mode 100644
index 000000000..33bda4b32
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-1.json
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+    "conv1d.weight": 9.19681042432785e-09,
+    "norm.weight": 3.91155481338501e-08,
+    "out_proj.weight": 1.8553691916167736e-10
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-2.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-2.json
new file mode 100644
index 000000000..c3d11374b
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-2.json
@@ -0,0 +1,32 @@
+{
+  "status": "passed",
+  "scope": "tp4_gdn_layer0",
+  "rank": 2,
+  "rl_kernel_commit": "9e6c4f24ce4b5e8888650e1062922711dbe565e0",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "cases": [
+    "full-chunk-8",
+    "full-chunk-64",
+    "full-chunk-256",
+    "full-chunk-1024",
+    "prompt-gradient",
+    "replicated-norm-gradient",
+    "hf-load-export"
+  ],
+  "gradient_max_abs": {
+    "A_log": 3.1377567211166024e-11,
+    "dt_bias": 3.069544618483633e-11,
+    "in_proj_qkvz.weight": 1.1714291758835316e-09,
+    "in_proj_ba.weight": 6.184563972055912e-11,
+    "conv1d.weight": 6.111804395914078e-09,
+    "norm.weight": 3.91155481338501e-08,
+    "out_proj.weight": 4.092726157978177e-10
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-3.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-3.json
new file mode 100644
index 000000000..c036f11fe
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-gdn/rank-3.json
@@ -0,0 +1,32 @@
+{
+  "status": "passed",
+  "scope": "tp4_gdn_layer0",
+  "rank": 3,
+  "rl_kernel_commit": "9e6c4f24ce4b5e8888650e1062922711dbe565e0",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "cases": [
+    "full-chunk-8",
+    "full-chunk-64",
+    "full-chunk-256",
+    "full-chunk-1024",
+    "prompt-gradient",
+    "replicated-norm-gradient",
+    "hf-load-export"
+  ],
+  "gradient_max_abs": {
+    "A_log": 8.094502845779061e-11,
+    "dt_bias": 7.958078640513122e-11,
+    "in_proj_qkvz.weight": 5.784386303275824e-10,
+    "in_proj_ba.weight": 3.979039320256561e-11,
+    "conv1d.weight": 5.093170329928398e-09,
+    "norm.weight": 3.91155481338501e-08,
+    "out_proj.weight": 3.67435859516263e-10
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-0.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-0.json
new file mode 100644
index 000000000..0fd10fafa
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-0.json
@@ -0,0 +1,45 @@
+{
+  "status": "passed",
+  "scope": "tp4_moe_block_layer0",
+  "rank": 0,
+  "rl_kernel_commit": "9e6c4f24ce4b5e8888650e1062922711dbe565e0",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "moe": {
+    "cases": [
+      "hf-load-export-all512experts",
+      "routes-replicated-8",
+      "full-chunk-reorder-8",
+      "routes-replicated-64",
+      "full-chunk-reorder-64",
+      "routes-replicated-256",
+      "full-chunk-reorder-256",
+      "routes-replicated-1024",
+      "full-chunk-reorder-1024",
+      "training-forward",
+      "all-parameter-gradient",
+      "replicated-router-gradient"
+    ],
+    "gradient_max_abs": {
+      "gate.weight": 1.9354047253727913e-09,
+      "experts.gate_up": 4.598405212163925e-09,
+      "experts.down": 7.8580342233181e-09,
+      "shared_expert.gate_proj.weight": 2.5890767574310303e-07,
+      "shared_expert.up_proj.weight": 4.3585896492004395e-07,
+      "shared_expert.down_proj.weight": 2.9243528842926025e-07,
+      "shared_expert_gate.weight": 5.513429641723633e-07
+    },
+    "forward_us": {
+      "8": 2407.167911529541,
+      "64": 2413.5360717773438,
+      "256": 1934.4639778137207,
+      "1024": 1961.6639614105225
+    }
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-1.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-1.json
new file mode 100644
index 000000000..651abe983
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-1.json
@@ -0,0 +1,45 @@
+{
+  "status": "passed",
+  "scope": "tp4_moe_block_layer0",
+  "rank": 1,
+  "rl_kernel_commit": "9e6c4f24ce4b5e8888650e1062922711dbe565e0",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "moe": {
+    "cases": [
+      "hf-load-export-all512experts",
+      "routes-replicated-8",
+      "full-chunk-reorder-8",
+      "routes-replicated-64",
+      "full-chunk-reorder-64",
+      "routes-replicated-256",
+      "full-chunk-reorder-256",
+      "routes-replicated-1024",
+      "full-chunk-reorder-1024",
+      "training-forward",
+      "all-parameter-gradient",
+      "replicated-router-gradient"
+    ],
+    "gradient_max_abs": {
+      "gate.weight": 1.9354047253727913e-09,
+      "experts.gate_up": 2.240994945168495e-09,
+      "experts.down": 5.558831617236137e-09,
+      "shared_expert.gate_proj.weight": 3.129243850708008e-07,
+      "shared_expert.up_proj.weight": 3.725290298461914e-07,
+      "shared_expert.down_proj.weight": 4.6938657760620117e-07,
+      "shared_expert_gate.weight": 5.513429641723633e-07
+    },
+    "forward_us": {
+      "8": 2461.6639614105225,
+      "64": 2415.616035461426,
+      "256": 2299.391984939575,
+      "1024": 1961.9840383529663
+    }
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-2.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-2.json
new file mode 100644
index 000000000..012e811d9
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-2.json
@@ -0,0 +1,45 @@
+{
+  "status": "passed",
+  "scope": "tp4_moe_block_layer0",
+  "rank": 2,
+  "rl_kernel_commit": "9e6c4f24ce4b5e8888650e1062922711dbe565e0",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "moe": {
+    "cases": [
+      "hf-load-export-all512experts",
+      "routes-replicated-8",
+      "full-chunk-reorder-8",
+      "routes-replicated-64",
+      "full-chunk-reorder-64",
+      "routes-replicated-256",
+      "full-chunk-reorder-256",
+      "routes-replicated-1024",
+      "full-chunk-reorder-1024",
+      "training-forward",
+      "all-parameter-gradient",
+      "replicated-router-gradient"
+    ],
+    "gradient_max_abs": {
+      "gate.weight": 1.9354047253727913e-09,
+      "experts.gate_up": 2.0227162167429924e-09,
+      "experts.down": 5.122274160385132e-09,
+      "shared_expert.gate_proj.weight": 2.337619662284851e-07,
+      "shared_expert.up_proj.weight": 3.9674341678619385e-07,
+      "shared_expert.down_proj.weight": 2.384185791015625e-07,
+      "shared_expert_gate.weight": 5.513429641723633e-07
+    },
+    "forward_us": {
+      "8": 2433.2799911499023,
+      "64": 2415.7440662384033,
+      "256": 2286.144018173218,
+      "1024": 1964.0640020370483
+    }
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-3.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-3.json
new file mode 100644
index 000000000..44189b0c3
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/tp4-moe/rank-3.json
@@ -0,0 +1,45 @@
+{
+  "status": "passed",
+  "scope": "tp4_moe_block_layer0",
+  "rank": 3,
+  "rl_kernel_commit": "9e6c4f24ce4b5e8888650e1062922711dbe565e0",
+  "tracked_tree_dirty": false,
+  "provider": "qwen3-next-shared-cuda-forward-v1",
+  "environment": {
+    "gpu": "NVIDIA B200",
+    "torch": "2.13.0+cu130",
+    "vllm": "0.30.0",
+    "nccl": "2.29.7"
+  },
+  "moe": {
+    "cases": [
+      "hf-load-export-all512experts",
+      "routes-replicated-8",
+      "full-chunk-reorder-8",
+      "routes-replicated-64",
+      "full-chunk-reorder-64",
+      "routes-replicated-256",
+      "full-chunk-reorder-256",
+      "routes-replicated-1024",
+      "full-chunk-reorder-1024",
+      "training-forward",
+      "all-parameter-gradient",
+      "replicated-router-gradient"
+    ],
+    "gradient_max_abs": {
+      "gate.weight": 1.9354047253727913e-09,
+      "experts.gate_up": 2.546585164964199e-09,
+      "experts.down": 5.296897143125534e-09,
+      "shared_expert.gate_proj.weight": 1.2665987014770508e-07,
+      "shared_expert.up_proj.weight": 2.644956111907959e-07,
+      "shared_expert.down_proj.weight": 2.998858690261841e-07,
+      "shared_expert_gate.weight": 5.513429641723633e-07
+    },
+    "forward_us": {
+      "8": 2456.5439224243164,
+      "64": 2408.4479808807373,
+      "256": 2331.712007522583,
+      "1024": 1955.8720588684082
+    }
+  }
+}

From c67ec14401b0847ac96441a24acfaac0b17b77ec Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 02:52:30 +0000
Subject: [PATCH 50/70] feat(qwen3-next): full-model chain with first-drift
 localisation

RFC #428 full_model_chain (CUDA).

- Engine adapters: the native vLLM rollout (exact-prefill scheduler, paged
  KV, traced response rows), the VIME/Megatron replay and training actor,
  engine weight import/export and the logprob adapter, all on the shared
  TP4 providers.
- Stage runners (preflight, layers, l2, train1, train2, train) with the
  validated acceptance contract (version 4) and scripts/qwen3_next_run.py,
  a launcher that works inside any four-GPU allocation: each stage gets a
  clean clone, an immutable run.json identified by a fresh execution_id,
  and refuses a predecessor that did not pass or pins another identity.
  Scheduler-independent helpers live in scripts/qwen3_next_runtime.py.
- Sub-node digests: both engines observe the normed mixer input, the mixer
  output, the normed MLP input, the selected expert ids and the MoE output
  of every layer, plus the embedding; the first divergence names the layer
  and sub-node.
- scripts/qwen3_next_chain_report.py writes a ChainGateReport-compatible
  report (schema qwen3-next-chain-v1, compatible with ws1-c10-c11-v5) with
  first_drift_family/layer/cell; scripts/qwen3_next_publish_evidence.py
  publishes a chain's records without site paths, hosts or users.
- Native rollout caps raised to 64 sequences, 16384 batched tokens and a
  4 GiB KV budget so the batch-invariance matrix runs under the same
  certified profile; the exact-prefill scheduler still fixes every chunk.
- The operator-case registry is regenerated from collection by
  scripts/qwen3_next_operator_cases.py.
- Packaging: transformers may be 5.13.1-5.17.0; a qwen3-next extra pins the
  audited runtime.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/ci.yml                      |   25 +
 .github/workflows/qwen3-next-provider-gpu.yml |    3 +-
 ci/run_qwen3_next_chain_gate.sh               |   27 +
 ci/run_qwen3_next_operator_gates.sh           |   10 +
 docs/usage/qwen3-next-chain.md                |   88 ++
 pyproject.toml                                |   10 +-
 rl_engine/integrations/qwen3_next_binary.py   |   71 ++
 .../integrations/qwen3_next_embedding.py      |   57 +
 .../integrations/qwen3_next_engine_weights.py |   88 ++
 rl_engine/integrations/qwen3_next_forward.py  |   45 +
 rl_engine/integrations/qwen3_next_logprob.py  |   89 ++
 .../qwen3_next_native_recompute.py            |  318 +++++
 .../integrations/qwen3_next_native_rollout.py |  777 ++++++++++++
 .../qwen3_next_native_training.py             |  386 ++++++
 .../integrations/qwen3_next_native_workers.py |   49 +
 rl_engine/integrations/qwen3_next_profile.py  |  143 +++
 rl_engine/integrations/qwen3_next_subnode.py  |  109 ++
 .../integrations/qwen3_next_tp_blocks.py      |    7 +-
 rl_engine/integrations/qwen3_next_training.py |  285 +++++
 rl_engine/integrations/qwen3_next_vime.py     |  402 ++++++
 .../integrations/qwen3_next_vime_runtime.py   |  178 +++
 rl_engine/integrations/qwen3_next_vllm.py     |  205 ++++
 .../integrations/qwen3_next_vllm_attention.py |  238 ++++
 .../integrations/qwen3_next_vllm_model.py     |  365 ++++++
 .../integrations/qwen3_next_vllm_worker.py    |   51 +
 .../kernels/gtest/qwen3_next_chain_report.py  |  268 ++++
 rl_engine/testing/pytest_case_manifest.py     |   80 ++
 rl_engine/testing/qwen3_next_binary.py        |   89 ++
 rl_engine/testing/qwen3_next_cases.py         |   69 ++
 .../testing/qwen3_next_export_schema.json     |   10 +
 rl_engine/testing/qwen3_next_l2.py            | 1047 ++++++++++++++++
 rl_engine/testing/qwen3_next_layer_cases.json |  122 ++
 .../testing/qwen3_next_operator_cases.json    |  293 +++++
 rl_engine/testing/qwen3_next_rl_fixture.py    |  150 +++
 scripts/qwen3_next_adapter_check.py           |  321 +++++
 scripts/qwen3_next_chain_report.py            |   99 ++
 scripts/qwen3_next_checkpoint.py              |  312 +++++
 scripts/qwen3_next_contract.py                |  371 ++++++
 scripts/qwen3_next_gate_common.py             |   12 +-
 scripts/qwen3_next_l2.py                      |  742 ++++++++++++
 scripts/qwen3_next_layer_contract.py          |  123 ++
 scripts/qwen3_next_layers.py                  |  342 ++++++
 scripts/qwen3_next_operator_cases.py          |  103 ++
 scripts/qwen3_next_operator_gates.py          |  151 +++
 scripts/qwen3_next_optimizer_checkpoint.py    |  307 +++++
 scripts/qwen3_next_preflight.py               |  252 ++++
 scripts/qwen3_next_publish_evidence.py        |  142 +++
 scripts/qwen3_next_rollout_smoke.py           |   73 ++
 scripts/qwen3_next_run.py                     |  337 ++++++
 scripts/qwen3_next_runtime.py                 |  772 ++++++++++++
 scripts/qwen3_next_tp_head_check.py           |  141 +++
 scripts/qwen3_next_tp_probe.py                |   54 +
 scripts/qwen3_next_training.py                | 1077 +++++++++++++++++
 scripts/qwen3_next_training_checkpoint.py     |  602 +++++++++
 scripts/qwen3_next_training_export.py         |  503 ++++++++
 setup.py                                      |    9 +-
 tests/check_qwen3_next_batch_invariance.py    |  453 +++++++
 tests/check_qwen3_next_engine_metadata.py     |  118 ++
 tests/check_qwen3_next_forward.py             |   55 +-
 tests/check_qwen3_next_multigpu.py            |   46 +
 tests/check_qwen3_next_native_rollout.py      |  318 +++++
 tests/check_qwen3_next_paged_attention.py     |   83 ++
 tests/check_qwen3_next_vime_weight_hooks.py   |   92 ++
 tests/check_qwen3_next_vllm_model_contract.py |  281 +++++
 tests/test_qwen3_next_binary_evidence.py      |   68 ++
 tests/test_qwen3_next_case_manifest.py        |  106 ++
 tests/test_qwen3_next_chain_report.py         |  149 +++
 tests/test_qwen3_next_checkpoint.py           |  222 ++++
 tests/test_qwen3_next_engine_weights.py       |   81 ++
 tests/test_qwen3_next_environment_identity.py |   41 +
 tests/test_qwen3_next_evidence_redaction.py   |  105 ++
 tests/test_qwen3_next_failure.py              |  136 +++
 tests/test_qwen3_next_forward_contract.py     |    3 +
 tests/test_qwen3_next_imported_binary.py      |  107 ++
 ..._qwen3_next_installed_provider_identity.py |   45 +
 tests/test_qwen3_next_l2.py                   |  719 +++++++++++
 tests/test_qwen3_next_layers.py               |  135 +++
 tests/test_qwen3_next_logprob_adapter.py      |   43 +
 tests/test_qwen3_next_native_recompute.py     |  232 ++++
 tests/test_qwen3_next_operator_gates.py       |   50 +
 tests/test_qwen3_next_profile.py              |  149 +++
 tests/test_qwen3_next_rl_fixture.py           |   58 +
 tests/test_qwen3_next_rotary_dtype.py         |   58 +
 tests/test_qwen3_next_run.py                  |  118 ++
 tests/test_qwen3_next_storage.py              |  334 +++++
 tests/test_qwen3_next_subnode.py              |   52 +
 tests/test_qwen3_next_training_audit.py       |  137 +++
 tests/test_qwen3_next_training_checkpoint.py  |  539 +++++++++
 tests/test_qwen3_next_training_closeout.py    |  430 +++++++
 tests/test_qwen3_next_training_export.py      |  288 +++++
 ...test_qwen3_next_vllm_attention_metadata.py |   75 ++
 91 files changed, 18919 insertions(+), 6 deletions(-)
 create mode 100755 ci/run_qwen3_next_chain_gate.sh
 create mode 100644 ci/run_qwen3_next_operator_gates.sh
 create mode 100644 docs/usage/qwen3-next-chain.md
 create mode 100644 rl_engine/integrations/qwen3_next_binary.py
 create mode 100644 rl_engine/integrations/qwen3_next_embedding.py
 create mode 100644 rl_engine/integrations/qwen3_next_engine_weights.py
 create mode 100644 rl_engine/integrations/qwen3_next_logprob.py
 create mode 100644 rl_engine/integrations/qwen3_next_native_recompute.py
 create mode 100644 rl_engine/integrations/qwen3_next_native_rollout.py
 create mode 100644 rl_engine/integrations/qwen3_next_native_training.py
 create mode 100644 rl_engine/integrations/qwen3_next_native_workers.py
 create mode 100644 rl_engine/integrations/qwen3_next_profile.py
 create mode 100644 rl_engine/integrations/qwen3_next_subnode.py
 create mode 100644 rl_engine/integrations/qwen3_next_training.py
 create mode 100644 rl_engine/integrations/qwen3_next_vime.py
 create mode 100644 rl_engine/integrations/qwen3_next_vime_runtime.py
 create mode 100644 rl_engine/integrations/qwen3_next_vllm.py
 create mode 100644 rl_engine/integrations/qwen3_next_vllm_attention.py
 create mode 100644 rl_engine/integrations/qwen3_next_vllm_model.py
 create mode 100644 rl_engine/integrations/qwen3_next_vllm_worker.py
 create mode 100644 rl_engine/kernels/gtest/qwen3_next_chain_report.py
 create mode 100644 rl_engine/testing/pytest_case_manifest.py
 create mode 100644 rl_engine/testing/qwen3_next_binary.py
 create mode 100644 rl_engine/testing/qwen3_next_cases.py
 create mode 100644 rl_engine/testing/qwen3_next_export_schema.json
 create mode 100644 rl_engine/testing/qwen3_next_l2.py
 create mode 100644 rl_engine/testing/qwen3_next_layer_cases.json
 create mode 100644 rl_engine/testing/qwen3_next_operator_cases.json
 create mode 100644 rl_engine/testing/qwen3_next_rl_fixture.py
 create mode 100644 scripts/qwen3_next_adapter_check.py
 create mode 100644 scripts/qwen3_next_chain_report.py
 create mode 100644 scripts/qwen3_next_checkpoint.py
 create mode 100644 scripts/qwen3_next_contract.py
 create mode 100644 scripts/qwen3_next_l2.py
 create mode 100644 scripts/qwen3_next_layer_contract.py
 create mode 100644 scripts/qwen3_next_layers.py
 create mode 100644 scripts/qwen3_next_operator_cases.py
 create mode 100644 scripts/qwen3_next_operator_gates.py
 create mode 100644 scripts/qwen3_next_optimizer_checkpoint.py
 create mode 100644 scripts/qwen3_next_preflight.py
 create mode 100644 scripts/qwen3_next_publish_evidence.py
 create mode 100644 scripts/qwen3_next_rollout_smoke.py
 create mode 100644 scripts/qwen3_next_run.py
 create mode 100644 scripts/qwen3_next_runtime.py
 create mode 100644 scripts/qwen3_next_tp_head_check.py
 create mode 100644 scripts/qwen3_next_tp_probe.py
 create mode 100644 scripts/qwen3_next_training.py
 create mode 100644 scripts/qwen3_next_training_checkpoint.py
 create mode 100644 scripts/qwen3_next_training_export.py
 create mode 100644 tests/check_qwen3_next_batch_invariance.py
 create mode 100644 tests/check_qwen3_next_engine_metadata.py
 create mode 100644 tests/check_qwen3_next_multigpu.py
 create mode 100644 tests/check_qwen3_next_native_rollout.py
 create mode 100644 tests/check_qwen3_next_paged_attention.py
 create mode 100644 tests/check_qwen3_next_vime_weight_hooks.py
 create mode 100644 tests/check_qwen3_next_vllm_model_contract.py
 create mode 100644 tests/test_qwen3_next_binary_evidence.py
 create mode 100644 tests/test_qwen3_next_case_manifest.py
 create mode 100644 tests/test_qwen3_next_chain_report.py
 create mode 100644 tests/test_qwen3_next_checkpoint.py
 create mode 100644 tests/test_qwen3_next_engine_weights.py
 create mode 100644 tests/test_qwen3_next_environment_identity.py
 create mode 100644 tests/test_qwen3_next_evidence_redaction.py
 create mode 100644 tests/test_qwen3_next_failure.py
 create mode 100644 tests/test_qwen3_next_imported_binary.py
 create mode 100644 tests/test_qwen3_next_installed_provider_identity.py
 create mode 100644 tests/test_qwen3_next_l2.py
 create mode 100644 tests/test_qwen3_next_layers.py
 create mode 100644 tests/test_qwen3_next_logprob_adapter.py
 create mode 100644 tests/test_qwen3_next_native_recompute.py
 create mode 100644 tests/test_qwen3_next_operator_gates.py
 create mode 100644 tests/test_qwen3_next_profile.py
 create mode 100644 tests/test_qwen3_next_rl_fixture.py
 create mode 100644 tests/test_qwen3_next_rotary_dtype.py
 create mode 100644 tests/test_qwen3_next_run.py
 create mode 100644 tests/test_qwen3_next_storage.py
 create mode 100644 tests/test_qwen3_next_subnode.py
 create mode 100644 tests/test_qwen3_next_training_audit.py
 create mode 100644 tests/test_qwen3_next_training_checkpoint.py
 create mode 100644 tests/test_qwen3_next_training_closeout.py
 create mode 100644 tests/test_qwen3_next_training_export.py
 create mode 100644 tests/test_qwen3_next_vllm_attention_metadata.py

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index d2cef82de..f50c73cef 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -68,6 +68,31 @@ jobs:
           python -m pytest rl_engine/tests/test_dispatch.py -v
           PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 python -m pytest tests/test_attention_correctness.py -q -rs
           python -m pytest tests/test_forward_invariance.py tests/test_tolerance_contract.py tests/test_ws1_workload.py tests/test_gradient_invariance.py tests/test_elementwise_inventory.py tests/test_four_judgment_matrix.py tests/test_op_checks.py tests/test_operator_inputs.py tests/test_profiler.py tests/test_kv_consistency.py tests/test_ws1_qwen3_dense.py tests/test_ws1_chain_integration.py tests/test_qwen3_next_norm.py tests/test_gdn_state_contract.py tests/test_tensor_identity.py tests/test_qwen3_next_forward_contract.py tests/test_qwen3_next_tp_blocks.py tests/test_qwen3_next_tp_gdn.py -q
+          python -m pytest -q \
+            tests/test_qwen3_next_binary_evidence.py \
+            tests/test_qwen3_next_case_manifest.py \
+            tests/test_qwen3_next_checkpoint.py \
+            tests/test_qwen3_next_engine_weights.py \
+            tests/test_qwen3_next_environment_identity.py \
+            tests/test_qwen3_next_failure.py \
+            tests/test_qwen3_next_imported_binary.py \
+            tests/test_qwen3_next_installed_provider_identity.py \
+            tests/test_qwen3_next_l2.py \
+            tests/test_qwen3_next_layers.py \
+            tests/test_qwen3_next_logprob_adapter.py \
+            tests/test_qwen3_next_native_recompute.py \
+            tests/test_qwen3_next_operator_gates.py \
+            tests/test_qwen3_next_profile.py \
+            tests/test_qwen3_next_rl_fixture.py \
+            tests/test_qwen3_next_rotary_dtype.py \
+            tests/test_qwen3_next_storage.py \
+            tests/test_qwen3_next_training_audit.py \
+            tests/test_qwen3_next_training_closeout.py \
+            tests/test_qwen3_next_training_export.py \
+            tests/test_qwen3_next_subnode.py \
+            tests/test_qwen3_next_chain_report.py \
+            tests/test_qwen3_next_run.py \
+            tests/test_qwen3_next_evidence_redaction.py
 
       - name: Run Cross-Configuration Contract Tests (CPU-safe)
         run: |
diff --git a/.github/workflows/qwen3-next-provider-gpu.yml b/.github/workflows/qwen3-next-provider-gpu.yml
index c045484bd..ca5bf6f4d 100644
--- a/.github/workflows/qwen3-next-provider-gpu.yml
+++ b/.github/workflows/qwen3-next-provider-gpu.yml
@@ -89,4 +89,5 @@ jobs:
             tests/check_qwen3_next_forward.py tests/check_qwen3_next_attention.py \
             tests/check_qwen3_next_gdn_bridge.py tests/check_qwen3_next_gdn_sequence.py \
             tests/check_qwen3_next_conv_bridge.py tests/check_qwen3_next_core_matrix.py \
-            tests/check_qwen3_next_shared_core.py
+            tests/check_qwen3_next_shared_core.py \
+            tests/check_qwen3_next_paged_attention.py tests/check_qwen3_next_batch_invariance.py
diff --git a/ci/run_qwen3_next_chain_gate.sh b/ci/run_qwen3_next_chain_gate.sh
new file mode 100755
index 000000000..ac7d8cec0
--- /dev/null
+++ b/ci/run_qwen3_next_chain_gate.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-Next chain-report adapter checks; with a report path, also validate that report.
+#   bash ci/run_qwen3_next_chain_gate.sh [chain-report.json]
+set -euo pipefail
+ROOT="$(cd "$(dirname "$0")/.." && pwd)"
+cd "$ROOT"
+PY="${PY:-python3}"
+export PYTHONPATH="$ROOT${PYTHONPATH:+:$PYTHONPATH}"
+"$PY" -m pytest -q tests/test_qwen3_next_chain_report.py
+if [ "$#" -ge 1 ]; then
+  "$PY" - "$1" <<'PY'
+import dataclasses, json, sys
+
+from rl_engine.kernels.gtest.chain_gate import ChainGateReport
+from rl_engine.kernels.gtest.qwen3_next_chain_report import COMPATIBLE_SCHEMA, SCHEMA_VERSION
+
+report = json.load(open(sys.argv[1]))
+missing = {f.name for f in dataclasses.fields(ChainGateReport)} - set(report)
+assert not missing, f"chain report lacks ChainGateReport fields: {sorted(missing)}"
+assert report["schema_version"] == SCHEMA_VERSION, report["schema_version"]
+assert report["compatible_schema"] == COMPATIBLE_SCHEMA, report["compatible_schema"]
+print(f"passed={report['passed']} first_drift={report['first_drift']} "
+      f"family={report['first_drift_family']} cells={len(report['cells'])}")
+sys.exit(0 if report["passed"] else 1)
+PY
+fi
diff --git a/ci/run_qwen3_next_operator_gates.sh b/ci/run_qwen3_next_operator_gates.sh
new file mode 100644
index 000000000..1086730a6
--- /dev/null
+++ b/ci/run_qwen3_next_operator_gates.sh
@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+# Required operator acceptance and diagnostics use separate declared case sets.
+set -euo pipefail
+ROOT="$(cd "$(dirname "$0")/.." && pwd)"
+cd "$ROOT"
+PY="${PY:-python3}"
+OUT="${QWEN3_NEXT_EVIDENCE_DIR:?Set QWEN3_NEXT_EVIDENCE_DIR to an artifact directory}"
+export PYTHONPATH="$ROOT${PYTHONPATH:+:$PYTHONPATH}"
+exec "$PY" scripts/qwen3_next_operator_gates.py --out "$OUT" \
+  --suite "${QWEN3_NEXT_OPERATOR_SUITE:-all}"
diff --git a/docs/usage/qwen3-next-chain.md b/docs/usage/qwen3-next-chain.md
new file mode 100644
index 000000000..01087fda9
--- /dev/null
+++ b/docs/usage/qwen3-next-chain.md
@@ -0,0 +1,88 @@
+# Qwen3-Next full-model chain (RFC #428 `full_model_chain`)
+
+The chain checks, bitwise, that Qwen3-Next-80B-A3B-Instruct gives the same
+response tokens, BF16 logits and FP32 selected logprobs in rollout (native vLLM
+prefill and decode) and in replay (VIME/Megatron recompute), on the pinned
+weights and again after two optimizer updates and a checkpoint reload. When a
+case differs, the report names the first layer, the sub-node inside it and its
+layer family.
+
+## Requirements
+
+* One node with four B200 GPUs, TP4 x EP1 (all 512 experts on every rank).
+* The audited runtime: CUDA 13.0.88, torch 2.13.0+cu130, vLLM 0.30.0, Triton
+  3.7.1, transformers 5.17.0 (`pip install -e ".[qwen3-next]"`), plus VIME and
+  its patched Megatron-LM in the same virtual environment for the replay and
+  training stages. The environment is fingerprinted at the start of every run;
+  installing anything into it between stages invalidates the chain.
+* The official checkpoint `Qwen/Qwen3-Next-80B-A3B-Instruct`, revision
+  `9c7f2fbe84465e40164a94cc16cd30b6999b0cc7`; every shard is hashed before the
+  first model load.
+* Host memory: 256 GB for preflight, 512 GB for `layers`/`l2`, 1.28 TB for the
+  training stages (`--memory` declares what the allocation grants), and shared
+  storage for the training checkpoints (about 1.1 TB each) and HF exports.
+
+## Running
+
+Inside an allocation with four GPUs, from a clean checkout of the commit to measure:
+
+```bash
+python scripts/qwen3_next_run.py --stage all \
+    --runs-root <runs> --checkpoint <checkpoint> --cuda-home <cuda-13.0> \
+    --python <venv>/bin/python --until l2
+python scripts/qwen3_next_run.py --stage all --from train1 --until train2 \
+    --previous <runs>/<...-l2-...> \
+    --runs-root <runs> --checkpoint <checkpoint> --cuda-home <cuda-13.0> --python <venv>/bin/python
+python scripts/qwen3_next_run.py --stage train --previous <runs>/<...-train2-...> \
+    --runs-root <runs> --checkpoint <checkpoint> --cuda-home <cuda-13.0> --python <venv>/bin/python
+```
+
+Each stage gets its own run directory with a clean clone of the commit, an
+immutable `run.json` (identified by a fresh `execution_id`) and, once it
+passes, a validated `result.json`; a later stage refuses a predecessor that
+did not pass or that pins a different source, environment, checkpoint,
+provider or topology identity.
+
+| Stage | What it checks | Typical time |
+| --- | --- | --- |
+| `preflight` | environment and versions, the mandatory operator cases (`rl_engine/testing/qwen3_next_operator_cases.json`) and norm C3/C4 gates, multi-GPU device guards, a four-rank TP probe, checkpoint hashes, a vLLM rollout smoke | 15 min |
+| `layers` | the real-weight TP4 gates (GDN, attention, MoE, GDN engine adapters, embedding/LM head) and the native scheduler on 16 cases; certifies the shared provider profile | 25 min |
+| `l2` | 16 cases (prompt 8/64/256/1024 x batch 1/4 x full/chunked prefill, 16 response tokens): rollout vs replay, raw bits, plus every layer and sub-node digest | 30 min |
+| `train1`, `train2` | one optimizer update each, checkpoint, HF export, L2 on the exported weights | 3-4 h each |
+| `train` | reload, L2, continuous and resumed step-3 controls, checkpoint fault injection | 4 h |
+
+## Reports
+
+```bash
+python scripts/qwen3_next_chain_report.py --last <runs>/<...-train-...> --out chain-report.json
+bash ci/run_qwen3_next_chain_gate.sh chain-report.json
+python scripts/qwen3_next_publish_evidence.py --last <runs>/<...-train-...> \
+    --chain-report chain-report.json --out docs/usage/evidence/qwen3-next-chain-<date>
+```
+
+`chain-report.json` has every field of the Qwen3-8B chain gate's report
+(`rl_engine.kernels.gtest.chain_gate.ChainGateReport`, schema `ws1-c10-c11-v5`)
+with `schema_version: qwen3-next-chain-v1`; each case at each weight version is
+a cell, and rollout/replay logits and logprobs are compared with
+`atol = rtol = 0`. A failing chain reports `first_drift` in the dense form
+`layer23.router:rollout->recompute`, plus `first_drift_layer`,
+`first_drift_family` (`embedding`, `norm`, `gdn`, `full_attention`, `moe` or
+`lm_head_logprob`) and `first_drift_cell`.
+
+The sub-nodes are observed at the same five points of every decoder layer in
+both engines (`rl_engine/integrations/qwen3_next_subnode.py`): the normed
+mixer input, the mixer output, the normed MLP input, the ten selected expert
+ids and the MoE output, plus the embedding once per model.
+
+`scripts/qwen3_next_publish_evidence.py` copies the stage records and the
+report, replaces site paths, host and user names with placeholders, refuses to
+publish if any survive, and writes `SHA256SUMS`.
+
+## Checks outside the default CPU suite
+
+`tests/check_qwen3_next_*.py` import vLLM and run in the self-hosted
+`Qwen3-Next-provider-GPU` workflow and in `preflight`.
+`tests/check_qwen3_next_vime_weight_hooks.py`,
+`tests/test_qwen3_next_training_checkpoint.py` and
+`tests/test_qwen3_next_vllm_attention_metadata.py` need the training
+environment (VIME, Megatron) and run there.
diff --git a/pyproject.toml b/pyproject.toml
index e4940c22f..461b28495 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,7 +18,7 @@ dependencies = [
     "tabulate",
     "numpy",
     "accelerate",
-    "transformers==5.13.1",
+    "transformers>=5.13.1,<=5.17.0",
 ]
 
 [project.scripts]
@@ -31,6 +31,14 @@ rl_kernel = "rl_engine.integrations.vllm_runtime:register_vllm_plugin"
 cuda = ["flashinfer-python>=0.6.0,<0.7", "nvidia-ml-py"]
 rocm = ["aiter"]
 vllm = ["vllm>=0.6.0"]
+# The audited Qwen3-Next runtime (RFC #428): every provider check pins these.
+qwen3-next = [
+    "torch==2.13.0",
+    "transformers==5.17.0",
+    "vllm==0.30.0",
+    "triton==3.7.1",
+    "flashinfer-python==0.6.18.post1",
+]
 drift-viewer = ["Pillow>=10", "PySide6>=6.6"]
 dev = ["pytest", "black", "isort", "ruff", "mypy", "pre-commit"]
 
diff --git a/rl_engine/integrations/qwen3_next_binary.py b/rl_engine/integrations/qwen3_next_binary.py
new file mode 100644
index 000000000..e883959af
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_binary.py
@@ -0,0 +1,71 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Identity of the native provider actually imported by a model worker."""
+
+import hashlib
+import importlib
+import importlib.machinery
+import os
+import re
+from pathlib import Path
+
+
+def validate_provider_identity(identity):
+    """Validate serialized execution evidence without importing GPU code."""
+    if (
+        not isinstance(identity, dict)
+        or set(identity) != {"module", "path", "sha256"}
+        or identity["module"] != "rl_engine._C"
+        or not isinstance(identity["path"], str)
+        or not Path(identity["path"]).is_absolute()
+        or not isinstance(identity["sha256"], str)
+        or re.fullmatch(r"[0-9a-f]{64}", identity["sha256"]) is None
+    ):
+        raise ValueError("Provider binary evidence must identify the actual native rl_engine._C")
+    return dict(identity)
+
+
+def imported_provider_identity():
+    """Really import the extension, hash its current bytes, and reject fallbacks.
+
+    Call at worker initialization and on both sides of each case. No cached file
+    digest can hide a replacement made between cases or while a case executes.
+    """
+    try:
+        module = importlib.import_module("rl_engine._C")
+    except (ImportError, OSError) as error:
+        raise RuntimeError(
+            "The required native rl_engine._C provider cannot be imported"
+        ) from error
+    spec = getattr(module, "__spec__", None)
+    filename = getattr(module, "__file__", None)
+    if (
+        getattr(module, "__name__", None) != "rl_engine._C"
+        or not isinstance(getattr(spec, "loader", None), importlib.machinery.ExtensionFileLoader)
+        or not isinstance(filename, str)
+        or not isinstance(getattr(spec, "origin", None), str)
+        or not any(filename.endswith(suffix) for suffix in importlib.machinery.EXTENSION_SUFFIXES)
+    ):
+        raise RuntimeError(
+            "rl_engine._C is not an imported native extension; fallback is forbidden"
+        )
+    path = Path(filename).resolve(strict=True)
+    if Path(spec.origin).resolve(strict=True) != path:
+        raise RuntimeError("Provider module file and import origin disagree")
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        before = os.fstat(stream.fileno())
+        for chunk in iter(lambda: stream.read(8 * 1024 * 1024), b""):
+            digest.update(chunk)
+        after = os.fstat(stream.fileno())
+
+    # Detect replacement of the pathname as well as writes to the opened file.
+    def fingerprint(stat):
+        return (stat.st_dev, stat.st_ino, stat.st_size, stat.st_mtime_ns, stat.st_ctime_ns)
+
+    if fingerprint(before) != fingerprint(after) or fingerprint(after) != fingerprint(path.stat()):
+        raise RuntimeError("Provider binary changed while its content identity was being recorded")
+    return validate_provider_identity(
+        {"module": "rl_engine._C", "path": str(path), "sha256": digest.hexdigest()}
+    )
diff --git a/rl_engine/integrations/qwen3_next_embedding.py b/rl_engine/integrations/qwen3_next_embedding.py
new file mode 100644
index 000000000..a29ea5b1b
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_embedding.py
@@ -0,0 +1,57 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Shared TP4 vocabulary embedding with a fixed-order, non-atomic VJP."""
+
+import torch
+import torch.distributed as dist
+
+from rl_engine.integrations.qwen3_next_tp import _reduce
+
+
+class _Embedding(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, tokens, weight, rank, group):
+        flat = tokens.reshape(-1)
+        local = flat - rank * 37984
+        valid = (local >= 0) & (local < 37984)
+        rows = local.clamp(0, 37983)
+        value = weight.index_select(0, rows)
+        value = torch.where(valid[:, None], value, torch.zeros_like(value))
+        ctx.save_for_backward(local, valid)
+        ctx.weight_shape, ctx.weight_dtype = weight.shape, weight.dtype
+        return _reduce(value, group).reshape(*tokens.shape, 2048)
+
+    @staticmethod
+    def backward(ctx, dy):
+        local, valid = ctx.saved_tensors
+        local = local[valid]
+        values = dy.reshape(-1, 2048)[valid]
+        gradient = torch.zeros(ctx.weight_shape, dtype=ctx.weight_dtype, device=dy.device)
+        order = torch.argsort(local, stable=True)
+        local, values = local[order], values[order]
+        unique, counts = torch.unique_consecutive(local, return_counts=True)
+        start = 0
+        # Every row is written once. Pairwise FP32 additions use a fixed tree
+        # over source token order; there is no floating-point scatter/atomic.
+        for row, count in zip(unique.cpu().tolist(), counts.cpu().tolist(), strict=True):
+            part = values[start : start + count].float()
+            while part.shape[0] > 1:
+                pairs = part.shape[0] // 2
+                merged = part[: 2 * pairs : 2] + part[1 : 2 * pairs : 2]
+                part = torch.cat((merged, part[-1:]), dim=0) if part.shape[0] % 2 else merged
+            gradient[row].copy_(part[0].to(ctx.weight_dtype))
+            start += count
+        return None, gradient, None, None
+
+
+def shared_embedding(tokens, weight, group):
+    if not dist.is_initialized() or group is None or dist.get_world_size(group) != 4:
+        raise ValueError("Shared Qwen embedding requires an explicit TP4 group")
+    if tokens.dtype != torch.int64 or tokens.device != weight.device:
+        raise ValueError("Embedding token IDs must be int64 on the weight device")
+    if weight.shape != (37984, 2048) or weight.dtype != torch.bfloat16:
+        raise ValueError("Each rank must own a BF16 [37984, 2048] embedding shard")
+    if weight.device.type != "cuda" or bool(((tokens < 0) | (tokens >= 151936)).any()):
+        raise ValueError("Embedding requires valid official vocabulary IDs on CUDA")
+    return _Embedding.apply(tokens, weight, dist.get_rank(group), group)
diff --git a/rl_engine/integrations/qwen3_next_engine_weights.py b/rl_engine/integrations/qwen3_next_engine_weights.py
new file mode 100644
index 000000000..a7a82e8ea
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_engine_weights.py
@@ -0,0 +1,88 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Explicit TP metadata and convolution layout conversion at engine boundaries."""
+
+import torch
+
+
+def conv_hf_to_rank_major(weight):
+    """Arrange HF [Q(all ranks), K(all ranks), V(all ranks)] for a dim-0 TP split."""
+    if weight.shape != (8192, 1, 4) or weight.dtype != torch.bfloat16:
+        raise ValueError("Qwen3-Next convolution must be BF16 [8192, 1, 4]")
+    q, k, v = weight.split((2048, 2048, 4096))
+    return torch.cat([part.chunk(4)[rank] for rank in range(4) for part in (q, k, v)])
+
+
+def conv_rank_major_to_hf(weight):
+    """Undo VIME's rank-major TP gather before exporting the official HF layout."""
+    if weight.shape != (8192, 1, 4) or weight.dtype != torch.bfloat16:
+        raise ValueError("Gathered Qwen3-Next convolution must be BF16 [8192, 1, 4]")
+    shards = [part.split((512, 512, 1024)) for part in weight.chunk(4)]
+    return torch.cat([shards[rank][segment] for segment in range(3) for rank in range(4)])
+
+
+def kv_hf_to_rank_major(weight):
+    if weight.shape != (512, 2048) or weight.dtype != torch.bfloat16:
+        raise ValueError("Official KV projection requires BF16 [512, 2048]")
+    first, second = weight.chunk(2)
+    return torch.cat((first, first, second, second))
+
+
+def kv_rank_major_to_hf(weight):
+    from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal
+
+    if weight.shape != (1024, 2048) or weight.dtype != torch.bfloat16:
+        raise ValueError("Gathered pair-replicated KV requires BF16 [1024, 2048]")
+    parts = weight.chunk(4)
+    assert_tensor_bitwise_equal(parts[0], parts[1], name="KV replica pair 0")
+    assert_tensor_bitwise_equal(parts[2], parts[3], name="KV replica pair 1")
+    return torch.cat((parts[0], parts[2]))
+
+
+def expert_hf_to_rank_major(gate, up):
+    if any(value.shape != (512, 2048) or value.dtype != torch.bfloat16 for value in (gate, up)):
+        raise ValueError("Official expert gate/up require BF16 [512, 2048]")
+    return torch.cat([value.chunk(4)[rank] for rank in range(4) for value in (gate, up)])
+
+
+def expert_rank_major_to_hf(weight):
+    if weight.shape != (1024, 2048) or weight.dtype != torch.bfloat16:
+        raise ValueError("Gathered fused expert requires BF16 [1024, 2048]")
+    ranks = [rank.chunk(2) for rank in weight.chunk(4)]
+    return tuple(torch.cat([ranks[rank][segment] for rank in range(4)]) for segment in range(2))
+
+
+def set_gdn_tp_metadata(module):
+    """Tell VIME/Megatron which weights are replicated and how TP gathers them.
+
+    Convolution uses stride one because import/export hooks handle its unequal
+    Q/K/V segments. The shared norm's gradient is already reduced by TP4GDN.
+    """
+    for name, parameter in module.named_parameters():
+        replicated = name == "norm.weight"
+        parameter.tensor_model_parallel = not replicated
+        parameter.partition_dim = -1 if replicated else (1 if name == "out_proj.weight" else 0)
+        parameter.partition_stride = 1
+        parameter.sequence_parallel = False
+        parameter.allreduce = True
+
+
+def validate_training_topology(config, args):
+    for name, expected in (
+        ("tensor_model_parallel_size", 4),
+        ("pipeline_model_parallel_size", 1),
+        ("context_parallel_size", 1),
+        ("expert_model_parallel_size", 1),
+    ):
+        if getattr(config, name, None) != expected:
+            raise ValueError(f"Shared Qwen3-Next requires {name}={expected}")
+    if getattr(config, "sequence_parallel", False) or getattr(args, "sequence_parallel", False):
+        raise ValueError("Shared Qwen3-Next requires sequence parallelism disabled")
+    if getattr(config, "fp32_residual_connection", False):
+        raise ValueError("Shared Qwen3-Next requires BF16 residual additions")
+    if any(
+        getattr(config, name, 0)
+        for name in ("attention_dropout", "hidden_dropout", "mtp_num_layers")
+    ):
+        raise ValueError("Shared Qwen3-Next requires dropout and MTP disabled")
diff --git a/rl_engine/integrations/qwen3_next_forward.py b/rl_engine/integrations/qwen3_next_forward.py
index 18c892261..872daed72 100644
--- a/rl_engine/integrations/qwen3_next_forward.py
+++ b/rl_engine/integrations/qwen3_next_forward.py
@@ -17,6 +17,8 @@
 import torch.nn.functional as F
 from torch.autograd.function import once_differentiable
 
+from rl_engine.kernels.logprob_contract import LogprobContract
+
 FORWARD_PROVIDER_ID = "qwen3-next-shared-cuda-forward-v1"
 
 
@@ -362,3 +364,46 @@ def shared_attention(q, k, v):
     if q.shape[2] > k.shape[2]:
         raise ValueError("Query tokens must fit within the supplied KV prefix")
     return _attention_op()(q, k, v, causal=True, scale=1 / 16)
+
+
+def shared_logprob(local_logits, targets, *, contract, tp_group=None, with_entropy=True):
+    """Return FP32 (selected logprob, LSE, optional entropy) with 64 vocab tiles.
+
+    The existing CUDA WS2 provider supplies forward and autograd. Native VIME
+    policy_loss requests entropy even with a zero regularization coefficient.
+    Both invocation modes therefore use its same entropy-capable path, keeping
+    selected-logprob forward/backward identical across training and rollout.
+    """
+    _cuda_tensors(local_logits, targets)
+    if not isinstance(contract, LogprobContract):
+        raise ValueError("An explicit LogprobContract is required")
+    if contract.sharding.real_vocab_size != 151936 or contract.sharding.padded_vocab_size != 151936:
+        raise ValueError("Qwen3-Next logprob requires the full 151936-entry vocabulary")
+    if contract.sharding.cp_world_size != 1 or contract.sharding.tp_world_size not in (1, 4):
+        raise ValueError("Qwen3-Next logprob requires CP1 and TP4 (TP1 for component tests)")
+    if local_logits.dtype not in (torch.bfloat16, torch.float32):
+        raise ValueError("Qwen3-Next logits must be BF16 or FP32")
+    if targets.dtype not in (torch.int32, torch.int64):
+        raise ValueError("Qwen3-Next targets must have integer token IDs")
+    if not isinstance(with_entropy, bool):
+        raise ValueError("with_entropy must be a bool")
+    if not torch.isfinite(local_logits).all():
+        raise ValueError("Qwen3-Next logits must be finite")
+    from rl_engine.kernels.ops.cuda.loss.vocab_parallel_logp import (
+        CudaVocabParallelLogprobOp,
+        native_tile_stats_available,
+    )
+
+    if not native_tile_stats_available():
+        raise RuntimeError("Qwen3-Next logprob requires rebuilt CUDA tile-stat kernels")
+    selected, lse, entropy = CudaVocabParallelLogprobOp().apply_with_entropy(
+        local_logits,
+        targets,
+        contract=contract,
+        tp_group=tp_group,
+        num_vocab_tiles=64,
+        validate=True,
+        with_entropy_grad=with_entropy,
+        deterministic=True,
+    )
+    return selected, lse, entropy if with_entropy else None
diff --git a/rl_engine/integrations/qwen3_next_logprob.py b/rl_engine/integrations/qwen3_next_logprob.py
new file mode 100644
index 000000000..4848ec020
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_logprob.py
@@ -0,0 +1,89 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Explicit shared Qwen TP4 logprob/entropy boundary for native VIME policy loss."""
+
+import torch
+import torch.distributed as dist
+
+from rl_engine.integrations.qwen3_next_forward import shared_logprob
+from rl_engine.kernels.logprob_contract import (
+    LogprobContract,
+    LogprobDType,
+    LogprobRole,
+    MaskSpec,
+    ReductionSpec,
+    ShardingSpec,
+)
+
+
+def qwen_logprob_contract(logits, *, rank, world_size, role=LogprobRole.TRAIN):
+    if world_size != 4 or type(rank) is not int or not 0 <= rank < 4:
+        raise ValueError("Full Qwen logprob execution requires TP4")
+    if logits.ndim != 2 or logits.shape[1] != 37984:
+        raise ValueError("Each Qwen TP4 rank must own 37984 vocabulary columns")
+    dtype = {torch.bfloat16: LogprobDType.BF16, torch.float32: LogprobDType.FP32}.get(logits.dtype)
+    if dtype is None:
+        raise ValueError("Qwen LM head produces BF16 logits, optionally promoted by VIME")
+    return LogprobContract(
+        role=role,
+        dtype=dtype,
+        mask=MaskSpec(num_tokens=logits.shape[0], active_mask=(True,) * logits.shape[0]),
+        sharding=ShardingSpec(
+            tp_rank=rank,
+            tp_world_size=4,
+            vocab_shard_bounds=tuple((index * 37984, (index + 1) * 37984) for index in range(4)),
+            real_vocab_size=151936,
+            padded_vocab_size=151936,
+            cp_rank=0,
+            cp_world_size=1,
+        ),
+        reduction=ReductionSpec(),
+    )
+
+
+def calculate_log_probs_and_entropy(
+    logits,
+    tokens,
+    tp_group,
+    with_entropy=False,
+    chunk_size=-1,
+    log_prob_keep_mask=None,
+    with_entropy_grad=True,
+):
+    """VIME's numerical callback; its policy loss and response alignment stay native."""
+    if log_prob_keep_mask is not None:
+        raise ValueError("Fixed Qwen eager profile does not support top-p replay")
+    if not dist.is_initialized() or tp_group is None:
+        raise ValueError("Shared Qwen logprob requires an explicit initialized TP4 group")
+    if logits.device.type != "cuda" or tokens.device != logits.device:
+        raise ValueError("Logits and target IDs must share a CUDA device")
+    if tokens.shape != (logits.shape[0],):
+        raise ValueError("Logprob target IDs must match the token rows")
+    contract = qwen_logprob_contract(
+        logits, rank=dist.get_rank(tp_group), world_size=dist.get_world_size(tp_group)
+    )
+    # Keep one fixed numerical path even when VIME requests chunking or metrics
+    # only. Its chunk_size is a memory hint; this explicit provider owns tiling.
+    selected, _lse, entropy = shared_logprob(
+        logits, tokens, contract=contract, tp_group=tp_group, with_entropy=True
+    )
+    if not with_entropy_grad:
+        entropy = entropy.detach()
+    return selected.unsqueeze(-1), entropy if with_entropy else None
+
+
+def install_vime_logprob_provider():
+    """Install explicitly in each worker, without modifying the shared VIME checkout."""
+    from megatron.core import mpu
+    from vime.backends.megatron_utils import loss
+
+    topology = (mpu.get_context_parallel_world_size(), mpu.get_tensor_model_parallel_world_size())
+    if topology != (1, 4):
+        raise ValueError("The Qwen native policy adapter supports TP4/CP1 only")
+    original = loss.calculate_log_probs_and_entropy
+    if original is calculate_log_probs_and_entropy:
+        return
+    if original.__module__ != "vime.utils.ppo_utils":
+        raise ValueError("Refusing to replace an unknown VIME logprob provider")
+    loss.calculate_log_probs_and_entropy = calculate_log_probs_and_entropy
diff --git a/rl_engine/integrations/qwen3_next_native_recompute.py b/rl_engine/integrations/qwen3_next_native_recompute.py
new file mode 100644
index 000000000..a59e89dc7
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_native_recompute.py
@@ -0,0 +1,318 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Independent native VIME full-model recomputation from frozen tokens only."""
+
+import hashlib
+import os
+import sys
+import traceback
+from datetime import timedelta
+from pathlib import Path
+from uuid import uuid4
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+from rl_engine.integrations import qwen3_next_subnode as subnode
+from rl_engine.testing.qwen3_next_l2 import (
+    embedding_digest,
+    layer_digest,
+    load_tensor_artifact,
+    save_tensor_artifact,
+    validate_model_artifact,
+    validate_native_worker_traces,
+)
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def response_rows(value, request):
+    """Keep original BF16 values at P-1..P+14 for every packed sample."""
+    batch = len(request["prompt_tokens"])
+    length = len(request["consumed_input_tokens"][0])
+    if value.dtype not in (torch.bfloat16, torch.int64) or value.numel() == 0:
+        raise ValueError("Response traces must preserve nonempty original BF16/int64 storage")
+    value = value.reshape(-1, value.shape[-1])
+    if value.shape[0] < batch * length:
+        raise ValueError("Packed model output does not cover every frozen sample")
+    positions = torch.tensor(request["prediction_positions"], device=value.device)
+    if positions.shape != (16,) or positions[-1] != length - 1:
+        raise ValueError("Response positions must end before consuming the last target token")
+    return torch.stack([value[index * length + positions] for index in range(batch)])
+
+
+def _rank_main(rank, cases, staging):
+    os.environ.update(LOCAL_RANK=str(rank), RANK=str(rank), WORLD_SIZE="4")
+    torch.cuda.set_device(rank)
+    dist.init_process_group(
+        "nccl",
+        init_method=f"file://{staging}/rendezvous",
+        rank=rank,
+        world_size=4,
+        timeout=timedelta(minutes=30),
+        device_id=torch.device("cuda", rank),
+    )
+    try:
+        from megatron.core import mpu
+        from megatron.core.packed_seq_params import PackedSeqParams
+        from qwen3_next_contract import atomic_json
+
+        from rl_engine.integrations.qwen3_next_binary import imported_provider_identity
+        from rl_engine.integrations.qwen3_next_forward import shared_logprob
+        from rl_engine.integrations.qwen3_next_logprob import qwen_logprob_contract
+        from rl_engine.integrations.qwen3_next_vime_runtime import create_actor
+
+        first = cases[0][0]
+        args, model, optimizer, scheduler = create_actor(
+            first["identity"]["checkpoint"], training=False
+        )
+        if optimizer is not None or scheduler is not None or len(model) != 1:
+            raise ValueError("L2 recompute requires one native actor without an optimizer")
+        raw = model[0]
+        while hasattr(raw, "module"):
+            raw = raw.module
+        if len(raw.decoder.layers) != 48:
+            raise ValueError("Native recompute must execute all 48 decoder layers")
+        model[0].eval()
+        provider_binary = imported_provider_identity()
+        group = mpu.get_tensor_model_parallel_group()
+        for case_index, (request, _output) in enumerate(cases):
+            if request["identity"] != first["identity"] or request["role"] != "recompute":
+                raise ValueError("A native actor batch cannot mix model identities or roles")
+            if rank == 0:
+                atomic_json(
+                    Path(staging).parent / "active-case.json",
+                    {
+                        "case_id": request["case_id"],
+                        "phase": "native_recompute",
+                        "request": str(Path(_output).with_suffix(".request.json")),
+                    },
+                )
+            consumed = torch.tensor(
+                request["consumed_input_tokens"], device="cuda", dtype=torch.long
+            )
+            batch, length = consumed.shape
+            if length != request["state_position"] + 1:
+                raise ValueError("Recompute recurrent-state position is inconsistent")
+            cu = torch.arange(batch + 1, device="cuda", dtype=torch.int32) * length
+            packed = PackedSeqParams(
+                cu_seqlens_q=cu,
+                cu_seqlens_kv=cu,
+                max_seqlen_q=length,
+                max_seqlen_kv=length,
+                qkv_format="thd",
+            )
+            traces, handles = {}, []
+            local_logits = []
+
+            def capture_output(index):
+                def hook(_module, _inputs, output):
+                    hidden = output[0] if isinstance(output, tuple) else output
+                    traces.setdefault(index, {})["output"] = (
+                        response_rows(hidden, request).detach().cpu()
+                    )
+
+                return hook
+
+            def capture_state(index):
+                def hook(state, indices, _cu):
+                    slots = indices[:batch].long()
+                    if (index + 1) % 4:
+                        values = {
+                            "convolution": state.convolution[slots],
+                            "recurrent_state": state.recurrent[slots],
+                        }
+                    else:
+                        chosen = slots.cpu().tolist()
+                        values = {
+                            "key": torch.cat([state.keys[i] for i in chosen]),
+                            "value": torch.cat([state.values[i] for i in chosen]),
+                        }
+                    traces.setdefault(index, {})["states"] = {
+                        key: value.detach().cpu() for key, value in values.items()
+                    }
+
+                return hook
+
+            def capture_logits(_module, _inputs, output):
+                if local_logits:
+                    raise ValueError(
+                        "Native L2 recompute must run exactly one full forward per case"
+                    )
+                local_logits.append(response_rows(output[0], request).detach())
+
+            def capture_subnode(index, name, value):
+                nodes = traces.setdefault(index, {}).setdefault("subnodes", {})
+                if name in nodes:
+                    raise ValueError(f"Sub-node {name} of layer {index} was observed twice")
+                nodes[name] = response_rows(value, request).detach().cpu()
+
+            subnode.install(raw.decoder.layers, subnode.vime_modules, capture_subnode)
+            embedding = raw.embedding.word_embeddings
+
+            def capture_embedding(_name, value):
+                if "embedding" in traces.setdefault("model", {}):
+                    raise ValueError("The embedding was observed twice in one forward")
+                traces["model"]["embedding"] = response_rows(value, request).detach().cpu()
+
+            embedding.subnode_hook = capture_embedding
+            for index, layer in enumerate(raw.decoder.layers):
+                layer.self_attention.trace_state_hook = capture_state(index)
+                handles.append(layer.register_forward_hook(capture_output(index)))
+            handles.append(raw.output_layer.register_forward_hook(capture_logits))
+            try:
+                with torch.no_grad():
+                    model[0](
+                        input_ids=consumed.reshape(1, -1),
+                        position_ids=None,
+                        attention_mask=None,
+                        labels=None,
+                        packed_seq_params=packed,
+                    )
+                if len(local_logits) != 1 or len(traces) != 49:
+                    raise ValueError("Native model omitted an output or diagnostic hook")
+                local = local_logits[0].reshape(-1, 37984)
+                targets = torch.tensor(
+                    request["response_tokens"], device="cuda", dtype=torch.long
+                ).reshape(-1)
+                selected, _lse, _entropy = shared_logprob(
+                    local,
+                    targets,
+                    contract=qwen_logprob_contract(local, rank=rank, world_size=4),
+                    tp_group=group,
+                    with_entropy=True,
+                )
+                peers = [torch.empty_like(local) for _ in range(4)]
+                dist.all_gather(peers, local.contiguous(), group=group)
+                record = {
+                    "rank": rank,
+                    "request_sha256": request["request_sha256"],
+                    "role": "recompute",
+                    "execution": request["execution"],
+                    "provider_binary": provider_binary,
+                    "layers": traces,
+                    "local_logits": local.reshape(batch, 16, 37984).cpu(),
+                    "responses": targets.reshape(batch, 16).cpu(),
+                    "logprobs": selected.reshape(batch, 16).cpu(),
+                }
+                if rank == 0:
+                    record["logits"] = torch.cat(peers, dim=-1).reshape(batch, 16, 151936).cpu()
+                save_tensor_artifact(Path(staging) / f"case-{case_index}-rank-{rank}.pt", record)
+                del peers, local
+                local_logits.clear()
+            finally:
+                for handle in handles:
+                    handle.remove()
+                for layer in raw.decoder.layers:
+                    layer.self_attention.trace_state_hook = None
+                subnode.remove(raw.decoder.layers, subnode.vime_modules)
+                embedding.subnode_hook = None
+            dist.barrier()
+    except BaseException:
+        # One rank must not take the others down by deadlock: tearing down the
+        # process group while its peers sit in a collective can block forever.
+        # Report, then exit so the spawning parent terminates
+        # the sibling ranks and the stage fails instead of running out its budget.
+        traceback.print_exc()
+        sys.stderr.flush()
+        os._exit(3)
+    finally:
+        dist.destroy_process_group()
+
+
+def _assemble(request, ranks, records):
+    if len(ranks) != 4 or [item.get("rank") for item in ranks] != list(range(4)):
+        raise ValueError("Recompute artifacts must cover all four distinct TP ranks")
+    result = {
+        "metadata": {
+            key: request[key]
+            for key in ("schema_version", "case_id", "role", "identity", "request_sha256")
+        },
+        "execution": request["execution"],
+        "prompt_tokens": torch.tensor(request["prompt_tokens"], dtype=torch.int64),
+        "response_tokens": torch.tensor(request["response_tokens"], dtype=torch.int64),
+        "prediction_positions": torch.tensor(request["prediction_positions"], dtype=torch.int64),
+        "logits": ranks[0]["logits"],
+        "logprobs": ranks[0]["logprobs"],
+        "layer_digests": [],
+        "embedding_digest": embedding_digest(ranks[0]["layers"]["model"]["embedding"]),
+        "debug_tensors": {},
+        "worker_traces": records,
+        "provider_binary": ranks[0]["provider_binary"],
+    }
+    for peer in ranks[1:]:
+        exact(
+            peer["layers"]["model"]["embedding"],
+            ranks[0]["layers"]["model"]["embedding"],
+            name="TP replicated embedding",
+        )
+    for peer in ranks:
+        if peer.get("provider_binary") != result["provider_binary"]:
+            raise ValueError("Recompute TP ranks loaded different CUDA provider binaries")
+        if peer["request_sha256"] != request["request_sha256"]:
+            raise ValueError("Rank result belongs to a different request")
+        exact(peer["logprobs"], result["logprobs"], name="TP selected logprob replica")
+    for layer in range(48):
+        hidden = ranks[0]["layers"][layer]["output"]
+        states = {}
+        for rank, peer in enumerate(ranks):
+            exact(peer["layers"][layer]["output"], hidden, name=f"TP layer {layer} replica")
+            states.update(
+                {
+                    f"tp{rank}.{name}": value
+                    for name, value in peer["layers"][layer]["states"].items()
+                }
+            )
+        nodes = ranks[0]["layers"][layer]["subnodes"]
+        for peer in ranks[1:]:
+            for name in subnode.SUBNODES:
+                exact(peer["layers"][layer]["subnodes"][name], nodes[name], name=f"TP {name}")
+        nodes = {name: nodes[name] for name in subnode.SUBNODES}
+        result["layer_digests"].append(
+            layer_digest(layer, hidden, states, request=request, subnodes=nodes)
+        )
+        if layer in request["trace_layers"]:
+            result["debug_tensors"][f"layer.{layer}.output"] = hidden
+            result["debug_tensors"].update(
+                {f"layer.{layer}.subnode.{name}": value for name, value in nodes.items()}
+            )
+            result["debug_tensors"].update(
+                {f"layer.{layer}.{name}": value for name, value in states.items()}
+            )
+    validate_model_artifact(result, request)
+    return result
+
+
+def run_recompute_cases(cases):
+    cases = [(request, str(output)) for request, output in cases]
+    if not cases:
+        raise ValueError("Native recompute batch cannot be empty")
+    staging = Path(cases[0][1]).parent / f"native-recompute-{uuid4().hex}"
+    staging.mkdir(parents=True, exist_ok=False)
+    # Spawn preserves the current isolated-venv interpreter. Each rank loads
+    # the real model once, and all ranks exit before another GPU role starts.
+    mp.spawn(_rank_main, args=(cases, str(staging)), nprocs=4, join=True)
+    for index, (request, output) in enumerate(cases):
+        files = [staging / f"case-{index}-rank-{rank}.pt" for rank in range(4)]
+        records = []
+        for rank, path in enumerate(files):
+            with path.open("rb") as stream:
+                digest = hashlib.file_digest(stream, "sha256").hexdigest()
+            records.append(
+                {
+                    "rank": rank,
+                    "path": str(path.absolute()),
+                    "sha256": digest,
+                    "request_sha256": request["request_sha256"],
+                }
+            )
+        artifact = _assemble(request, [load_tensor_artifact(path) for path in files], records)
+        validate_native_worker_traces(artifact, request, artifact_root=staging.parent)
+        # Retain original per-rank hidden/state tensors for historical validation
+        # and first-divergence diagnosis after old HF exports have been retired.
+        save_tensor_artifact(Path(output), artifact)
+
+
+def run_recompute_case(request, output):
+    run_recompute_cases([(request, output)])
diff --git a/rl_engine/integrations/qwen3_next_native_rollout.py b/rl_engine/integrations/qwen3_next_native_rollout.py
new file mode 100644
index 000000000..912354597
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_native_rollout.py
@@ -0,0 +1,777 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Actual vLLM scheduler rollout, with raw shared-provider L2 observations.
+
+Qualification leaves backend capabilities closed; certified execution requires
+the source/environment-bound profile certificate. The scheduler audits actual decisions;
+configuration hints alone are never reported as executed prefill chunks.
+"""
+
+import gc
+import hashlib
+import os
+from pathlib import Path
+
+import torch
+from vllm.v1.core.sched.interface import PauseState
+from vllm.v1.core.sched.scheduler import Scheduler
+from vllm.v1.sample.logits_processor import AdapterLogitsProcessor
+
+from rl_engine.integrations import qwen3_next_subnode as subnode
+from rl_engine.integrations.qwen3_next_binary import (
+    imported_provider_identity,
+    validate_provider_identity,
+)
+from rl_engine.testing.qwen3_next_l2 import (
+    embedding_digest,
+    json_digest,
+    layer_digest,
+    load_tensor_artifact,
+    save_tensor_artifact,
+    validate_debug_capture,
+    validate_model_artifact,
+)
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+NATIVE_ROLLOUT_PROFILE = {
+    # Caps, not schedules: ExactPrefillScheduler fixes every step's chunks. They
+    # are sized so up to 64 concurrent requests fit one certified profile.
+    "kv_cache_memory_bytes_per_gpu": 4 << 30,
+    "max_model_len": 1040,
+    "max_num_seqs": 64,
+    "max_num_batched_tokens": 16384,
+    "scheduler": "rl_engine.integrations.qwen3_next_native_rollout.ExactPrefillScheduler",
+    "async_scheduling": False,
+    "prefix_caching": False,
+    "weights_dtype": "bfloat16",
+    "recurrent_state_dtype": "float32",
+    "model_runner": "v1",
+}
+
+
+def capabilities():
+    return {
+        "rollout": True,
+        "debug_layers": True,
+        "architecture_layers": 48,
+        "tp": 4,
+        "batched_cases": True,
+        "native_scheduler": True,
+        "scheduler_batch_invariance_verified": False,
+        "full_model_l2_verified": False,
+    }
+
+
+def runtime_profile(config):
+    scheduler = config.scheduler_config.scheduler_cls
+    if isinstance(scheduler, type):
+        scheduler = scheduler.__module__ + "." + scheduler.__qualname__
+    actual = {
+        "kv_cache_memory_bytes_per_gpu": config.cache_config.kv_cache_memory_bytes,
+        "max_model_len": config.model_config.max_model_len,
+        "max_num_seqs": config.scheduler_config.max_num_seqs,
+        "max_num_batched_tokens": config.scheduler_config.max_num_batched_tokens,
+        "scheduler": scheduler,
+        "async_scheduling": config.scheduler_config.async_scheduling,
+        "prefix_caching": config.cache_config.enable_prefix_caching,
+        "weights_dtype": str(config.model_config.dtype).removeprefix("torch."),
+        "recurrent_state_dtype": config.cache_config.mamba_ssm_cache_dtype,
+        "model_runner": "v2" if config.use_v2_model_runner else "v1",
+    }
+    if actual != NATIVE_ROLLOUT_PROFILE:
+        raise ValueError(f"Native runtime changed the explicit rollout profile: {actual}")
+    return actual
+
+
+def validate_request(request):
+    if request.get("role") != "rollout" or request.get("schema_version") != 1:
+        raise ValueError("A version1 rollout request is required")
+    digest_input = {key: value for key, value in request.items() if key != "request_sha256"}
+    if json_digest(digest_input) != request.get("request_sha256"):
+        raise ValueError("Rollout request identity mismatch")
+    prompts, samples = request["prompt_tokens"], request["sample_ids"]
+    batch = len(prompts)
+    if (
+        batch not in (1, 4)
+        or len(samples) != batch
+        or any(type(sample) is not int for sample in samples)
+        or len(set(samples)) != batch
+    ):
+        raise ValueError("Rollout requires one or four uniquely identified samples")
+    length = len(prompts[0])
+    if length not in (8, 64, 256, 1024) or any(len(row) != length for row in prompts):
+        raise ValueError("Rollout prompts must use one official matrix length")
+    responses = request["response_tokens"]
+    for rows in (prompts, responses or []):
+        if any(type(token) is not int or not 0 <= token < 151936 for row in rows for token in row):
+            raise ValueError("Rollout tokens must be integers in the official vocabulary")
+    if responses is not None and (
+        len(responses) != batch or any(len(row) != 16 for row in responses)
+    ):
+        raise ValueError("Frozen responses must have exactly sixteen tokens per sample")
+    chunks = request["prefill_chunks"]
+    if (
+        not chunks
+        or any(type(value) is not int or value <= 0 for value in chunks)
+        or sum(chunks) != length
+    ):
+        raise ValueError("Exact positive prefill chunks must cover the prompt")
+    expected_execution = {
+        "rollout_mode": "autoregressive",
+        "prefill_chunks": chunks,
+        "response_mode": "generate" if responses is None else "teacher_forced",
+    }
+    if (
+        request["execution"] != expected_execution
+        or request["response_length"] != 16
+        or request["vocab_size"] != 151936
+        or request["logits_dtype"] != "bfloat16"
+        or request["logprobs_dtype"] != "float32"
+        or request["prediction_positions"] != list(range(length - 1, length + 15))
+        or request["consumed_response_tokens"] != 15
+        or request["state_position"] != length + 14
+        or request["required_layer_digests"] != list(range(48))
+        or request["generation"]
+        != {"temperature": 0.0, "top_p": 1.0, "top_k": -1, "seed": 1234, "ignore_eos": True}
+    ):
+        raise ValueError("Rollout execution or response offsets differ from the pinned contract")
+    if responses is None:
+        if batch != 4 or chunks != [length] or request["consumed_input_tokens"] is not None:
+            raise ValueError("Only baseline B4 full prefill may define frozen responses")
+    elif request["consumed_input_tokens"] != [
+        prompt + response[:-1] for prompt, response in zip(prompts, responses, strict=True)
+    ]:
+        raise ValueError("Consumed input must end before response token sixteen")
+    if any(type(layer) is not int or layer not in range(48) for layer in request["trace_layers"]):
+        raise ValueError("Debug layer indices must be in [0,48)")
+    identity = request["identity"]
+    if identity["architecture_layers"] != 48 or identity["topology"] != {
+        "tp": 4,
+        "pp": 1,
+        "cp": 1,
+        "ep": 1,
+    }:
+        raise ValueError("Rollout cannot change depth or topology")
+    if not Path(identity["checkpoint"]).is_absolute():
+        raise ValueError("Checkpoint identity must use an absolute shared path")
+    if (
+        identity["provider_config"].get("native_rollout_kv_cache_memory_bytes")
+        != NATIVE_ROLLOUT_PROFILE["kv_cache_memory_bytes_per_gpu"]
+    ):
+        raise ValueError("Provider identity must pin the native rollout cache budget")
+    return batch, length
+
+
+def next_scheduled_tokens(computed, prompt_length, chunks):
+    boundary = 0
+    for size in chunks:
+        if computed == boundary:
+            return size
+        boundary += size
+    if prompt_length <= computed < prompt_length + 15:
+        return 1
+    raise ValueError("Native scheduler is not at an exact requested chunk/decode boundary")
+
+
+class ExactPrefillScheduler(Scheduler):
+    """Use native cache allocation/scheduling while enforcing one frozen case at a time."""
+
+    def schedule(self, throttle_prefills=False):
+        active = [request for request in self.requests.values() if not request.is_finished()]
+        if not active:
+            return super().schedule(throttle_prefills)
+        descriptors = [
+            (request.sampling_params.extra_args or {}).get("shared_l2") for request in active
+        ]
+        if any(item is None for item in descriptors):
+            raise ValueError("Exact Qwen scheduler accepts only identified L2 requests")
+        first = descriptors[0]
+        contract = (
+            first["request_sha256"],
+            first["batch_size"],
+            first["prompt_length"],
+            first["prefill_chunks"],
+        )
+        if any(
+            (
+                item["request_sha256"],
+                item["batch_size"],
+                item["prompt_length"],
+                item["prefill_chunks"],
+            )
+            != contract
+            for item in descriptors
+        ):
+            raise ValueError("Different L2 cases cannot share a scheduler batch")
+        if len(active) < first["batch_size"]:
+            # EngineCore may receive the four add-request messages separately.
+            # Its normal paused scheduler emits no work until all are admitted.
+            previous = self._pause_state
+            self._pause_state = PauseState.PAUSED_ALL
+            try:
+                result = super().schedule(throttle_prefills)
+            finally:
+                self._pause_state = previous
+            if result.total_num_scheduled_tokens:
+                raise RuntimeError("The native scheduler violated the input batch barrier")
+            return result
+        if len(active) != first["batch_size"] or len(
+            {item["sample_id"] for item in descriptors}
+        ) != len(active):
+            raise ValueError("L2 scheduler sample cardinality mismatch")
+        counts = {request.num_computed_tokens for request in active}
+        if len(counts) != 1:
+            raise RuntimeError("Native requests lost their synchronized exact-chunk boundary")
+        computed = counts.pop()
+        count = next_scheduled_tokens(computed, first["prompt_length"], first["prefill_chunks"])
+        self.scheduler_config.long_prefill_token_threshold = count
+        result = super().schedule(throttle_prefills)
+        expected = {request.request_id: count for request in active}
+        if result.num_scheduled_tokens != expected or result.preempted_req_ids:
+            raise RuntimeError(
+                "exact_scheduler_mismatch: "
+                f"expected {expected}, actual {result.num_scheduled_tokens}"
+            )
+        return result
+
+
+class ForceFrozenTokens(AdapterLogitsProcessor):
+    """Sampling-only teacher forcing; raw model logits are observed beforehand."""
+
+    def is_argmax_invariant(self):
+        return False
+
+    def new_req_logits_processor(self, params):
+        descriptor = (params.extra_args or {}).get("shared_l2")
+        if descriptor is None:
+            raise ValueError("L2 sampling requires explicit request metadata")
+        frozen = descriptor["response_tokens"]
+        if frozen is None:
+            return None
+        if len(frozen) != 16 or any(
+            type(token) is not int or not 0 <= token < 151936 for token in frozen
+        ):
+            raise ValueError("Teacher forcing requires sixteen frozen official token IDs")
+
+        def force(output_ids, logits):
+            index = len(output_ids)
+            if index not in range(16):
+                raise ValueError("Teacher forcing exceeded the fixed response length")
+            selected = torch.full_like(logits, float("-inf"))
+            selected[frozen[index]] = 0
+            return selected
+
+        return force
+
+
+def file_digest(path):
+    digest = hashlib.sha256()
+    with Path(path).open("rb") as stream:
+        for chunk in iter(lambda: stream.read(8 * 1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+class RolloutTrace:
+    """Worker-local observations of real engine buffers, keyed by immutable sample IDs."""
+
+    def __init__(self, worker, request, output_directory):
+        from rl_engine.integrations.qwen3_next_vllm_model import RLKernelQwen3NextForCausalLM
+
+        self.batch, self.prompt_length = validate_request(request)
+        self.worker, self.request, self.directory = worker, request, Path(output_directory)
+        self.provider_binary = imported_provider_identity()
+        if self.provider_binary != worker.shared_provider_binary:
+            raise RuntimeError("Provider binary changed after native worker initialization")
+        self.rank = worker.rank
+        self.native_runtime_profile = runtime_profile(worker.vllm_config)
+        self.provider_execution_mode = worker.shared_provider_execution_mode
+        expected_mode = (
+            "certified"
+            if request["identity"].get("scheduler_profile_certificate")
+            else "qualification"
+        )
+        if self.provider_execution_mode != expected_mode:
+            raise ValueError("Worker provider certification differs from the immutable request")
+        self.model = worker.get_model()
+        if not isinstance(self.model, RLKernelQwen3NextForCausalLM) or worker.use_v2_model_runner:
+            raise ValueError(
+                "Trace requires the actual shared model and native V1 GPU model runner"
+            )
+        self.row_by_sample = {sample: row for row, sample in enumerate(request["sample_ids"])}
+        self.request_rows, self.progress = {}, {}
+        self.current, self.schedule = [], []
+        self.logits = torch.empty(self.batch, 16, 151936, dtype=torch.bfloat16)
+        self.local_logits = torch.empty(self.batch, 16, 37984, dtype=torch.bfloat16)
+        self.logprobs = torch.empty(self.batch, 16, dtype=torch.float32)
+        self.responses = torch.full((self.batch, 16), -1, dtype=torch.int64)
+        self.hidden = torch.empty(48, self.batch, 16, 2048, dtype=torch.bfloat16)
+        self.hidden_written = torch.zeros(48, self.batch, 16, dtype=torch.bool)
+        self.states = [
+            (
+                {"convolution": [None] * self.batch, "recurrent_state": [None] * self.batch}
+                if (layer + 1) % 4
+                else {"key": [None] * self.batch, "value": [None] * self.batch}
+            )
+            for layer in range(48)
+        ]
+        self.model.model.shared_forward_hook = self.begin_forward
+        self.model.model.shared_trace_hook = self.layer
+        self.model.shared_logits_hook = self.record_logits
+        self.subnodes = subnode.SubnodeBuffer(self.batch)
+        subnode.install(self.model.model.layers, subnode.vllm_modules, self.subnode)
+        self.model.model.subnode_hook = self.embedding
+
+    def embedding(self, name, packed):
+        for row, begin, end, computed in self.current:
+            for offset in range(end - begin):
+                response = computed + offset - (self.prompt_length - 1)
+                if 0 <= response < 16:
+                    self.subnodes.put_embedding(row, response, packed[begin + offset])
+
+    def subnode(self, layer_index, name, packed):
+        for row, begin, end, computed in self.current:
+            for offset in range(end - begin):
+                response = computed + offset - (self.prompt_length - 1)
+                if 0 <= response < 16:
+                    self.subnodes.put(layer_index, name, row, response, packed[begin + offset])
+
+    def before_execute(self, scheduled):
+        for new in scheduled.scheduled_new_reqs:
+            descriptor = (new.sampling_params.extra_args or {}).get("shared_l2", {})
+            if descriptor.get("request_sha256") != self.request["request_sha256"]:
+                raise ValueError("Scheduler request belongs to a different L2 case")
+            row = self.row_by_sample[descriptor["sample_id"]]
+            if (
+                new.prompt_token_ids != self.request["prompt_tokens"][row]
+                or new.num_computed_tokens != 0
+            ):
+                raise ValueError("Native prompt differs or prefix reuse was applied")
+            if new.req_id in self.request_rows or row in self.request_rows.values():
+                raise ValueError("Native scheduler duplicated a sample")
+            self.request_rows[new.req_id], self.progress[new.req_id] = row, 0
+        self.scheduled = scheduled
+        if not scheduled.total_num_scheduled_tokens:
+            return
+        if len(scheduled.num_scheduled_tokens) != self.batch or scheduled.preempted_req_ids:
+            raise RuntimeError("The actual native step did not preserve its required batch")
+        step = {}
+        for req_id, count in scheduled.num_scheduled_tokens.items():
+            computed = self.progress[req_id]
+            expected = next_scheduled_tokens(
+                computed, self.prompt_length, self.request["prefill_chunks"]
+            )
+            if count != expected:
+                raise RuntimeError(
+                    "Observed worker tokens disagree with requested prefill/decode chunks"
+                )
+            step[req_id] = (self.request_rows[req_id], computed, count)
+        self.step = step
+
+    def begin_forward(self, input_ids, positions):
+        ids = list(self.worker.model_runner.input_batch.req_ids)
+        if set(ids) != set(self.step):
+            raise ValueError("Actual model input batch differs from SchedulerOutput")
+        self.current = []
+        begin = 0
+        for req_id in ids:
+            row, computed, count = self.step[req_id]
+            end = begin + count
+            if positions[begin:end].cpu().tolist() != list(range(computed, computed + count)):
+                raise ValueError("Native absolute positions differ from the scheduled offsets")
+            known = self.request["prompt_tokens"][row] + self.responses[row].tolist()
+            if input_ids[begin:end].cpu().tolist() != known[computed : computed + count]:
+                raise ValueError(
+                    "Native engine consumed tokens other than the prompt/frozen response prefix"
+                )
+            self.current.append((row, begin, end, computed))
+            self.progress[req_id] = computed + count
+            begin = end
+        if begin != self.scheduled.total_num_scheduled_tokens:
+            raise ValueError("Packed model input does not cover actual scheduled tokens")
+        self.schedule.append(
+            {
+                "samples": [self.request["sample_ids"][row] for row, _, _, _ in self.current],
+                "computed_before": [computed for _, _, _, computed in self.current],
+                "token_counts": [end - begin for _, begin, end, _ in self.current],
+            }
+        )
+
+    def layer(self, layer_index, output, layer):
+        from vllm.forward_context import get_forward_context
+        from vllm.model_executor.layers.mamba.mamba_utils import is_conv_state_dim_first
+
+        from rl_engine.integrations.qwen3_next_vllm import metadata_sequences
+        from rl_engine.integrations.qwen3_next_vllm_attention import SharedAttentionMetadata
+
+        if output.dtype != torch.bfloat16 or output.shape[1:] != (2048,):
+            raise ValueError("Layer observation did not preserve original BF16 hidden states")
+        metadata = get_forward_context().attn_metadata
+        if not isinstance(metadata, dict):
+            raise ValueError("Native engine did not supply per-layer attention metadata")
+        for row, begin, end, computed in self.current:
+            for offset in range(end - begin):
+                response = computed + offset - (self.prompt_length - 1)
+                if 0 <= response < 16:
+                    if self.hidden_written[layer_index, row, response]:
+                        raise ValueError("A response hidden state was observed more than once")
+                    self.hidden[layer_index, row, response].copy_(
+                        output[begin + offset].detach().cpu()
+                    )
+                    self.hidden_written[layer_index, row, response] = True
+        if not any(
+            computed + end - begin == self.prompt_length + 15
+            for _, begin, end, computed in self.current
+        ):
+            return
+        if (layer_index + 1) % 4:
+            module = layer.linear_attn
+            meta = metadata[module.prefix]
+            indices, cu, _reset = metadata_sequences(
+                meta, self.scheduled.total_num_scheduled_tokens
+            )
+            if cu.tolist() != [0] + [end for _, _, end, _ in self.current]:
+                raise ValueError("GDN metadata reordered sequences without matching model inputs")
+            conv, recurrent = module.kv_cache
+            if not is_conv_state_dim_first():
+                conv = conv.transpose(-1, -2)
+            for (row, begin, end, computed), slot in zip(
+                self.current, indices.tolist(), strict=True
+            ):
+                if computed + end - begin == self.prompt_length + 15:
+                    self.states[layer_index]["convolution"][row] = conv[slot].detach().cpu().clone()
+                    self.states[layer_index]["recurrent_state"][row] = (
+                        recurrent[slot].detach().cpu().clone()
+                    )
+        else:
+            attention = layer.self_attn.attn
+            meta, cache = metadata[attention.layer_name], attention.kv_cache
+            if not isinstance(meta, SharedAttentionMetadata):
+                raise ValueError("Full attention did not use shared native paged-KV metadata")
+            if list(meta.query_start_loc) != [0] + [end for _, _, end, _ in self.current]:
+                raise ValueError("Full attention boundaries differ from actual packed input")
+            block_size = cache.shape[2]
+            for index, (row, begin, end, computed) in enumerate(self.current):
+                length = computed + end - begin
+                if length != self.prompt_length + 15:
+                    continue
+                if meta.seq_lens[index] != length:
+                    raise ValueError("Full attention prefix length differs from consumed inputs")
+                blocks = meta.block_table[index, : (length + block_size - 1) // block_size].long()
+                values = cache[blocks, 0].reshape(-1, 512)[:length]
+                self.states[layer_index]["key"][row] = (
+                    values[:, :256].reshape(1, length, 256).detach().cpu().clone()
+                )
+                self.states[layer_index]["value"][row] = (
+                    values[:, 256:].reshape(1, length, 256).detach().cpu().clone()
+                )
+
+    def record_logits(self, hidden, local, full):
+        from rl_engine.integrations.qwen3_next_forward import shared_logprob
+        from rl_engine.integrations.qwen3_next_logprob import qwen_logprob_contract
+        from rl_engine.kernels.logprob_contract import LogprobRole
+
+        if full.shape != (len(self.current), 151936) or local.shape != (len(self.current), 37984):
+            raise ValueError(
+                "Native sampling rows do not correspond one-to-one with active requests"
+            )
+        eligible = [
+            (index, row, computed + end - begin - self.prompt_length)
+            for index, (row, begin, end, computed) in enumerate(self.current)
+            if computed + end - begin >= self.prompt_length
+        ]
+        if not eligible:
+            return
+        rows = torch.tensor([index for index, _, _ in eligible], device=local.device)
+        targets = torch.tensor(
+            [
+                (
+                    int(full[index].argmax())
+                    if self.request["response_tokens"] is None
+                    else self.request["response_tokens"][row][position]
+                )
+                for index, row, position in eligible
+            ],
+            device=local.device,
+            dtype=torch.int64,
+        )
+        selected = local.index_select(0, rows)
+        contract = qwen_logprob_contract(
+            selected, rank=self.rank, world_size=4, role=LogprobRole.INFER
+        )
+        logp, _, _ = shared_logprob(
+            selected, targets, contract=contract, tp_group=self.model.shared_tp_group
+        )
+        for count, (index, row, position) in enumerate(eligible):
+            if position not in range(16) or self.responses[row, position] != -1:
+                raise ValueError("Sampling observation has a duplicate or invalid response offset")
+            self.logits[row, position].copy_(full[index].detach().cpu())
+            self.local_logits[row, position].copy_(local[index].detach().cpu())
+            self.logprobs[row, position].copy_(logp[count].detach().cpu())
+            self.responses[row, position] = int(targets[count])
+
+    def finish(self):
+        if imported_provider_identity() != self.provider_binary:
+            raise RuntimeError("Provider binary changed during the rollout case")
+        if not self.hidden_written.all() or bool((self.responses < 0).any()):
+            raise RuntimeError("Rollout did not observe every response position in every layer")
+        for layer in range(48):
+            self.subnodes.layer(layer)
+        if len(self.schedule) != len(self.request["prefill_chunks"]) + 15:
+            raise RuntimeError("Actual scheduler step count differs from the exact chunk contract")
+        states = []
+        for layer in self.states:
+            if any(value is None for values in layer.values() for value in values):
+                raise RuntimeError("A final convolution/recurrent/KV state was not observed")
+            states.append({name: torch.stack(values) for name, values in layer.items()})
+        payload = {
+            "rank": self.rank,
+            "request_sha256": self.request["request_sha256"],
+            "logits": self.logits,
+            "local_logits": self.local_logits,
+            "logprobs": self.logprobs,
+            "responses": self.responses,
+            "hidden": self.hidden,
+            "states": states,
+            "subnodes": {name: self.subnodes.values[name] for name in subnode.SUBNODES},
+            "embedding": self.subnodes.embedding_rows(),
+            "schedule": self.schedule,
+            "native_runtime_profile": self.native_runtime_profile,
+            "provider_execution_mode": self.provider_execution_mode,
+            "provider_binary": self.provider_binary,
+        }
+        self.directory.mkdir(parents=True, exist_ok=True)
+        path = self.directory / f"rank-{self.rank}.pt"
+        save_tensor_artifact(path, payload)
+        self.model.model.shared_forward_hook = self.model.model.shared_trace_hook = None
+        self.model.shared_logits_hook = None
+        subnode.remove(self.model.model.layers, subnode.vllm_modules)
+        self.model.model.subnode_hook = None
+        return {
+            "rank": self.rank,
+            "path": str(path),
+            "sha256": file_digest(path),
+            "request_sha256": self.request["request_sha256"],
+        }
+
+
+def assemble_trace(request, records, sampled_responses):
+    if len(records) != 4 or sorted(record["rank"] for record in records) != list(range(4)):
+        raise ValueError("Rollout trace requires all four unique TP ranks")
+    shards = []
+    for record in sorted(records, key=lambda value: value["rank"]):
+        if (
+            record["request_sha256"] != request["request_sha256"]
+            or file_digest(record["path"]) != record["sha256"]
+        ):
+            raise ValueError("Worker trace identity/hash mismatch")
+        shard = load_tensor_artifact(record["path"])
+        if shard["rank"] != record["rank"] or shard["request_sha256"] != request["request_sha256"]:
+            raise ValueError("Worker trace content identity mismatch")
+        shards.append(shard)
+    first = shards[0]
+    provider_binary = validate_provider_identity(first.get("provider_binary"))
+    for shard in shards[1:]:
+        if validate_provider_identity(shard.get("provider_binary")) != provider_binary:
+            raise ValueError("TP workers imported different provider binaries")
+    for shard in shards[1:]:
+        for name in ("logits", "logprobs", "responses", "hidden"):
+            exact(shard[name], first[name], name=f"TP replicated rollout {name}")
+        for name in subnode.SUBNODES:
+            exact(shard["subnodes"][name], first["subnodes"][name], name=f"TP replicated {name}")
+        exact(shard["embedding"], first["embedding"], name="TP replicated embedding")
+        if shard["schedule"] != first["schedule"]:
+            raise ValueError("TP workers observed different scheduler decisions")
+        if shard["native_runtime_profile"] != first["native_runtime_profile"]:
+            raise ValueError("TP workers use different native runtime profiles")
+        if shard["provider_execution_mode"] != first["provider_execution_mode"]:
+            raise ValueError("TP workers use different provider certification modes")
+    if first["native_runtime_profile"] != NATIVE_ROLLOUT_PROFILE:
+        raise ValueError("Worker artifacts do not identify the pinned native runtime")
+    expected_mode = (
+        "certified" if request["identity"].get("scheduler_profile_certificate") else "qualification"
+    )
+    if first["provider_execution_mode"] != expected_mode:
+        raise ValueError("Worker artifacts do not match the requested certification mode")
+    exact(
+        torch.cat([shard["local_logits"] for shard in shards], dim=-1),
+        first["logits"],
+        name="raw LM-head vocabulary gather",
+    )
+    exact(
+        torch.tensor(sampled_responses, dtype=torch.int64),
+        first["responses"],
+        name="actual native sampled response",
+    )
+    layers, debug = [], {}
+    for layer in range(48):
+        states = {
+            f"tp{rank}.{name}": value
+            for rank, shard in enumerate(shards)
+            for name, value in shard["states"][layer].items()
+        }
+        hidden = first["hidden"][layer]
+        nodes = {name: first["subnodes"][name][layer] for name in subnode.SUBNODES}
+        layers.append(layer_digest(layer, hidden, states, request=request, subnodes=nodes))
+        if layer in request["trace_layers"]:
+            debug[f"layer.{layer}.output"] = hidden
+            debug.update({f"layer.{layer}.subnode.{name}": value for name, value in nodes.items()})
+            debug.update({f"layer.{layer}.{name}": value for name, value in states.items()})
+    artifact = {
+        "metadata": {
+            key: request[key]
+            for key in ("schema_version", "case_id", "role", "identity", "request_sha256")
+        },
+        "execution": request["execution"],
+        "prompt_tokens": torch.tensor(request["prompt_tokens"], dtype=torch.int64),
+        "response_tokens": first["responses"],
+        "prediction_positions": torch.tensor(request["prediction_positions"], dtype=torch.int64),
+        "logits": first["logits"],
+        "logprobs": first["logprobs"],
+        "layer_digests": layers,
+        "embedding_digest": embedding_digest(first["embedding"]),
+        "debug_tensors": debug,
+        "native_scheduler_steps": first["schedule"],
+        "worker_traces": records,
+        "native_runtime_profile": first["native_runtime_profile"],
+        "provider_execution_mode": first["provider_execution_mode"],
+        "provider_binary": provider_binary,
+    }
+    validate_model_artifact(artifact, request)
+    validate_debug_capture(artifact, request)
+    return artifact
+
+
+def _new_engine(identity):
+    from vllm import LLM
+
+    from rl_engine.integrations.qwen3_next_profile import activate_for_identity
+    from rl_engine.integrations.qwen3_next_vllm_model import register_shared_model
+
+    activate_for_identity(identity)
+    if os.environ.get("VLLM_USE_V2_MODEL_RUNNER") != "0":
+        raise RuntimeError(
+            "Set VLLM_USE_V2_MODEL_RUNNER=0 before importing the native rollout worker"
+        )
+    if torch.cuda.device_count() != 4:
+        raise RuntimeError("Native rollout requires exactly four visible GPUs")
+    return LLM(**engine_arguments(identity, register_shared_model()))
+
+
+def engine_arguments(identity, architecture):
+    """Keep explicit engine arguments independently checkable against pinned vLLM."""
+    return dict(
+        model=identity["checkpoint"],
+        tokenizer=identity["checkpoint"],
+        skip_tokenizer_init=True,
+        trust_remote_code=False,
+        hf_overrides={"architectures": [architecture]},
+        additional_config={"qwen3_next_shared_identity": identity, "gdn_prefill_backend": "triton"},
+        tensor_parallel_size=4,
+        pipeline_parallel_size=1,
+        distributed_executor_backend="mp",
+        dtype="bfloat16",
+        seed=1234,
+        enforce_eager=True,
+        enable_prefix_caching=False,
+        enable_chunked_prefill=True,
+        max_model_len=1040,
+        max_num_seqs=NATIVE_ROLLOUT_PROFILE["max_num_seqs"],
+        max_num_batched_tokens=NATIVE_ROLLOUT_PROFILE["max_num_batched_tokens"],
+        long_prefill_token_threshold=1,
+        scheduler_cls="rl_engine.integrations.qwen3_next_native_rollout.ExactPrefillScheduler",
+        async_scheduling=False,
+        worker_cls="rl_engine.integrations.qwen3_next_vllm_worker.SharedQwenWorker",
+        logits_processors=["rl_engine.integrations.qwen3_next_native_rollout:ForceFrozenTokens"],
+        kv_cache_dtype="bfloat16",
+        mamba_cache_dtype="bfloat16",
+        mamba_ssm_cache_dtype="float32",
+        mamba_cache_mode="none",
+        generation_config="vllm",
+        gpu_memory_utilization=0.85,
+        kv_cache_memory_bytes=NATIVE_ROLLOUT_PROFILE["kv_cache_memory_bytes_per_gpu"],
+        attention_config={"backend": "CUSTOM"},
+        kernel_config={
+            "moe_backend": "triton",
+            "enable_flashinfer_autotune": False,
+            "enable_jit_warmup": False,
+        },
+        compilation_config={"mode": 0, "cudagraph_mode": "NONE"},
+    )
+
+
+def run_rollout_cases(cases):
+    from vllm import SamplingParams
+
+    engine, identity = None, None
+    try:
+        for request, output in cases:
+            batch, length = validate_request(request)
+            if engine is None:
+                identity = request["identity"]
+                engine = _new_engine(identity)
+            elif request["identity"] != identity:
+                raise ValueError(
+                    "One native rollout process cannot silently change weight/provider identity"
+                )
+            output = Path(output).resolve()
+            records = engine.collective_rpc(
+                "configure_l2_trace", args=(request, str(output.with_suffix(".worker-traces")))
+            )
+            if len(records) != 4 or any(
+                record["request_sha256"] != request["request_sha256"] for record in records
+            ):
+                raise RuntimeError("All TP workers must acknowledge the exact trace request")
+            sampling = []
+            for row, sample in enumerate(request["sample_ids"]):
+                descriptor = {
+                    "request_sha256": request["request_sha256"],
+                    "sample_id": sample,
+                    "batch_size": batch,
+                    "prompt_length": length,
+                    "prefill_chunks": request["prefill_chunks"],
+                    "response_tokens": (
+                        None
+                        if request["response_tokens"] is None
+                        else request["response_tokens"][row]
+                    ),
+                }
+                sampling.append(
+                    SamplingParams(
+                        temperature=0,
+                        top_p=1,
+                        top_k=-1,
+                        seed=1234,
+                        ignore_eos=True,
+                        max_tokens=16,
+                        detokenize=False,
+                        extra_args={"shared_l2": descriptor},
+                    )
+                )
+            generated = engine.generate(
+                [{"prompt_token_ids": row} for row in request["prompt_tokens"]],
+                sampling,
+                use_tqdm=False,
+            )
+            if len(generated) != batch or any(
+                len(item.outputs) != 1 or item.prompt_token_ids != request["prompt_tokens"][row]
+                for row, item in enumerate(generated)
+            ):
+                raise RuntimeError(
+                    "Native engine did not return one identified completion per prompt"
+                )
+            responses = [list(item.outputs[0].token_ids) for item in generated]
+            if any(len(row) != 16 for row in responses):
+                raise RuntimeError("Native engine did not generate sixteen tokens")
+            records = engine.collective_rpc("collect_l2_trace")
+            save_tensor_artifact(output, assemble_trace(request, records, responses))
+    finally:
+        if engine is not None:
+            engine.llm_engine.engine_core.shutdown()
+            del engine
+            gc.collect()
+
+
+def run_rollout_case(request, output):
+    run_rollout_cases([(request, output)])
diff --git a/rl_engine/integrations/qwen3_next_native_training.py b/rl_engine/integrations/qwen3_next_native_training.py
new file mode 100644
index 000000000..e252de3c7
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_native_training.py
@@ -0,0 +1,386 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Native four-rank RL updates and restart controls, separated from rollout GPUs."""
+
+import json
+import os
+import re
+import sys
+import traceback
+from datetime import timedelta
+from pathlib import Path
+from uuid import uuid4
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+from rl_engine.testing.qwen3_next_l2 import load_tensor_artifact, validate_fixture
+from rl_engine.testing.qwen3_next_rl_fixture import PolicyFixture
+
+
+def policy_fixture(path):
+    frozen = load_tensor_artifact(path)
+    validate_fixture(frozen)
+    return PolicyFixture.freeze(
+        torch.cat((frozen["prompts"]["64"], frozen["responses"]["64"]), dim=1),
+        frozen["old_logprobs"]["64"],
+        baseline_identity=frozen["baseline_identity"],
+    )
+
+
+def active_parameter_groups(model):
+    """Every parameter tensor in the full actor must participate in the update.
+
+    Routed experts use one fused parameter per layer: unselected expert rows
+    may be zero, while at least one selected row must carry a real gradient.
+    """
+    from rl_engine.integrations.qwen3_next_training import named_trainable_parameters
+
+    parameters = named_trainable_parameters(model)
+    validate_active_parameter_names(parameters)
+    return {name: (name,) for name in parameters}
+
+
+def validate_active_parameter_names(parameters):
+    layers = {
+        int(match.group(1))
+        for name in parameters
+        for match in [re.search(r"(?:^|\.)decoder\.layers\.(\d+)\.", name)]
+        if match
+    }
+    if layers != set(range(48)):
+        raise ValueError("Active parameters must cover exactly the full 48 decoder layers")
+    for layer in range(48):
+        for component in ("self_attention.", "mlp.", "pre_mlp_layernorm."):
+            prefix = f"decoder.layers.{layer}.{component}"
+            if not any(prefix in name for name in parameters):
+                raise ValueError(f"Full actor omits an active parameter path: {prefix}")
+    for component in ("embedding.", "output_layer.", "final_layernorm."):
+        if not any(component in name for name in parameters):
+            raise ValueError(f"Full actor omits {component}")
+
+
+def policy_step(args, model, optimizer, scheduler, fixture, *, step, required_groups):
+    """Track consumed samples only after native loss/backward/update/scheduler succeed."""
+    from rl_engine.integrations.qwen3_next_training import native_policy_update
+
+    before, after = (step - 1) * 4, step * 4
+    if args.consumed_train_samples != before or scheduler.num_steps != before:
+        raise ValueError("Native update starts from inconsistent sample/scheduler progress")
+    audit = native_policy_update(
+        args, model, optimizer, scheduler, fixture, step=step, required_groups=required_groups
+    )
+    if scheduler.num_steps != after or args.consumed_train_samples != before:
+        raise ValueError("Native VIME scheduler/sample-counter behavior differs from pinned API")
+    # Pinned VIME train_one_step advances the scheduler but owns no consumed
+    # sample counter. The controller advances that counter after its success.
+    args.consumed_train_samples = after
+    audit["progress"] = {
+        "consumed_before": before,
+        "consumed_after": after,
+        "scheduler_before": before,
+        "scheduler_after": after,
+        "sample_counter_owner": "qwen3_next_native_training.policy_step",
+    }
+    return audit
+
+
+def retire_prior_checkpoints(checkpoint, root, identity):
+    """Called only after all-rank publication/load verifies the successor bytes."""
+    from qwen3_next_runtime import retire_directory
+
+    checkpoint, root = Path(checkpoint), Path(root)
+    if (
+        root.is_symlink()
+        or checkpoint.is_symlink()
+        or checkpoint.resolve().parent != root.resolve()
+    ):
+        raise ValueError("Checkpoint retention is restricted to this run's direct directory")
+    checkpoint, root = checkpoint.resolve(), root.resolve()
+    owner = root / "RUN_OWNER.json"
+    if owner.is_symlink() or json.loads(owner.read_text()) != identity:
+        raise ValueError("Checkpoint retention lacks a matching run owner")
+    successor = json.loads((checkpoint / "COMPLETE.json").read_text())
+    if successor.get("identity") != identity or successor.get("status") != "complete":
+        raise ValueError("Checkpoint successor is not a published state for this run")
+    retired = []
+    for path in root.iterdir():
+        if (
+            not path.is_dir()
+            or path == checkpoint
+            or path.suffix == ".partial"
+            or path.name.startswith(".")
+        ):
+            continue
+        marker = path / "COMPLETE.json"
+        if path.is_symlink() or marker.is_symlink():
+            raise ValueError("Checkpoint retention cannot follow symlinks")
+        previous = json.loads(marker.read_text())
+        if (
+            previous.get("identity") != identity
+            or previous.get("status") != "complete"
+            or previous.get("step", -1) >= successor["step"]
+        ):
+            raise ValueError("Checkpoint retention found an unrelated or ambiguous successor")
+        retirement = retire_directory(
+            path,
+            root,
+            identity,
+            proof={
+                "complete": previous,
+                "successor": str(checkpoint),
+                "successor_complete": successor,
+            },
+        )
+        retired.append({"path": str(path), "complete": previous, "retirement": retirement})
+    return retired
+
+
+def _rank_main(rank, request, staging):
+    from qwen3_next_contract import atomic_json
+
+    os.environ.update(RANK=str(rank), LOCAL_RANK=str(rank), WORLD_SIZE="4")
+    torch.cuda.set_device(rank)
+    dist.init_process_group(
+        "nccl",
+        init_method=f"file://{staging}/rendezvous",
+        rank=rank,
+        world_size=4,
+        timeout=timedelta(minutes=60),
+        device_id=torch.device("cuda", rank),
+    )
+    try:
+        from megatron.core.tensor_parallel.random import get_cuda_rng_tracker
+        from qwen3_next_training_checkpoint import (
+            _rank_zero_call,
+            load_training_checkpoint,
+            save_training_checkpoint,
+            training_state_identity,
+        )
+        from qwen3_next_training_export import export_training_weights
+
+        from rl_engine.integrations.qwen3_next_vime_runtime import create_actor
+        from rl_engine.testing.qwen3_next_binary import verify_imported_provider
+
+        fixture = policy_fixture(request["fixture"])
+        manifest = request["manifest"]
+        identity = request["identity"]
+        provider_binary = verify_imported_provider(
+            request["provider_binary"], source_tree=identity["source_tree"]
+        )
+        if identity["fixture_sha256"] != fixture.content_sha256:
+            raise ValueError("Native RL worker fixture identity changed")
+        args, model, optimizer, scheduler = create_actor(manifest["checkpoint"], training=True)
+        if optimizer is None or scheduler is None or len(model) != 1:
+            raise ValueError("Native RL worker requires the actual optimizer and scheduler")
+        tracker = get_cuda_rng_tracker()
+        groups = active_parameter_groups(model)
+        phase = request["phase"]
+        record = {
+            "rank": rank,
+            "phase": phase,
+            "identity": identity,
+            "pid": os.getpid(),
+            "provider_binary": provider_binary,
+            "native_entrypoints": [
+                "vime.backends.megatron_utils.model.setup_model_and_optimizer",
+                "vime.backends.megatron_utils.model.train_one_step",
+                "vime.backends.megatron_utils.loss.policy_loss_function",
+            ],
+        }
+        restored = None
+        if request.get("checkpoint"):
+            restored = load_training_checkpoint(
+                args,
+                model,
+                optimizer,
+                scheduler,
+                fixture,
+                request["checkpoint"],
+                identity=identity,
+                tracker=tracker,
+            )
+            record["restored"] = restored
+        expected_previous = {
+            "update1": 0,
+            "update2": 1,
+            "reload": 2,
+            "continuous": 0,
+            "resumed": 2,
+        }[phase]
+        actual_previous = restored["step"] if restored else 0
+        recover_published_update = phase in ("update1", "update2") and actual_previous == (
+            1 if phase == "update1" else 2
+        )
+        if actual_previous != expected_previous and not recover_published_update:
+            raise ValueError("Native RL update resumed from the wrong complete step")
+        if phase in ("continuous", "resumed"):
+            record["controls"] = {}
+            for step in (1, 2, 3) if phase == "continuous" else (3,):
+                audit = policy_step(
+                    args, model, optimizer, scheduler, fixture, step=step, required_groups=groups
+                )
+                state = training_state_identity(
+                    args,
+                    model,
+                    optimizer,
+                    scheduler,
+                    fixture,
+                    step=step,
+                    sample_position=step * 4,
+                    identity=identity,
+                    tracker=tracker,
+                )
+                expected = request["expected_states"][str(step)][rank] if step < 3 else None
+                if expected is not None and state != expected:
+                    differing = sorted(
+                        key
+                        for key in expected.keys() | state.keys()
+                        if expected.get(key) != state.get(key)
+                    )
+                    atomic_json(
+                        Path(staging) / f"divergence-rank-{rank}.json",
+                        {
+                            "rank": rank,
+                            "step": step,
+                            "differing": differing,
+                            "expected": expected,
+                            "actual": state,
+                            "update": audit,
+                        },
+                    )
+                    raise ValueError(
+                        f"Continuous control diverged from original update {step} "
+                        f"on rank {rank}: {', '.join(differing)}"
+                    )
+                record["controls"][str(step)] = {"update": audit, "state": state}
+            verify_imported_provider(
+                request["provider_binary"], source_tree=identity["source_tree"]
+            )
+            atomic_json(Path(staging) / f"rank-{rank}.json", record)
+            dist.barrier()
+            return
+        step = 1 if phase == "update1" else 2
+        if recover_published_update:
+            record["update"] = restored["rank_evidence"]["update_evidence"]
+            if record["update"].get("step") != step:
+                raise ValueError("Published checkpoint omits the committed update evidence")
+            record["recovered_committed_update"] = True
+            checkpoint = request["checkpoint"]
+        elif phase != "reload":
+            record["update"] = policy_step(
+                args, model, optimizer, scheduler, fixture, step=step, required_groups=groups
+            )
+            saved = save_training_checkpoint(
+                args,
+                model,
+                optimizer,
+                scheduler,
+                fixture,
+                request["checkpoint_root"],
+                step=step,
+                sample_position=step * 4,
+                identity=identity,
+                tracker=tracker,
+                export_bytes=request["export_bytes"],
+                update_evidence=record["update"],
+            )
+            record["saved"] = saved
+            checkpoint = saved["checkpoint"]
+        else:
+            checkpoint = request["checkpoint"]
+        record["retired_checkpoints"] = _rank_zero_call(
+            lambda: retire_prior_checkpoints(checkpoint, request["checkpoint_root"], identity),
+            "retire previous complete checkpoints after verified successor",
+        )
+        checkpoint_bytes = sum(
+            path.stat().st_size for path in Path(checkpoint).rglob("*") if path.is_file()
+        )
+        # Export must reflect the reloaded BF16 actor in the reload phase. Never
+        # reuse the update2 export as purported evidence that loading worked.
+        record["export"] = export_training_weights(
+            args,
+            model,
+            request["export_root"],
+            step=step,
+            weight_version={"update1": "update-1", "update2": "update-2", "reload": "reload-2"}[
+                phase
+            ],
+            identity=identity,
+            base_checkpoint=manifest["checkpoint"],
+            base_checkpoint_identity=manifest["checkpoint_identity"],
+            checkpoint_bytes=checkpoint_bytes,
+            retained_complete_bytes=checkpoint_bytes,
+        )
+        record["state"] = training_state_identity(
+            args,
+            model,
+            optimizer,
+            scheduler,
+            fixture,
+            step=step,
+            sample_position=step * 4,
+            identity=identity,
+            tracker=tracker,
+        )
+        verify_imported_provider(request["provider_binary"], source_tree=identity["source_tree"])
+        atomic_json(Path(staging) / f"rank-{rank}.json", record)
+        dist.barrier()
+    except BaseException:
+        # One rank must not take the others down by deadlock: tearing down the
+        # process group while its peers sit in a collective can block forever.
+        # Report, then exit so the spawning parent terminates
+        # the sibling ranks and the stage fails instead of running out its budget.
+        traceback.print_exc()
+        sys.stderr.flush()
+        os._exit(3)
+    finally:
+        dist.destroy_process_group()
+
+
+def run_training_phase(request, output):
+    """One fresh native actor lifetime, then all CUDA processes exit."""
+    from qwen3_next_contract import atomic_json
+
+    if request["phase"] not in ("update1", "update2", "reload", "continuous", "resumed"):
+        raise ValueError("Unknown native RL phase")
+    if torch.cuda.device_count() != 4:
+        raise ValueError("Native training requires exactly four visible GPUs")
+    identity, manifest = request["identity"], request["manifest"]
+    if (
+        identity.get("topology") != {"tp": 4, "pp": 1, "cp": 1, "ep": 1}
+        or identity.get("architecture_layers") != 48
+        or identity.get("checkpoint") != manifest.get("checkpoint")
+        or identity.get("checkpoint_identity") != manifest.get("checkpoint_identity")
+    ):
+        raise ValueError("Native training request changed base checkpoint/depth/topology")
+    staging = Path(output).parent / f"native-training-{uuid4().hex}"
+    staging.mkdir(parents=True, exist_ok=False)
+    mp.spawn(_rank_main, args=(request, str(staging)), nprocs=4, join=True)
+    records = [json.loads((staging / f"rank-{rank}.json").read_text()) for rank in range(4)]
+    for rank, record in enumerate(records):
+        if (record["rank"], record["phase"], record["identity"]) != (
+            rank,
+            request["phase"],
+            request["identity"],
+        ):
+            raise ValueError("Native RL rank record identity mismatch")
+        if record.get("export") != records[0].get("export"):
+            raise ValueError("Native RL ranks disagree on the published HF export")
+        if record.get("provider_binary") != records[0].get("provider_binary"):
+            raise ValueError("Native RL ranks imported different CUDA provider binaries")
+    result = {
+        "schema_version": 1,
+        "status": "passed",
+        "phase": request["phase"],
+        "identity": request["identity"],
+        "ranks": records,
+        "export": records[0].get("export"),
+        "checkpoint": records[0].get("saved", {}).get("checkpoint", request.get("checkpoint")),
+        "all_rank_processes_exited": True,
+        "provider_binary": request["provider_binary"],
+    }
+    atomic_json(output, result)
+    return result
diff --git a/rl_engine/integrations/qwen3_next_native_workers.py b/rl_engine/integrations/qwen3_next_native_workers.py
new file mode 100644
index 000000000..f101e1133
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_native_workers.py
@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Implemented native model entrypoints; availability is not acceptance evidence."""
+
+
+def capabilities():
+    from rl_engine.integrations import qwen3_next_native_recompute as recompute
+    from rl_engine.integrations import qwen3_next_native_rollout as rollout
+
+    ready = rollout.capabilities()
+    if any(
+        not callable(getattr(module, name, None))
+        for module, names in (
+            (rollout, ("run_rollout_case", "run_rollout_cases")),
+            (recompute, ("run_recompute_case", "run_recompute_cases")),
+        )
+        for name in names
+    ):
+        raise RuntimeError("The native model worker implementation is incomplete")
+    return {
+        **ready,
+        "recompute": True,
+        "independent_processes_required": True,
+        "full_model_l2_verified": False,
+        "training_and_reload_verified": False,
+    }
+
+
+def run_rollout_cases(cases):
+    from rl_engine.integrations.qwen3_next_native_rollout import run_rollout_cases as run
+
+    # Preserve the lazy generator: baseline generation freezes response tokens
+    # before the subsequent teacher-forced cases are constructed.
+    return run(cases)
+
+
+def run_rollout_case(request, output):
+    return run_rollout_cases([(request, output)])
+
+
+def run_recompute_cases(cases):
+    from rl_engine.integrations.qwen3_next_native_recompute import run_recompute_cases as run
+
+    return run(cases)
+
+
+def run_recompute_case(request, output):
+    return run_recompute_cases([(request, output)])
diff --git a/rl_engine/integrations/qwen3_next_profile.py b/rl_engine/integrations/qwen3_next_profile.py
new file mode 100644
index 000000000..8a7ae2f63
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_profile.py
@@ -0,0 +1,143 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Activate only the bounded CUSTOM profile after verifying native layer evidence.
+
+Capability is process-local and false by default. Qualification runs use BI=0;
+strict runs use BI=1 plus the immutable predecessor certificate in their model
+identity. Generic vLLM GDN_ATTN registration is never modified.
+"""
+
+import json
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+_CERTIFIED = None
+ROOT = Path(__file__).resolve().parents[2]
+
+
+def profile_is_certified():
+    return _CERTIFIED is not None and os.environ.get("VLLM_BATCH_INVARIANT") == "1"
+
+
+def validate_certified_weights(identity, certified):
+    """The base certificate is not a certificate for newly updated weight bytes."""
+    from rl_engine.testing.qwen3_next_l2 import validate_weight_identity
+
+    validate_weight_identity(identity)
+    if identity.get("weight_version", "baseline") == "baseline":
+        if any(
+            identity.get(key) != certified.get(key) for key in ("checkpoint", "checkpoint_identity")
+        ):
+            raise ValueError("Baseline weights differ from the certified official checkpoint")
+        return
+    if any(
+        (
+            identity.get("base_checkpoint") != certified.get("checkpoint"),
+            identity.get("base_checkpoint_identity") != certified.get("checkpoint_identity"),
+        )
+    ):
+        raise ValueError("Updated export refers to a different certified official base")
+    from qwen3_next_training_export import validate_training_export
+
+    verified = validate_training_export(identity["checkpoint"], identity["checkpoint_identity"])
+    if verified != identity["checkpoint_identity"]:
+        raise ValueError("Actual updated weight files differ from the pinned export identity")
+    training = verified["training_identity"]
+    fields = (
+        "source_commit",
+        "source_tree",
+        "environment_sha256",
+        "provider_config",
+        "topology",
+        "checkpoint",
+        "checkpoint_identity",
+    )
+    if any(training.get(key) != certified.get(key) for key in fields):
+        raise ValueError(
+            "Updated export training provenance differs from the certified base profile"
+        )
+
+
+def activate_for_identity(identity):
+    global _CERTIFIED
+    certificate = identity.get("scheduler_profile_certificate")
+    mode = os.environ.get("VLLM_BATCH_INVARIANT")
+    if certificate is None:
+        if mode != "0" or _CERTIFIED is not None:
+            raise ValueError("Uncertified shared providers require explicit qualification BI=0")
+        return "qualification"
+    if mode != "1":
+        raise ValueError("A certified shared profile must execute strict BI=1")
+    sys.path.insert(0, str(ROOT / "scripts"))
+    from qwen3_next_contract import validate_result
+    from qwen3_next_l2 import model_identity
+    from qwen3_next_runtime import file_digest, identity_digest, verify_environment
+
+    record_key = identity_digest({"certificate": certificate, "identity": identity})
+    if _CERTIFIED == record_key:
+        return "certified"
+    if _CERTIFIED is not None:
+        raise ValueError("A process cannot switch its certified shared provider identity")
+    path = Path(certificate["path"])
+    if (
+        not path.is_absolute()
+        or path.name != "result.json"
+        or file_digest(path) != certificate["sha256"]
+    ):
+        raise ValueError("Shared profile certificate path/content identity mismatch")
+    if os.environ.get("QWEN_SHARED_PROFILE_RESULT", str(path)) != str(path):
+        raise ValueError("Scheduler certificate environment disagrees with the worker request")
+    manifest = json.loads((path.parent / "run.json").read_text())
+    result = json.loads(path.read_text())
+    if manifest.get("stage") != "layers":
+        raise ValueError("Only completed native layer/scheduler evidence can certify CUSTOM")
+    certified = model_identity(manifest)
+    fields = (
+        "source_commit",
+        "source_tree",
+        "environment_sha256",
+        "provider_config",
+        "topology",
+        "architecture_layers",
+        "weights_dtype",
+        "recurrent_state_dtype",
+    )
+    if any(identity.get(key) != certified.get(key) for key in fields):
+        raise ValueError(
+            "Scheduler certificate belongs to another source/environment/provider profile"
+        )
+    validate_certified_weights(identity, certified)
+    provider = identity["provider_config"]
+    if provider.get("profile") != "qwen3_next_tp4_shared_eager_v1":
+        raise ValueError("Only the explicit Qwen3-Next TP4 eager profile can be certified")
+    if (
+        subprocess.check_output(["git", "rev-parse", "HEAD^{tree}"], cwd=ROOT, text=True).strip()
+        != identity["source_tree"]
+    ):
+        raise ValueError("Current source tree differs from the certified provider")
+    if subprocess.run(["git", "diff", "--quiet", "HEAD"], cwd=ROOT).returncode:
+        raise ValueError("Current provider source has uncommitted changes")
+    for name, expected in provider["sources"].items():
+        if file_digest(ROOT / name) != expected:
+            raise ValueError(f"Certified provider source changed: {name}")
+    for name, expected in provider["runtime_env"].items():
+        if os.environ.get(name) != expected:
+            raise ValueError(f"Certified runtime setting differs: {name}")
+    cuda = manifest["cuda_identity"]
+    if (
+        os.environ.get("CUDA_HOME") != cuda["home"]
+        or file_digest(Path(cuda["home"]) / "bin/nvcc") != cuda["nvcc_sha256"]
+    ):
+        raise ValueError("CUDA toolkit differs from the certified environment")
+    verify_environment(manifest["environment"], require_venv=True)
+    validate_result(manifest, result, run_dir=path.parent)
+    _CERTIFIED = record_key
+    return "certified"
+
+
+def activate_profile_from_environment(identity):
+    """Compatibility spelling; a path without its pinned request digest is insufficient."""
+    return activate_for_identity(identity)
diff --git a/rl_engine/integrations/qwen3_next_subnode.py b/rl_engine/integrations/qwen3_next_subnode.py
new file mode 100644
index 000000000..cc9fc79cd
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_subnode.py
@@ -0,0 +1,109 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Sub-node observations inside each Qwen3-Next decoder layer, for first-drift localisation.
+
+Both the rollout model (vLLM) and the replay model (VIME) call a module's
+``subnode_hook(name, packed_rows)`` at the same five points of every decoder
+layer, in this order:
+
+* ``input_layernorm`` - the normed mixer input;
+* ``mixer`` - the GDN or full-attention output after the TP reduction;
+* ``post_attention_layernorm`` - the normed MLP input;
+* ``router`` - the ten selected expert ids per token;
+* ``mlp`` - the MoE output after the TP reduction.
+
+The embedding output is observed once per model as ``embedding`` (on the
+embedding module itself) so a drift there is not attributed to layer 0's norm.
+
+A trace owner binds the layer index, maps packed token rows to the 16
+response positions of each sample, and stores them in :class:`SubnodeBuffer`.
+Observing copies values and never changes one.
+"""
+
+from functools import partial
+
+import torch
+
+SUBNODES = ("input_layernorm", "mixer", "post_attention_layernorm", "router", "mlp")
+EMBEDDING = "embedding"
+LAYERS, RESPONSE_LENGTH, HIDDEN, TOP_K = 48, 16, 2048, 10
+
+
+def subnode_layout(name):
+    if name == "router":
+        return TOP_K, torch.int64
+    if name in SUBNODES or name == EMBEDDING:
+        return HIDDEN, torch.bfloat16
+    raise ValueError(f"Unknown Qwen3-Next sub-node: {name}")
+
+
+class SubnodeBuffer:
+    """[48, B, 16, width] host copies of every sub-node, each written exactly once."""
+
+    def __init__(self, batch):
+        self.batch = batch
+        self.values, self.written = {}, {}
+        for name in SUBNODES:
+            width, dtype = subnode_layout(name)
+            self.values[name] = torch.zeros(LAYERS, batch, RESPONSE_LENGTH, width, dtype=dtype)
+            self.written[name] = torch.zeros(LAYERS, batch, RESPONSE_LENGTH, dtype=torch.bool)
+        self.embedding = torch.zeros(batch, RESPONSE_LENGTH, HIDDEN, dtype=torch.bfloat16)
+        self.embedding_written = torch.zeros(batch, RESPONSE_LENGTH, dtype=torch.bool)
+
+    def put_embedding(self, row, response, value):
+        if value.dtype != torch.bfloat16 or value.shape != (HIDDEN,):
+            raise ValueError("The embedding must keep its original BF16 [2048] storage")
+        if self.embedding_written[row, response]:
+            raise ValueError("The embedding of one response position was observed twice")
+        self.embedding[row, response].copy_(value.detach().cpu())
+        self.embedding_written[row, response] = True
+
+    def embedding_rows(self):
+        if not bool(self.embedding_written.all()):
+            raise ValueError("The embedding was not observed at every response position")
+        return self.embedding
+
+    def put(self, layer, name, row, response, value):
+        width, dtype = subnode_layout(name)
+        if value.dtype != dtype or value.shape != (width,):
+            raise ValueError(f"Sub-node {name} must keep its original {dtype} [{width}] storage")
+        if self.written[name][layer, row, response]:
+            raise ValueError(f"Sub-node {name} of layer {layer} was observed twice")
+        self.values[name][layer, row, response].copy_(value.detach().cpu())
+        self.written[name][layer, row, response] = True
+
+    def put_rows(self, layer, name, values):
+        """Store a complete [B, 16, width] block, as the replay model produces it."""
+        for row in range(self.batch):
+            for response in range(RESPONSE_LENGTH):
+                self.put(layer, name, row, response, values[row, response])
+
+    def layer(self, layer):
+        missing = [name for name in SUBNODES if not bool(self.written[name][layer].all())]
+        if missing:
+            raise ValueError(f"Layer {layer} sub-nodes were not all observed: {missing}")
+        return {name: self.values[name][layer] for name in SUBNODES}
+
+
+def install(layers, modules_of, record):
+    """Bind ``record(layer_index, name, packed_rows)`` to every hook-bearing module."""
+    for index, layer in enumerate(layers):
+        for module in modules_of(layer):
+            module.subnode_hook = partial(record, index)
+
+
+def remove(layers, modules_of):
+    for layer in layers:
+        for module in modules_of(layer):
+            module.subnode_hook = None
+
+
+def vllm_modules(layer):
+    """vLLM ``SharedVLLMDecoderLayer``: the layer itself and its MoE."""
+    return (layer, layer.mlp)
+
+
+def vime_modules(layer):
+    """MCore layer with the shared VIME providers: mixer, pre-MLP norm and MoE."""
+    return (layer.self_attention, layer.pre_mlp_layernorm, layer.mlp)
diff --git a/rl_engine/integrations/qwen3_next_tp_blocks.py b/rl_engine/integrations/qwen3_next_tp_blocks.py
index 7a45c1ad2..ccb04d978 100644
--- a/rl_engine/integrations/qwen3_next_tp_blocks.py
+++ b/rl_engine/integrations/qwen3_next_tp_blocks.py
@@ -304,6 +304,9 @@ def assemble_moe_weight(name, values):
 class TP4MoE(nn.Module):
     """All 512 experts at EP1 with intermediate width 128 per TP rank."""
 
+    # Set by a trace owner (see qwen3_next_subnode); receives the route indices.
+    subnode_hook = None
+
     def __init__(self, *, group, device):
         super().__init__()
         self.group = _tp_group(group)
@@ -355,12 +358,14 @@ def forward(self, hidden):
         if hidden.ndim != 2 or hidden.shape[1] != 2048 or hidden.dtype != torch.bfloat16:
             raise ValueError("MoE hidden must be BF16 [tokens, 2048]")
         copied = _CopyToTP.apply(hidden, self.group)
-        routed, _ = shared_moe(
+        routed, routes = shared_moe(
             copied,
             _CopyToTP.apply(self.gate.weight, self.group),
             self.experts.gate_up,
             self.experts.down,
         )
+        if self.subnode_hook is not None:
+            self.subnode_hook("router", routes.indices)
         gate = shared_linear(copied, self.shared_expert.gate_proj.weight)
         up = shared_linear(copied, self.shared_expert.up_proj.weight)
         activated = (F.silu(gate.float()) * up.float()).to(hidden.dtype)
diff --git a/rl_engine/integrations/qwen3_next_training.py b/rl_engine/integrations/qwen3_next_training.py
new file mode 100644
index 000000000..753242ad8
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_training.py
@@ -0,0 +1,285 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Observe native VIME updates before its train_one_step clears gradients.
+
+This module does not implement an optimizer or loss. The native training entry
+points remain responsible for both; this observer rejects missing evidence.
+"""
+
+import hashlib
+import math
+import random
+import struct
+from contextlib import contextmanager
+
+import numpy as np
+import torch
+
+from rl_engine.testing.qwen3_next_rl_fixture import require_policy_configuration
+
+
+def tensor_digest(value, *, chunk_bytes=64 * 1024 * 1024):
+    """Hash original tensor bytes with bounded host memory, including dtype/shape."""
+    if value.layout != torch.strided or value.device.type == "meta":
+        raise ValueError("Only materialized strided tensors have a state identity")
+    digest = hashlib.sha256()
+    digest.update(str((str(value.dtype), tuple(value.shape))).encode())
+    flat = value.detach().contiguous().reshape(-1)
+    chunk_size = max(1, chunk_bytes // value.element_size())
+    for start in range(0, flat.numel(), chunk_size):
+        part = flat[start : start + chunk_size].cpu()
+        if (part.is_floating_point() or part.is_complex()) and not torch.isfinite(part).all():
+            raise ValueError("Nonfinite tensor in training state")
+        digest.update(part.view(torch.uint8).numpy().tobytes())
+    return digest.hexdigest()
+
+
+def state_digest(state):
+    """Canonical recursive identity for optimizer/scheduler/RNG state, without pickle."""
+    digest = hashlib.sha256()
+
+    def add(raw):
+        digest.update(struct.pack("!Q", len(raw)))
+        digest.update(raw)
+
+    def visit(value):
+        add(type(value).__name__.encode())
+        if isinstance(value, torch.Tensor):
+            add(tensor_digest(value).encode())
+        elif isinstance(value, np.ndarray):
+            add(str((value.dtype.str, value.shape)).encode())
+            if value.dtype.hasobject:
+                raise TypeError("Object arrays cannot identify recovery state")
+            add(value.tobytes(order="C"))
+        elif isinstance(value, dict):
+            if any(type(key) not in (str, int) for key in value):
+                raise TypeError("Recovery state keys must be strings or integers")
+            for key in sorted(value, key=lambda key: (type(key).__name__, str(key))):
+                visit(key)
+                visit(value[key])
+        elif isinstance(value, (list, tuple)):
+            for item in value:
+                visit(item)
+        elif value is None or type(value) in (bool, int, str, bytes):
+            add(repr(value).encode())
+        elif type(value) is float:
+            if not math.isfinite(value):
+                raise ValueError("Nonfinite scalar in training state")
+            add(struct.pack("!d", value))
+        else:
+            raise TypeError(f"Unsupported recovery state type: {type(value)!r}")
+
+    visit(state)
+    return digest.hexdigest()
+
+
+def capture_rng_state(*, tracker=None):
+    return {
+        "python": random.getstate(),
+        "numpy": np.random.get_state(),
+        "torch_cpu": torch.get_rng_state().clone(),
+        "torch_cuda": [value.clone() for value in torch.cuda.get_rng_state_all()],
+        "megatron": (
+            None
+            if tracker is None
+            else {name: value.clone() for name, value in tracker.get_states().items()}
+        ),
+    }
+
+
+def restore_rng_state(state, *, tracker=None):
+    if len(state["torch_cuda"]) != torch.cuda.device_count():
+        raise ValueError("RNG restore requires the original visible GPU topology")
+    if (state["megatron"] is None) != (tracker is None):
+        raise ValueError("Megatron RNG tracker must be restored explicitly")
+    random.setstate(state["python"])
+    np.random.set_state(state["numpy"])
+    torch.set_rng_state(state["torch_cpu"])
+    torch.cuda.set_rng_state_all(state["torch_cuda"])
+    if tracker is not None:
+        tracker.set_states({name: value.clone() for name, value in state["megatron"].items()})
+
+
+def named_trainable_parameters(model):
+    result = {}
+    seen = set()
+    for index, chunk in enumerate(model):
+        for name, parameter in chunk.named_parameters():
+            if parameter.requires_grad and id(parameter) not in seen:
+                result[f"chunk{index}.{name}"] = parameter
+                seen.add(id(parameter))
+    if not result:
+        raise ValueError("Native training model has no trainable parameters")
+    return result
+
+
+def _any_rank_nonzero(flags):
+    """Combine per-rank nonzero flags across the tensor-parallel ranks (max)."""
+    import torch.distributed as dist
+
+    if not dist.is_available() or not dist.is_initialized() or dist.get_world_size() == 1:
+        return flags
+    reduced = torch.tensor(flags, dtype=torch.int32, device=torch.cuda.current_device())
+    dist.all_reduce(reduced, op=dist.ReduceOp.MAX)
+    return [bool(value) for value in reduced.tolist()]
+
+
+def gradient_evidence(parameters, *, required_groups, reduce_flags=_any_rank_nonzero):
+    """Unselected experts may be zero; every declared active path must be nonzero.
+
+    "Nonzero" is judged across the tensor-parallel group, not per rank: a
+    vocab-parallel shard such as the embedding of a rank whose vocabulary slice
+    contains none of the batch's tokens legitimately receives an all-zero
+    gradient while the full parameter does train. Every rank evaluates the same
+    parameter list in the same order, so the flag exchange is a fixed-order
+    collective that all ranks enter together. The per-rank value is kept as
+    ``nonzero_local``.
+    """
+    if not required_groups or any(not prefixes for prefixes in required_groups.values()):
+        raise ValueError("Predeclare the active parameter groups before training")
+    matched = {group: [] for group in required_groups}
+    evidence = {}
+    names = []
+    local_flags = []
+    for name, parameter in parameters.items():
+        gradient = getattr(parameter, "main_grad", None)
+        if gradient is None:
+            gradient = parameter.grad
+        if gradient is None:
+            evidence[name] = {"present": False, "nonzero": False, "nonzero_local": False}
+            continue
+        if gradient.shape != parameter.shape or not torch.isfinite(gradient).all():
+            raise ValueError(f"Invalid/nonfinite gradient for {name}")
+        nonzero_local = bool(torch.count_nonzero(gradient))
+        evidence[name] = {
+            "present": True,
+            "nonzero": nonzero_local,
+            "nonzero_local": nonzero_local,
+            "dtype": str(gradient.dtype),
+        }
+        names.append(name)
+        local_flags.append(nonzero_local)
+    group_flags = list(reduce_flags(local_flags)) if names else []
+    if len(group_flags) != len(names):
+        raise ValueError("Nonzero-gradient flags did not come back one per parameter")
+    for name, nonzero in zip(names, group_flags, strict=True):
+        evidence[name]["nonzero"] = bool(nonzero)
+        for group, prefixes in required_groups.items():
+            if nonzero and any(prefix in name for prefix in prefixes):
+                matched[group].append(name)
+    missing = [group for group, names_ in matched.items() if not names_]
+    if missing:
+        raise ValueError(f"Active paths have no finite nonzero gradient: {missing}")
+    return {"parameters": evidence, "active_groups": matched}
+
+
+@contextmanager
+def observe_native_optimizer_step(optimizer, model, *, required_groups, evidence):
+    """Wrap only step(), preserving the native loss, backward, clipping and update."""
+    original = optimizer.step
+    had_own = "step" in vars(optimizer)
+    previous = vars(optimizer).get("step")
+    parameters = named_trainable_parameters(model)
+    calls = 0
+
+    def observed(*args, **kwargs):
+        nonlocal calls
+        calls += 1
+        if calls != 1:
+            raise ValueError("One train_one_step call must perform exactly one optimizer step")
+        evidence["gradients"] = gradient_evidence(parameters, required_groups=required_groups)
+        before = {name: tensor_digest(value) for name, value in parameters.items()}
+        result = original(*args, **kwargs)
+        if not isinstance(result, tuple) or len(result) != 3 or not bool(result[0]):
+            raise ValueError("Native Megatron optimizer did not report a successful update")
+        norm = float(result[1])
+        if not math.isfinite(norm) or norm <= 0:
+            raise ValueError("Native optimizer must report a finite nonzero gradient norm")
+        after = {name: tensor_digest(value) for name, value in parameters.items()}
+        changed = [name for name in before if before[name] != after[name]]
+        if not changed:
+            raise ValueError("Optimizer reported success without changing any model parameter")
+        evidence.update(
+            {
+                "grad_norm": norm,
+                "parameter_hashes_before": before,
+                "parameter_hashes_after": after,
+                "changed_parameters": changed,
+            }
+        )
+        return result
+
+    optimizer.step = observed
+    try:
+        yield
+        if calls != 1:
+            raise ValueError("Native training did not call the optimizer exactly once")
+    finally:
+        if had_own:
+            optimizer.step = previous
+        else:
+            del optimizer.step
+
+
+def native_policy_update(args, model, optimizer, scheduler, fixture, *, step, required_groups):
+    """Execute the actual installed VIME train_one_step with its clipped policy loss."""
+    require_policy_configuration(args)
+    fixture.validate()
+    if step not in (1, 2, 3):
+        raise ValueError("The fixed acceptance sequence uses updates 1, 2 and recovery control 3")
+    from megatron.core.distributed import finalize_model_grads
+    from megatron.core.utils import get_model_config
+    from vime.backends.megatron_utils import loss as native_loss
+    from vime.backends.megatron_utils.data import DataIterator
+    from vime.backends.megatron_utils.model import train_one_step
+
+    if train_one_step.__module__ != "vime.backends.megatron_utils.model":
+        raise ValueError("Native VIME training entry point was replaced")
+    if native_loss.policy_loss_function.__module__ != "vime.backends.megatron_utils.loss":
+        raise ValueError("Native VIME policy loss was replaced")
+    for chunk in model:
+        chunk.train()
+        config = get_model_config(chunk)
+        if config.hidden_dropout != 0 or config.attention_dropout != 0:
+            raise ValueError("The fixed eager profile requires zero dropout")
+        config.grad_scale_func = optimizer.scale_loss
+        config.finalize_model_grads_func = finalize_model_grads
+        config.timers = None
+    batch = fixture.batch([0, 1, 2, 3], device=torch.cuda.current_device())
+    batch["tokens"] = batch.pop("unconcat_tokens")
+    iterator = DataIterator(batch, [[0], [1], [2], [3]])
+    original_policy_loss = native_loss.policy_loss_function
+    policy_calls = 0
+
+    def observed_loss(*loss_args, **loss_kwargs):
+        nonlocal policy_calls
+        policy_calls += 1
+        return original_policy_loss(*loss_args, **loss_kwargs)
+
+    evidence = {"step": step, "fixture": fixture.content_sha256, "sample_position": step * 4}
+    native_loss.policy_loss_function = observed_loss
+    try:
+        with observe_native_optimizer_step(
+            optimizer, model, required_groups=required_groups, evidence=evidence
+        ):
+            metrics, norm = train_one_step(
+                args,
+                rollout_id=0,
+                step_id=step - 1,
+                data_iterator=[iterator],
+                model=model,
+                optimizer=optimizer,
+                opt_param_scheduler=scheduler,
+                num_microbatches=4,
+                step_global_batch_size=4,
+            )
+    finally:
+        native_loss.policy_loss_function = original_policy_loss
+    if policy_calls != 4 or iterator.offset != 4:
+        raise ValueError("Native policy loss must consume exactly the four fixed samples")
+    if not math.isfinite(float(norm)):
+        raise ValueError("Native training returned a nonfinite gradient norm")
+    evidence.update({"policy_loss_calls": policy_calls, "metrics": metrics})
+    fixture.validate()
+    return evidence
diff --git a/rl_engine/integrations/qwen3_next_vime.py b/rl_engine/integrations/qwen3_next_vime.py
new file mode 100644
index 000000000..ff789dc8d
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_vime.py
@@ -0,0 +1,402 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""VIME ModuleSpec and HF weight hooks for the explicit shared TP4 GDN provider.
+
+    --spec rl_engine.integrations.qwen3_next_vime get_qwen3_next_shared_spec
+
+Installing this spec installs Qwen3-Next-only weight conversion hooks in this
+training process. Other model families keep their existing conversion paths.
+The decoder spec covers both attention families, routed/shared experts, and
+decoder norms. Call install_vime_model_providers after constructing the GPT
+model to replace its embedding, final norm and output layer as well.
+"""
+
+import re
+
+import torch
+from megatron.core import mpu
+from megatron.core.tensor_parallel.layers import (
+    set_defaults_if_not_set_tensor_model_parallel_attributes,
+)
+from megatron.core.transformer.identity_op import IdentityOp
+from megatron.core.transformer.spec_utils import ModuleSpec
+from torch import nn
+from vime_plugins.models.hf_attention import HuggingfaceAttention
+
+from rl_engine.integrations.qwen3_next_engine_weights import (
+    conv_hf_to_rank_major,
+    conv_rank_major_to_hf,
+    expert_hf_to_rank_major,
+    expert_rank_major_to_hf,
+    kv_hf_to_rank_major,
+    kv_rank_major_to_hf,
+    set_gdn_tp_metadata,
+    validate_training_topology,
+)
+from rl_engine.integrations.qwen3_next_provider import GDNProviderConfig
+from rl_engine.integrations.qwen3_next_tp import _CopyToTP
+from rl_engine.integrations.qwen3_next_tp_blocks import TP4FullAttention, TP4MoE
+from rl_engine.integrations.qwen3_next_tp_gdn import TP4GDN
+
+
+class SharedInputNorm(nn.Module):
+    def __init__(self, eps, device, weight=None):
+        super().__init__()
+        self.eps = eps
+        if weight is not None and not isinstance(weight, nn.Parameter):
+            raise ValueError("Shared decoder norm weight must be an existing Parameter")
+        if weight is not None and (weight.shape != (2048,) or weight.dtype != torch.bfloat16):
+            raise ValueError("Shared decoder norm requires a BF16 [2048] weight")
+        self.weight = (
+            weight
+            if weight is not None
+            else nn.Parameter(torch.zeros(2048, dtype=torch.bfloat16, device=device))
+        )
+        self.weight.tensor_model_parallel = False
+        self.weight.partition_dim = -1
+        self.weight.partition_stride = 1
+        self.weight.sequence_parallel = False
+
+    def forward(self, value):
+        from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp
+
+        return Qwen3NextRMSNormCudaOp()(value, self.weight, eps=self.eps)
+
+
+class SharedVIMELMHead(nn.Module):
+    """Megatron output-layer API with shared GEMM and the original weight name.
+
+    A forward hook on this module observes BF16 logits before Float16Module
+    widens the model output for VIME's policy loss.
+    """
+
+    def __init__(self, weight, group):
+        super().__init__()
+        if not isinstance(weight, nn.Parameter):
+            raise ValueError("Shared TP4 LM head weight must be an existing Parameter")
+        if weight.shape != (37984, 2048) or weight.dtype != torch.bfloat16:
+            raise ValueError("Shared TP4 LM head requires BF16 [37984, 2048] weight")
+        if torch.distributed.get_world_size(group) != 4:
+            raise ValueError("Shared LM head requires TP4")
+        self.weight = weight
+        self.weight.tensor_model_parallel = True
+        self.weight.partition_dim = 0
+        self.weight.partition_stride = 1
+        self.group = group
+        self.gather_output = False
+        self.sequence_parallel = False
+
+    def forward(self, input_, weight=None, runtime_gather_output=None):
+        from megatron.core.tensor_parallel.mappings import gather_from_tensor_model_parallel_region
+
+        from rl_engine.integrations.qwen3_next_forward import shared_linear
+
+        if self.sequence_parallel:
+            raise ValueError("Shared LM head requires sequence parallelism disabled")
+        selected = self.weight if weight is None else weight
+        if selected.shape != self.weight.shape or selected.dtype != torch.bfloat16:
+            raise ValueError("Shared LM head received an incompatible tied weight")
+        value = shared_linear(_CopyToTP.apply(input_, self.group), selected)
+        gather = self.gather_output if runtime_gather_output is None else runtime_gather_output
+        if gather:
+            value = gather_from_tensor_model_parallel_region(value, group=self.group)
+        return value, None
+
+
+class SharedVIMEGDN(HuggingfaceAttention):
+    def __init__(
+        self, args, config, layer_number, cp_comm_type="p2p", pg_collection=None, name=None
+    ):
+        validate_training_topology(config, args)
+        super().__init__(args, config, layer_number, cp_comm_type, pg_collection)
+        if self.hf_config.hidden_size != 2048:
+            raise ValueError("Shared VIME GDN requires the pinned Qwen3-Next architecture")
+        group = (
+            pg_collection.tp if pg_collection is not None else mpu.get_tensor_model_parallel_group()
+        )
+        device = torch.device("cuda", torch.cuda.current_device())
+        self.linear_attn = TP4GDN(
+            group=group, device=device, config=GDNProviderConfig(), eps=self.hf_config.rms_norm_eps
+        )
+        set_gdn_tp_metadata(self.linear_attn)
+        self.input_layernorm = SharedInputNorm(self.hf_config.rms_norm_eps, device)
+        self.trace_state_hook = None
+        self.subnode_hook = None
+
+    def hf_forward(self, hidden_states, packed_seq_params):
+        if hidden_states.ndim != 3 or hidden_states.shape[0] != 1:
+            raise ValueError("VIME packed GDN expects [1, tokens, 2048]")
+        if packed_seq_params is None:
+            raise ValueError("VIME GDN requires packed sequence boundaries")
+        cu = packed_seq_params.cu_seqlens_q.to(device="cpu")
+        hidden = self.input_layernorm(hidden_states[0])
+        if self.subnode_hook is not None:
+            self.subnode_hook("input_layernorm", hidden)
+        indices = torch.arange(1, cu.numel(), device=hidden.device, dtype=torch.int32)
+        initial = self.linear_attn.initial_state(cu.numel())
+        output, state = self.linear_attn(hidden, initial, indices, cu)
+        if self.trace_state_hook is not None:
+            self.trace_state_hook(state, indices, cu)
+        if self.subnode_hook is not None:
+            self.subnode_hook("mixer", output)
+        return output.unsqueeze(0)
+
+
+class SharedVIMEFullAttention(HuggingfaceAttention):
+    def __init__(
+        self, args, config, layer_number, cp_comm_type="p2p", pg_collection=None, name=None
+    ):
+        validate_training_topology(config, args)
+        super().__init__(args, config, layer_number, cp_comm_type, pg_collection)
+        group = pg_collection.tp if pg_collection else mpu.get_tensor_model_parallel_group()
+        device = torch.device("cuda", torch.cuda.current_device())
+        rope = getattr(self.hf_config, "rope_parameters", None)
+        if rope is None:
+            rope = {"rope_theta": self.hf_config.rope_theta, "partial_rotary_factor": 0.25}
+        if any(
+            (
+                rope.get("partial_rotary_factor") != 0.25,
+                rope.get("rope_type", "default") != "default",
+            )
+        ):
+            raise ValueError("Shared full attention requires default RoPE with rotary dimension 64")
+        self.self_attn = TP4FullAttention(
+            group=group,
+            device=device,
+            eps=self.hf_config.rms_norm_eps,
+            rope_theta=rope["rope_theta"],
+        )
+        self.input_layernorm = SharedInputNorm(self.hf_config.rms_norm_eps, device)
+        self.trace_state_hook = None
+        self.subnode_hook = None
+        for parameter in self.parameters():
+            set_defaults_if_not_set_tensor_model_parallel_attributes(parameter)
+            parameter.sequence_parallel = False
+
+    def hf_forward(self, hidden_states, packed_seq_params):
+        if hidden_states.ndim != 3 or hidden_states.shape[0] != 1 or packed_seq_params is None:
+            raise ValueError("Shared VIME attention requires packed [1, tokens, 2048]")
+        hidden = self.input_layernorm(hidden_states[0])
+        if self.subnode_hook is not None:
+            self.subnode_hook("input_layernorm", hidden)
+        cu = packed_seq_params.cu_seqlens_q.to(device="cpu")
+        indices = torch.arange(1, cu.numel(), device=hidden.device, dtype=torch.int32)
+        output, state = self.self_attn(
+            hidden, self.self_attn.initial_state(cu.numel()), indices, cu
+        )
+        if self.trace_state_hook is not None:
+            self.trace_state_hook(state, indices, cu)
+        if self.subnode_hook is not None:
+            self.subnode_hook("mixer", output)
+        return output.unsqueeze(0)
+
+
+class SharedVIMEMoE(TP4MoE):
+    def __init__(self, config, submodules=None):
+        validate_training_topology(config, config)
+        if config.expert_tensor_parallel_size != 4:
+            raise ValueError("Shared MoE requires expert tensor parallel size 4")
+        super().__init__(
+            group=mpu.get_tensor_model_parallel_group(),
+            device=torch.device("cuda", torch.cuda.current_device()),
+        )
+        # Native VIME only recognizes stride-two FC1 names; our paired loader
+        # already packs complete per-rank gate/up blocks before the generic split.
+        self.experts.gate_up.partition_stride = 1
+        for parameter in self.parameters():
+            set_defaults_if_not_set_tensor_model_parallel_attributes(parameter)
+            parameter.sequence_parallel = False
+
+    def forward(self, hidden_states, padding_mask=None):
+        if padding_mask is not None and bool(padding_mask.any()):
+            raise ValueError("Shared VIME MoE requires packed tokens without padding")
+        value = super().forward(hidden_states.reshape(-1, 2048))
+        if self.subnode_hook is not None:
+            self.subnode_hook("mlp", value)
+        return value.reshape_as(hidden_states), None
+
+
+class SharedDecoderNorm(SharedInputNorm):
+    def __init__(self, config, hidden_size, eps):
+        if hidden_size != 2048 or config.params_dtype != torch.bfloat16:
+            raise ValueError("Shared decoder norm requires the official BF16 architecture")
+        super().__init__(eps, torch.device("cuda", torch.cuda.current_device()))
+        # Set by a trace owner: this instance is a layer's pre-MLP norm.
+        self.subnode_hook = None
+
+    def forward(self, value):
+        output = super().forward(value)
+        if self.subnode_hook is not None:
+            self.subnode_hook("post_attention_layernorm", output.reshape(-1, output.shape[-1]))
+        return output
+
+
+class SharedVIMEEmbedding(nn.Module):
+    def __init__(self, weight, group):
+        super().__init__()
+        if weight.shape != (37984, 2048) or weight.dtype != torch.bfloat16:
+            raise ValueError("Shared embedding requires the official BF16 TP4 weight")
+        self.weight = weight
+        self.group = group
+        self.subnode_hook = None
+
+    def forward(self, tokens):
+        from rl_engine.integrations.qwen3_next_embedding import shared_embedding
+
+        output = shared_embedding(tokens, self.weight, self.group)
+        if self.subnode_hook is not None:
+            self.subnode_hook("embedding", output)
+        return output
+
+
+def install_vime_model_providers(model):
+    """Replace outer GPT providers while retaining each original parameter object."""
+    group = mpu.get_tensor_model_parallel_group()
+    validate_training_topology(model.config, model.config)
+    if len(model.decoder.layers) != 48:
+        raise ValueError("The complete provider requires all 48 decoder layers")
+    for layer in model.decoder.layers:
+        if not isinstance(layer.self_attention, (SharedVIMEGDN, SharedVIMEFullAttention)):
+            raise ValueError("Construct the model with get_qwen3_next_shared_spec first")
+        if not isinstance(layer.mlp, SharedVIMEMoE):
+            raise ValueError("Shared decoder MoE provider is missing")
+    model.embedding.word_embeddings = SharedVIMEEmbedding(
+        model.embedding.word_embeddings.weight, group
+    )
+    previous = model.decoder.final_layernorm
+    model.decoder.final_layernorm = SharedInputNorm(
+        model.config.layernorm_epsilon, previous.weight.device, weight=previous.weight
+    )
+    model.output_layer = SharedVIMELMHead(model.output_layer.weight, group)
+    return model
+
+
+def install_vime_gdn_weight_hooks():
+    """Install exact shared-model layouts in VIME's real load/export entrypoints."""
+    import vime.backends.megatron_utils.hf_to_megatron as load_api
+    import vime.backends.megatron_utils.megatron_to_hf as export_api
+    from vime.backends.megatron_utils.hf_to_megatron.common import strip_mcore_wrappers
+
+    original_load = load_api._LOADERS["qwen3_next"]
+    if getattr(original_load, "_rl_kernel_tp4_gdn", False):
+        return
+    original_export = export_api.convert_qwen3_next_to_hf
+
+    def layer_parts(name):
+        match = re.fullmatch(r"decoder\.layers\.(\d+)\.(.+)", strip_mcore_wrappers(name))
+        return match.groups() if match else (None, None)
+
+    def direct_moe(rest):
+        return rest in {
+            "mlp.gate.weight",
+            "mlp.shared_expert_gate.weight",
+            "mlp.shared_expert.gate_proj.weight",
+            "mlp.shared_expert.up_proj.weight",
+            "mlp.shared_expert.down_proj.weight",
+        }
+
+    def load(name, reader, config):
+        layer, rest = layer_parts(name)
+        prefix = f"model.layers.{layer}"
+        if rest == "mlp.experts.gate_up":
+            value = torch.empty((512, 1024, 2048), dtype=torch.bfloat16)
+            for expert in range(512):
+                gate, up = (
+                    reader.get_tensor(f"{prefix}.mlp.experts.{expert}.{part}_proj.weight")
+                    for part in ("gate", "up")
+                )
+                value[expert].copy_(expert_hf_to_rank_major(gate, up))
+            return value
+        if rest == "mlp.experts.down":
+            value = torch.empty((512, 2048, 512), dtype=torch.bfloat16)
+            for expert in range(512):
+                weight = reader.get_tensor(f"{prefix}.mlp.experts.{expert}.down_proj.weight")
+                if weight.shape != (2048, 512) or weight.dtype != torch.bfloat16:
+                    raise ValueError("Official expert down projection requires BF16 [2048, 512]")
+                value[expert].copy_(weight)
+            return value
+        if direct_moe(rest):
+            return reader.get_tensor(f"{prefix}.{rest}")
+        value = original_load(name, reader, config)
+        if name.endswith("self_attention.linear_attn.conv1d.weight"):
+            if mpu.get_tensor_model_parallel_world_size() != 4:
+                raise ValueError("The shared GDN weight loader requires TP4")
+            value = conv_hf_to_rank_major(value)
+        if rest in (
+            "self_attention.self_attn.k_proj.weight",
+            "self_attention.self_attn.v_proj.weight",
+        ):
+            value = kv_hf_to_rank_major(value)
+        return value
+
+    def export(args, name, value):
+        layer, rest = layer_parts(name)
+        prefix = f"model.layers.{layer}"
+        if rest == "mlp.experts.gate_up":
+            if value.shape != (512, 1024, 2048):
+                raise ValueError("Gathered routed gate/up shape is not the complete TP4 tensor")
+            return [
+                (f"{prefix}.mlp.experts.{expert}.{part}_proj.weight", weight)
+                for expert in range(512)
+                for part, weight in zip(("gate", "up"), expert_rank_major_to_hf(value[expert]))
+            ]
+        if rest == "mlp.experts.down":
+            if value.shape != (512, 2048, 512) or value.dtype != torch.bfloat16:
+                raise ValueError("Gathered routed down shape is not the complete TP4 tensor")
+            return [
+                (f"{prefix}.mlp.experts.{expert}.down_proj.weight", value[expert])
+                for expert in range(512)
+            ]
+        if direct_moe(rest):
+            return [(f"{prefix}.{rest}", value)]
+        if name.endswith("self_attention.linear_attn.conv1d.weight"):
+            if mpu.get_tensor_model_parallel_world_size() != 4:
+                raise ValueError("The shared GDN weight exporter requires TP4")
+            value = conv_rank_major_to_hf(value)
+        if rest in (
+            "self_attention.self_attn.k_proj.weight",
+            "self_attention.self_attn.v_proj.weight",
+        ):
+            value = kv_rank_major_to_hf(value)
+        return original_export(args, name, value)
+
+    load._rl_kernel_tp4_gdn = True
+    load_api._LOADERS["qwen3_next"] = load
+    export_api.convert_qwen3_next_to_hf = export
+
+
+def get_qwen3_next_shared_spec(args, config, vp_stage=None):
+    """Use the explicit provider for every decoder normalization and sublayer."""
+    from vime_plugins.models.qwen3_next import Attention, get_qwen3_next_spec
+
+    validate_training_topology(config, args)
+    if config.expert_tensor_parallel_size != 4:
+        raise ValueError("Shared model requires expert tensor parallel size 4")
+    if vp_stage is not None:
+        raise ValueError("The shared GDN profile does not use virtual pipeline stages")
+    spec = get_qwen3_next_spec(args, config, vp_stage)
+    replaced = 0
+    for layer in spec.layer_specs:
+        if layer.submodules.self_attention.module is Attention:
+            layer.submodules.self_attention = ModuleSpec(
+                module=SharedVIMEGDN, params={"args": args}
+            )
+            replaced += 1
+        else:
+            layer.submodules.self_attention = ModuleSpec(
+                module=SharedVIMEFullAttention, params={"args": args}
+            )
+        layer.submodules.input_layernorm = IdentityOp
+        layer.submodules.pre_mlp_layernorm = SharedDecoderNorm
+        layer.submodules.mlp = ModuleSpec(module=SharedVIMEMoE)
+        # The native TE sharded-state map refers to fused norm/projection names.
+        # Shared adapters retain explicit HF-compatible norm parameter names.
+        layer.submodules.sharded_state_dict_keys_map = {}
+    if replaced != 36:
+        raise ValueError(f"Pinned Qwen3-Next must contain 36 GDN layers, found {replaced}")
+    if len(spec.layer_specs) != 48:
+        raise ValueError("Pinned Qwen3-Next must contain all 48 decoder layers")
+    spec.layer_norm = SharedDecoderNorm
+    install_vime_gdn_weight_hooks()
+    return spec
diff --git a/rl_engine/integrations/qwen3_next_vime_runtime.py b/rl_engine/integrations/qwen3_next_vime_runtime.py
new file mode 100644
index 000000000..36df0bca7
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_vime_runtime.py
@@ -0,0 +1,178 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Construct the native VIME/Megatron actor for the fixed Qwen TP4 profile."""
+
+import copy
+import os
+import sys
+from pathlib import Path
+
+from rl_engine.testing.qwen3_next_rl_fixture import (
+    OPTIMIZER_SETTINGS,
+    POLICY_SETTINGS,
+    require_policy_configuration,
+)
+
+
+def model_provider(pre_process=True, post_process=True, vp_stage=None):
+    """Native GPTModel construction, with providers installed before DDP/optimizer."""
+    from megatron.training.global_vars import get_args
+    from vime.backends.megatron_utils.model_provider import get_model_provider_func
+
+    from rl_engine.integrations.qwen3_next_vime import install_vime_model_providers
+
+    args = copy.copy(get_args())
+    args.custom_model_provider_path = None
+    factory = get_model_provider_func(args, "actor")
+    model = factory(pre_process=pre_process, post_process=post_process, vp_stage=vp_stage)
+    install_vime_model_providers(model)
+    return model
+
+
+def runtime_argv(checkpoint, *, training):
+    """Use native argument parsing/validation; no precision-policy overrides."""
+    checkpoint = str(Path(checkpoint).absolute())
+    values = {
+        "hf-checkpoint": checkpoint,
+        "load": checkpoint,
+        "actor-num-nodes": 1,
+        "actor-num-gpus-per-node": 4,
+        "num-gpus-per-node": 4,
+        "tensor-model-parallel-size": 4,
+        "pipeline-model-parallel-size": 1,
+        "context-parallel-size": 1,
+        "expert-model-parallel-size": 1,
+        "expert-tensor-parallel-size": 4,
+        "num-attention-heads": 16,
+        "num-query-groups": 2,
+        "kv-channels": 256,
+        "num-layers": 48,
+        "hidden-size": 2048,
+        "ffn-hidden-size": 5120,
+        "normalization": "RMSNorm",
+        "position-embedding-type": "rope",
+        "norm-epsilon": 1e-6,
+        "rotary-percent": 0.25,
+        "rotary-base": 10000000,
+        "vocab-size": 151936,
+        "make-vocab-size-divisible-by": 1,
+        "padded-vocab-size": 151936,
+        "moe-ffn-hidden-size": 512,
+        "moe-shared-expert-intermediate-size": 512,
+        "moe-router-score-function": "softmax",
+        "moe-router-dtype": "fp32",
+        "moe-router-topk": 10,
+        "num-experts": 512,
+        "moe-layer-freq": "[" + ",".join(["1"] * 48) + "]",
+        "moe-aux-loss-coeff": 0,
+        "moe-token-dispatcher-type": "alltoall",
+        "hidden-dropout": 0,
+        "attention-dropout": 0,
+        "seq-length": 1040,
+        "max-position-embeddings": 262144,
+        "global-batch-size": 4,
+        "micro-batch-size": 1,
+        "data-pad-size-multiplier": 1,
+        "num-rollout": 3 if training else 0,
+        "rollout-batch-size": 4,
+        "n-samples-per-prompt": 1,
+        "rollout-num-gpus": 0,
+        "advantage-estimator": "grpo",
+        "eps-clip": 0.2,
+        "eps-clip-high": 0.2,
+        "kl-coef": 0,
+        "kl-loss-coef": 0,
+        "entropy-coef": 0,
+        "optimizer": "adam",
+        "lr": 1e-6,
+        "lr-decay-style": "constant",
+        "adam-beta1": 0.9,
+        "adam-beta2": 0.98,
+        "adam-eps": 1e-8,
+        "weight-decay": 0.1,
+        "clip-grad": 1.0,
+        "optimizer-offload-fraction": 1.0,
+        "custom-model-provider-path": (
+            "rl_engine.integrations.qwen3_next_vime_runtime.model_provider"
+        ),
+    }
+    switches = [
+        "debug-train-only",
+        "disable-bias-linear",
+        "qk-layernorm",
+        "group-query-attention",
+        "use-gated-attention",
+        "apply-layernorm-1p",
+        "swiglu",
+        "untie-embeddings-and-output-weights",
+        "attention-output-gate",
+        "moe-shared-expert-gate",
+        "moe-grouped-gemm",
+        "use-rollout-logprobs",
+        "disable-compute-advantages-and-returns",
+        "optimizer-cpu-offload",
+        "use-precision-aware-optimizer",
+        # Megatron's fused weight-gradient accumulation needs APEX's
+        # fused_weight_gradient_mlp_cuda, which the pinned environment does not
+        # ship; the unfused path accumulates the same GEMM result into main_grad.
+        "no-gradient-accumulation-fusion",
+    ]
+    argv = ["qwen3-next-native-actor"]
+    for name, value in values.items():
+        argv.extend((f"--{name}", str(value)))
+    argv.extend(f"--{name}" for name in switches)
+    argv.extend(("--spec", "rl_engine.integrations.qwen3_next_vime", "get_qwen3_next_shared_spec"))
+    return argv
+
+
+def create_actor(checkpoint, *, training):
+    """Call real native setup/load after the caller initializes Torch distributed."""
+    import torch
+    import torch.distributed as dist
+    from vime.backends.megatron_utils.initialize import init
+    from vime.utils.arguments import parse_args
+
+    if sys.prefix == sys.base_prefix:
+        raise RuntimeError("Native VIME actor must actually run inside the isolated venv")
+    if not dist.is_initialized() or dist.get_world_size() != 4 or torch.cuda.device_count() != 4:
+        raise ValueError("Native VIME actor requires four initialized ranks and visible GPUs")
+    saved_argv = sys.argv
+    try:
+        sys.argv = runtime_argv(checkpoint, training=training)
+        args = parse_args()
+    finally:
+        sys.argv = saved_argv
+    precision = {name: getattr(args, name, None) for name in ("optimization_dtype", "reduce_dtype")}
+    for name, value in {**POLICY_SETTINGS, **OPTIMIZER_SETTINGS}.items():
+        setattr(args, name, value)
+    args.rank, args.local_rank = dist.get_rank(), int(os.environ["LOCAL_RANK"])
+    args.world_size = 4
+    args.num_rollout = 3 if training else 0
+    args.sequence_parallel = False
+    args.fp32_residual_connection = False
+    args.overlap_grad_reduce = False
+    args.overlap_param_gather = False
+    args.recompute_granularity = None
+    args.use_critic = False
+    require_policy_configuration(args)
+    if any(getattr(args, name, None) != value for name, value in precision.items()):
+        raise ValueError("Optimizer/reduction precision changed during profile setup")
+    init(args)
+    # vime's actor bootstrap creates a gloo group next to the NCCL one; the HF weight
+    # exporter (HfWeightIteratorDirect) gathers its parameter metadata over it.
+    # This runtime bypasses that bootstrap, so create the group here on every rank.
+    from vime.utils.distributed_utils import init_gloo_group
+
+    init_gloo_group()
+    if (args.vocab_size, args.padded_vocab_size) != (151936, 151936):
+        raise ValueError("Tokenizer initialization changed the official LM-head vocabulary")
+    from vime.backends.megatron_utils.checkpoint import load_checkpoint
+    from vime.backends.megatron_utils.model import setup_model_and_optimizer
+
+    from rl_engine.integrations.qwen3_next_logprob import install_vime_logprob_provider
+
+    model, optimizer, scheduler = setup_model_and_optimizer(args, role="actor")
+    load_checkpoint(model, optimizer, scheduler, checkpointing_context={})
+    install_vime_logprob_provider()
+    return args, model, optimizer, scheduler
diff --git a/rl_engine/integrations/qwen3_next_vllm.py b/rl_engine/integrations/qwen3_next_vllm.py
new file mode 100644
index 000000000..e5b03afdb
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_vllm.py
@@ -0,0 +1,205 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Actual vLLM GDN layer/cache adapter for the shared TP4 provider.
+
+The CUSTOM backend deliberately does not advertise batch invariance. Registering
+this adapter cannot bypass vLLM's strict capability check. Real metadata/step
+acceptance must pass before a separately reviewed capability registration.
+"""
+
+import torch
+from torch import nn
+from vllm.distributed import get_tp_group
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.mamba.gdn.qwen_gdn_linear_attn import QwenGatedDeltaNetAttention
+from vllm.model_executor.layers.mamba.mamba_utils import is_conv_state_dim_first
+from vllm.v1.attention.backend import AttentionCGSupport
+from vllm.v1.attention.backends.gdn_attn import (
+    GDNAttentionBackend,
+    GDNAttentionMetadata,
+    GDNAttentionMetadataBuilder,
+)
+from vllm.v1.attention.backends.registry import MambaAttentionBackendEnum, register_backend
+
+from rl_engine.integrations.qwen3_next_provider import GDNProviderConfig, GDNState
+from rl_engine.integrations.qwen3_next_tp import _ReduceFromTP
+from rl_engine.integrations.qwen3_next_tp_gdn import TP4GDN
+
+
+class SharedGDNMetadataBuilder(GDNAttentionMetadataBuilder):
+    """Use vLLM's scheduler ordering and record resets even for fresh one-token prompts."""
+
+    _cudagraph_support = AttentionCGSupport.NEVER
+
+    def build(self, common_prefix_len, common_attn_metadata, **kwargs):
+        if common_prefix_len:
+            raise ValueError("Shared GDN does not enable prefix reuse")
+        metadata = super().build(common_prefix_len, common_attn_metadata, **kwargs)
+        if metadata.num_spec_decodes or metadata.spec_sequence_masks is not None:
+            raise ValueError("Shared GDN does not enable speculative decoding")
+        metadata.has_initial_state = common_attn_metadata.compute_num_computed_tokens() > 0
+        return metadata
+
+
+class SharedGDNBackend(GDNAttentionBackend):
+    @staticmethod
+    def get_name():
+        return "RL_KERNEL_QWEN3_NEXT_GDN"
+
+    @staticmethod
+    def get_builder_cls():
+        return SharedGDNMetadataBuilder
+
+    @staticmethod
+    def supports_batch_invariance():
+        from rl_engine.integrations.qwen3_next_profile import profile_is_certified
+
+        return profile_is_certified()
+
+
+def metadata_sequences(metadata, tokens):
+    """Extract active packed sequences, excluding zero-token padding without touching null state."""
+    if not isinstance(metadata, GDNAttentionMetadata):
+        raise ValueError("Shared GDN requires actual vLLM GDNAttentionMetadata")
+    if metadata.num_spec_decodes or metadata.spec_sequence_masks is not None:
+        raise ValueError("Speculative GDN metadata is outside the shared profile")
+    cu = metadata.non_spec_query_start_loc
+    indices = metadata.non_spec_state_indices_tensor
+    if cu is None or indices is None:
+        raise ValueError("GDN metadata lacks packed sequence boundaries or state slots")
+    cu = cu.to(device="cpu", dtype=torch.int64)
+    if cu.ndim != 1 or cu.numel() != indices.numel() + 1:
+        raise ValueError("GDN metadata boundaries and slot counts disagree")
+    lengths = cu.diff()
+    if cu[0] != 0 or cu[-1] != tokens or bool((lengths < 0).any()):
+        raise ValueError("GDN metadata boundaries must cover exactly the actual tokens")
+    active = torch.nonzero(lengths > 0, as_tuple=True)[0]
+    chosen = indices.index_select(0, active.to(indices.device))
+    if bool((chosen <= 0).any()) or chosen.unique().numel() != chosen.numel():
+        raise ValueError("Active GDN requests require distinct positive state slots")
+    compact_cu = torch.cat((torch.zeros(1, dtype=torch.int64), lengths[active].cumsum(0)))
+    has_initial = metadata.has_initial_state
+    if has_initial is None or has_initial.numel() != indices.numel():
+        raise ValueError("Shared GDN metadata must declare initial state for every request")
+    reset = chosen[
+        ~has_initial.to(device=indices.device, dtype=torch.bool)[active.to(indices.device)]
+    ]
+    return chosen, compact_cu, reset
+
+
+def _register_backend():
+    path = "rl_engine.integrations.qwen3_next_vllm.SharedGDNBackend"
+    try:
+        current = MambaAttentionBackendEnum.CUSTOM.get_path()
+    except ValueError:
+        current = None
+    if current is not None and current != path:
+        raise RuntimeError("A different custom Mamba backend is already registered")
+    register_backend(MambaAttentionBackendEnum.CUSTOM, path, is_mamba=True)
+
+
+class SharedQwenGDN(QwenGatedDeltaNetAttention):
+    """Preserve real vLLM HF weight loaders and bind shared forward to its owned cache."""
+
+    def __init__(self, config, vllm_config, prefix="", **kwargs):
+        parallel = vllm_config.parallel_config
+        if parallel.tensor_parallel_size != 4 or parallel.pipeline_parallel_size != 1:
+            raise ValueError("Shared vLLM GDN requires TP4 and PP1")
+        if getattr(parallel, "decode_context_parallel_size", 1) != 1:
+            raise ValueError("Shared vLLM GDN requires CP1")
+        if getattr(parallel, "prefill_context_parallel_size", 1) != 1:
+            raise ValueError("Shared vLLM GDN requires prefill CP1")
+        if vllm_config.model_config.dtype != torch.bfloat16:
+            raise ValueError("Shared vLLM GDN requires BF16 weights")
+        if vllm_config.quant_config is not None or vllm_config.speculative_config is not None:
+            raise ValueError("Shared GDN requires unquantized weights and no speculation")
+        if vllm_config.cache_config.enable_prefix_caching:
+            raise ValueError("Shared GDN requires prefix reuse disabled")
+        if vllm_config.cache_config.mamba_cache_mode != "none":
+            raise ValueError("Shared GDN requires the non-reusing mamba cache mode")
+        if kwargs.get("reduce_results", True) is not True:
+            raise ValueError("Shared GDN performs its own TP output reduction")
+        if not vllm_config.model_config.enforce_eager:
+            raise ValueError("Shared GDN currently requires eager execution")
+        if not kwargs.get("gqa_interleaved_layout", False) or config.hidden_size != 2048:
+            raise ValueError("Shared GDN requires Qwen3-Next interleaved TP4 projections")
+        _register_backend()
+        super().__init__(config, vllm_config, prefix, **kwargs)
+        if self.get_state_dtype() != (torch.bfloat16, torch.float32):
+            raise ValueError("Shared GDN requires BF16 convolution and FP32 recurrent state")
+        # HF stores A_log and dt_bias in BF16; upstream widens A_log at allocation.
+        # Keep the checkpoint weight identity, widening only at the recurrence call.
+        for name in ("A_log", "dt_bias"):
+            original = getattr(self, name)
+            parameter = nn.Parameter(original.to(dtype=torch.bfloat16), requires_grad=False)
+            parameter.weight_loader = original.weight_loader
+            setattr(self, name, parameter)
+        self.group = get_tp_group().device_group
+        self.provider_config = GDNProviderConfig()
+        self.eps = config.rms_norm_eps
+
+    @property
+    def mamba_type(self):
+        return MambaAttentionBackendEnum.CUSTOM
+
+    @torch.no_grad()
+    def forward(self, hidden_states):
+        """Match the installed vLLM layer contract: one input, one returned tensor."""
+        output = torch.empty_like(hidden_states)
+        self._forward_into(hidden_states, output)
+        return output
+
+    @torch.no_grad()
+    def _forward_into(self, hidden_states, output):
+        context = get_forward_context()
+        metadata = context.attn_metadata
+        metadata = metadata.get(self.prefix) if isinstance(metadata, dict) else None
+        if metadata is None:
+            # vLLM's pre-cache memory profiling has no request or persistent state.
+            from rl_engine.integrations.qwen3_next_forward import shared_linear
+
+            qkvz = shared_linear(hidden_states, self.in_proj_qkvz.weight)
+            ba = shared_linear(hidden_states, self.in_proj_ba.weight)
+            zeros = qkvz.new_zeros((ba.shape[0], 1024))
+            projected = shared_linear(zeros, self.out_proj.weight)
+            output.copy_(_ReduceFromTP.apply(projected, self.group))
+            del qkvz, ba
+            return
+        tokens = metadata.num_actual_tokens
+        if tokens > hidden_states.shape[0] or output.shape != hidden_states.shape:
+            raise ValueError("vLLM GDN buffers do not cover the actual tokens")
+        indices, cu, reset = metadata_sequences(metadata, tokens)
+        output.zero_()
+        if not tokens:
+            return
+        conv = self.kv_cache[0]
+        if not is_conv_state_dim_first():
+            conv = conv.transpose(-1, -2)
+        recurrent = self.kv_cache[1]
+        if reset.numel():
+            state = GDNState(conv.clone(), recurrent.clone())
+            state.convolution.index_fill_(0, reset.long(), 0)
+            state.recurrent.index_fill_(0, reset.long(), 0)
+        else:
+            state = GDNState(conv, recurrent)
+        value, next_state = TP4GDN.forward(self, hidden_states[:tokens], state, indices, cu)
+        trace_state_hook = getattr(self, "trace_state_hook", None)
+        if trace_state_hook is not None:
+            trace_state_hook(next_state, indices, cu)
+        output[:tokens].copy_(value)
+        conv.copy_(next_state.convolution)
+        recurrent.copy_(next_state.recurrent)
+
+
+def register_shared_gdn_adapter():
+    """Opt in to the actual GDN layer adapter, without claiming strict capability."""
+    from vllm.model_executor.custom_op import op_registry_oot
+
+    _register_backend()
+    existing = op_registry_oot.get("QwenGatedDeltaNetAttention")
+    if existing is SharedQwenGDN:
+        return
+    if existing is not None:
+        raise RuntimeError("A different out-of-tree Qwen GDN layer is already registered")
+    QwenGatedDeltaNetAttention.register_oot(SharedQwenGDN)
diff --git a/rl_engine/integrations/qwen3_next_vllm_attention.py b/rl_engine/integrations/qwen3_next_vllm_attention.py
new file mode 100644
index 000000000..b6d872b42
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_vllm_attention.py
@@ -0,0 +1,238 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Eager vLLM paged-KV adapter for the shared D=256 causal attention core.
+
+The scheduler owns physical blocks and slot mappings. This backend neither
+reconstructs request IDs nor retains a separate Python KV cache. Capability
+registration remains conservative until the real scheduler matrix passes.
+"""
+
+from dataclasses import dataclass
+
+import torch
+from vllm.v1.attention.backend import (
+    AttentionBackend,
+    AttentionImpl,
+    AttentionMetadata,
+    AttentionMetadataBuilder,
+    AttentionType,
+)
+
+from rl_engine.integrations.qwen3_next_forward import shared_attention
+
+
+@dataclass
+class SharedAttentionMetadata(AttentionMetadata):
+    query_start_loc: tuple[int, ...]
+    seq_lens: tuple[int, ...]
+    block_table: torch.Tensor
+    slot_mapping: torch.Tensor
+    num_actual_tokens: int
+
+
+def validate_boundaries(starts, lengths, num_actual_tokens):
+    if len(starts) != len(lengths) + 1 or not starts or starts[0] != 0:
+        raise ValueError("Attention boundaries must start at zero and cover every request")
+    if starts[-1] != num_actual_tokens or num_actual_tokens < 0:
+        raise ValueError("Attention boundaries must exactly cover actual scheduled tokens")
+    for start, end, length in zip(starts[:-1], starts[1:], lengths, strict=True):
+        if end < start or length < end - start or length > 1040:
+            raise ValueError("Invalid Qwen eager query/prefix length")
+
+
+class SharedAttentionBuilder(AttentionMetadataBuilder[SharedAttentionMetadata]):
+    def __init__(self, kv_cache_spec, layer_names, vllm_config, device):
+        super().__init__(kv_cache_spec, layer_names, vllm_config, device)
+        parallel = vllm_config.parallel_config
+        topology = (
+            parallel.tensor_parallel_size,
+            parallel.pipeline_parallel_size,
+            parallel.decode_context_parallel_size,
+            parallel.prefill_context_parallel_size,
+        )
+        if topology != (4, 1, 1, 1):
+            raise ValueError("Shared Qwen attention requires TP4/PP1/CP1")
+        if not vllm_config.model_config.enforce_eager or vllm_config.speculative_config is not None:
+            raise ValueError("Shared Qwen attention requires eager execution without speculation")
+        if vllm_config.cache_config.enable_prefix_caching:
+            raise ValueError("Prefix reuse is outside the fixed Qwen eager profile")
+
+    def build(self, common_prefix_len, common_attn_metadata, fast_build=False):
+        if common_prefix_len != 0:
+            raise ValueError("Shared-prefix attention is outside the validated profile")
+        common = common_attn_metadata
+        if isinstance(common.causal, torch.Tensor):
+            causal = bool(common.causal.all())
+        else:
+            causal = common.causal is True
+        if not causal:
+            raise ValueError("Only causal Qwen attention is supported")
+        # Use exact device lengths, not optimistic CPU bounds from spec decode.
+        starts = tuple(common.query_start_loc[: common.num_reqs + 1].cpu().tolist())
+        lengths = tuple(common.seq_lens[: common.num_reqs].cpu().tolist())
+        validate_boundaries(starts, lengths, common.num_actual_tokens)
+        return SharedAttentionMetadata(
+            starts,
+            lengths,
+            common.block_table_tensor[: common.num_reqs],
+            common.slot_mapping[: common.num_actual_tokens],
+            common.num_actual_tokens,
+        )
+
+
+class SharedAttentionImpl(AttentionImpl[SharedAttentionMetadata]):
+    def __init__(
+        self,
+        num_heads,
+        head_size,
+        scale,
+        num_kv_heads=None,
+        alibi_slopes=None,
+        sliding_window=None,
+        kv_cache_dtype="auto",
+        logits_soft_cap=None,
+        attn_type=AttentionType.DECODER,
+        kv_sharing_target_layer_name=None,
+        **kwargs,
+    ):
+        if (num_heads, head_size, num_kv_heads, scale) != (4, 256, 1, 1 / 16):
+            raise ValueError("Shared Qwen attention requires four Q heads and one D256 KV head")
+        unsupported = (
+            alibi_slopes is not None,
+            sliding_window is not None,
+            logits_soft_cap not in (None, 0),
+            attn_type != AttentionType.DECODER,
+            kv_sharing_target_layer_name is not None,
+            kv_cache_dtype not in ("auto", "bfloat16"),
+        )
+        if any(unsupported) or kwargs:
+            raise ValueError("Unsupported extension to the fixed Qwen attention profile")
+        self.num_heads, self.head_size, self.num_kv_heads = 4, 256, 1
+        self.scale, self.kv_cache_dtype = scale, kv_cache_dtype
+
+    def forward(
+        self,
+        layer,
+        query,
+        key,
+        value,
+        kv_cache,
+        attn_metadata,
+        output,
+        output_scale=None,
+        output_block_scale=None,
+    ):
+        if output_scale is not None or output_block_scale is not None:
+            raise ValueError("Quantized output is outside the BF16 eager profile")
+        if attn_metadata is None:
+            # vLLM allocation profiling has no requests or valid cache to read.
+            return output.zero_()
+        metadata = attn_metadata
+        n = metadata.num_actual_tokens
+        validate_boundaries(metadata.query_start_loc, metadata.seq_lens, n)
+        if any(
+            t.dtype != torch.bfloat16 or t.device != query.device
+            for t in (query, key, value, kv_cache, output)
+        ):
+            raise ValueError("Q/K/V/cache/output must be BF16 on the same CUDA device")
+        if query.device.type != "cuda":
+            raise ValueError("Shared Qwen attention executes on CUDA")
+        if query.shape[1:] != (4, 256) or key.shape[1:] != (1, 256) or value.shape != key.shape:
+            raise ValueError("Invalid projected Q/K/V shape")
+        # vLLM 0.30 materializes logical cache [blocks, KV heads, tokens, K+V].
+        if kv_cache.ndim != 4 or kv_cache.shape[1] != 1 or kv_cache.shape[-1] != 512:
+            raise ValueError("Unexpected vLLM cache layout; no layout guessing is permitted")
+        if min(query.shape[0], key.shape[0], output.shape[0]) < n:
+            raise ValueError("Projected tensors do not cover every scheduled token")
+        slots = metadata.slot_mapping.to(device=query.device, dtype=torch.long)
+        if slots.shape != (n,) or slots.unique().numel() != n:
+            raise ValueError("Active KV write slots must be unique and match scheduled tokens")
+        block_size = kv_cache.shape[2]
+        if bool(((slots < 0) | (slots >= kv_cache.shape[0] * block_size)).any()):
+            raise ValueError("Active KV write slot is null or outside the allocated cache")
+        plans = []
+        for row, (start, end, length) in enumerate(
+            zip(
+                metadata.query_start_loc[:-1],
+                metadata.query_start_loc[1:],
+                metadata.seq_lens,
+                strict=True,
+            )
+        ):
+            if start == end:
+                continue
+            needed = (length + block_size - 1) // block_size
+            physical = metadata.block_table[row, :needed].to(device=query.device, dtype=torch.long)
+            if physical.numel() != needed or physical.unique().numel() != needed:
+                raise ValueError("Request KV blocks are incomplete or repeated")
+            if bool(((physical < 0) | (physical >= kv_cache.shape[0])).any()):
+                raise ValueError("Request KV block is outside the allocated cache")
+            logical = torch.arange(length, device=query.device)
+            physical_slots = physical[logical // block_size] * block_size + logical % block_size
+            if not torch.equal(physical_slots[-(end - start) :], slots[start:end]):
+                raise ValueError("Block table and write slots disagree about absolute positions")
+            plans.append((start, end, length, physical))
+        if any(not torch.isfinite(tensor[:n]).all() for tensor in (query, key, value)):
+            raise ValueError("Projected attention inputs must be finite")
+        # Validate every request before mutating any scheduler-owned cache slot.
+        blocks, offsets = slots // block_size, slots % block_size
+        kv_cache[blocks, 0, offsets, :256] = key[:n, 0]
+        kv_cache[blocks, 0, offsets, 256:] = value[:n, 0]
+        output.zero_()
+        for start, end, length, physical in plans:
+            cached = kv_cache[physical, 0].reshape(-1, 512)[:length]
+            if not torch.isfinite(cached).all():
+                raise ValueError("Request KV prefix contains nonfinite values")
+            k = cached[:, :256].reshape(1, 1, length, 256).contiguous()
+            v = cached[:, 256:].reshape(1, 1, length, 256).contiguous()
+            q = query[start:end].transpose(0, 1).unsqueeze(0).contiguous()
+            attended = shared_attention(q, k, v)
+            output[start:end].copy_(attended.squeeze(0).transpose(0, 1))
+        return output
+
+
+class SharedAttentionBackend(AttentionBackend):
+    supported_dtypes = [torch.bfloat16]
+    supported_kv_cache_dtypes = ["auto", "bfloat16"]
+    forward_includes_kv_cache_update = True
+
+    @staticmethod
+    def get_name():
+        # Attention.__init__ indexes AttentionBackendEnum with this value.
+        return "CUSTOM"
+
+    @staticmethod
+    def get_impl_cls():
+        return SharedAttentionImpl
+
+    @staticmethod
+    def get_builder_cls():
+        return SharedAttentionBuilder
+
+    @classmethod
+    def get_supported_head_sizes(cls):
+        return [256]
+
+    @classmethod
+    def supports_kv_connector(cls):
+        return False
+
+    @classmethod
+    def supports_device_cpu_query_lens_mismatch(cls):
+        return False
+
+    @classmethod
+    def supports_batch_invariance(cls):
+        from rl_engine.integrations.qwen3_next_profile import profile_is_certified
+
+        return profile_is_certified()
+
+
+def register_shared_attention_backend():
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum, register_backend
+
+    register_backend(
+        AttentionBackendEnum.CUSTOM,
+        "rl_engine.integrations.qwen3_next_vllm_attention.SharedAttentionBackend",
+    )
diff --git a/rl_engine/integrations/qwen3_next_vllm_model.py b/rl_engine/integrations/qwen3_next_vllm_model.py
new file mode 100644
index 000000000..445c6ca5f
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_vllm_model.py
@@ -0,0 +1,365 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Opt-in vLLM model using shared Qwen3-Next arithmetic and native weight loaders.
+
+Construction preserves every native Parameter and loader. The existing modules
+are bound to subclasses before weights load; no second copy of the 80B model is
+allocated. Only the selected eager profile is supported. Registering it does not
+declare scheduler batch invariance or completion of full-model L2 acceptance.
+"""
+
+from importlib.metadata import version
+
+import torch
+import torch.nn.functional as F
+from vllm.distributed import get_tp_group
+from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
+    UnquantizedFusedMoEMethod,
+)
+from vllm.model_executor.models.qwen3_next import (
+    Qwen3NextAttention,
+    Qwen3NextDecoderLayer,
+    Qwen3NextForCausalLM,
+    Qwen3NextModel,
+    Qwen3NextSparseMoeBlock,
+)
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+from rl_engine.distributed.collectives import collective_for_group
+from rl_engine.integrations.qwen3_next_forward import (
+    FORWARD_PROVIDER_ID,
+    shared_linear,
+    shared_logprob,
+    shared_moe,
+)
+from rl_engine.integrations.qwen3_next_logprob import qwen_logprob_contract
+from rl_engine.integrations.qwen3_next_tp import _ReduceFromTP
+from rl_engine.integrations.qwen3_next_tp_blocks import TP4FullAttention
+from rl_engine.kernels.logprob_contract import LogprobRole
+
+ARCHITECTURE = "RLKernelQwen3NextForCausalLM"
+
+
+def profile_capabilities():
+    return {
+        "provider": FORWARD_PROVIDER_ID,
+        "architecture": ARCHITECTURE,
+        "layers": 48,
+        "tensor_parallel_size": 4,
+        "scheduler_batch_invariance_verified": False,
+        "full_model_l2_verified": False,
+    }
+
+
+def validate_profile(vllm_config):
+    if version("vllm") != "0.30.0":
+        raise ValueError("Shared model requires pinned vLLM 0.30.0")
+    model, parallel = vllm_config.model_config, vllm_config.parallel_config
+    config = model.hf_text_config
+    required = {
+        "hidden_size": 2048,
+        "num_hidden_layers": 48,
+        "num_attention_heads": 16,
+        "num_key_value_heads": 2,
+        "head_dim": 256,
+        "vocab_size": 151936,
+        "num_experts": 512,
+        "num_experts_per_tok": 10,
+        "moe_intermediate_size": 512,
+        "shared_expert_intermediate_size": 512,
+        "rms_norm_eps": 1e-6,
+        "max_position_embeddings": 262144,
+    }
+    for name, expected in required.items():
+        if getattr(config, name, None) != expected:
+            raise ValueError(f"Shared model requires official {name}={expected}")
+    expected_types = [
+        "full_attention" if index % 4 == 3 else "linear_attention" for index in range(48)
+    ]
+    if list(config.layer_types) != expected_types:
+        raise ValueError("Shared model requires the official 36 GDN / 12 full-attention layers")
+    rope = config.rope_parameters
+    if (
+        rope.get("rope_theta") != 10000000
+        or rope.get("partial_rotary_factor") != 0.25
+        or rope.get("rope_type", "default") != "default"
+    ):
+        raise ValueError("Shared model requires default theta=1e7 and rotary dimension 64")
+    topology = tuple(
+        getattr(parallel, name, 1)
+        for name in (
+            "tensor_parallel_size",
+            "pipeline_parallel_size",
+            "data_parallel_size",
+            "decode_context_parallel_size",
+            "prefill_context_parallel_size",
+        )
+    )
+    if topology != (4, 1, 1, 1, 1):
+        raise ValueError("Shared model requires TP4/PP1/DP1/CP1")
+    unsupported = (
+        model.dtype != torch.bfloat16,
+        not model.enforce_eager,
+        vllm_config.quant_config is not None,
+        vllm_config.speculative_config is not None,
+        vllm_config.lora_config is not None,
+        vllm_config.cache_config.enable_prefix_caching,
+        parallel.enable_expert_parallel,
+        parallel.enable_eplb,
+        parallel.use_sequence_parallel_moe,
+        parallel.enable_dbo,
+        getattr(config, "attention_dropout", 0) != 0,
+        getattr(config, "mlp_only_layers", []),
+        getattr(config, "decoder_sparse_step", 1) != 1,
+        getattr(config, "layer_scale", False),
+        not getattr(config, "norm_topk_prob", True),
+        getattr(config, "hidden_act", "silu") != "silu",
+        getattr(config, "qkv_bias", False),
+        not getattr(config, "attn_output_gate", True),
+        not getattr(config, "is_causal", True),
+        getattr(config, "tie_word_embeddings", False),
+    )
+    if any(unsupported):
+        raise ValueError("Unsupported extension to the fixed BF16 eager shared model profile")
+    # This selects the native plain-weight allocation, not the execution path.
+    # The shared MoE below owns all forward arithmetic and prevents repacking.
+    if vllm_config.kernel_config.moe_backend != "triton":
+        raise ValueError("Set kernel_config.moe_backend='triton' for plain native BF16 allocation")
+    if vllm_config.attention_config.backend != AttentionBackendEnum.CUSTOM:
+        raise ValueError("Set attention_config.backend='CUSTOM' for the shared paged-KV backend")
+
+
+def _rms(value, module):
+    from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp
+
+    return Qwen3NextRMSNormCudaOp()(value, module.weight, eps=1e-6)
+
+
+def _require_weight(value, shape, name):
+    if value.dtype != torch.bfloat16 or value.shape != shape or not value.is_contiguous():
+        raise ValueError(f"{name} must remain contiguous BF16 {shape}")
+
+
+class SharedPlainBF16MoEMethod(UnquantizedFusedMoEMethod):
+    """Retain HF-loaded plain tensors; the shared model never calls native MoE."""
+
+    def process_weights_after_loading(self, layer):
+        _require_weight(layer.w13_weight, (512, 256, 2048), "routed gate/up")
+        _require_weight(layer.w2_weight, (512, 2048, 128), "routed down")
+        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
+
+    def apply(self, *args, **kwargs):
+        raise RuntimeError("Use SharedVLLMMoE; native MoE arithmetic is outside this provider")
+
+    def apply_monolithic(self, *args, **kwargs):
+        raise RuntimeError("Use SharedVLLMMoE; native MoE arithmetic is outside this provider")
+
+
+class SharedVLLMAttention(Qwen3NextAttention):
+    def forward(self, positions, hidden_states):
+        if positions.ndim != 1 or positions.shape[0] != hidden_states.shape[0]:
+            raise ValueError("Shared text attention requires one absolute position per token")
+        if bool(((positions < 0) | (positions >= 262144)).any()):
+            raise ValueError("Attention positions exceed the official RoPE range")
+        # Keep the three GEMM invocations identical to the training TP4 module;
+        # native qkv_proj owns one packed allocation and all three HF loaders.
+        q_weight, k_weight, v_weight = self.qkv_proj.weight.split((2048, 256, 256))
+        query, gate = shared_linear(hidden_states, q_weight).reshape(-1, 4, 512).chunk(2, -1)
+        key = shared_linear(hidden_states, k_weight).reshape(-1, 1, 256)
+        value = shared_linear(hidden_states, v_weight)
+        query, key = _rms(query, self.q_norm), _rms(key, self.k_norm)
+        query = TP4FullAttention._rotate(self, query, positions).reshape(-1, 1024)
+        key = TP4FullAttention._rotate(self, key, positions).reshape(-1, 256)
+        attended = self.attn(query, key, value).reshape(-1, 4, 256) * gate.sigmoid()
+        local = shared_linear(attended.reshape(-1, 1024), self.o_proj.weight)
+        return _ReduceFromTP.apply(local, self.shared_tp_group)
+
+
+class SharedVLLMMoE(Qwen3NextSparseMoeBlock):
+    subnode_hook = None
+
+    def forward(self, hidden_states, already_sequence_parallel=False):
+        if already_sequence_parallel:
+            raise ValueError("Shared MoE does not enable sequence parallelism")
+        routed = self.experts.routed_experts
+        output, routes = shared_moe(
+            hidden_states, self.gate.weight, routed.w13_weight, routed.w2_weight
+        )
+        if self.subnode_hook is not None:
+            self.subnode_hook("router", routes.indices)
+        gate_weight, up_weight = self.shared_expert.gate_up_proj.weight.chunk(2, dim=0)
+        gate = shared_linear(hidden_states, gate_weight)
+        up = shared_linear(hidden_states, up_weight)
+        activated = (F.silu(gate.float()) * up.float()).to(torch.bfloat16)
+        shared = shared_linear(activated, self.shared_expert.down_proj.weight)
+        shared_gate = shared_linear(hidden_states, self.shared_expert_gate.weight).sigmoid()
+        return _ReduceFromTP.apply(output + shared * shared_gate, self.shared_tp_group)
+
+
+class SharedVLLMDecoderLayer(Qwen3NextDecoderLayer):
+    # Set by a trace owner to observe the sub-node outputs named in
+    # qwen3_next_subnode.SUBNODES; observing never changes a value.
+    subnode_hook = None
+
+    def forward(self, hidden_states, residual, positions, **kwargs):
+        trace = self.subnode_hook
+        residual = hidden_states if residual is None else hidden_states + residual
+        hidden_states = _rms(residual, self.input_layernorm)
+        if trace is not None:
+            trace("input_layernorm", hidden_states)
+        if self.layer_type == "linear_attention":
+            hidden_states = self.linear_attn(hidden_states=hidden_states)
+        else:
+            hidden_states = self.self_attn(positions, hidden_states)
+        if trace is not None:
+            trace("mixer", hidden_states)
+        residual = residual + hidden_states
+        hidden_states = _rms(residual, self.post_attention_layernorm)
+        if trace is not None:
+            trace("post_attention_layernorm", hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        if trace is not None:
+            trace("mlp", hidden_states)
+        return hidden_states, residual
+
+
+class SharedVLLMModel(Qwen3NextModel):
+    subnode_hook = None
+
+    def embed_input_ids(self, input_ids):
+        from rl_engine.integrations.qwen3_next_embedding import shared_embedding
+
+        if input_ids.ndim != 1 or input_ids.dtype not in (torch.int32, torch.int64):
+            raise ValueError("Shared embedding requires a packed integer token vector")
+        return shared_embedding(input_ids.long(), self.embed_tokens.weight, self.shared_tp_group)
+
+    def forward(self, input_ids, positions, intermediate_tensors=None, inputs_embeds=None):
+        if intermediate_tensors is not None or inputs_embeds is not None:
+            raise ValueError("Shared text-only PP1 profile takes token IDs directly")
+        if self.shared_forward_hook is not None:
+            self.shared_forward_hook(input_ids, positions)
+        hidden_states = self.embed_input_ids(input_ids)
+        if self.subnode_hook is not None:
+            self.subnode_hook("embedding", hidden_states)
+        residual = None
+        for index, layer in enumerate(self.layers):
+            hidden_states, residual = layer(hidden_states, residual, positions)
+            if self.shared_trace_hook is not None:
+                self.shared_trace_hook(index, hidden_states + residual, layer)
+        return _rms(hidden_states + residual, self.norm)
+
+
+def _bind_native_model(model, group):
+    """Attach shared behavior without replacing parameters or their HF loaders."""
+    from rl_engine.integrations.qwen3_next_vllm import SharedQwenGDN
+    from rl_engine.integrations.qwen3_next_vllm_attention import SharedAttentionBackend
+
+    if len(model.layers) != 48 or (model.start_layer, model.end_layer) != (0, 48):
+        raise ValueError("The native model must own all 48 layers under PP1")
+    _require_weight(model.embed_tokens.weight, (37984, 2048), "embedding")
+    _require_weight(model.norm.weight, (2048,), "final norm")
+    for layer in model.layers:
+        layer.__class__ = SharedVLLMDecoderLayer
+        if layer.layer_type == "linear_attention":
+            if not isinstance(layer.linear_attn, SharedQwenGDN):
+                raise ValueError("Native GDN was not constructed with the shared cache adapter")
+        else:
+            attention = layer.self_attn
+            if attention.attn.attn_backend is not SharedAttentionBackend:
+                raise ValueError("Full attention must use the explicit shared paged-KV backend")
+            _require_weight(attention.qkv_proj.weight, (2560, 2048), "attention QKV/gate")
+            _require_weight(attention.o_proj.weight, (2048, 1024), "attention output")
+            attention.__class__ = SharedVLLMAttention
+            attention.shared_tp_group = group
+            # TP4FullAttention._rotate derives FP32 inverse frequencies from this
+            # float on every call, so both engines rotate with identical tables.
+            attention.rope_theta = 10000000.0
+        moe = layer.mlp
+        if (
+            not isinstance(moe, Qwen3NextSparseMoeBlock)
+            or moe.shared_expert is None
+            or moe.is_fused_shared_expert_enabled
+            or moe.replicate_shared_expert
+        ):
+            raise ValueError(
+                "Every layer requires all512 unfused TP experts plus a sharded shared expert"
+            )
+        routed = moe.experts.routed_experts
+        if type(routed.quant_method) is not UnquantizedFusedMoEMethod:
+            raise ValueError("Shared MoE requires native unquantized BF16 parameter loaders")
+        _require_weight(routed.w13_weight, (512, 256, 2048), "routed gate/up")
+        _require_weight(routed.w2_weight, (512, 2048, 128), "routed down")
+        _require_weight(moe.shared_expert.gate_up_proj.weight, (256, 2048), "shared gate/up")
+        _require_weight(moe.shared_expert.down_proj.weight, (2048, 128), "shared down")
+        routed.quant_method.__class__ = SharedPlainBF16MoEMethod
+        moe.__class__ = SharedVLLMMoE
+        moe.shared_tp_group = group
+    model.__class__ = SharedVLLMModel
+    model.shared_tp_group, model.shared_trace_hook = group, None
+    model.shared_forward_hook = None
+
+
+class RLKernelQwen3NextForCausalLM(Qwen3NextForCausalLM):
+    def __init__(self, *, vllm_config, prefix=""):
+        validate_profile(vllm_config)
+        from rl_engine.integrations.qwen3_next_vllm import register_shared_gdn_adapter
+        from rl_engine.integrations.qwen3_next_vllm_attention import (
+            register_shared_attention_backend,
+        )
+
+        register_shared_gdn_adapter()
+        register_shared_attention_backend()
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        self.shared_tp_group = get_tp_group().device_group
+        _bind_native_model(self.model, self.shared_tp_group)
+        _require_weight(self.lm_head.weight, (37984, 2048), "LM head")
+        self.shared_logits_hook = None
+
+    def forward(
+        self, input_ids, positions, intermediate_tensors=None, inputs_embeds=None, **kwargs
+    ):
+        # Call the explicit eager method instead of the native compile wrapper.
+        return self.model.forward(input_ids, positions, intermediate_tensors, inputs_embeds)
+
+    def compute_local_logits(self, hidden_states):
+        return shared_linear(hidden_states, self.lm_head.weight)
+
+    def compute_logits(self, hidden_states):
+        local = self.compute_local_logits(hidden_states)
+        if not torch.isfinite(local).all():
+            raise ValueError("Shared model produced non-finite logits")
+        source = local.t().contiguous()
+        collective = collective_for_group(
+            self.shared_tp_group, min_size_bytes=source.numel() * source.element_size()
+        )
+        logits = collective.all_gather(source).t().contiguous()
+        if self.shared_logits_hook is not None:
+            self.shared_logits_hook(hidden_states, local, logits)
+        return logits
+
+    def compute_selected_logprobs(self, hidden_states, targets):
+        """The L2/fixture path; native sampler logprobs are not provider evidence."""
+        local = self.compute_local_logits(hidden_states)
+        contract = qwen_logprob_contract(
+            local,
+            rank=get_tp_group().rank_in_group,
+            world_size=4,
+            role=LogprobRole.INFER,
+        )
+        return shared_logprob(local, targets, contract=contract, tp_group=self.shared_tp_group)[0]
+
+
+def register_shared_model():
+    """Call in every vLLM process before constructing the explicit architecture."""
+    from vllm.model_executor.models import ModelRegistry
+
+    path = "rl_engine.integrations.qwen3_next_vllm_model:RLKernelQwen3NextForCausalLM"
+    if ARCHITECTURE in ModelRegistry.get_supported_archs():
+        if ModelRegistry.models[ARCHITECTURE].load_model_cls() is not RLKernelQwen3NextForCausalLM:
+            raise ValueError(
+                "A different model is already registered under the shared architecture"
+            )
+    else:
+        ModelRegistry.register_model(ARCHITECTURE, path)
+    return ARCHITECTURE
diff --git a/rl_engine/integrations/qwen3_next_vllm_worker.py b/rl_engine/integrations/qwen3_next_vllm_worker.py
new file mode 100644
index 000000000..9b854daec
--- /dev/null
+++ b/rl_engine/integrations/qwen3_next_vllm_worker.py
@@ -0,0 +1,51 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""vLLM spawned-worker registration for the explicit shared Qwen architecture."""
+
+from vllm.v1.worker.gpu_worker import Worker
+
+from rl_engine.integrations.qwen3_next_binary import imported_provider_identity
+from rl_engine.integrations.qwen3_next_vllm_model import register_shared_model
+
+
+class SharedQwenWorker(Worker):
+    """Select through LLM(worker_cls=...) so spawn never relies on parent globals."""
+
+    def __init__(self, *args, **kwargs):
+        config = kwargs.get("vllm_config", args[0] if args else None)
+        identity = (
+            config.additional_config.get("qwen3_next_shared_identity")
+            if config is not None
+            else None
+        )
+        mode = None
+        if identity is not None:
+            from rl_engine.integrations.qwen3_next_profile import activate_for_identity
+
+            mode = activate_for_identity(identity)
+        register_shared_model()
+        self.shared_provider_binary = imported_provider_identity()
+        super().__init__(*args, **kwargs)
+        self.shared_l2_trace = None
+        self.shared_provider_execution_mode = mode
+
+    def configure_l2_trace(self, request, output_directory):
+        from rl_engine.integrations.qwen3_next_native_rollout import RolloutTrace
+
+        if self.shared_l2_trace is not None:
+            raise RuntimeError("The previous rollout trace has not been collected")
+        self.shared_l2_trace = RolloutTrace(self, request, output_directory)
+        return {"rank": self.rank, "request_sha256": request["request_sha256"]}
+
+    def collect_l2_trace(self):
+        if self.shared_l2_trace is None:
+            raise RuntimeError("No active rollout trace")
+        result = self.shared_l2_trace.finish()
+        self.shared_l2_trace = None
+        return result
+
+    def execute_model(self, scheduler_output):
+        if self.shared_l2_trace is not None:
+            self.shared_l2_trace.before_execute(scheduler_output)
+        return super().execute_model(scheduler_output)
diff --git a/rl_engine/kernels/gtest/qwen3_next_chain_report.py b/rl_engine/kernels/gtest/qwen3_next_chain_report.py
new file mode 100644
index 000000000..3326dd0af
--- /dev/null
+++ b/rl_engine/kernels/gtest/qwen3_next_chain_report.py
@@ -0,0 +1,268 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Qwen3-Next full-model chain as a WS1 chain-gate report.
+
+The Qwen3-Next chain runs as stages (``scripts/qwen3_next_run.py``): L2 on the
+pinned weights, then L2 again after each of two optimizer updates and after a
+reload. Each L2 stage writes a 16-case raw-bit matrix comparing rollout
+(native vLLM prefill + decode) with replay (VIME recompute). This module turns
+those matrices into the same report shape as the Qwen3-8B chain gate
+(:class:`rl_engine.kernels.gtest.chain_gate.ChainGateReport`), built from the
+same dataclasses, so the two models' reports can be read side by side.
+
+Every case of every weight version becomes one cell. Bitwise rollout/replay
+equality of logits and selected logprobs becomes one
+:class:`TensorComparisonDetail` each, with ``atol = rtol = 0``. The first
+divergence names a decoder layer and a sub-node (see
+``rl_engine.integrations.qwen3_next_subnode``), which this report also maps to
+a layer family.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import Any
+
+import torch
+
+from rl_engine.kernels.gtest.chain_gate import CellOutput, ChainGateReport
+from rl_engine.kernels.gtest.forward_invariance import TensorComparisonDetail
+from rl_engine.kernels.gtest.tolerance import (
+    BackendProvenance,
+    compute_logprob_aggregates,
+    default_clip_interval,
+    judge_logprob_aggregates,
+    resolve_comparison_roles,
+)
+
+SCHEMA_VERSION = "qwen3-next-chain-v1"
+COMPATIBLE_SCHEMA = "ws1-c10-c11-v5"
+PROFILE = "qwen3_next_tp4_shared_eager_v1"
+WEIGHT_VERSIONS = ("baseline", "update1", "update2", "reload")
+FAMILIES = ("embedding", "norm", "gdn", "full_attention", "moe", "lm_head_logprob")
+_TRAIN_INFER_KIND = "train_infer_logprob_parity"
+DISCLAIMER = (
+    "Qwen3-Next-80B-A3B-Instruct, TP4 x EP1, BF16, strict shared providers. Cells are the "
+    "16-case raw-bit L2 matrix (prompt 8/64/256/1024 x batch 1/4 x full/chunked prefill, 16 "
+    "response tokens) at each weight version; rollout is native vLLM prefill and decode, "
+    "replay is VIME recompute. Gradients are judged by the training stages (two updates, "
+    "reload and resumption), not by C11 gradient rows, so gradient fields are empty here."
+)
+
+
+def family_of(layer: int | None, node: str | None) -> str | None:
+    """Map a first divergence to the layer family that produced it."""
+    if node is None:
+        return None
+    if node == "embedding":
+        return "embedding"
+    if node in ("logits", "logprobs", "response_tokens", "lm_head_logprob"):
+        return "lm_head_logprob"
+    if layer is None or layer not in range(48):
+        raise ValueError(f"Divergence node {node!r} needs a decoder layer, got {layer!r}")
+    if node in ("input_layernorm", "post_attention_layernorm"):
+        return "norm"
+    if node in ("router", "mlp"):
+        return "moe"
+    if node in ("mixer", "output", "state"):
+        return "full_attention" if (layer + 1) % 4 == 0 else "gdn"
+    raise ValueError(f"Unknown divergence node {node!r}")
+
+
+def first_drift_of(record: Mapping[str, Any]) -> tuple[str | None, int | None, str | None]:
+    """``(dense first_drift, layer, family)`` of one L2 case record."""
+    if record["passed"]:
+        return None, None, None
+    divergence = record.get("first_divergent_node")
+    if divergence is not None:
+        layer, node = divergence["layer"], divergence["node"]
+        label = "embedding" if node == "embedding" else f"layer{layer}.{node}"
+    else:
+        layer = None
+        difference = record.get("first_difference") or {}
+        node = difference.get("tensor", "lm_head_logprob")
+        label = node
+    return f"{label}:rollout->recompute", layer, family_of(layer, node)
+
+
+def _detail(name, cell_id, rollout, recompute, roles) -> TensorComparisonDetail:
+    lhs, rhs = rollout.double(), recompute.double()
+    error = (lhs - rhs).abs()
+    return TensorComparisonDetail(
+        tensor_name=name,
+        config_pair=(f"{cell_id}/rollout", f"{cell_id}/recompute"),
+        shape=tuple(rollout.shape),
+        dtype=str(rollout.dtype).replace("torch.", ""),
+        max_abs_error=float(error.max()),
+        mean_abs_error=float(error.mean()),
+        max_rel_error=float((error / rhs.abs().clamp_min(1e-12)).max()),
+        atol=0.0,
+        rtol=0.0,
+        passed=bool(torch.equal(rollout, recompute)),
+        judgment="forward_invariance",
+        comparison_lhs_role=roles.comparison_lhs_role,
+        comparison_rhs_role=roles.comparison_rhs_role,
+    )
+
+
+def build_report(
+    matrices: Mapping[str, Mapping[str, Any]],
+    artifacts: Mapping[str, Mapping[str, Mapping[str, Any]]],
+    *,
+    contract: Mapping[str, Any],
+    measured_commit: str,
+    weight_source: str,
+    weight_hash: str,
+    device: str,
+    gpu_name: str | None,
+    compute_capability: str | None,
+) -> dict[str, Any]:
+    """Build the chain report from L2 matrices and their loaded case artifacts.
+
+    ``matrices[version]`` is an ``l2-matrix.json`` report; ``artifacts[version]
+    [case_id][role]`` is the loaded rollout/recompute artifact of that case.
+    """
+    if not matrices or set(matrices) - set(WEIGHT_VERSIONS):
+        raise ValueError(f"Weight versions must be a subset of {WEIGHT_VERSIONS}")
+    roles = resolve_comparison_roles(contract, "forward_invariance")
+    train_roles = resolve_comparison_roles(contract, _TRAIN_INFER_KIND)
+    clip = default_clip_interval(contract)
+    cells, invariance, decode_prefill = {}, [], []
+    lhs_all, rhs_all = [], []
+    first = None
+    for version in WEIGHT_VERSIONS:
+        if version not in matrices:
+            continue
+        matrix = matrices[version]
+        for case_id in matrix["case_ids"]:
+            record = matrix["cases"][case_id]
+            rollout = artifacts[version][case_id]["rollout"]
+            recompute = artifacts[version][case_id]["recompute"]
+            cell_id = f"{version}/{case_id}"
+            drift, layer, family = first_drift_of(record)
+            if drift is not None and first is None:
+                first = (drift, layer, family, cell_id)
+            batch = rollout["logprobs"].shape[0]
+            cells[cell_id] = CellOutput(
+                cell_id=cell_id,
+                selected_logp={
+                    (f"sample{row}", position): rollout["logprobs"][row, position]
+                    for row in range(batch)
+                    for position in range(16)
+                },
+                loss=None,
+                grads={},
+                first_drift_node=drift,
+                node_digests={
+                    "embedding": rollout["embedding_digest"]["sha256"],
+                    **{
+                        f"layer{item['layer']}": item["output_sha256"]
+                        for item in rollout["layer_digests"]
+                    },
+                },
+                requested_backend=PROFILE,
+                actual_backend=PROFILE,
+            )
+            for name in ("logprobs", "logits"):
+                invariance.append(_detail(name, cell_id, rollout[name], recompute[name], roles))
+            lhs_all.append(rollout["logprobs"].reshape(-1))
+            rhs_all.append(recompute["logprobs"].reshape(-1))
+            if case_id.endswith("-chunked"):
+                full = artifacts[version][case_id.replace("-chunked", "-full")]["rollout"]
+                verdict = judge_logprob_aggregates(
+                    compute_logprob_aggregates(
+                        rollout["logprobs"].reshape(-1),
+                        full["logprobs"].reshape(-1),
+                        torch.ones(rollout["logprobs"].numel(), dtype=torch.bool),
+                        contract=contract,
+                        report_kind=_TRAIN_INFER_KIND,
+                        clip_interval=clip,
+                        comparison_lhs_role=train_roles.comparison_lhs_role,
+                        comparison_rhs_role=train_roles.comparison_rhs_role,
+                    ),
+                    contract,
+                    execution_dtype="bf16",
+                )
+                decode_prefill.append((cell_id, verdict))
+    lhs, rhs = torch.cat(lhs_all), torch.cat(rhs_all)
+    train_infer = judge_logprob_aggregates(
+        compute_logprob_aggregates(
+            lhs,
+            rhs,
+            torch.ones(lhs.numel(), dtype=torch.bool),
+            contract=contract,
+            report_kind=_TRAIN_INFER_KIND,
+            clip_interval=clip,
+            comparison_lhs_role=train_roles.comparison_lhs_role,
+            comparison_rhs_role=train_roles.comparison_rhs_role,
+        ),
+        contract,
+        execution_dtype="bf16",
+    )
+    passed = (
+        first is None
+        and all(matrix["status"] == "passed" for matrix in matrices.values())
+        and all(item.passed for item in invariance)
+    )
+    report = ChainGateReport(
+        backend_profile=PROFILE,
+        workload_id="qwen3-next-l2-matrix-16",
+        fixture_hash=matrices[next(iter(matrices))]["fixture"]["content_sha256"],
+        config_fingerprint={"model": "Qwen3-Next-80B-A3B-Instruct", "tp": 4, "ep": 1, "pp": 1},
+        weight_source=weight_source,
+        weight_hash=weight_hash,
+        seed=1234,
+        workload_seed=1234,
+        device=device,
+        gpu_name=gpu_name,
+        compute_capability=compute_capability,
+        backend_provenance=BackendProvenance(
+            backend_profile=PROFILE,
+            requested_backend=PROFILE,
+            actual_backend=PROFILE,
+            execution_dtype="bf16",
+            accumulation_dtype="fp32",
+            output_dtype="bf16",
+            reference_dtype="bf16",
+            candidate_tf32_enabled=False,
+            reference_tf32_enabled=False,
+        ),
+        runtime_backend_observations={},
+        backward_runtime_observations={},
+        cells=cells,
+        invariance=tuple(invariance),
+        gradient_invariance=(),
+        accuracy=(),
+        gradient_accuracy=(),
+        accuracy_aggregates=(),
+        train_infer=train_infer,
+        train_infer_bn=None,
+        decode_prefill=tuple(decode_prefill),
+        first_drift=None if first is None else first[0],
+        aggregates=train_infer,
+        gradient_scope="training_stage_reports",
+        required_grad_names=(),
+        all_parameter_gradients=False,
+        representative_case_ids=tuple(cells),
+        workflow_url=None,
+        c8_evidence_path="",
+        c8_source_commit=None,
+        passed=passed,
+        backward_executed="update1" in matrices,
+        train_infer_executed=True,
+        accuracy_executed=False,
+        gradient_accuracy_executed=False,
+        disclaimer=DISCLAIMER,
+    )
+    payload = report.to_dict()
+    payload.update(
+        schema_version=SCHEMA_VERSION,
+        compatible_schema=COMPATIBLE_SCHEMA,
+        measured_commit=measured_commit,
+        weight_versions=[version for version in WEIGHT_VERSIONS if version in matrices],
+        first_drift_cell=None if first is None else first[3],
+        first_drift_layer=None if first is None else first[1],
+        first_drift_family=None if first is None else first[2],
+    )
+    return payload
diff --git a/rl_engine/testing/pytest_case_manifest.py b/rl_engine/testing/pytest_case_manifest.py
new file mode 100644
index 000000000..7371727c7
--- /dev/null
+++ b/rl_engine/testing/pytest_case_manifest.py
@@ -0,0 +1,80 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Pytest plugin enforcing Qwen3-Next's declared mandatory case identities."""
+
+import json
+import time
+from pathlib import Path
+
+from rl_engine.testing.qwen3_next_cases import execution_verdict, load_cases
+
+
+def pytest_addoption(parser):
+    group = parser.getgroup("qwen3-next acceptance")
+    group.addoption("--case-manifest", required=True)
+    group.addoption("--case-suite", choices=("norm", "core", "all", "diagnostics"), required=True)
+    group.addoption("--case-report", required=True)
+
+
+def pytest_configure(config):
+    plugin = CaseRecorder(
+        load_cases(Path(config.getoption("--case-manifest")), config.getoption("--case-suite")),
+        Path(config.getoption("--case-report")),
+    )
+    config.pluginmanager.register(plugin, "qwen3-next-case-recorder")
+
+
+class CaseRecorder:
+    def __init__(self, expected, output):
+        self.expected = expected
+        self.output = output
+        self.collected = []
+        self.outcomes = {}
+        self.active = None
+
+    def _progress(self, status="running"):
+        """Observable progress is separate from the final acceptance artifact."""
+        self.output.parent.mkdir(parents=True, exist_ok=True)
+        target = self.output.with_suffix(".progress.json")
+        partial = target.with_suffix(".partial")
+        payload = json.dumps(
+            {
+                "status": status,
+                "active_case": self.active,
+                "observed_at": time.time(),
+                "collected": len(self.collected),
+                "required": len(self.expected),
+                "finished_calls": sum("call" in phases for phases in self.outcomes.values()),
+                "outcomes": self.outcomes,
+            },
+            indent=2,
+        )
+        partial.write_text(payload + "\n")
+        partial.replace(target)
+
+    def pytest_collection_finish(self, session):
+        self.collected = [item.nodeid for item in session.items]
+        self._progress()
+
+    def pytest_runtest_logstart(self, nodeid, location):
+        self.active = nodeid
+        self._progress()
+
+    def pytest_runtest_logreport(self, report):
+        outcome = "skipped" if hasattr(report, "wasxfail") else report.outcome
+        self.outcomes.setdefault(report.nodeid, {})[report.when] = outcome
+        self._progress()
+
+    def pytest_sessionfinish(self, session, exitstatus):
+        result = execution_verdict(self.expected, self.collected, self.outcomes)
+        result["pytest_exit_code"] = int(exitstatus)
+        result["passed"] = result["passed"] and exitstatus == 0
+        self.output.parent.mkdir(parents=True, exist_ok=True)
+        partial = self.output.with_suffix(self.output.suffix + ".partial")
+        partial.write_text(json.dumps(result, indent=2) + "\n")
+        partial.replace(self.output)
+        self.active = None
+        self._progress("complete")
+        if not result["passed"] and exitstatus == 0:
+            session.exitstatus = 1
diff --git a/rl_engine/testing/qwen3_next_binary.py b/rl_engine/testing/qwen3_next_binary.py
new file mode 100644
index 000000000..41ad8165b
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_binary.py
@@ -0,0 +1,89 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Bind each native model execution to its freshly built and archived CUDA binary."""
+
+import hashlib
+import json
+import os
+import shutil
+from pathlib import Path
+
+
+def _digest(path):
+    with Path(path).open("rb") as stream:
+        return hashlib.file_digest(stream, "sha256").hexdigest()
+
+
+def archive_provider_binary(source, output, *, source_tree):
+    source, output = Path(source).absolute(), Path(output).absolute()
+    binaries = list((source / "rl_engine").glob("_C.cpython*.so"))
+    if len(binaries) != 1 or not source_tree:
+        raise ValueError("Expected one freshly built CUDA provider and its source tree")
+    binary = binaries[0]
+    digest = _digest(binary)
+    archive = output / "provider-binary.so"
+    temporary = archive.with_suffix(".partial")
+    shutil.copyfile(binary, temporary)
+    with temporary.open("rb") as stream:
+        os.fsync(stream.fileno())
+    if _digest(temporary) != digest:
+        raise ValueError("CUDA provider changed while archiving its execution identity")
+    temporary.replace(archive)
+    payload = {
+        "schema_version": 1,
+        "source_tree": source_tree,
+        "actual": {"module": "rl_engine._C", "path": str(binary), "sha256": digest},
+        "archive": {"path": str(archive), "sha256": digest},
+    }
+    path = output / "provider-binary.json"
+    path.write_text(json.dumps(payload, indent=2) + "\n")
+    with path.open("rb") as stream:
+        os.fsync(stream.fileno())
+    return {"path": str(path), "sha256": _digest(path)}
+
+
+def validate_binary_record(record, *, source_tree=None, artifact_root=None):
+    """Check archived bytes, without depending on a subsequently rebuilt live file."""
+    if not isinstance(record, dict) or set(record) != {"path", "sha256"}:
+        raise ValueError("Native execution requires a pinned CUDA binary evidence record")
+
+    def checked(item):
+        path = Path(item["path"])
+        if not path.is_absolute() or not path.is_file():
+            raise ValueError("CUDA binary evidence requires an existing absolute path")
+        if artifact_root is not None and not path.resolve().is_relative_to(
+            Path(artifact_root).resolve()
+        ):
+            raise ValueError("CUDA binary evidence escaped its owning stage")
+        if _digest(path) != item["sha256"]:
+            raise ValueError("CUDA provider evidence content changed")
+        return path
+
+    payload = json.loads(checked(record).read_text())
+    if payload.get("schema_version") != 1 or not payload.get("source_tree"):
+        raise ValueError("Unsupported CUDA provider evidence")
+    if source_tree is not None and payload["source_tree"] != source_tree:
+        raise ValueError("CUDA provider belongs to another source tree")
+    actual = payload.get("actual", {})
+    if set(actual) != {"module", "path", "sha256"} or actual["module"] != "rl_engine._C":
+        raise ValueError("The actual native CUDA extension identity is missing")
+    if not Path(actual["path"]).is_absolute() or not Path(actual["path"]).name.startswith(
+        "_C.cpython"
+    ):
+        raise ValueError("Provider identity does not name the actual native extension")
+    archive = payload.get("archive", {})
+    if archive.get("sha256") != actual["sha256"]:
+        raise ValueError("Archived binary differs from the binary imported by workers")
+    checked(archive)
+    return actual
+
+
+def verify_imported_provider(record, *, source_tree):
+    from rl_engine.integrations.qwen3_next_binary import imported_provider_identity
+
+    expected = validate_binary_record(record, source_tree=source_tree)
+    actual = imported_provider_identity()
+    if actual != expected:
+        raise ValueError("Worker imported a different CUDA binary than its stage built")
+    return actual
diff --git a/rl_engine/testing/qwen3_next_cases.py b/rl_engine/testing/qwen3_next_cases.py
new file mode 100644
index 000000000..72e1794c7
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_cases.py
@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Pinned operator case identities and complete execution verdicts."""
+
+import hashlib
+import json
+from pathlib import Path
+
+
+def load_cases(path: Path, suite: str) -> list[str]:
+    manifest = json.loads(path.read_text())
+    if manifest.get("schema_version") != 1:
+        raise ValueError("Unsupported operator case manifest schema")
+    groups = manifest["suites"]
+    all_ids = [case for cases in groups.values() for case in cases]
+    if len(all_ids) != len(set(all_ids)):
+        raise ValueError("Operator case IDs must be unique across suites")
+    names = ("norm", "core") if suite == "all" else (suite,)
+    cases = sorted(case for name in names for case in groups[name])
+    if not cases or any(not isinstance(case, str) or "::test_" not in case for case in cases):
+        raise ValueError("Operator suites must contain nonempty pytest case IDs")
+    return cases
+
+
+def case_digest(cases: list[str]) -> str:
+    return hashlib.sha256(("\n".join(sorted(cases)) + "\n").encode()).hexdigest()
+
+
+def execution_verdict(expected: list[str], collected: list[str], outcomes: dict) -> dict:
+    """Require the complete declared collection and successful setup/call/teardown."""
+    missing = sorted(set(expected) - set(collected))
+    unexpected = sorted(set(collected) - set(expected))
+    duplicates = len(collected) != len(set(collected))
+    counts = {"tests": len(expected), "failures": 0, "errors": 0, "skipped": 0}
+    incomplete = []
+    for case in expected:
+        phases = outcomes.get(case, {})
+        if any(value == "skipped" for value in phases.values()):
+            counts["skipped"] += 1
+        elif phases.get("setup") == "failed" or phases.get("teardown") == "failed":
+            counts["errors"] += 1
+        elif phases.get("call") == "failed":
+            counts["failures"] += 1
+        elif phases != {"setup": "passed", "call": "passed", "teardown": "passed"}:
+            incomplete.append(case)
+    passed = not any(
+        (
+            missing,
+            unexpected,
+            duplicates,
+            incomplete,
+            counts["failures"],
+            counts["errors"],
+            counts["skipped"],
+        )
+    )
+    return {
+        "passed": passed,
+        "expected_case_ids": expected,
+        "expected_case_ids_sha256": case_digest(expected),
+        "collected_case_ids": collected,
+        "missing": missing,
+        "unexpected": unexpected,
+        "duplicate_collection": duplicates,
+        "incomplete": incomplete,
+        "counts": counts,
+        "outcomes": outcomes,
+    }
diff --git a/rl_engine/testing/qwen3_next_export_schema.json b/rl_engine/testing/qwen3_next_export_schema.json
new file mode 100644
index 000000000..e1389c4f5
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_export_schema.json
@@ -0,0 +1,10 @@
+{
+  "schema_version": 1,
+  "checkpoint_revision": "9c7f2fbe84465e40164a94cc16cd30b6999b0cc7",
+  "tensor_count": 75944,
+  "weight_schema_sha256": "babe9f7e7cf398ad6a8607ee03bb6549139b26fcdd919bb65e9d22b059536361",
+  "weights_dtype": "bfloat16",
+  "architecture_layers": 48,
+  "encoding": "sha256 of sorted compact JSON mapping every HF tensor name to dtype and shape",
+  "scope": "Official safetensors header schema only; no numerical or full-model L2 acceptance"
+}
diff --git a/rl_engine/testing/qwen3_next_l2.py b/rl_engine/testing/qwen3_next_l2.py
new file mode 100644
index 000000000..7f8778072
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_l2.py
@@ -0,0 +1,1047 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Frozen full-model Qwen3-Next inputs and original-dtype L2 artifacts.
+
+Native workers receive only JSON requests (tokens, weights and provider identity).
+They write a weights_only-safe torch artifact with metadata, prompt_tokens,
+response_tokens, prediction_positions, BF16 logits [B,16,151936], FP32 logprobs
+[B,16], and layer_digests for all 48 layers. Recurrent debug states describe the
+same prediction positions: P-1 through P+14, before consuming response token 16.
+No rollout hidden tensor or recurrent state is a recompute input.
+"""
+
+import hashlib
+import json
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+import torch
+
+from rl_engine.integrations.qwen3_next_subnode import SUBNODES, subnode_layout
+from rl_engine.testing.tensor_identity import tensor_bitwise_equal
+
+VOCAB_SIZE = 151936
+RESPONSE_LENGTH = 16
+PROMPT_LENGTHS = (8, 64, 256, 1024)
+FROZEN_TEXTS = (
+    "The capital of France is Paris. Explain the route from the library to the river. ",
+    "Compute seven plus five, then describe a careful way to check the answer. ",
+    "A robot observes a red cup beside a blue plate on a wooden kitchen table. ",
+    "Write a precise explanation of how rain reaches a mountain stream in autumn. ",
+)
+NATIVE_WORKER_MODULE = "rl_engine.integrations.qwen3_next_native_workers"
+
+
+# The native rollout cache budget every certified profile pins (4 GiB per GPU).
+NATIVE_KV_CACHE_BYTES = 4 << 30
+
+
+def json_digest(value):
+    return hashlib.sha256(
+        json.dumps(value, sort_keys=True, separators=(",", ":")).encode()
+    ).hexdigest()
+
+
+def tensor_digest(value):
+    value = value.detach().cpu().contiguous()
+    digest = hashlib.sha256(str((str(value.dtype), tuple(value.shape))).encode())
+    digest.update(value.reshape(-1).view(torch.uint8).numpy().tobytes())
+    return digest.hexdigest()
+
+
+@dataclass(frozen=True)
+class L2Case:
+    prompt_length: int
+    batch_size: int
+    prefill: str
+
+    @property
+    def case_id(self):
+        return f"p{self.prompt_length:04d}-b{self.batch_size}-{self.prefill}"
+
+    @property
+    def chunks(self):
+        if self.prefill == "full":
+            return [self.prompt_length]
+        remaining = self.prompt_length - 1
+        chunks = [1, min(63, remaining)]
+        remaining -= chunks[-1]
+        while remaining:
+            size = min(64, remaining)
+            chunks.append(size)
+            remaining -= size
+        return chunks
+
+
+def case_matrix():
+    return [
+        L2Case(length, batch, prefill)
+        for length in PROMPT_LENGTHS
+        for batch in (1, 4)
+        for prefill in ("full", "chunked")
+    ]
+
+
+def prompt_fixture(tokenizer, tokenizer_identity):
+    encoded = [tokenizer.encode(text, add_special_tokens=False) for text in FROZEN_TEXTS]
+    if any(
+        not row or any(type(token) is not int or not 0 <= token < VOCAB_SIZE for token in row)
+        for row in encoded
+    ):
+        raise ValueError("Pinned tokenizer produced an empty or out-of-range prompt")
+    prompts = {}
+    for length in PROMPT_LENGTHS:
+        prompts[str(length)] = torch.tensor(
+            [(row * ((length + len(row) - 1) // len(row)))[:length] for row in encoded],
+            dtype=torch.int64,
+        )
+        if torch.unique(prompts[str(length)], dim=0).shape[0] != 4:
+            raise ValueError("The four pinned prompt samples must be distinct")
+    identity = {
+        "texts": list(FROZEN_TEXTS),
+        "tokenizer": tokenizer_identity,
+        "prompt_hashes": {key: tensor_digest(value) for key, value in prompts.items()},
+    }
+    return {
+        "schema_version": 1,
+        "prompts": prompts,
+        "prompt_identity": identity,
+        "prompt_sha256": json_digest(identity),
+    }
+
+
+def freeze_responses(prompts, baseline_artifacts, *, baseline_identity, baseline_records):
+    responses, old_logprobs = {}, {}
+    for length in PROMPT_LENGTHS:
+        artifact = baseline_artifacts[str(length)]
+        response = artifact["response_tokens"]
+        selected = artifact["logprobs"]
+        if response.shape != (4, RESPONSE_LENGTH) or response.dtype != torch.int64:
+            raise ValueError(
+                "Baseline generation must produce sixteen real tokens for four samples"
+            )
+        if (
+            selected.shape != (4, RESPONSE_LENGTH)
+            or selected.dtype != torch.float32
+            or not torch.isfinite(selected).all()
+        ):
+            raise ValueError("Baseline logprobs must be finite FP32 [4,16]")
+        if bool(((response < 0) | (response >= VOCAB_SIZE)).any()):
+            raise ValueError("Baseline response token is outside the official vocabulary")
+        responses[str(length)] = response.detach().cpu().clone()
+        old_logprobs[str(length)] = selected.detach().cpu().clone()
+    fixture = {
+        **prompts,
+        "responses": responses,
+        "old_logprobs": old_logprobs,
+        "baseline_identity": baseline_identity,
+        "baseline_artifacts": baseline_records,
+    }
+    fixture["content_sha256"] = fixture_digest(fixture)
+    return fixture
+
+
+def fixture_digest(fixture):
+    identity = {
+        "prompt_sha256": fixture["prompt_sha256"],
+        "baseline_identity": fixture["baseline_identity"],
+        "baseline_artifacts": fixture["baseline_artifacts"],
+        "responses": {key: tensor_digest(value) for key, value in fixture["responses"].items()},
+        "old_logprobs": {
+            key: tensor_digest(value) for key, value in fixture["old_logprobs"].items()
+        },
+    }
+    return json_digest(identity)
+
+
+def validate_fixture(fixture):
+    expected_keys = {str(length) for length in PROMPT_LENGTHS}
+    if fixture.get("schema_version") != 1 or any(
+        set(fixture[name]) != expected_keys
+        for name in ("prompts", "responses", "old_logprobs", "baseline_artifacts")
+    ):
+        raise ValueError("Incomplete frozen L2 input matrix")
+    for record in fixture["baseline_artifacts"].values():
+        if (
+            set(record) != {"path", "sha256"}
+            or not Path(record["path"]).is_absolute()
+            or len(record["sha256"]) != 64
+        ):
+            raise ValueError("Baseline generation artifact identity is incomplete")
+    for length in PROMPT_LENGTHS:
+        key = str(length)
+        prompt, response, logprobs = (
+            fixture[name][key] for name in ("prompts", "responses", "old_logprobs")
+        )
+        if (
+            prompt.shape != (4, length)
+            or prompt.dtype != torch.int64
+            or tensor_digest(prompt) != fixture["prompt_identity"]["prompt_hashes"][key]
+        ):
+            raise ValueError("Frozen prompt was changed")
+        if (
+            response.shape != (4, RESPONSE_LENGTH)
+            or response.dtype != torch.int64
+            or bool(((response < 0) | (response >= VOCAB_SIZE)).any())
+        ):
+            raise ValueError("Invalid frozen response")
+        if (
+            logprobs.shape != (4, RESPONSE_LENGTH)
+            or logprobs.dtype != torch.float32
+            or not torch.isfinite(logprobs).all()
+        ):
+            raise ValueError("Invalid frozen baseline logprobs")
+    if (
+        json_digest(fixture["prompt_identity"]) != fixture["prompt_sha256"]
+        or fixture_digest(fixture) != fixture["content_sha256"]
+    ):
+        raise ValueError("Frozen L2 fixture content identity mismatch")
+
+
+def save_tensor_artifact(path, payload):
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    temporary = path.with_suffix(path.suffix + ".partial")
+    with temporary.open("wb") as stream:
+        torch.save(payload, stream)
+        stream.flush()
+        os.fsync(stream.fileno())
+    temporary.replace(path)
+    directory = os.open(path.parent, os.O_RDONLY | os.O_DIRECTORY)
+    try:
+        os.fsync(directory)
+    finally:
+        os.close(directory)
+
+
+def load_tensor_artifact(path):
+    return torch.load(path, map_location="cpu", weights_only=True)
+
+
+def validate_weight_identity(identity):
+    """Bind mutable export bytes separately from the fixed official model identity.
+
+    This is the JSON boundary check. The native loaders and profile activation also
+    call validate_training_export to rehash and inspect the actual exported files.
+    """
+    if identity.get("provider_config", {}).get("profile") != "qwen3_next_tp4_shared_eager_v1":
+        return
+    version = identity.get("weight_version")
+    if not isinstance(version, str) or not version:
+        raise ValueError("Official shared model identity requires an explicit weight version")
+    actual = identity.get("checkpoint_identity", {})
+    if version == "baseline":
+        if actual.get("kind") == "qwen3_next_native_hf_export":
+            raise ValueError("An updated native export cannot masquerade as the official baseline")
+        if any(key in identity for key in ("base_checkpoint", "base_checkpoint_identity")) and any(
+            (
+                identity.get("base_checkpoint") != identity.get("checkpoint"),
+                identity.get("base_checkpoint_identity") != actual,
+            )
+        ):
+            raise ValueError("Baseline identity cannot redirect its official base checkpoint")
+        return
+    if any(key not in identity for key in ("base_checkpoint", "base_checkpoint_identity")):
+        raise ValueError("Updated weights require a separately pinned official base identity")
+    if any(
+        (
+            not Path(identity["checkpoint"]).is_absolute(),
+            not Path(identity["base_checkpoint"]).is_absolute(),
+            identity["checkpoint"] == identity["base_checkpoint"],
+            actual.get("schema_version") != 1,
+            actual.get("kind") != "qwen3_next_native_hf_export",
+            actual.get("status") != "complete",
+            actual.get("weight_version") != version,
+            actual.get("base_checkpoint") != identity["base_checkpoint"],
+            actual.get("base_checkpoint_identity") != identity["base_checkpoint_identity"],
+            actual.get("architecture_layers") != 48,
+            actual.get("weights_dtype") != "bfloat16",
+            type(actual.get("step")) is not int,
+            actual.get("step", 0) < 1,
+        )
+    ):
+        raise ValueError("Updated checkpoint identity differs from its published native export")
+    training = actual.get("training_identity", {})
+    fields = ("source_commit", "source_tree", "environment_sha256", "provider_config", "topology")
+    if any(training.get(key) != identity.get(key) for key in fields) or any(
+        (
+            training.get("checkpoint") != identity["base_checkpoint"],
+            training.get("checkpoint_identity") != identity["base_checkpoint_identity"],
+        )
+    ):
+        raise ValueError("Native export training provenance differs from the model/base identity")
+
+
+def worker_request(case, fixture, *, role, identity, generate=False, trace_layers=None):
+    if role not in ("rollout", "recompute"):
+        raise ValueError("Unknown native model role")
+    validate_weight_identity(identity)
+    if generate and (role != "rollout" or case.batch_size != 4 or case.prefill != "full"):
+        raise ValueError("Only initial B4 full-prefill rollout may define frozen responses")
+    if not generate:
+        validate_fixture(fixture)
+    prompt = fixture["prompts"][str(case.prompt_length)][: case.batch_size]
+    request = {
+        "schema_version": 1,
+        "case_id": case.case_id,
+        "role": role,
+        "identity": identity,
+        "sample_ids": list(range(case.batch_size)),
+        "prompt_tokens": prompt.tolist(),
+        "response_tokens": (
+            None
+            if generate
+            else fixture["responses"][str(case.prompt_length)][: case.batch_size].tolist()
+        ),
+        "response_length": RESPONSE_LENGTH,
+        "prediction_positions": list(
+            range(case.prompt_length - 1, case.prompt_length + RESPONSE_LENGTH - 1)
+        ),
+        "consumed_response_tokens": RESPONSE_LENGTH - 1,
+        "rollout_prefill_chunks": case.chunks,
+        "prefill_chunks": case.chunks if role == "rollout" else None,
+        "consumed_input_tokens": (
+            None
+            if generate
+            else [
+                left + right[:-1]
+                for left, right in zip(
+                    prompt.tolist(),
+                    fixture["responses"][str(case.prompt_length)][: case.batch_size].tolist(),
+                )
+            ]
+        ),
+        "state_position": case.prompt_length + RESPONSE_LENGTH - 2,
+        "execution": (
+            {
+                "rollout_mode": "autoregressive",
+                "prefill_chunks": case.chunks,
+                "response_mode": "generate" if generate else "teacher_forced",
+            }
+            if role == "rollout"
+            else {
+                "recompute_mode": "full_from_tokens",
+                "input_sequence_length": case.prompt_length + RESPONSE_LENGTH - 1,
+            }
+        ),
+        "fixture_sha256": fixture["prompt_sha256"] if generate else fixture["content_sha256"],
+        "logits_dtype": "bfloat16",
+        "logprobs_dtype": "float32",
+        "vocab_size": VOCAB_SIZE,
+        "trace_layers": list(trace_layers or []),
+        "required_layer_digests": list(range(48)),
+        "generation": {
+            "temperature": 0.0,
+            "top_p": 1.0,
+            "top_k": -1,
+            "seed": 1234,
+            "ignore_eos": True,
+        },
+    }
+    request["request_sha256"] = json_digest(request)
+    return request
+
+
+def layer_state_names(layer):
+    kinds = ("convolution", "recurrent_state") if (layer + 1) % 4 else ("key", "value")
+    return sorted(f"tp{rank}.{kind}" for rank in range(4) for kind in kinds)
+
+
+def layer_digest(layer, output, states, *, request=None, subnodes=None):
+    """Hash each sample's real layer outputs, sub-nodes and all TP-owned state tensors."""
+    if (
+        layer not in range(48)
+        or output.ndim != 3
+        or output.shape[1:] != (16, 2048)
+        or output.dtype != torch.bfloat16
+    ):
+        raise ValueError(
+            "Layer diagnostics require original BF16 response hidden states [B,16,2048]"
+        )
+    batch = output.shape[0]
+    if sorted(states) != layer_state_names(layer) or any(
+        not isinstance(value, torch.Tensor) or value.shape[0] != batch or not value.numel()
+        for value in states.values()
+    ):
+        raise ValueError("Actual convolution/recurrent or KV state tensors are required")
+    shapes = (
+        native_state_shapes(request)
+        if request is not None
+        and request["identity"].get("provider_config", {}).get("profile")
+        == "qwen3_next_tp4_shared_eager_v1"
+        else None
+    )
+    for name, value in states.items():
+        if shapes is not None:
+            validate_native_state_storage(value, name.rsplit(".", 1)[-1], shapes)
+        if "recurrent_state" in name and value.dtype != torch.float32:
+            raise ValueError("Recurrent diagnostic state must preserve FP32")
+        if not torch.isfinite(value).all():
+            raise ValueError("Nonfinite recurrent/KV diagnostic state")
+    if not torch.isfinite(output).all():
+        raise ValueError("Nonfinite layer diagnostic output")
+    if subnodes is None or tuple(subnodes) != SUBNODES:
+        raise ValueError(f"Layer diagnostics require the sub-nodes {SUBNODES} in order")
+    for name, value in subnodes.items():
+        width, dtype = subnode_layout(name)
+        if value.dtype != dtype or tuple(value.shape) != (batch, 16, width):
+            raise ValueError(f"Sub-node {name} must be original {dtype} [B,16,{width}]")
+    subnode_hashes = {
+        name: [tensor_digest(value[index]) for index in range(batch)]
+        for name, value in subnodes.items()
+    }
+    outputs = [tensor_digest(output[index]) for index in range(batch)]
+    state_hashes = [
+        json_digest({name: tensor_digest(value[index]) for name, value in sorted(states.items())})
+        for index in range(batch)
+    ]
+    return {
+        "layer": layer,
+        "sample_output_sha256": outputs,
+        "sample_state_sha256": state_hashes,
+        "output_sha256": json_digest({"layer": layer, "samples": outputs}),
+        "state_sha256": json_digest({"layer": layer, "samples": state_hashes}),
+        "state_names": sorted(states),
+        "sample_subnode_sha256": subnode_hashes,
+        "subnode_sha256": json_digest({"layer": layer, "subnodes": subnode_hashes}),
+    }
+
+
+def embedding_digest(values):
+    """Per-sample digests of the [B, 16, 2048] BF16 embedding rows at the response positions."""
+    if values.dtype != torch.bfloat16 or values.ndim != 3 or values.shape[1:] != (16, 2048):
+        raise ValueError("Embedding diagnostics require original BF16 [B,16,2048] rows")
+    samples = [tensor_digest(values[index]) for index in range(values.shape[0])]
+    return {"sample_sha256": samples, "sha256": json_digest({"embedding": samples})}
+
+
+def first_divergent_node(reference, candidate):
+    """The first (layer, sub-node) whose sample digests differ, in execution order.
+
+    Within a layer the sub-nodes are compared in SUBNODES order, then the layer
+    output and the recurrent/KV state. Returns ``{"layer", "node"}`` or None.
+    """
+    batch = candidate["logits"].shape[0]
+    left, right = reference["embedding_digest"], candidate["embedding_digest"]
+    if left["sample_sha256"][:batch] != right["sample_sha256"]:
+        return {"layer": 0, "node": "embedding"}
+    for left, right in zip(reference["layer_digests"], candidate["layer_digests"]):
+        for node in SUBNODES:
+            if left["sample_subnode_sha256"][node][:batch] != right["sample_subnode_sha256"][node]:
+                return {"layer": right["layer"], "node": node}
+        for node, name in (("output", "sample_output_sha256"), ("state", "sample_state_sha256")):
+            if left[name][:batch] != right[name]:
+                return {"layer": right["layer"], "node": node}
+    return None
+
+
+def first_divergent_layer(reference, candidate):
+    batch = candidate["logits"].shape[0]
+    for left, right in zip(reference["layer_digests"], candidate["layer_digests"]):
+        for name in ("sample_output_sha256", "sample_state_sha256"):
+            if left[name][:batch] != right[name]:
+                return right["layer"]
+    return None
+
+
+def validate_model_artifact(artifact, request):
+    validate_weight_identity(request["identity"])
+    batch = len(request["prompt_tokens"])
+    metadata = artifact.get("metadata", {})
+    if metadata != {
+        key: request[key]
+        for key in ("schema_version", "case_id", "role", "identity", "request_sha256")
+    }:
+        raise ValueError("Model artifact provenance differs from its independent worker request")
+    if artifact.get("execution") != request["execution"]:
+        raise ValueError("Artifact execution mode does not match its rollout/recompute contract")
+    profile = request["identity"].get("provider_config", {})
+    if profile.get("profile") == "qwen3_next_tp4_shared_eager_v1" and request["role"] == "rollout":
+        validate_native_scheduler_execution(artifact, request)
+    expected = {
+        "logits": ((batch, RESPONSE_LENGTH, VOCAB_SIZE), torch.bfloat16),
+        "logprobs": ((batch, RESPONSE_LENGTH), torch.float32),
+        "response_tokens": ((batch, RESPONSE_LENGTH), torch.int64),
+        "prompt_tokens": ((batch, len(request["prompt_tokens"][0])), torch.int64),
+        "prediction_positions": ((RESPONSE_LENGTH,), torch.int64),
+    }
+    for name, (shape, dtype) in expected.items():
+        tensor = artifact.get(name)
+        if (
+            not isinstance(tensor, torch.Tensor)
+            or tensor.device.type != "cpu"
+            or tensor.dtype != dtype
+            or tuple(tensor.shape) != shape
+        ):
+            raise ValueError(f"{name}: expected CPU {dtype} {shape}, preserving original storage")
+        if tensor.is_floating_point() and not torch.isfinite(tensor).all():
+            raise ValueError(f"{name}: nonfinite full-model artifact")
+    if not torch.equal(
+        artifact["prompt_tokens"], torch.tensor(request["prompt_tokens"], dtype=torch.int64)
+    ) or not torch.equal(
+        artifact["prediction_positions"],
+        torch.tensor(request["prediction_positions"], dtype=torch.int64),
+    ):
+        raise ValueError("Prompt or response prediction offsets do not match")
+    if request["response_tokens"] is not None and not torch.equal(
+        artifact["response_tokens"], torch.tensor(request["response_tokens"], dtype=torch.int64)
+    ):
+        raise ValueError("Worker changed frozen response tokens")
+    if bool(
+        ((artifact["response_tokens"] < 0) | (artifact["response_tokens"] >= VOCAB_SIZE)).any()
+    ):
+        raise ValueError("Generated token is outside the official vocabulary")
+    embedding = artifact.get("embedding_digest", {})
+    samples = embedding.get("sample_sha256", [])
+    if len(samples) != batch or embedding.get("sha256") != json_digest({"embedding": samples}):
+        raise ValueError("The artifact must identify every sample's embedding rows")
+    layers = artifact.get("layer_digests", [])
+    if [layer.get("layer") for layer in layers] != list(range(48)):
+        raise ValueError("Full 48-layer diagnostic identities are required")
+    for layer in layers:
+        if layer.get("state_names") != layer_state_names(layer["layer"]):
+            raise ValueError("Per-layer state identities must cover every TP rank")
+        for name in ("output", "state"):
+            values = layer.get(f"sample_{name}_sha256", [])
+            if len(values) != batch:
+                raise ValueError("Every layer must identify every sample's output and state")
+            if any(
+                not isinstance(value, str)
+                or len(value) != 64
+                or any(char not in "0123456789abcdef" for char in value)
+                for value in values
+            ):
+                raise ValueError("Invalid per-sample layer tensor digest")
+            if layer.get(f"{name}_sha256") != json_digest(
+                {"layer": layer["layer"], "samples": values}
+            ):
+                raise ValueError("Layer digest is not bound to its sample tensor identities")
+        subnodes = layer.get("sample_subnode_sha256", {})
+        if tuple(subnodes) != SUBNODES or any(
+            len(values) != batch or any(not isinstance(v, str) or len(v) != 64 for v in values)
+            for values in subnodes.values()
+        ):
+            raise ValueError("Every layer must identify every sample's sub-node outputs")
+        if layer.get("subnode_sha256") != json_digest(
+            {"layer": layer["layer"], "subnodes": subnodes}
+        ):
+            raise ValueError("Layer sub-node digest is not bound to its sample identities")
+    for name, value in artifact.get("debug_tensors", {}).items():
+        if (
+            not isinstance(value, torch.Tensor)
+            or value.device.type != "cpu"
+            or not torch.isfinite(value).all()
+        ):
+            raise ValueError("Debug layer/state tensors must be finite CPU tensors")
+        if "recurrent_state" in name and value.dtype != torch.float32:
+            raise ValueError("Recurrent state diagnostics must preserve FP32 storage")
+
+
+def validate_debug_capture(artifact, request):
+    tensors = artifact.get("debug_tensors", {})
+    for layer in request["trace_layers"]:
+        record = artifact["layer_digests"][layer]
+        prefix = f"layer.{layer}."
+        names = [prefix + "output"] + [prefix + name for name in record["state_names"]]
+        names += [f"{prefix}subnode.{name}" for name in SUBNODES]
+        if any(name not in tensors for name in names):
+            raise ValueError("First-divergence capture is missing layer output or state tensors")
+        actual = layer_digest(
+            layer,
+            tensors[prefix + "output"],
+            {name: tensors[prefix + name] for name in record["state_names"]},
+            request=request,
+            subnodes={name: tensors[f"{prefix}subnode.{name}"] for name in SUBNODES},
+        )
+        if actual != record:
+            raise ValueError("Saved first-divergence tensors differ from their layer identities")
+
+
+def validate_native_scheduler_execution(artifact, request):
+    expected = {
+        "kv_cache_memory_bytes_per_gpu": NATIVE_KV_CACHE_BYTES,
+        "max_model_len": 1040,
+        "max_num_seqs": 64,
+        "max_num_batched_tokens": 16384,
+        "scheduler": "rl_engine.integrations.qwen3_next_native_rollout.ExactPrefillScheduler",
+        "async_scheduling": False,
+        "prefix_caching": False,
+        "weights_dtype": "bfloat16",
+        "recurrent_state_dtype": "float32",
+        "model_runner": "v1",
+    }
+    if artifact.get("native_runtime_profile") != expected:
+        raise ValueError("Native rollout runtime differs from its fixed scheduler/cache profile")
+    mode = (
+        "certified" if "scheduler_profile_certificate" in request["identity"] else "qualification"
+    )
+    if artifact.get("provider_execution_mode") != mode:
+        raise ValueError("Native rollout execution mode differs from its certificate identity")
+    if (
+        request["identity"]["provider_config"].get("native_rollout_kv_cache_memory_bytes")
+        != NATIVE_KV_CACHE_BYTES
+    ):
+        raise ValueError("The submitted profile did not pin the native cache budget")
+    sizes = [*request["prefill_chunks"], *([1] * request["consumed_response_tokens"])]
+    steps = artifact.get("native_scheduler_steps", [])
+    if len(steps) != len(sizes):
+        raise ValueError("Native scheduler did not execute every requested prefill/decode boundary")
+    samples = request["sample_ids"]
+    consumed = 0
+    for step, size in zip(steps, sizes):
+        if set(step) != {"samples", "computed_before", "token_counts"} or any(
+            (
+                sorted(step["samples"]) != sorted(samples),
+                len(set(step["samples"])) != len(samples),
+                step["computed_before"] != [consumed] * len(samples),
+                step["token_counts"] != [size] * len(samples),
+            )
+        ):
+            raise ValueError("Native scheduler executed a different sample/chunk/offset matrix")
+        consumed += size
+
+
+def compare_artifacts(rollout, recompute):
+    comparisons = {
+        name: tensor_bitwise_equal(rollout[name], recompute[name])
+        for name in ("response_tokens", "logits", "logprobs")
+    }
+    first_layer = first_divergent_layer(rollout, recompute)
+    first_node = first_divergent_node(rollout, recompute)
+    comparisons["layer_states"] = first_node is None
+    first_difference = None
+    for name, passed in comparisons.items():
+        if passed:
+            continue
+        if name == "layer_states":
+            break
+        left, right = rollout[name], recompute[name]
+        if left.shape == right.shape and left.dtype == right.dtype:
+            width = left.element_size()
+            changed = (
+                left.contiguous().reshape(-1).view(torch.uint8)
+                != right.contiguous().reshape(-1).view(torch.uint8)
+            ).nonzero()
+            if changed.numel():
+                flat = int(changed[0, 0]) // width
+                first_difference = {
+                    "tensor": name,
+                    "flat_element": flat,
+                    "byte_offset": int(changed[0, 0]),
+                }
+        break
+    return {
+        "passed": all(comparisons.values()),
+        "comparisons": comparisons,
+        "first_divergent_layer": first_layer,
+        "first_divergent_node": first_node,
+        "first_difference": first_difference,
+    }
+
+
+def native_state_shapes(request):
+    """Complete canonical per-rank state after P prompt plus 15 response inputs.
+
+    TP4GDN owns 2048 convolution channels with three prior samples and eight
+    128x128 recurrent heads. Full attention owns one 256-wide KV head per rank;
+    the paired replicas remain independently recorded and compared.
+    """
+    prompts = request["prompt_tokens"]
+    batch = len(prompts)
+    if batch not in (1, 4) or any(len(row) != len(prompts[0]) for row in prompts):
+        raise ValueError("Native state evidence requires the fixed equal-length batch1/4 matrix")
+    prompt_length = len(prompts[0])
+    consumed = prompt_length + RESPONSE_LENGTH - 1
+    if (
+        prompt_length not in PROMPT_LENGTHS
+        or request.get("consumed_response_tokens") != RESPONSE_LENGTH - 1
+        or request.get("state_position") != consumed - 1
+    ):
+        raise ValueError("Native state position must consume the prompt and exactly 15 responses")
+    return {
+        "convolution": (batch, 2048, 3),
+        "recurrent_state": (batch, 8, 128, 128),
+        "key": (batch, 1, consumed, 256),
+        "value": (batch, 1, consumed, 256),
+    }
+
+
+def validate_native_state_storage(value, name, expected_shapes):
+    """One dtype/shape/finite boundary shared by assembly and stored trace audits."""
+    if not isinstance(value, torch.Tensor) or tuple(value.shape) != expected_shapes[name]:
+        raise ValueError(
+            f"Original worker must retain complete {name} state shape "
+            f"{expected_shapes[name]} after the consumed prompt/response prefix"
+        )
+    dtype = torch.float32 if name == "recurrent_state" else torch.bfloat16
+    if value.dtype != dtype or not torch.isfinite(value).all():
+        raise ValueError("Original worker state violates BF16/FP32 finite storage")
+
+
+def validate_native_worker_traces(artifact, request, *, artifact_root=None):
+    """Rehash four actual traces and reconstruct every layer and state digest."""
+    profile = request["identity"].get("provider_config", {}).get("profile")
+    if profile != "qwen3_next_tp4_shared_eager_v1":
+        return
+    records = artifact.get("worker_traces", [])
+    if len(records) != 4 or sorted(record.get("rank", -1) for record in records) != [0, 1, 2, 3]:
+        raise ValueError("Native model needs one original trace from each TP rank")
+    binary = artifact.get("provider_binary", {})
+    digest = binary.get("sha256", "")
+    if (
+        binary.get("module") != "rl_engine._C"
+        or not isinstance(binary.get("path"), str)
+        or not Path(binary["path"]).is_absolute()
+        or not isinstance(digest, str)
+        or len(digest) != 64
+        or any(character not in "0123456789abcdef" for character in digest)
+    ):
+        raise ValueError("Native model must identify the actual imported CUDA provider binary")
+    batch = len(request["sample_ids"])
+    expected_state_shapes = native_state_shapes(request)
+    state_hashes = [[{} for _ in range(batch)] for _ in range(48)]
+    for record in records:
+        rank = record["rank"]
+        if record.get("request_sha256") != request["request_sha256"]:
+            raise ValueError("Worker trace request identity mismatch")
+        trace = load_tensor_artifact(checked_artifact_path(record, artifact_root=artifact_root))
+        provenance = {
+            "rank": rank,
+            "request_sha256": request["request_sha256"],
+            "provider_binary": binary,
+        }
+        if request["role"] == "rollout":
+            provenance.update(
+                native_runtime_profile=artifact["native_runtime_profile"],
+                provider_execution_mode=artifact["provider_execution_mode"],
+                schedule=artifact["native_scheduler_steps"],
+            )
+        else:
+            provenance.update(role="recompute", execution=request["execution"])
+        if any(trace.get(key) != value for key, value in provenance.items()):
+            raise ValueError("Worker trace rank/runtime/scheduler provenance mismatch")
+        tensors = {
+            "logprobs": artifact["logprobs"],
+            "responses": artifact["response_tokens"],
+            "local_logits": artifact["logits"][..., rank * 37984 : (rank + 1) * 37984],
+        }
+        # Recompute keeps the gathered full logits on rank zero, and every rank
+        # retains its original local shard and independently selected logprobs.
+        if request["role"] == "rollout" or rank == 0:
+            tensors["logits"] = artifact["logits"]
+        for key, expected in tensors.items():
+            if not isinstance(trace.get(key), torch.Tensor) or not tensor_bitwise_equal(
+                trace[key], expected
+            ):
+                raise ValueError(f"Original worker {key} bytes contradict the assembled artifact")
+        if request["role"] == "recompute":
+            layers = trace.get("layers", {})
+            if not isinstance(layers, dict) or set(layers) != {*range(48), "model"}:
+                raise ValueError("Original recompute worker must retain all 48 layer traces")
+            outputs = [layers[layer].get("output") for layer in range(48)]
+            if any(
+                not isinstance(value, torch.Tensor)
+                or value.shape != (batch, 16, 2048)
+                or value.dtype != torch.bfloat16
+                for value in outputs
+            ):
+                raise ValueError("Original recompute worker must retain all 48 BF16 layer outputs")
+            hidden = torch.stack(outputs)
+            states = [layers[layer].get("states", {}) for layer in range(48)]
+            subnodes = {
+                name: torch.stack([layers[layer]["subnodes"][name] for layer in range(48)])
+                for name in SUBNODES
+            }
+            embedding = layers["model"]["embedding"]
+        else:
+            hidden = trace.get("hidden")
+            states = trace.get("states", [])
+            subnodes, embedding = trace.get("subnodes", {}), trace.get("embedding")
+        if embedding_digest(embedding) != artifact["embedding_digest"]:
+            raise ValueError("Original worker embedding rows contradict the assembled artifact")
+        for name in SUBNODES:
+            for layer in range(48):
+                if [
+                    tensor_digest(subnodes[name][layer, sample]) for sample in range(batch)
+                ] != artifact["layer_digests"][layer]["sample_subnode_sha256"][name]:
+                    raise ValueError(
+                        f"Original worker {name} rows contradict the assembled artifact"
+                    )
+        if (
+            not isinstance(hidden, torch.Tensor)
+            or hidden.shape != (48, batch, 16, 2048)
+            or hidden.dtype != torch.bfloat16
+        ):
+            raise ValueError("Original worker must retain all 48 BF16 layer outputs")
+        if not torch.isfinite(hidden).all() or len(states) != 48:
+            raise ValueError("Original worker has nonfinite outputs or missing layer states")
+        for layer in range(48):
+            expected_names = (
+                ("key", "value") if (layer + 1) % 4 == 0 else ("convolution", "recurrent_state")
+            )
+            if set(states[layer]) != set(expected_names):
+                raise ValueError("Worker trace state types differ from the official layer layout")
+            for sample in range(batch):
+                if (
+                    tensor_digest(hidden[layer, sample])
+                    != artifact["layer_digests"][layer]["sample_output_sha256"][sample]
+                ):
+                    raise ValueError("Original worker layer output contradicts its declared digest")
+            for name, value in states[layer].items():
+                validate_native_state_storage(value, name, expected_state_shapes)
+                for sample in range(batch):
+                    state_hashes[layer][sample][f"tp{rank}.{name}"] = tensor_digest(value[sample])
+    for layer in range(48):
+        if [json_digest(values) for values in state_hashes[layer]] != artifact["layer_digests"][
+            layer
+        ]["sample_state_sha256"]:
+            raise ValueError("Original TP state bytes contradict the assembled layer digests")
+
+
+def compare_batch_prefill(reference, candidate):
+    batch = candidate["logits"].shape[0]
+    compared = {
+        name: tensor_bitwise_equal(reference[name][:batch], candidate[name])
+        for name in ("response_tokens", "logits", "logprobs")
+    }
+    compared["layer_states"] = first_divergent_node(reference, candidate) is None
+    return compared
+
+
+def validate_matrix_report(report, *, expected_identity=None, artifact_root=None):
+    """A passed label alone cannot close the full-model matrix."""
+    expected_ids = [case.case_id for case in case_matrix()]
+    required = {
+        "schema_version": 1,
+        "status": "passed",
+        "scope": "qwen3_next_full_model_l2",
+        "raw_bit_comparison": True,
+        "logits_dtype": "bfloat16",
+        "logprobs_dtype": "float32",
+        "response_length": 16,
+        "vocab_size": 151936,
+        "case_ids": expected_ids,
+    }
+    if any(report.get(key) != value for key, value in required.items()) or set(
+        report.get("cases", {})
+    ) != set(expected_ids):
+        raise ValueError("Full-model L2 requires the complete sixteen-case raw-bit matrix")
+    identity = report.get("identity", {})
+    if identity.get("architecture_layers") != 48 or identity.get("topology") != {
+        "tp": 4,
+        "pp": 1,
+        "cp": 1,
+        "ep": 1,
+    }:
+        raise ValueError("Full-model L2 cannot change model depth or topology")
+    processes = report.get("processes", {})
+    if (
+        set(processes) != {"rollout", "recompute"}
+        or any(
+            row.get("exit_code") != 0
+            or row.get("gpu_release_verified") is not True
+            or type(row.get("pid")) is not int
+            or row["pid"] <= 0
+            for row in processes.values()
+        )
+        or processes["rollout"]["pid"] == processes["recompute"]["pid"]
+    ):
+        raise ValueError("Independent process completion and GPU release evidence are required")
+    checks = {"response_tokens", "logits", "logprobs", "layer_states"}
+    for case in report["cases"].values():
+        compared = case.get("comparisons", {})
+        if (
+            case.get("passed") is not True
+            or set(compared) != checks
+            or any(value is not True for value in compared.values())
+        ):
+            raise ValueError(
+                "Every response token, full logits, logprob and layer/state identity must pass"
+            )
+        groups = [case.get("batch_prefill", {})]
+        if identity.get("weight_version") == "baseline":
+            groups.append(case.get("baseline_generation", {}))
+        for group in groups:
+            if set(group) != {"rollout", "recompute"} or any(
+                set(row) != checks or any(value is not True for value in row.values())
+                for row in group.values()
+            ):
+                raise ValueError(
+                    "Batch, chunk and actual baseline generation comparisons are mandatory"
+                )
+        if set(case.get("artifacts", {})) != {"rollout", "recompute"}:
+            raise ValueError("Both independent full-vocabulary tensor artifacts are required")
+    _validate_report_artifacts(
+        report,
+        roles=("rollout", "recompute"),
+        expected_identity=expected_identity,
+        artifact_root=artifact_root,
+    )
+
+
+def checked_artifact_path(record, *, artifact_root=None):
+    """Rehash bytes before parsing; artifact paths cannot escape this attempt."""
+    if not isinstance(record, dict) or not {"path", "sha256"} <= record.keys():
+        raise ValueError("Tensor evidence requires a path and SHA256")
+    path = Path(record["path"])
+    if not path.is_absolute():
+        raise ValueError("Tensor artifact path must be absolute")
+    path = path.resolve(strict=True)
+    if artifact_root is not None and not path.is_relative_to(Path(artifact_root).resolve()):
+        raise ValueError("Tensor artifact escaped its current attempt")
+    with path.open("rb") as stream:
+        digest = hashlib.file_digest(stream, "sha256").hexdigest()
+    if digest != record["sha256"]:
+        raise ValueError("Tensor artifact content digest changed")
+    return path
+
+
+def _validate_report_artifacts(report, *, roles, expected_identity, artifact_root):
+    identity = report["identity"]
+    if expected_identity is not None and identity != expected_identity:
+        raise ValueError("Model matrix source/environment/provider identity mismatch")
+    from rl_engine.testing.qwen3_next_binary import validate_binary_record
+
+    binary = validate_binary_record(
+        report.get("provider_binary"),
+        source_tree=identity.get("source_tree"),
+        artifact_root=artifact_root,
+    )
+    # Inputs may be the explicit frozen baseline shared by subsequent training
+    # runs. Their bytes are pinned separately; outputs must be in this attempt.
+    fixture = load_tensor_artifact(checked_artifact_path(report.get("fixture")))
+    validate_fixture(fixture)
+    if fixture["content_sha256"] != report["fixture"].get("content_sha256"):
+        raise ValueError("Matrix frozen-input content identity mismatch")
+    baselines = {}
+    for length, record in fixture["baseline_artifacts"].items():
+        baseline = load_tensor_artifact(checked_artifact_path(record))
+        original_identity = baseline.get("metadata", {}).get("identity", {})
+        if json_digest(original_identity) != fixture["baseline_identity"]:
+            raise ValueError("Baseline generation provenance changed")
+        if identity.get("weight_version") == "baseline" and original_identity != identity:
+            raise ValueError("Baseline weights/provider differ from the matrix")
+        baseline_request = worker_request(
+            L2Case(int(length), 4, "full"),
+            fixture,
+            role="rollout",
+            identity=original_identity,
+            generate=True,
+        )
+        validate_model_artifact(baseline, baseline_request)
+        validate_native_worker_traces(baseline, baseline_request)
+        baselines[length] = baseline
+    worker_records = report.get("worker_results", {})
+    if set(worker_records) != set(roles):
+        raise ValueError("Independent worker result artifacts are missing")
+    workers = {}
+    for role in roles:
+        worker = json.loads(
+            checked_artifact_path(
+                worker_records[role],
+                artifact_root=artifact_root,
+            ).read_text()
+        )
+        if any(
+            worker.get(key) != value
+            for key, value in {
+                "status": "passed",
+                "role": role,
+                "identity": identity,
+                "fixture_sha256": fixture["content_sha256"],
+                "pid": report["processes"][role]["pid"],
+                "provider_binary": binary,
+            }.items()
+        ) or set(worker.get("cases", {})) != {case.case_id for case in case_matrix()}:
+            raise ValueError("Independent worker provenance or case inventory mismatch")
+        workers[role] = worker
+    for case in case_matrix():
+        row = report["cases"][case.case_id]
+        outputs = {}
+        for role in roles:
+            record = row["artifacts"][role]
+            path = checked_artifact_path(record, artifact_root=artifact_root)
+            request = worker_request(case, fixture, role=role, identity=identity)
+            worker_record = workers[role]["cases"][case.case_id]
+            if worker_record != {**record, "request_sha256": request["request_sha256"]}:
+                raise ValueError("Worker and matrix refer to different model output artifacts")
+            outputs[role] = load_tensor_artifact(path)
+            if outputs[role].get("provider_binary") != binary:
+                raise ValueError("Model output used a different native CUDA provider")
+            validate_model_artifact(outputs[role], request)
+            validate_native_worker_traces(outputs[role], request, artifact_root=artifact_root)
+            reference_record = report["cases"][L2Case(case.prompt_length, 4, "full").case_id][
+                "artifacts"
+            ][role]
+            reference = load_tensor_artifact(
+                checked_artifact_path(reference_record, artifact_root=artifact_root)
+            )
+            actual = compare_batch_prefill(reference, outputs[role])
+            if not all(actual.values()) or actual != row["batch_prefill"][role]:
+                raise ValueError("Batch/prefill tensor bytes contradict matrix acceptance")
+            if identity.get("weight_version") == "baseline":
+                actual = compare_batch_prefill(baselines[str(case.prompt_length)], outputs[role])
+                if not all(actual.values()) or actual != row["baseline_generation"][role]:
+                    raise ValueError("Generated baseline tensor bytes contradict matrix acceptance")
+        if len(roles) == 2:
+            actual = compare_artifacts(outputs["rollout"], outputs["recompute"])
+            if not actual["passed"] or actual["comparisons"] != row["comparisons"]:
+                raise ValueError("Independent full-model tensor bytes contradict matrix acceptance")
+
+
+def validate_scheduler_report(report, *, expected_identity=None, artifact_root=None):
+    expected_ids = [case.case_id for case in case_matrix()]
+    required = {
+        "schema_version": 1,
+        "status": "passed",
+        "raw_bit_comparison": True,
+        "scope": "qwen3_next_native_scheduler_only",
+        "case_ids": expected_ids,
+        "full_model_l2": "not_evaluated",
+    }
+    if any(report.get(key) != value for key, value in required.items()):
+        raise ValueError("Actual scheduler requires its complete native sixteen-case matrix")
+    if set(report.get("cases", {})) != set(expected_ids):
+        raise ValueError("Scheduler matrix omitted mandatory cases")
+    identity = report.get("identity", {})
+    if identity.get("architecture_layers") != 48 or identity.get("topology") != {
+        "tp": 4,
+        "pp": 1,
+        "cp": 1,
+        "ep": 1,
+    }:
+        raise ValueError("Scheduler gate must execute the complete TP4 model")
+    processes = report.get("processes", {})
+    process = processes.get("rollout", {})
+    if set(processes) != {"rollout"} or any(
+        (
+            process.get("exit_code") != 0,
+            process.get("gpu_release_verified") is not True,
+            type(process.get("pid")) is not int,
+            process.get("pid", 0) <= 0,
+        )
+    ):
+        raise ValueError("Scheduler process completion and memory release evidence missing")
+    checks = {"response_tokens", "logits", "logprobs", "layer_states"}
+    for row in report["cases"].values():
+        if row.get("passed") is not True or set(row.get("artifacts", {})) != {"rollout"}:
+            raise ValueError("Scheduler case failed or has no native tensor artifact")
+        for field in ("batch_prefill", "baseline_generation"):
+            group = row.get(field, {})
+            values = group.get("rollout", {})
+            if any(
+                (
+                    set(group) != {"rollout"},
+                    set(values) != checks,
+                    not all(value is True for value in values.values()),
+                )
+            ):
+                raise ValueError("Scheduler batch/prefill/raw-bit comparisons are incomplete")
+    _validate_report_artifacts(
+        report,
+        roles=("rollout",),
+        expected_identity=expected_identity,
+        artifact_root=artifact_root,
+    )
diff --git a/rl_engine/testing/qwen3_next_layer_cases.json b/rl_engine/testing/qwen3_next_layer_cases.json
new file mode 100644
index 000000000..7610bd7c4
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_layer_cases.json
@@ -0,0 +1,122 @@
+{
+  "schema_version": 1,
+  "gates": {
+    "gdn": {
+      "script": "scripts/qwen3_next_tp_gdn_check.py",
+      "output_option": "--output",
+      "scope": "tp4_gdn_layer0",
+      "groups": {
+        "cases": [
+          "full-chunk-8",
+          "full-chunk-64",
+          "full-chunk-256",
+          "full-chunk-1024",
+          "prompt-gradient",
+          "replicated-norm-gradient",
+          "hf-load-export"
+        ]
+      }
+    },
+    "attention": {
+      "script": "scripts/qwen3_next_tp_attention_check.py",
+      "output_option": "--output",
+      "scope": "tp4_full_attention_layer3",
+      "groups": {
+        "attention.cases": [
+          "hf-load-export",
+          "full-chunk-8",
+          "full-chunk-64",
+          "full-chunk-256",
+          "full-chunk-1024",
+          "batch4-varlen-reorder",
+          "sixteen-token-decode",
+          "prompt-gradient",
+          "kv-pair-gradient",
+          "qk-norm-gradient"
+        ]
+      }
+    },
+    "moe": {
+      "script": "scripts/qwen3_next_tp_moe_check.py",
+      "output_option": "--output",
+      "scope": "tp4_moe_block_layer0",
+      "groups": {
+        "moe.cases": [
+          "hf-load-export-all512experts",
+          "routes-replicated-8",
+          "full-chunk-reorder-8",
+          "routes-replicated-64",
+          "full-chunk-reorder-64",
+          "routes-replicated-256",
+          "full-chunk-reorder-256",
+          "routes-replicated-1024",
+          "full-chunk-reorder-1024",
+          "training-forward",
+          "all-parameter-gradient",
+          "replicated-router-gradient"
+        ]
+      }
+    },
+    "adapters": {
+      "script": "scripts/qwen3_next_adapter_check.py",
+      "output_option": "--out",
+      "scope": "gdn_engine_adapter_component",
+      "groups": {
+        "cases": [
+          "batch1-length8",
+          "batch1-length64",
+          "batch1-length256",
+          "batch1-length1024",
+          "batch4-length8",
+          "batch4-length64",
+          "batch4-length256",
+          "batch4-length1024",
+          "mixed-prefill-decode16-reorder"
+        ]
+      },
+      "fields": {
+        "prompt_gradient": "passed",
+        "hf_conv_roundtrip": "passed",
+        "batch_invariance_capability_registered": false
+      }
+    },
+    "heads": {
+      "script": "scripts/qwen3_next_tp_head_check.py",
+      "output_option": "--output",
+      "scope": "tp4_embedding_lm_head_only",
+      "groups": {
+        "cases": [
+          "hf-embedding-roundtrip",
+          "hf-lm-head-roundtrip",
+          "replicated-norm-gradient",
+          "nonzero-finite-embedding-head-gradient",
+          "embedding-repeat-100",
+          "batch-invariant-prompt8",
+          "batch-invariant-prompt64",
+          "batch-invariant-prompt256",
+          "batch-invariant-prompt1024",
+          "logprob-original-and-promoted-logits",
+          "entropy-interface"
+        ]
+      }
+    }
+  },
+  "scheduler_case_ids": [
+    "p0008-b1-full",
+    "p0008-b1-chunked",
+    "p0008-b4-full",
+    "p0008-b4-chunked",
+    "p0064-b1-full",
+    "p0064-b1-chunked",
+    "p0064-b4-full",
+    "p0064-b4-chunked",
+    "p0256-b1-full",
+    "p0256-b1-chunked",
+    "p0256-b4-full",
+    "p0256-b4-chunked",
+    "p1024-b1-full",
+    "p1024-b1-chunked",
+    "p1024-b4-full",
+    "p1024-b4-chunked"
+  ]
+}
diff --git a/rl_engine/testing/qwen3_next_operator_cases.json b/rl_engine/testing/qwen3_next_operator_cases.json
new file mode 100644
index 000000000..5da204ee9
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_operator_cases.json
@@ -0,0 +1,293 @@
+{
+  "schema_version": 1,
+  "description": "Mandatory norm/core contracts; alternative-provider approximate comparisons are diagnostics only. Generated by scripts/qwen3_next_operator_cases.py; case changes require review before a frozen run.",
+  "suites": {
+    "norm": [
+      "tests/check_qwen3_next_norm_providers.py::test_custom_op_has_distinct_native_and_cuda_paths",
+      "tests/check_qwen3_next_norm_providers.py::test_gdn_decode_provider_env_defaults_are_recorded",
+      "tests/check_qwen3_next_norm_providers.py::test_our_gated_op_is_batch_invariant",
+      "tests/check_qwen3_next_norm_providers.py::test_vllm_gated_norm_is_batch_invariant[forward_cuda]",
+      "tests/check_qwen3_next_norm_providers.py::test_vllm_gated_norm_is_batch_invariant[forward_native]",
+      "tests/test_qwen3_next_norm.py::test_bad_weight_shape_raises",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[16]",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[1]",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[2]",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[32]",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[48]",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[64]",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[8]",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_slice[128]",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_slice[2048]",
+      "tests/test_qwen3_next_norm.py::test_batch_invariance_with_padding",
+      "tests/test_qwen3_next_norm.py::test_call_matches_forward",
+      "tests/test_qwen3_next_norm.py::test_chunked_reduction_is_slice_invariant_on_device[128]",
+      "tests/test_qwen3_next_norm.py::test_chunked_reduction_is_slice_invariant_on_device[2048]",
+      "tests/test_qwen3_next_norm.py::test_cuda_default_offset_preserves_plain_convention",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_backward_matches_autograd_golden",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[16]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[1]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[2]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[32]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[48]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[512]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[64]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[8]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_dgate_keeps_signed_zero_weight[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_dgate_keeps_signed_zero_weight[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_dgate_keeps_signed_zero_weight[dtype2]",
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+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[1024-1]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[1024-4]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[65-1]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[65-4]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[8-1]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[8-4]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_invalid_indices_do_not_mutate_cache[bad_slots0]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_invalid_indices_do_not_mutate_cache[bad_slots1]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_invalid_indices_do_not_mutate_cache[bad_slots2]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_invalid_indices_do_not_mutate_cache[bad_slots3]",
+      "tests/check_qwen3_next_shared_core.py::test_independent_training_forward_matches_inference_and_retains_gradients",
+      "tests/check_qwen3_next_shared_core.py::test_shared_core_chunk_and_decode_state_match[1024]",
+      "tests/check_qwen3_next_shared_core.py::test_shared_core_chunk_and_decode_state_match[8]",
+      "tests/check_qwen3_next_shared_core.py::test_unimplemented_provider_profile_is_rejected"
+    ],
+    "diagnostics": [
+      "tests/check_gdn_recurrent_golden.py::test_bf16_state_rounding_stays_within_fixed_fixture_bound",
+      "tests/check_gdn_recurrent_golden.py::test_conv_dim_first_false_matches_the_transposed_layout",
+      "tests/check_gdn_recurrent_golden.py::test_conv_forward_fp32_and_call_entry_points",
+      "tests/check_gdn_recurrent_golden.py::test_conv_golden_is_batch_invariant[False-None-state_dtype0]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_golden_is_batch_invariant[False-None-state_dtype1]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_golden_is_batch_invariant[False-silu-state_dtype0]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_golden_is_batch_invariant[False-silu-state_dtype1]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_golden_is_batch_invariant[True-None-state_dtype0]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_golden_is_batch_invariant[True-None-state_dtype1]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_golden_is_batch_invariant[True-silu-state_dtype0]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_golden_is_batch_invariant[True-silu-state_dtype1]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_null_block_id_is_skipped",
+      "tests/check_gdn_recurrent_golden.py::test_conv_output_bf16_cache_matches_rounded_product_path[17]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_output_bf16_cache_matches_rounded_product_path[1]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_output_matches_provider_with_fp32_cache[17]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_output_matches_provider_with_fp32_cache[1]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_output_matches_provider_with_fp32_cache[4]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_output_matches_provider_with_fp32_cache[64]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_provider_preserves_bf16_product_cancellation",
+      "tests/check_gdn_recurrent_golden.py::test_conv_rejects_unknown_activation_and_bad_shapes",
+      "tests/check_gdn_recurrent_golden.py::test_conv_state_update_is_bitwise_exact[17]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_state_update_is_bitwise_exact[1]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_state_update_is_bitwise_exact[4]",
+      "tests/check_gdn_recurrent_golden.py::test_conv_state_update_is_bitwise_exact[64]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_is_batch_invariant[state_dtype0]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_is_batch_invariant[state_dtype1]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_matches_packed_decode_provider[bf16-17]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_matches_packed_decode_provider[bf16-1]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_matches_packed_decode_provider[bf16-4]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_matches_packed_decode_provider[bf16-64]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_matches_packed_decode_provider[fp32-17]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_matches_packed_decode_provider[fp32-1]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_matches_packed_decode_provider[fp32-4]",
+      "tests/check_gdn_recurrent_golden.py::test_golden_matches_packed_decode_provider[fp32-64]",
+      "tests/check_gdn_recurrent_golden.py::test_null_block_id_is_skipped_by_both",
+      "tests/check_gdn_recurrent_golden.py::test_recurrent_call_matches_forward",
+      "tests/check_gdn_recurrent_golden.py::test_recurrent_forward_fp32_widens_the_state[state_dtype0]",
+      "tests/check_gdn_recurrent_golden.py::test_recurrent_forward_fp32_widens_the_state[state_dtype1]",
+      "tests/check_gdn_recurrent_golden.py::test_recurrent_without_in_kernel_l2norm_differs",
+      "tests/check_qwen3_next_batch_invariance.py::test_flashinfer_chunked_prefill_deviation_stays_rounding_scale",
+      "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_split_matches_one_pass[100]",
+      "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_split_matches_one_pass[128]",
+      "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_split_matches_one_pass[192]",
+      "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_split_matches_one_pass[64]",
+      "tests/check_qwen3_next_norm_providers.py::test_ours_tracks_each_vllm_path_within_bounds[forward_cuda]",
+      "tests/check_qwen3_next_norm_providers.py::test_ours_tracks_each_vllm_path_within_bounds[forward_native]",
+      "tests/check_qwen3_next_norm_providers.py::test_vllm_paths_disagree_only_within_bounds[dtype0-0.02-0.05]",
+      "tests/check_qwen3_next_norm_providers.py::test_vllm_paths_disagree_only_within_bounds[dtype1-1e-05-1.0]"
+    ]
+  }
+}
diff --git a/rl_engine/testing/qwen3_next_rl_fixture.py b/rl_engine/testing/qwen3_next_rl_fixture.py
new file mode 100644
index 000000000..3d9cbaba8
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_rl_fixture.py
@@ -0,0 +1,150 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Frozen inputs for real VIME policy-loss/optimizer recovery acceptance."""
+
+import hashlib
+import json
+from dataclasses import dataclass
+
+import torch
+
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal
+
+POLICY_SETTINGS = {
+    "loss_type": "policy_loss",
+    "advantage_estimator": "grpo",
+    "use_rollout_logprobs": True,
+    "disable_compute_advantages_and_returns": True,
+    "normalize_advantages": False,
+    "eps_clip": 0.2,
+    "eps_clip_high": 0.2,
+    "eps_clip_c": None,
+    "entropy_coef": 0.0,
+    "kl_coef": 0.0,
+    "kl_loss_coef": 0.0,
+    "opd_kl_coef": 0.0,
+    "use_kl_loss": False,
+    "use_tis": False,
+    "use_opsm": False,
+    "get_mismatch_metrics": False,
+    "custom_pg_loss_reducer_function_path": None,
+    "global_batch_size": 4,
+    "micro_batch_size": 1,
+    "use_dynamic_batch_size": False,
+    "rollout_temperature": 1.0,
+    "rollout_top_p": 1.0,
+    "rollout_top_k": -1,
+    "seed": 1234,
+}
+OPTIMIZER_SETTINGS = {
+    "optimizer": "adam",
+    "lr": 1e-6,
+    "lr_decay_style": "constant",
+    "weight_decay": 0.1,
+    "adam_beta1": 0.9,
+    "adam_beta2": 0.98,
+    "adam_eps": 1e-8,
+    "clip_grad": 1.0,
+    "optimizer_cpu_offload": True,
+    "optimizer_offload_fraction": 1.0,
+    "overlap_cpu_optimizer_d2h_h2d": False,
+    "use_precision_aware_optimizer": True,
+    "use_distributed_optimizer": True,
+    "use_stateless_adam": False,
+    "no_save_optim": False,
+    "no_save_rng": False,
+}
+
+
+def _tensor_digest(tensor):
+    value = tensor.detach().cpu().contiguous()
+    digest = hashlib.sha256()
+    digest.update(str((str(value.dtype), tuple(value.shape))).encode())
+    digest.update(value.reshape(-1).view(torch.uint8).numpy().tobytes())
+    return digest.hexdigest()
+
+
+@dataclass(frozen=True)
+class PolicyFixture:
+    tokens: torch.Tensor
+    old_logprobs: torch.Tensor
+    baseline_identity: str
+    content_sha256: str
+
+    @classmethod
+    def freeze(cls, tokens, old_logprobs, *, baseline_identity):
+        if tokens.shape != (4, 80) or tokens.dtype != torch.int64:
+            raise ValueError("Policy fixture requires four int64 prompt64+response16 samples")
+        if old_logprobs.shape != (4, 16) or old_logprobs.dtype != torch.float32:
+            raise ValueError("Baseline selected logprobs must be FP32 [4, 16]")
+        if not torch.isfinite(old_logprobs).all() or old_logprobs.requires_grad:
+            raise ValueError("Baseline logprobs must be finite, frozen rollout values")
+        if not baseline_identity or not isinstance(baseline_identity, str):
+            raise ValueError("A verified baseline run/content identity is required")
+        if bool(((tokens < 0) | (tokens >= 151936)).any()):
+            raise ValueError("Token ID is outside the official Qwen3-Next vocabulary")
+        if torch.unique(tokens, dim=0).shape[0] != 4:
+            raise ValueError("The four policy samples must be distinct")
+        tokens = tokens.detach().cpu().clone()
+        old_logprobs = old_logprobs.detach().cpu().clone()
+        digest = cls._identity(tokens, old_logprobs, baseline_identity)
+        return cls(tokens, old_logprobs, baseline_identity, digest)
+
+    @staticmethod
+    def _identity(tokens, old_logprobs, baseline_identity):
+        payload = {
+            "tokens": _tensor_digest(tokens),
+            "old_logprobs": _tensor_digest(old_logprobs),
+            "baseline": baseline_identity,
+            "advantages": [1.0, -0.5, 0.75, -0.25],
+            "response_mask": [1] * 16,
+            "sample_order": [[0], [1], [2], [3]],
+            "policy": POLICY_SETTINGS,
+            "optimizer": OPTIMIZER_SETTINGS,
+        }
+        return hashlib.sha256(json.dumps(payload, sort_keys=True).encode()).hexdigest()
+
+    def validate(self):
+        if (
+            self._identity(self.tokens, self.old_logprobs, self.baseline_identity)
+            != self.content_sha256
+        ):
+            raise ValueError("Frozen policy fixture was modified")
+
+    def verify_initial_recompute(self, logprobs):
+        self.validate()
+        assert_tensor_bitwise_equal(
+            logprobs.cpu(), self.old_logprobs, name="baseline policy logprobs"
+        )
+
+    def batch(self, sample_ids, *, device):
+        """Produce native VIME loss fields; no rollout hidden/state tensors are used."""
+        self.validate()
+        if not sample_ids or any(type(i) is not int or i not in range(4) for i in sample_ids):
+            raise ValueError("Policy sample IDs must be nonempty integers in [0, 4)")
+        if len(set(sample_ids)) != len(sample_ids):
+            raise ValueError("A microbatch cannot duplicate policy samples")
+        advantages = [1.0, -0.5, 0.75, -0.25]
+        return {
+            "unconcat_tokens": [self.tokens[i].to(device=device) for i in sample_ids],
+            "rollout_log_probs": [self.old_logprobs[i].to(device=device) for i in sample_ids],
+            "advantages": [
+                torch.full((16,), advantages[i], dtype=torch.float32, device=device)
+                for i in sample_ids
+            ],
+            "loss_masks": [torch.ones(16, dtype=torch.int32, device=device) for _ in sample_ids],
+            "rollout_mask_sums": torch.full(
+                (len(sample_ids),), 16, dtype=torch.float32, device=device
+            ),
+            "total_lengths": [80] * len(sample_ids),
+            "response_lengths": [16] * len(sample_ids),
+            "partition": list(sample_ids),
+        }
+
+
+def require_policy_configuration(args):
+    """Reject drift rather than changing an already constructed training configuration."""
+    for name, expected in {**POLICY_SETTINGS, **OPTIMIZER_SETTINGS}.items():
+        if not hasattr(args, name) or getattr(args, name) != expected:
+            raise ValueError(f"Policy acceptance requires {name}={expected!r}")
diff --git a/scripts/qwen3_next_adapter_check.py b/scripts/qwen3_next_adapter_check.py
new file mode 100644
index 000000000..0705abf54
--- /dev/null
+++ b/scripts/qwen3_next_adapter_check.py
@@ -0,0 +1,321 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Four-rank real VIME/vLLM GDN adapter acceptance, scoped below complete-model L2."""
+
+import argparse
+import json
+import os
+from pathlib import Path
+from types import SimpleNamespace
+
+import torch
+import torch.distributed as dist
+
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def run(checkpoint, output):
+    from megatron.core import mpu
+    from megatron.core.packed_seq_params import PackedSeqParams
+    from megatron.core.transformer.spec_utils import build_module
+    from megatron.core.transformer.transformer_config import TransformerConfig
+    from vime.backends.megatron_utils import hf_to_megatron as load_api
+    from vime.backends.megatron_utils import megatron_to_hf as export_api
+    from vime.backends.megatron_utils.hf_to_megatron.common import (
+        SafetensorReader,
+        shard_mcore_tensor,
+    )
+    from vime.backends.megatron_utils.update_weight.common import all_gather_param
+    from vllm.config.vllm import set_current_vllm_config
+    from vllm.distributed import init_distributed_environment, initialize_model_parallel
+    from vllm.engine.arg_utils import EngineArgs
+    from vllm.forward_context import set_forward_context
+    from vllm.model_executor.layers.mamba.mamba_utils import is_conv_state_dim_first
+    from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+    from vllm.v1.attention.backend import CommonAttentionMetadata
+
+    from rl_engine.integrations.qwen3_next_vime import get_qwen3_next_shared_spec
+    from rl_engine.integrations.qwen3_next_vllm import SharedGDNMetadataBuilder, SharedQwenGDN
+
+    rank, local_rank = int(os.environ["RANK"]), int(os.environ["LOCAL_RANK"])
+    torch.cuda.set_device(local_rank)
+    dist.init_process_group("nccl")
+    if dist.get_world_size() != 4:
+        raise ValueError("GDN engine adapters require exactly four ranks")
+    mpu.initialize_model_parallel(tensor_model_parallel_size=4)
+    # vLLM 0.30.0's initialize_model_parallel reads get_current_vllm_config(), so the
+    # engine config must exist before the vLLM process groups are created.
+    vconfig = EngineArgs(
+        model=str(checkpoint),
+        skip_tokenizer_init=True,
+        dtype="bfloat16",
+        tensor_parallel_size=4,
+        max_model_len=2048,
+        max_num_seqs=4,
+        enforce_eager=True,
+        enable_prefix_caching=False,
+        mamba_cache_dtype="bfloat16",
+        mamba_ssm_cache_dtype="float32",
+        distributed_executor_backend="external_launcher",
+        additional_config={"gdn_prefill_backend": "triton"},
+    ).create_engine_config()
+    with set_current_vllm_config(vconfig):
+        init_distributed_environment(4, rank, "env://", local_rank, "nccl")
+        initialize_model_parallel(4)
+    args = SimpleNamespace(
+        hf_checkpoint=str(checkpoint),
+        num_experts=512,
+        sequence_parallel=False,
+        hidden_size=2048,
+        num_attention_heads=16,
+        num_query_groups=2,
+        kv_channels=256,
+    )
+    config = TransformerConfig(
+        num_layers=48,
+        hidden_size=2048,
+        num_attention_heads=16,
+        num_query_groups=2,
+        kv_channels=256,
+        ffn_hidden_size=5120,
+        num_moe_experts=512,
+        moe_ffn_hidden_size=512,
+        moe_router_topk=10,
+        moe_shared_expert_intermediate_size=512,
+        tensor_model_parallel_size=4,
+        expert_tensor_parallel_size=4,
+        pipeline_model_parallel_size=1,
+        context_parallel_size=1,
+        expert_model_parallel_size=1,
+        sequence_parallel=False,
+        attention_dropout=0,
+        hidden_dropout=0,
+        params_dtype=torch.bfloat16,
+        bf16=True,
+        normalization="RMSNorm",
+        add_bias_linear=False,
+    )
+    spec = get_qwen3_next_shared_spec(args, config)
+    training = build_module(
+        spec.layer_specs[0].submodules.self_attention, config=config, layer_number=1
+    )
+    reader = SafetensorReader(checkpoint)
+    prefix = "module.module.decoder.layers.0.self_attention."
+    with torch.no_grad():
+        for name, parameter in training.named_parameters():
+            full_name = prefix + name
+            full = load_api._LOADERS["qwen3_next"](full_name, reader, training.hf_config)
+            local = shard_mcore_tensor(full_name, full, parameter)
+            if local.shape != parameter.shape:
+                raise ValueError(f"Incorrect training shard shape: {full_name}")
+            parameter.copy_(local.to(device=parameter.device))
+        conv_name = prefix + "linear_attn.conv1d.weight"
+        conv = all_gather_param(conv_name, training.linear_attn.conv1d.weight)
+        converted = export_api.convert_qwen3_next_to_hf(args, conv_name, conv)
+        exact(converted[0][1].cpu(), reader.get_tensor("model.layers.0.linear_attn.conv1d.weight"))
+
+    with set_current_vllm_config(vconfig):
+        previous_dtype = torch.get_default_dtype()
+        torch.set_default_dtype(torch.bfloat16)
+        try:
+            rollout = SharedQwenGDN(
+                vconfig.model_config.hf_text_config,
+                vconfig,
+                prefix="model.layers.0.linear_attn",
+                gqa_interleaved_layout=True,
+            ).cuda()
+        finally:
+            torch.set_default_dtype(previous_dtype)
+        for name, parameter in rollout.named_parameters():
+            value = reader.get_tensor(f"model.layers.0.linear_attn.{name}")
+            getattr(parameter, "weight_loader", default_weight_loader)(parameter, value)
+            exact(parameter, dict(training.linear_attn.named_parameters())[name], name=name)
+        cache_spec = rollout.get_kv_cache_spec(vconfig)
+        builder = SharedGDNMetadataBuilder(
+            cache_spec, [rollout.prefix], vconfig, torch.device("cuda")
+        )
+
+        def initial_cache():
+            # Deliberately dirty slots prove the metadata resets a new prompt.
+            return tuple(
+                torch.full((8, *shape), 0.125, dtype=dtype, device="cuda")
+                for shape, dtype in zip(rollout.get_state_shape(), rollout.get_state_dtype())
+            )
+
+        def step(hidden, lengths, computed, slots):
+            cu = torch.tensor([0, *torch.tensor(lengths).cumsum(0).tolist()], dtype=torch.int32)
+            seq = torch.tensor(lengths, dtype=torch.int32) + torch.tensor(
+                computed, dtype=torch.int32
+            )
+            common = CommonAttentionMetadata(
+                query_start_loc=cu.cuda(),
+                query_start_loc_cpu=cu,
+                seq_lens=seq.cuda(),
+                num_reqs=len(lengths),
+                num_actual_tokens=sum(lengths),
+                max_query_len=max(lengths),
+                max_seq_len=int(seq.max()),
+                block_table_tensor=torch.tensor(slots, dtype=torch.int32, device="cuda")[:, None],
+                slot_mapping=torch.zeros(sum(lengths), dtype=torch.int64, device="cuda"),
+            )
+            metadata = builder.build(0, common)
+            with set_forward_context({rollout.prefix: metadata}, vconfig):
+                return rollout(hidden_states=training.input_layernorm(hidden))
+
+        checked = []
+        generator = torch.Generator(device="cuda").manual_seed(713)
+        for batch in (1, 4):
+            for length in (8, 64, 256, 1024):
+                hidden = torch.randn(
+                    batch * length, 2048, generator=generator, device="cuda", dtype=torch.bfloat16
+                )
+                cu = torch.arange(batch + 1, dtype=torch.int32, device="cuda") * length
+                packed = PackedSeqParams(
+                    qkv_format="thd",
+                    cu_seqlens_q=cu,
+                    cu_seqlens_kv=cu,
+                    max_seqlen_q=length,
+                    max_seqlen_kv=length,
+                )
+                with torch.no_grad():
+                    train_out, _ = training(hidden.unsqueeze(1), None, packed_seq_params=packed)
+                    rollout.kv_cache = initial_cache()
+                    whole = step(
+                        hidden.clone(), [length] * batch, [0] * batch, list(range(1, batch + 1))
+                    )
+                    exact(whole, train_out[:, 0], name=f"VIME/vLLM B{batch} L{length}")
+                    final = tuple(state.clone() for state in rollout.kv_cache)
+                    for cut in (1, 63, 64, 65, 100, 528, 576):
+                        if cut >= length:
+                            continue
+                        rollout.kv_cache = initial_cache()
+                        pieces = [[] for _ in range(batch)]
+                        for lo, hi in ((0, cut), (cut, length)):
+                            order = list(range(batch)) if lo == 0 else list(reversed(range(batch)))
+                            rows = [
+                                row
+                                for seq in order
+                                for row in range(seq * length + lo, seq * length + hi)
+                            ]
+                            part = step(
+                                hidden[rows],
+                                [hi - lo] * batch,
+                                [lo] * batch,
+                                [seq + 1 for seq in order],
+                            )
+                            for position, seq in enumerate(order):
+                                pieces[seq].append(
+                                    part[position * (hi - lo) : (position + 1) * (hi - lo)]
+                                )
+                        exact(
+                            torch.cat([torch.cat(parts) for parts in pieces]),
+                            whole,
+                            name=f"chunk cut={cut}",
+                        )
+                        for current, expected in zip(rollout.kv_cache, final):
+                            exact(current, expected, name="owned rollout cache")
+                checked.append(f"batch{batch}-length{length}")
+
+        # Exercise a fresh one-token prompt in the same real scheduler batch as
+        # prefill requests, then sixteen reordered decode steps on owned caches.
+        prompt_lengths = [1, 64, 256, 1024]
+        lengths = [length + 16 for length in prompt_lengths]
+        sequences = [
+            torch.randn(length, 2048, generator=generator, device="cuda", dtype=torch.bfloat16)
+            for length in lengths
+        ]
+        complete = torch.cat(sequences)
+        cu = torch.tensor(
+            [0, *torch.tensor(lengths).cumsum(0).tolist()], device="cuda", dtype=torch.int32
+        )
+        packed = PackedSeqParams(
+            qkv_format="thd",
+            cu_seqlens_q=cu,
+            cu_seqlens_kv=cu,
+            max_seqlen_q=max(lengths),
+            max_seqlen_kv=max(lengths),
+        )
+        with torch.no_grad():
+            training_complete, _ = training(complete.unsqueeze(1), None, packed_seq_params=packed)
+            rollout.kv_cache = initial_cache()
+            whole = step(complete, lengths, [0] * 4, [1, 2, 3, 4])
+            exact(whole, training_complete[:, 0], name="mixed VIME/vLLM complete")
+            final = tuple(state.clone() for state in rollout.kv_cache)
+            rollout.kv_cache = initial_cache()
+            first = step(
+                torch.cat([value[:length] for value, length in zip(sequences, prompt_lengths)]),
+                prompt_lengths,
+                [0] * 4,
+                [1, 2, 3, 4],
+            )
+            pieces = [[part] for part in first.split(prompt_lengths)]
+            for position in range(16):
+                order = [3, 1, 0, 2] if position % 2 else [2, 0, 3, 1]
+                positions = [length + position for length in prompt_lengths]
+                outputs = step(
+                    torch.cat(
+                        [
+                            sequences[index][positions[index] : positions[index] + 1]
+                            for index in order
+                        ]
+                    ),
+                    [1] * 4,
+                    [prompt_lengths[index] + position for index in order],
+                    [index + 1 for index in order],
+                )
+                for index, value in zip(order, outputs.split(1)):
+                    pieces[index].append(value)
+            exact(
+                torch.cat([torch.cat(parts) for parts in pieces]),
+                whole,
+                name="sixteen decode steps",
+            )
+            for current, expected in zip(rollout.kv_cache, final):
+                exact(current, expected, name="mixed owned rollout cache")
+        checked.append("mixed-prefill-decode16-reorder")
+
+        hidden = torch.randn(
+            8, 1, 2048, generator=generator, device="cuda", dtype=torch.bfloat16
+        ).requires_grad_()
+        cu = torch.tensor([0, 8], device="cuda", dtype=torch.int32)
+        packed = PackedSeqParams(
+            qkv_format="thd", cu_seqlens_q=cu, cu_seqlens_kv=cu, max_seqlen_q=8, max_seqlen_kv=8
+        )
+        value, _ = training(hidden, None, packed_seq_params=packed)
+        value[4:].float().square().sum().backward()
+        if hidden.grad is None or not all(
+            (torch.isfinite(hidden.grad).all(), bool(hidden.grad[:4].abs().max() > 0))
+        ):
+            raise AssertionError("Actual VIME response loss lost finite nonzero prompt gradients")
+        grads = [
+            parameter.grad for parameter in training.parameters() if parameter.grad is not None
+        ]
+        if not grads or any(not torch.isfinite(grad).all() for grad in grads):
+            raise AssertionError("Actual VIME GDN parameter gradients must be finite")
+        from rl_engine.integrations.qwen3_next_binary import imported_provider_identity
+
+        result = {
+            "status": "passed",
+            "scope": "gdn_engine_adapter_component",
+            "rank": rank,
+            "provider_binary": imported_provider_identity(),
+            "cases": checked,
+            "prompt_gradient": "passed",
+            "hf_conv_roundtrip": "passed",
+            "full_model_l2": "not_evaluated",
+            "batch_invariance_capability_registered": False,
+            "conv_dim_first": is_conv_state_dim_first(),
+        }
+        output.mkdir(parents=True, exist_ok=True)
+        (output / f"rank-{rank}.json").write_text(json.dumps(result, indent=2) + "\n")
+        dist.barrier()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--checkpoint", type=Path, required=True)
+    parser.add_argument("--out", type=Path, required=True)
+    args = parser.parse_args()
+    run(args.checkpoint, args.out)
diff --git a/scripts/qwen3_next_chain_report.py b/scripts/qwen3_next_chain_report.py
new file mode 100644
index 000000000..df01bee54
--- /dev/null
+++ b/scripts/qwen3_next_chain_report.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Write the WS1 chain-gate report for a finished Qwen3-Next chain.
+
+    python scripts/qwen3_next_chain_report.py --last <train run dir> --out chain-report.json
+
+Follows ``previous`` from the given run back to the ``l2`` stage, reads every
+passed stage's L2 matrix (``baseline`` from ``l2``, ``update1``/``update2``
+from ``train1``/``train2``, ``reload`` from ``train``) and the hash-checked case
+artifacts it names, and writes ``rl_engine.kernels.gtest.qwen3_next_chain_report``'s
+report. Exits 1 when the chain did not pass.
+"""
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from rl_engine.kernels.gtest.qwen3_next_chain_report import build_report  # noqa: E402
+from rl_engine.kernels.gtest.tolerance import load_contract  # noqa: E402
+from rl_engine.testing.qwen3_next_l2 import (  # noqa: E402
+    checked_artifact_path,
+    load_tensor_artifact,
+)
+
+VERSION_OF_STAGE = {"l2": "baseline", "train1": "update1", "train2": "update2", "train": "reload"}
+
+
+def chain_runs(last):
+    runs, run = [], Path(last).resolve()
+    while run is not None:
+        manifest = json.loads((run / "run.json").read_text())
+        if manifest["stage"] in VERSION_OF_STAGE:
+            runs.append((manifest, run))
+        if manifest["stage"] == "l2":
+            break
+        run = Path(manifest["previous"]) if manifest.get("previous") else None
+    if not runs or runs[-1][0]["stage"] != "l2":
+        raise SystemExit("The chain does not lead back to an l2 run")
+    commits = {manifest["source_commit"] for manifest, _ in runs}
+    if len(commits) != 1:
+        raise SystemExit(f"The chain mixes source commits: {sorted(commits)}")
+    return list(reversed(runs))
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--last", type=Path, required=True, help="last passed stage run")
+    parser.add_argument("--out", type=Path, required=True)
+    args = parser.parse_args()
+    matrices, artifacts = {}, {}
+    gpu_name = None
+    for manifest, run in chain_runs(args.last):
+        result = json.loads((run / "result.json").read_text())
+        if result.get("status") != "passed":
+            raise SystemExit(f"{manifest['stage']} did not pass")
+        attempt = run / "attempts" / result["attempt"]
+        matrix = json.loads((run / result["evidence"]["l2_matrix"]["path"]).read_text())
+        version = VERSION_OF_STAGE[manifest["stage"]]
+        matrices[version] = matrix
+        artifacts[version] = {
+            case_id: {
+                role: load_tensor_artifact(checked_artifact_path(record, artifact_root=attempt))
+                for role, record in matrix["cases"][case_id]["artifacts"].items()
+            }
+            for case_id in matrix["case_ids"]
+        }
+        environment = attempt / "environment.json"
+        if gpu_name is None and environment.is_file():
+            gpu_name = json.loads(environment.read_text()).get("gpu_name")
+    identity = manifest["checkpoint_identity"]
+    report = build_report(
+        matrices,
+        artifacts,
+        contract=load_contract(),
+        measured_commit=manifest["source_commit"],
+        weight_source=f"Qwen/Qwen3-Next-80B-A3B-Instruct@{identity['revision']}",
+        weight_hash=identity["expected_content_hash"],
+        device="cuda",
+        gpu_name=gpu_name,
+        compute_capability=None,
+    )
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.out.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n")
+    print(
+        f"chain passed={report['passed']} first_drift={report['first_drift']} "
+        f"family={report['first_drift_family']} cells={len(report['cells'])}"
+    )
+    if not report["passed"]:
+        raise SystemExit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_checkpoint.py b/scripts/qwen3_next_checkpoint.py
new file mode 100644
index 000000000..2d3e9036a
--- /dev/null
+++ b/scripts/qwen3_next_checkpoint.py
@@ -0,0 +1,312 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Publish and recover complete all-rank checkpoints on the shared filesystem.
+
+Training owns serialization and barriers. This module never loads tensor files;
+all four ranks must finish model, optimizer, RNG and progress before publishing.
+"""
+
+import json
+import os
+import shutil
+from pathlib import Path
+
+from qwen3_next_contract import atomic_json
+from qwen3_next_runtime import file_digest
+
+ROLES = frozenset(("model", "optimizer", "rng", "progress"))
+
+
+def _sync_directory(path):
+    fd = os.open(path, os.O_RDONLY | os.O_DIRECTORY)
+    try:
+        os.fsync(fd)
+    finally:
+        os.close(fd)
+
+
+def _file(root, relative):
+    relative = Path(relative)
+    if relative.is_absolute() or ".." in relative.parts:
+        raise ValueError("Checkpoint file must be relative to its checkpoint directory")
+    path = root / relative
+    if path.is_symlink() or not path.resolve().is_relative_to(root.resolve()) or not path.is_file():
+        raise ValueError(f"Missing or external checkpoint file: {relative}")
+    return path
+
+
+def write_rank_manifest(staging, *, rank, world_size, identity, step, sample_position, files):
+    staging = Path(staging)
+    if world_size != 4 or not 0 <= rank < world_size or step < 0 or sample_position < 0:
+        raise ValueError("Checkpoint requires four ranks and nonnegative progress")
+    if staging.suffix != ".partial" or (staging / "COMPLETE.json").exists():
+        raise ValueError("Rank output requires an unpublished .partial checkpoint")
+    if set(files) != ROLES or any(not paths for paths in files.values()):
+        raise ValueError("Every rank must save model, optimizer, RNG and progress")
+    records = {}
+    seen = set()
+    for role, paths in files.items():
+        records[role] = []
+        for relative in paths:
+            if str(relative) in seen:
+                raise ValueError("A checkpoint file cannot fulfill multiple state roles")
+            seen.add(str(relative))
+            path = _file(staging, relative)
+            with path.open("rb") as stream:
+                os.fsync(stream.fileno())
+            records[role].append(
+                {
+                    "path": str(relative),
+                    "size": path.stat().st_size,
+                    "sha256": file_digest(path),
+                }
+            )
+    manifest = {
+        "schema_version": 1,
+        "rank": rank,
+        "world_size": world_size,
+        "identity": identity,
+        "step": step,
+        "sample_position": sample_position,
+        "files": records,
+    }
+    atomic_json(staging / f"rank-{rank}.manifest.json", manifest)
+    return manifest
+
+
+def _validate_ranks(root, *, world_size, identity, verify_files=True):
+    if world_size != 4:
+        raise ValueError("Checkpoint topology must use four ranks")
+    manifests = []
+    used_paths = set()
+    progress = None
+    for rank in range(world_size):
+        manifest = json.loads((root / f"rank-{rank}.manifest.json").read_text())
+        if (manifest.get("schema_version"), manifest.get("rank"), manifest.get("world_size")) != (
+            1,
+            rank,
+            world_size,
+        ):
+            raise ValueError("Checkpoint rank topology mismatch")
+        if manifest.get("identity") != identity:
+            raise ValueError("Checkpoint model/provider/environment identity mismatch")
+        current = (manifest.get("step"), manifest.get("sample_position"))
+        if any(
+            not isinstance(value, int) or isinstance(value, bool) or value < 0 for value in current
+        ):
+            raise ValueError("Invalid checkpoint progress")
+        if progress is not None and current != progress:
+            raise ValueError("Checkpoint ranks have different progress")
+        progress = current
+        files = manifest.get("files", {})
+        if set(files) != ROLES or any(not paths for paths in files.values()):
+            raise ValueError("Checkpoint omits required optimizer/RNG/model/progress state")
+        for records in files.values():
+            for record in records:
+                if record["path"] in used_paths:
+                    raise ValueError("Checkpoint ranks must own distinct state files")
+                used_paths.add(record["path"])
+                path = _file(root, record["path"])
+                if path.stat().st_size != record["size"]:
+                    raise ValueError(f"Checkpoint state size mismatch: {record['path']}")
+                if verify_files and file_digest(path) != record["sha256"]:
+                    raise ValueError(f"Checkpoint state checksum mismatch: {record['path']}")
+        manifests.append(manifest)
+    return manifests
+
+
+def publish_checkpoint(staging, destination, *, world_size, identity):
+    staging, destination = Path(staging), Path(destination)
+    if (
+        staging.suffix != ".partial"
+        or destination.suffix == ".partial"
+        or destination.name.startswith(".")
+    ):
+        raise ValueError("Publish from a .partial directory to a visible completed directory")
+    if staging.parent.resolve() != destination.parent.resolve() or destination.exists():
+        raise ValueError("Checkpoint publication needs a new sibling destination")
+    manifests = _validate_ranks(staging, world_size=world_size, identity=identity)
+    complete = {
+        "schema_version": 1,
+        "status": "complete",
+        "world_size": world_size,
+        "identity": identity,
+        "step": manifests[0]["step"],
+        "sample_position": manifests[0]["sample_position"],
+        "rank_manifests": {
+            str(rank): file_digest(staging / f"rank-{rank}.manifest.json")
+            for rank in range(world_size)
+        },
+    }
+    atomic_json(staging / "COMPLETE.json", complete)
+    # Sync nested directory entries as well as file contents before publication.
+    directories = sorted(
+        (path for path in staging.rglob("*") if path.is_dir()),
+        key=lambda path: len(path.parts),
+        reverse=True,
+    )
+    for directory in directories:
+        _sync_directory(directory)
+    _sync_directory(staging)
+    staging.rename(destination)
+    _sync_directory(destination.parent)
+    atomic_json(
+        destination.parent / "LATEST.json",
+        {
+            "path": destination.name,
+            "step": complete["step"],
+            "complete_sha256": file_digest(destination / "COMPLETE.json"),
+        },
+    )
+    return complete
+
+
+def validate_checkpoint(path, expected_identity, *, verify_files=True):
+    """Validate a published checkpoint's markers, manifests and files.
+
+    ``verify_files=False`` checks every state file's presence and size but not its
+    content digest; callers use it where the content is verified anyway when the
+    files are read (the native restore compares every loaded tensor against the
+    saved digests), so a 1.1 TB checkpoint is not rehashed before each phase.
+    """
+    path = Path(path)
+    if path.suffix == ".partial" or path.name.startswith("."):
+        raise ValueError("Unpublished checkpoint cannot be loaded")
+    complete = json.loads((path / "COMPLETE.json").read_text())
+    if complete.get("schema_version") != 1 or complete.get("status") != "complete":
+        raise ValueError("Checkpoint has no supported completion marker")
+    if complete.get("identity") != expected_identity:
+        raise ValueError("Checkpoint model/provider/environment identity mismatch")
+    manifests = _validate_ranks(
+        path,
+        world_size=complete["world_size"],
+        identity=expected_identity,
+        verify_files=verify_files,
+    )
+    if complete.get("rank_manifests") != {
+        str(rank): file_digest(path / f"rank-{rank}.manifest.json") for rank in range(4)
+    }:
+        raise ValueError("Checkpoint rank manifest checksum mismatch")
+    if (complete.get("step"), complete.get("sample_position")) != (
+        manifests[0]["step"],
+        manifests[0]["sample_position"],
+    ):
+        raise ValueError("Completion marker progress mismatch")
+    return complete
+
+
+def resume_interrupted_publications(root, expected_identity):
+    """Publish every fully written ``.partial`` checkpoint a killed job left unpublished.
+
+    A preempted job can die after all four ranks wrote their files and manifests
+    but before rank 0 validated and renamed the directory. Such a directory is
+    publishable as is: publication rehashes every file against the manifests,
+    so an incomplete or damaged one is left for the normal retirement path.
+    Returns journal recovery records.
+    """
+    root = Path(root)
+    records = []
+    for staging in sorted(root.glob("*.partial")):
+        if not staging.is_dir() or staging.is_symlink() or (staging / "COMPLETE.json").exists():
+            continue
+        marker = staging / "STAGING.json"
+        if not marker.is_file():
+            continue
+        metadata = json.loads(marker.read_text())
+        if metadata.get("identity") != expected_identity or metadata.get("status") != "staging":
+            continue
+        if not all((staging / f"rank-{rank}.manifest.json").is_file() for rank in range(4)):
+            continue
+        destination = staging.with_suffix("")
+        if destination.exists():
+            continue
+        try:
+            complete = publish_checkpoint(
+                staging, destination, world_size=4, identity=expected_identity
+            )
+        except (OSError, ValueError, KeyError) as exc:
+            records.append(
+                {
+                    "reason": "interrupted_publication_not_resumable",
+                    "staging": staging.name,
+                    "error": str(exc)[:300],
+                }
+            )
+            continue
+        records.append(
+            {
+                "reason": "interrupted_publication_resumed",
+                "checkpoint": destination.name,
+                "step": complete["step"],
+            }
+        )
+    return records
+
+
+def newest_complete_checkpoint(root, expected_identity, *, verify_files=True):
+    """Return a recovery record, retaining rejected and interrupted candidates."""
+    root = Path(root)
+    candidates, rejected, interrupted = [], [], []
+    for path in sorted(root.iterdir()):
+        if not path.is_dir():
+            continue
+        if path.suffix == ".partial" or path.name.startswith("."):
+            interrupted.append(path.name)
+            continue
+        try:
+            complete = validate_checkpoint(path, expected_identity, verify_files=verify_files)
+        except (OSError, ValueError, KeyError) as exc:
+            rejected.append({"path": path.name, "reason": str(exc)})
+        else:
+            candidates.append((complete["step"], path, complete))
+    if not candidates and rejected:
+        raise ValueError(f"No valid published checkpoint remains: {rejected}")
+    chosen = max(candidates, key=lambda item: (item[0], item[1].name)) if candidates else None
+    return {
+        "checkpoint": str(chosen[1]) if chosen else None,
+        "step": chosen[0] if chosen else None,
+        "sample_position": chosen[2]["sample_position"] if chosen else None,
+        "rejected": rejected,
+        "interrupted": interrupted,
+    }
+
+
+def require_checkpoint_disk_space(
+    root, *, checkpoint_bytes, export_bytes, retained_complete_bytes=0, headroom_bytes=16 * 1024**3
+):
+    """Budget last+next checkpoint and exports before serializing, without deletion.
+
+    checkpoint_bytes includes model, optimizer/master weights, RNG and progress.
+    retained_complete_bytes discounts only already retained complete checkpoint
+    bytes; unfinished directories are never counted as available or reusable.
+    """
+    if (
+        any(
+            type(value) is not int or value < 0
+            for value in (checkpoint_bytes, export_bytes, retained_complete_bytes, headroom_bytes)
+        )
+        or checkpoint_bytes == 0
+    ):
+        raise ValueError("Checkpoint disk estimates must be nonnegative integer byte counts")
+    retained = min(retained_complete_bytes, checkpoint_bytes)
+    required = 2 * checkpoint_bytes + export_bytes + headroom_bytes - retained
+    usage = shutil.disk_usage(root)
+    record = {
+        "scope": "checkpoint_disk_budget",
+        "checkpoint_bytes": checkpoint_bytes,
+        "last_plus_next_bytes": 2 * checkpoint_bytes,
+        "export_bytes": export_bytes,
+        "retained_complete_bytes": retained,
+        "headroom_bytes": headroom_bytes,
+        "required_free_bytes": required,
+        "actual_free_bytes": usage.free,
+        "status": "passed" if usage.free >= required else "insufficient_disk",
+    }
+    atomic_json(Path(root) / "disk-budget.json", record)
+    if usage.free < required:
+        raise RuntimeError(
+            f"insufficient_disk: need {required} free bytes for last+next checkpoint "
+            f"and exports, have {usage.free}"
+        )
+    return record
diff --git a/scripts/qwen3_next_contract.py b/scripts/qwen3_next_contract.py
new file mode 100644
index 000000000..88d55dd6c
--- /dev/null
+++ b/scripts/qwen3_next_contract.py
@@ -0,0 +1,371 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Versioned acceptance checks shared by the stage runners and the launcher."""
+
+import hashlib
+import json
+import re
+from pathlib import Path
+
+from qwen3_next_runtime import atomic_json, file_digest, identity_digest  # noqa: F401
+
+CONTRACT_VERSION = 4
+SCOPES = {
+    "components": "qwen3_next_components_only",
+    "diagnostics": "qwen3_next_operator_diagnostics",
+    "preflight": "four_gpu_infrastructure_and_operators_only",
+    "layers": "qwen3_next_layer_and_scheduler_gates",
+    "l2": "qwen3_next_full_model_l2",
+    "train1": "qwen3_next_training_update1",
+    "train2": "qwen3_next_training_update2",
+    "train": "qwen3_next_training_and_reload",
+}
+# Stages that evaluate the full model against updated weights (an L2 matrix each).
+TRAIN_STAGES = ("train1", "train2", "train")
+IDENTITY_FIELDS = (
+    "contract_version",
+    "source_tree",
+    "environment_sha256",
+    "cuda_identity",
+    "checkpoint_identity",
+    "provider_config",
+    "topology",
+    "operator_requirements",
+    "layer_requirements",
+)
+
+
+def checkpoint_identity(checkpoint):
+    checkpoint = Path(checkpoint)
+    metadata = {
+        str(path.relative_to(checkpoint)): file_digest(path)
+        for path in sorted(checkpoint.iterdir())
+        if path.is_file() and path.suffix != ".safetensors"
+    }
+    index = json.loads((checkpoint / "model.safetensors.index.json").read_text())
+    reference = json.loads(
+        (
+            Path(__file__).resolve().parents[1] / "rl_engine/testing/qwen3_next_norm_manifest.json"
+        ).read_text()
+    )["model_identity"]
+    snapshot = reference["weight_snapshot"]
+    if (
+        checkpoint.name != reference["revision"]
+        or metadata.get(snapshot["index_file"]) != snapshot["index_sha256"]
+    ):
+        raise ValueError("Checkpoint metadata does not match the official pinned revision")
+    expected_shards = {item["filename"]: item for item in snapshot["shards"]}
+    if set(index["weight_map"].values()) != expected_shards.keys():
+        raise ValueError("Checkpoint shard inventory differs from the official revision")
+    shards = {}
+    for filename, expected in sorted(expected_shards.items()):
+        stat = (checkpoint / filename).stat()
+        if stat.st_size != expected["size_bytes"]:
+            raise ValueError(f"Checkpoint shard size mismatch: {filename}")
+        shards[filename] = {
+            "size": stat.st_size,
+            "mtime_ns": stat.st_mtime_ns,
+            "ctime_ns": stat.st_ctime_ns,
+            "expected_sha256": expected["sha256"],
+        }
+    return {
+        "revision": checkpoint.name,
+        "metadata": metadata,
+        "shards": shards,
+        "expected_content_hash": snapshot["content_hash"],
+        "full_hash_verification": "required_before_model_load",
+    }
+
+
+def operator_requirements(root, suite):
+    registry = json.loads(
+        (Path(root) / "rl_engine/testing/qwen3_next_operator_cases.json").read_text()
+    )
+    if registry.get("schema_version") != 1:
+        raise ValueError("Unsupported mandatory operator case registry")
+    suites = registry["suites"]
+    cases = sorted(suites["norm"] + suites["core"] if suite == "all" else suites[suite])
+    if not cases or len(cases) != len(set(cases)):
+        raise ValueError("Mandatory operator case registry is empty or duplicated")
+    gates = (
+        sorted(
+            f"{op}-{gate}.json"
+            for op in ("qwen3_next_rms_norm", "rms_norm_gated")
+            for gate in ("forward", "gradient")
+        )
+        if suite in ("all", "norm")
+        else []
+    )
+    return {
+        "suite": suite,
+        "case_ids": cases,
+        "case_ids_sha256": hashlib.sha256(("\n".join(cases) + "\n").encode()).hexdigest(),
+        "norm_gates": gates,
+    }
+
+
+def latest_attempt(run_dir, scheduler_attempt=None):
+    values = [
+        int(path.name)
+        for path in (Path(run_dir) / "attempts").glob("*")
+        if path.is_dir() and path.name.isdigit()
+    ]
+    if scheduler_attempt is not None:
+        if not str(scheduler_attempt).isdigit():
+            raise ValueError("Invalid scheduler attempt identity")
+        values.append(int(scheduler_attempt))
+    return str(max(values)) if values else None
+
+
+def acceptance_identity(manifest):
+    return {field: manifest[field] for field in IDENTITY_FIELDS}
+
+
+def result_envelope(manifest, attempt, *, evidence):
+    return {
+        **acceptance_identity(manifest),
+        "source_commit": manifest["source_commit"],
+        "run_id": manifest["run_id"],
+        "stage": manifest["stage"],
+        "suite": manifest.get("suite", "all"),
+        "execution_id": manifest["execution_id"],
+        "attempt": str(attempt),
+        "status": "diagnostic_completed" if manifest["stage"] == "diagnostics" else "passed",
+        "phase": "complete",
+        "scope": SCOPES[manifest["stage"]],
+        "full_model_l2": "not_evaluated",
+        "evidence": evidence,
+    }
+
+
+def evidence_record(path, root):
+    return {"path": str(Path(path).relative_to(root)), "sha256": file_digest(path)}
+
+
+def validate_partial_training_evidence(manifest, root, evidence, *, attempt_root):
+    """train1/train2: the progress record, its last phase's worker export and L2 matrix agree.
+
+    A training phase's L2 runs on that phase's exported weights, so its matrix
+    identity is the run's model identity with the export as the checkpoint (the
+    pinned base recorded as ``base_checkpoint``), exactly as the stage built it.
+    """
+    from qwen3_next_l2 import model_identity
+    from qwen3_next_training import STAGE_LAST_PHASE, read_record, stage_phases
+
+    from rl_engine.testing.qwen3_next_l2 import validate_matrix_report
+
+    stage = manifest["stage"]
+    progress = json.loads((root / evidence["updates"]["path"]).read_text())
+    phases = list(stage_phases(stage))
+    last = STAGE_LAST_PHASE[stage]
+    if (
+        progress.get("status") != "passed"
+        or progress.get("scope") != SCOPES[stage]
+        or progress.get("identity") != model_identity(manifest)
+        or progress.get("phases") != phases
+        or any(phase not in progress.get("workers", {}) for phase in phases)
+    ):
+        raise ValueError("Training progress identity, scope or phases mismatch")
+    matrix_record = progress.get("l2_reports", {}).get(last, {})
+    if matrix_record.get("sha256") != evidence["l2_matrix"]["sha256"]:
+        raise ValueError("Training progress does not reference the accepted L2 matrix")
+    worker = read_record(progress["workers"][last])
+    export = worker.get("export") or {}
+    if (
+        export.get("weight_version") is None
+        or export.get("base_checkpoint") != manifest["checkpoint"]
+    ):
+        raise ValueError("Training phase export does not derive from the pinned base checkpoint")
+    report = json.loads((root / evidence["l2_matrix"]["path"]).read_text())
+    validate_matrix_report(
+        report,
+        expected_identity=model_identity({**manifest, **export}),
+        artifact_root=attempt_root,
+    )
+
+
+def validate_result(manifest, result, *, run_dir=None, attempt=None):
+    if manifest.get("contract_version") != CONTRACT_VERSION:
+        raise ValueError("Historical acceptance contract cannot authorize a new stage")
+    expected = {
+        **acceptance_identity(manifest),
+        "source_commit": manifest["source_commit"],
+        "run_id": manifest["run_id"],
+        "stage": manifest["stage"],
+        "suite": manifest.get("suite", "all"),
+        "status": "diagnostic_completed" if manifest["stage"] == "diagnostics" else "passed",
+        "phase": "complete",
+        "scope": SCOPES[manifest["stage"]],
+    }
+    for field, value in expected.items():
+        if result.get(field) != value:
+            raise ValueError(f"Acceptance identity mismatch: {field}")
+    if not re.fullmatch(r"[0-9a-f]{32}", str(manifest.get("execution_id", ""))):
+        raise ValueError("Run has no launcher execution identity")
+    if result.get("execution_id") != manifest["execution_id"]:
+        raise ValueError("Acceptance execution identity mismatch")
+    if not str(result.get("attempt", "")).isdigit():
+        raise ValueError("Acceptance has no attempt identity")
+    if run_dir is not None:
+        attempt = latest_attempt(run_dir, attempt)
+    if attempt is not None and result["attempt"] != str(attempt):
+        raise ValueError("Acceptance belongs to a stale attempt")
+    if manifest["stage"] in ("l2", *TRAIN_STAGES) and result.get("full_model_l2") != "passed":
+        raise ValueError("Full-model L2 evidence is required")
+    if (
+        manifest["stage"] not in ("l2", *TRAIN_STAGES)
+        and result.get("full_model_l2") != "not_evaluated"
+    ):
+        raise ValueError("Component and layer acceptance cannot claim full-model L2")
+    if manifest["stage"] == "train" and result.get("training_and_reload") != "passed":
+        raise ValueError("Two updates and reload evidence is required")
+    evidence = result.get("evidence", {})
+    required = {"operators", "environment"}
+    if manifest["stage"] == "preflight":
+        required |= {"device_guards", "tp4", "checkpoint", "rollout_smoke"}
+    if manifest["stage"] == "components" and manifest.get("suite") in ("norm", "all"):
+        required.add("device_guards")
+    if manifest["stage"] in ("l2", *TRAIN_STAGES):
+        required |= {"l2_matrix"}
+    if manifest["stage"] == "l2":
+        required.add("provider_binary")
+    if manifest["stage"] == "layers":
+        required |= {"layers", "checkpoint", "provider_binary"}
+    if manifest["stage"] == "train":
+        required |= {"updates", "reload"}
+    if manifest["stage"] in ("train1", "train2"):
+        required |= {"updates"}
+    if not required <= evidence.keys():
+        raise ValueError(f"Incomplete acceptance evidence: {sorted(required - evidence.keys())}")
+    if run_dir is not None:
+        root = Path(run_dir).resolve()
+        attempt_root = root / "attempts" / result["attempt"]
+        for record in evidence.values():
+            path = (root / record["path"]).resolve()
+            if not path.is_relative_to(attempt_root) or file_digest(path) != record["sha256"]:
+                raise ValueError("Acceptance evidence content/attempt mismatch")
+        operators = json.loads((root / evidence["operators"]["path"]).read_text())
+        if (
+            operators.get("status") != expected["status"]
+            or operators.get("git_commit") != manifest["source_commit"]
+        ):
+            raise ValueError("Operator evidence must match the completed source run")
+        requirement = manifest["operator_requirements"]
+        if manifest["stage"] == "diagnostics":
+            if (
+                operators.get("diagnostic_only") is not True
+                or operators.get("case_ids_sha256") != requirement["case_ids_sha256"]
+            ):
+                raise ValueError("Diagnostic evidence case identity mismatch")
+            return identity_digest(expected)
+        case_ids = operators.get("mandatory_case_ids", [])
+        if (
+            case_ids != requirement["case_ids"]
+            or operators.get("case_ids_sha256") != requirement["case_ids_sha256"]
+        ):
+            raise ValueError("Evidence differs from the pinned mandatory case registry")
+        gates = operators.get("norm_gates", {})
+        if sorted(gates) != requirement["norm_gates"] or any(
+            gate.get("passed") is not True for gate in gates.values()
+        ):
+            raise ValueError("Norm C3/C4 evidence is missing or failed")
+        expected_digest = hashlib.sha256(("\n".join(sorted(case_ids)) + "\n").encode()).hexdigest()
+        counts = operators.get("provider_tests", {})
+        if (
+            not case_ids
+            or len(case_ids) != len(set(case_ids))
+            or len(case_ids) != counts.get("tests")
+            or operators.get("case_ids_sha256") != expected_digest
+        ):
+            raise ValueError("Mandatory operator case identities do not match the evidence")
+        if not counts.get("tests") or any(
+            counts.get(key, 0) for key in ("skipped", "errors", "failures")
+        ):
+            raise ValueError("Required operator tests must pass without skips")
+        if manifest["stage"] in ("layers", "l2", *TRAIN_STAGES):
+            from qwen3_next_l2 import model_identity
+
+            if manifest["stage"] == "layers":
+                from qwen3_next_layer_contract import validate_layer_report
+
+                checkpoint = json.loads((root / evidence["checkpoint"]["path"]).read_text())
+                if any(
+                    checkpoint.get(key) != value
+                    for key, value in {
+                        "revision": manifest["checkpoint_identity"]["revision"],
+                        "content_hash": manifest["checkpoint_identity"]["expected_content_hash"],
+                        "shards_verified": len(manifest["checkpoint_identity"]["shards"]),
+                    }.items()
+                ):
+                    raise ValueError(
+                        "Layer checkpoint verification differs from the pinned snapshot"
+                    )
+                report = json.loads((root / evidence["layers"]["path"]).read_text())
+                validate_layer_report(
+                    report,
+                    manifest["layer_requirements"],
+                    model_identity(manifest),
+                    root=attempt_root,
+                )
+            else:
+                from rl_engine.testing.qwen3_next_l2 import validate_matrix_report
+
+                certificate = manifest.get("scheduler_profile_certificate", {})
+                certificate_path = Path(certificate.get("path", ""))
+                if not certificate_path.is_absolute() or file_digest(
+                    certificate_path
+                ) != certificate.get("sha256"):
+                    raise ValueError("Strict L2 requires its pinned native scheduler certificate")
+                certified_manifest = json.loads((certificate_path.parent / "run.json").read_text())
+                certified_result = json.loads(certificate_path.read_text())
+                if certified_manifest.get("stage") != "layers" or acceptance_identity(
+                    certified_manifest
+                ) != acceptance_identity(manifest):
+                    raise ValueError("Strict L2 certificate belongs to another provider identity")
+                validate_result(
+                    certified_manifest, certified_result, run_dir=certificate_path.parent
+                )
+                if manifest["stage"] == "train":
+                    from qwen3_next_training import validate_training_report
+
+                    if evidence["updates"]["sha256"] != evidence["reload"]["sha256"]:
+                        raise ValueError(
+                            "Update and reload evidence must name the same complete report"
+                        )
+                    report = json.loads((root / evidence["updates"]["path"]).read_text())
+                    sections = {
+                        "baseline",
+                        "update1",
+                        "update2",
+                        "reload",
+                        "continuous_step3",
+                        "resumed_step3",
+                        "checkpoint",
+                        "fault_injection",
+                    }
+                    if (
+                        report.get("identity") != model_identity(manifest)
+                        or set(report.get("required_sections", [])) != sections
+                    ):
+                        raise ValueError("Training report identity or required sections mismatch")
+                    if (
+                        report.get("l2_reports", {}).get("reload", {}).get("sha256")
+                        != evidence["l2_matrix"]["sha256"]
+                    ):
+                        raise ValueError(
+                            "Training reload does not reference the accepted full L2 matrix"
+                        )
+                    validate_training_report(report, verify_files=True)
+                elif manifest["stage"] in ("train1", "train2"):
+                    validate_partial_training_evidence(
+                        manifest, root, evidence, attempt_root=attempt_root
+                    )
+                else:
+                    report = json.loads((root / evidence["l2_matrix"]["path"]).read_text())
+                    validate_matrix_report(
+                        report,
+                        expected_identity=model_identity(manifest),
+                        artifact_root=attempt_root,
+                    )
+    return identity_digest(expected)
diff --git a/scripts/qwen3_next_gate_common.py b/scripts/qwen3_next_gate_common.py
index 00642c8de..8280e2d6c 100644
--- a/scripts/qwen3_next_gate_common.py
+++ b/scripts/qwen3_next_gate_common.py
@@ -117,7 +117,17 @@ def run_gate(description, scope, gate):
         args.output.mkdir(parents=True, exist_ok=True)
         path = args.output / f"rank-{rank}.json"
         partial = path.with_suffix(".json.partial")
-        payload = {"status": "passed", "scope": scope, "rank": rank, **provenance(), **result}
+        from rl_engine.integrations.qwen3_next_binary import imported_provider_identity
+
+        payload = {
+            "status": "passed",
+            "scope": scope,
+            "full_model_l2": "not_evaluated",
+            "rank": rank,
+            "provider_binary": imported_provider_identity(),
+            **provenance(),
+            **result,
+        }
         partial.write_text(json.dumps(payload, indent=2) + "\n")
         partial.replace(path)
         dist.barrier()
diff --git a/scripts/qwen3_next_l2.py b/scripts/qwen3_next_l2.py
new file mode 100644
index 000000000..b0a191c45
--- /dev/null
+++ b/scripts/qwen3_next_l2.py
@@ -0,0 +1,742 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Independent native rollout/recompute processes; no numerical fallback."""
+
+import argparse
+import importlib
+import json
+import os
+import shutil
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from qwen3_next_contract import (  # noqa: E402
+    atomic_json,
+    checkpoint_identity,
+    evidence_record,
+    result_envelope,
+    validate_result,
+)
+from qwen3_next_runtime import (  # noqa: E402
+    failure_fields,
+    file_digest,
+    memory_limits,
+    parse_memory_request,
+    subprocess_failure,
+    verify_checkpoint,
+    verify_environment,
+)
+
+from rl_engine.testing.qwen3_next_binary import (  # noqa: E402
+    archive_provider_binary,
+    validate_binary_record,
+    verify_imported_provider,
+)
+from rl_engine.testing.qwen3_next_l2 import (  # noqa: E402
+    NATIVE_WORKER_MODULE,
+    PROMPT_LENGTHS,
+    L2Case,
+    case_matrix,
+    compare_artifacts,
+    compare_batch_prefill,
+    first_divergent_layer,
+    first_divergent_node,
+    json_digest,
+    load_tensor_artifact,
+    prompt_fixture,
+    save_tensor_artifact,
+    validate_debug_capture,
+    validate_fixture,
+    validate_matrix_report,
+    validate_model_artifact,
+    worker_request,
+)
+
+
+def check_ready():
+    try:
+        module = importlib.import_module(NATIVE_WORKER_MODULE)
+    except ModuleNotFoundError as exc:
+        raise RuntimeError(f"native_workers_not_ready: {exc}") from exc
+    required = {
+        "rollout": True,
+        "recompute": True,
+        "debug_layers": True,
+        "architecture_layers": 48,
+        "tp": 4,
+        "batched_cases": True,
+    }
+    actual = module.capabilities()
+    if any(actual.get(key) != value for key, value in required.items()):
+        raise RuntimeError(f"native_workers_not_ready: required {required}, received {actual}")
+    if any(
+        not callable(getattr(module, name, None))
+        for name in (
+            "run_rollout_case",
+            "run_recompute_case",
+            "run_rollout_cases",
+            "run_recompute_cases",
+        )
+    ):
+        raise RuntimeError("native_workers_not_ready: actual model worker entrypoints missing")
+    return module, actual
+
+
+def model_identity(manifest):
+    identity = {
+        **(
+            {"scheduler_profile_certificate": manifest["scheduler_profile_certificate"]}
+            if "scheduler_profile_certificate" in manifest
+            else {}
+        ),
+        "source_commit": manifest["source_commit"],
+        "source_tree": manifest["source_tree"],
+        "environment_sha256": manifest["environment_sha256"],
+        "checkpoint": manifest["checkpoint"],
+        "weight_version": manifest.get("weight_version", "baseline"),
+        "checkpoint_identity": manifest["checkpoint_identity"],
+        "provider_config": manifest["provider_config"],
+        "topology": manifest["topology"],
+        "architecture_layers": 48,
+        "weights_dtype": "bfloat16",
+        "recurrent_state_dtype": "float32",
+    }
+    if manifest.get("weight_version", "baseline") != "baseline":
+        identity.update(
+            base_checkpoint=manifest["base_checkpoint"],
+            base_checkpoint_identity=manifest["base_checkpoint_identity"],
+        )
+    from rl_engine.testing.qwen3_next_l2 import validate_weight_identity
+
+    validate_weight_identity(identity)
+    return identity
+
+
+def verify_before_model_load(manifest, output):
+    if manifest.get("weight_version", "baseline") != "baseline":
+        verified = verify_updated_weights(manifest)
+    else:
+        if checkpoint_identity(manifest["checkpoint"]) != manifest["checkpoint_identity"]:
+            raise RuntimeError("checkpoint_mismatch: official weight snapshot changed")
+        reference = json.loads(
+            (ROOT / "rl_engine/testing/qwen3_next_norm_manifest.json").read_text()
+        )["model_identity"]
+        verified = verify_checkpoint(Path(manifest["checkpoint"]), reference)
+    atomic_json(output / "weight-verification.json", verified)
+    return verified
+
+
+def verify_updated_weights(manifest):
+    from qwen3_next_training_export import validate_training_export
+
+    verified = validate_training_export(manifest["checkpoint"], manifest["checkpoint_identity"])
+    expected = {
+        "weight_version": manifest["weight_version"],
+        "base_checkpoint": manifest["base_checkpoint"],
+        "base_checkpoint_identity": manifest["base_checkpoint_identity"],
+    }
+    if any(verified.get(key) != value for key, value in expected.items()):
+        raise RuntimeError("checkpoint_mismatch: updated weights/base/version provenance differs")
+    for key in (
+        "source_commit",
+        "source_tree",
+        "environment_sha256",
+        "provider_config",
+        "topology",
+    ):
+        if verified["training_identity"].get(key) != manifest[key]:
+            raise RuntimeError(f"checkpoint_mismatch: updated weights changed {key}")
+    return verified
+
+
+def run_one(module, role, request, output):
+    output.parent.mkdir(parents=True, exist_ok=True)
+    atomic_json(output.with_suffix(".request.json"), request)
+    getattr(module, f"run_{role}_case")(request, output)
+    if not output.is_file():
+        raise RuntimeError("Native worker did not produce a full-vocabulary tensor artifact")
+    artifact = load_tensor_artifact(output)
+    validate_model_artifact(artifact, request)
+    validate_debug_capture(artifact, request)
+    return artifact
+
+
+def worker_batch(
+    manifest,
+    output,
+    role,
+    fixture_path,
+    *,
+    debug_request=None,
+    native_worker=None,
+    provider_binary=None,
+):
+    if manifest.get("weight_version", "baseline") != "baseline" and not fixture_path.is_file():
+        raise RuntimeError("Updated weights must reuse the already frozen baseline input fixture")
+    module, capabilities = check_ready() if native_worker is None else native_worker
+    verify_environment(manifest["environment"], require_venv=True)
+    output.mkdir(parents=True, exist_ok=debug_request is not None)
+    verify_before_model_load(manifest, output)
+    identity = model_identity(manifest)
+    actual_binary = None
+    if provider_binary is not None:
+        actual_binary = verify_imported_provider(
+            provider_binary, source_tree=manifest["source_tree"]
+        )
+    elif manifest["provider_config"].get("profile") == "qwen3_next_tp4_shared_eager_v1":
+        raise RuntimeError("Native worker requires the stage's pinned CUDA binary evidence")
+    if debug_request is not None:
+        request = json.loads(debug_request.read_text())
+        if request["identity"] != identity or request["role"] != role:
+            raise ValueError("Debug request identity mismatch")
+        artifact = run_one(module, role, request, output / "debug.pt")
+        if not artifact.get("debug_tensors"):
+            raise RuntimeError("First divergence rerun did not save layer/recurrent-state tensors")
+        return
+    fixture = None
+    if fixture_path.is_file():
+        fixture = load_tensor_artifact(fixture_path)
+        validate_fixture(fixture)
+    elif role != "rollout":
+        raise RuntimeError("Recompute requires the already frozen baseline token fixture")
+    records, pending = {}, []
+
+    def cases():
+        nonlocal fixture
+        if fixture is None:
+            from transformers import AutoTokenizer
+
+            from rl_engine.testing.qwen3_next_l2 import freeze_responses
+
+            tokenizer = AutoTokenizer.from_pretrained(
+                manifest["checkpoint"], local_files_only=True, trust_remote_code=False
+            )
+            prompts = prompt_fixture(tokenizer, manifest["checkpoint_identity"]["metadata"])
+            baseline, baseline_records = {}, {}
+            for length in PROMPT_LENGTHS:
+                case = L2Case(length, 4, "full")
+                request = worker_request(case, prompts, role=role, identity=identity, generate=True)
+                path = output / "baseline" / f"{case.case_id}.pt"
+                path.parent.mkdir(parents=True, exist_ok=True)
+                atomic_json(path.with_suffix(".request.json"), request)
+                atomic_json(
+                    output / "active-case.json",
+                    {
+                        "case_id": case.case_id,
+                        "phase": "baseline_generation",
+                        "request": str(path.with_suffix(".request.json")),
+                    },
+                )
+                yield request, path
+                artifact = load_tensor_artifact(path)
+                validate_model_artifact(artifact, request)
+                if actual_binary is not None and artifact.get("provider_binary") != actual_binary:
+                    raise ValueError("Baseline generation used a different CUDA provider")
+                baseline[str(length)] = artifact
+                baseline_records[str(length)] = {"path": str(path), "sha256": file_digest(path)}
+            fixture = freeze_responses(
+                prompts,
+                baseline,
+                baseline_identity=json_digest(identity),
+                baseline_records=baseline_records,
+            )
+            validate_fixture(fixture)
+            save_tensor_artifact(fixture_path, fixture)
+        tokenizer_identity = manifest.get(
+            "base_checkpoint_identity", manifest["checkpoint_identity"]
+        )
+        if fixture["prompt_identity"]["tokenizer"] != tokenizer_identity["metadata"]:
+            raise RuntimeError("Frozen input tokenizer identity mismatch")
+        for case in case_matrix():
+            request = worker_request(case, fixture, role=role, identity=identity)
+            path = output / f"{case.case_id}.pt"
+            atomic_json(path.with_suffix(".request.json"), request)
+            atomic_json(
+                output / "active-case.json",
+                {
+                    "case_id": case.case_id,
+                    "phase": (
+                        "preparing_recompute_requests" if role == "recompute" else "frozen_inputs"
+                    ),
+                    "request": str(path.with_suffix(".request.json")),
+                },
+            )
+            # Recompute materializes this iterator before one four-rank model
+            # lifetime. Its artifacts therefore exist only after the batch returns.
+            # Rollout's baseline loop above stays lazy to freeze generated tokens.
+            pending.append((request, path))
+            yield request, path
+
+    getattr(module, f"run_{role}_cases")(cases())
+    if [request["case_id"] for request, _ in pending] != [case.case_id for case in case_matrix()]:
+        raise RuntimeError("Native batch worker did not consume every required case")
+    for request, path in pending:
+        artifact = load_tensor_artifact(path)
+        validate_model_artifact(artifact, request)
+        if actual_binary is not None and artifact.get("provider_binary") != actual_binary:
+            raise ValueError("Native rank traces used a different CUDA provider")
+        records[request["case_id"]] = {
+            "path": str(path),
+            "sha256": file_digest(path),
+            "request_sha256": request["request_sha256"],
+        }
+    if manifest.get("weight_version", "baseline") != "baseline":
+        verify_updated_weights(manifest)
+    elif checkpoint_identity(manifest["checkpoint"]) != manifest["checkpoint_identity"]:
+        raise RuntimeError("checkpoint_mismatch: weights changed during model execution")
+    if provider_binary is not None:
+        verify_imported_provider(provider_binary, source_tree=manifest["source_tree"])
+    atomic_json(
+        output / "worker-result.json",
+        {
+            "status": "passed",
+            "role": role,
+            "pid": os.getpid(),
+            "identity": identity,
+            "capabilities": capabilities,
+            "fixture_sha256": fixture["content_sha256"],
+            "cases": records,
+            "provider_binary": actual_binary,
+        },
+    )
+
+
+def run_process(command, *, log_path, timeout=4 * 3600, cleanup_grace_seconds=10):
+    with Path(log_path).open("w") as log:
+        child = subprocess.Popen(
+            command, stdout=log, stderr=subprocess.STDOUT, start_new_session=True
+        )
+        try:
+            code = child.wait(timeout=timeout)
+        except BaseException:
+            from qwen3_next_runtime import wait_owned_child
+
+            # The leader may exit on TERM while workers in its group ignore it.
+            # Finish the bounded group cleanup even after the leader has exited.
+            wait_owned_child(child, lambda: True, grace_seconds=cleanup_grace_seconds)
+            raise
+    if code:
+        raise subprocess_failure(code, log_path)
+    return {"pid": child.pid, "exit_code": code, "log": str(log_path)}
+
+
+def gpu_probe(output):
+    import torch
+
+    if torch.cuda.device_count() != 4 or any(
+        "B200" not in torch.cuda.get_device_name(index) for index in range(4)
+    ):
+        raise RuntimeError("topology_mismatch: four allocated B200 GPUs required")
+    atomic_json(
+        output, {"uuids": [str(torch.cuda.get_device_properties(index).uuid) for index in range(4)]}
+    )
+
+
+def wait_gpu_release(uuids, *, timeout=30):
+    wanted = {uuid.lower().removeprefix("gpu-") for uuid in uuids}
+    deadline = time.monotonic() + timeout
+    while True:
+        output = subprocess.check_output(
+            [
+                "nvidia-smi",
+                "--query-compute-apps=pid,gpu_uuid,used_gpu_memory",
+                "--format=csv,noheader,nounits",
+            ],
+            text=True,
+            timeout=10,
+        )
+        remaining = [
+            line
+            for line in output.splitlines()
+            if len(line.split(",")) == 3
+            and line.split(",")[1].strip().lower().removeprefix("gpu-") in wanted
+        ]
+        if not remaining:
+            return
+        if time.monotonic() >= deadline:
+            raise RuntimeError(f"gpu_memory_not_released: {remaining}")
+        time.sleep(0.25)
+
+
+def run_matrix(manifest_path, output, fixture_path):
+    """Return evidence after two independent processes; never mark training complete."""
+    manifest = json.loads(Path(manifest_path).read_text())
+    output = Path(output)
+    output.mkdir(parents=True, exist_ok=True)
+    check_ready()
+    verify_environment(manifest["environment"], require_venv=True)
+    memory = memory_limits()
+    requested = parse_memory_request(manifest["memory"])
+    if (
+        memory["effective_bytes"] is None
+        or memory["effective_bytes"] < requested
+        or memory["available_bytes"] is None
+        or memory["available_bytes"] < requested
+    ):
+        raise RuntimeError("insufficient_memory: L2 allocation cannot satisfy its pinned budget")
+    prebuilt = manifest.get("prebuilt_provider_binary")
+    if prebuilt is None:
+        run_process(
+            [manifest["python"], str(ROOT / "setup.py"), "build_ext", "--inplace"],
+            log_path=output / "build.log",
+            timeout=1800,
+        )
+    else:
+        expected = validate_binary_record(prebuilt, source_tree=manifest["source_tree"])
+        path = Path(expected["path"])
+        if path.parent != ROOT / "rl_engine" or file_digest(path) != expected["sha256"]:
+            raise ValueError("Training's prebuilt CUDA provider changed before L2")
+    provider_binary = archive_provider_binary(ROOT, output, source_tree=manifest["source_tree"])
+    probe_path = output / "gpu-probe.json"
+    run_process(
+        [manifest["python"], str(Path(__file__)), "--gpu-probe", "--output", str(probe_path)],
+        log_path=output / "gpu-probe.log",
+        timeout=180,
+    )
+    uuids = json.loads(probe_path.read_text())["uuids"]
+    wait_gpu_release(uuids)
+    atomic_json(
+        output / "environment.json",
+        {
+            "environment": manifest["environment"],
+            "memory": memory,
+            "gpu_uuids": uuids,
+            "execution_gpus": 4,
+        },
+    )
+    if manifest.get("input_fixture"):
+        if (
+            not manifest.get("input_fixture_sha256")
+            or file_digest(fixture_path) != manifest["input_fixture_sha256"]
+        ):
+            raise RuntimeError(
+                "input_fixture_mismatch: external baseline fixture must be pinned by file SHA256"
+            )
+    processes = {}
+    for role in ("rollout", "recompute"):
+        command = [
+            manifest["python"],
+            str(Path(__file__)),
+            "--worker",
+            role,
+            "--manifest",
+            str(manifest_path),
+            "--output",
+            str(output / role),
+            "--fixture",
+            str(fixture_path),
+            "--provider-binary",
+            json.dumps(provider_binary),
+        ]
+        processes[role] = run_process(command, log_path=output / f"{role}.log")
+        wait_gpu_release(uuids)
+        processes[role]["gpu_release_verified"] = True
+        worker_result = json.loads((output / role / "worker-result.json").read_text())
+        if (
+            worker_result.get("pid") != processes[role]["pid"]
+            or worker_result.get("status") != "passed"
+            or worker_result.get("identity") != model_identity(manifest)
+            or sorted(worker_result.get("cases", {}))
+            != sorted(case.case_id for case in case_matrix())
+        ):
+            raise RuntimeError("Independent native worker did not finish the required matrix")
+        atomic_json(output / "processes.json", processes)
+    fixture = load_tensor_artifact(fixture_path)
+    validate_fixture(fixture)
+    records, first_failure = {}, None
+    baseline_outputs = {}
+    for length, record in fixture["baseline_artifacts"].items():
+        if file_digest(record["path"]) != record["sha256"]:
+            raise RuntimeError("Baseline generation artifact was changed")
+        baseline_outputs[length] = load_tensor_artifact(record["path"])
+        original_identity = baseline_outputs[length]["metadata"]["identity"]
+        if json_digest(original_identity) != fixture["baseline_identity"]:
+            raise RuntimeError("Baseline generation provenance was changed")
+        baseline_request = worker_request(
+            L2Case(int(length), 4, "full"),
+            fixture,
+            role="rollout",
+            identity=original_identity,
+            generate=True,
+        )
+        validate_model_artifact(baseline_outputs[length], baseline_request)
+    identity = model_identity(manifest)
+    for case in case_matrix():
+        artifacts = {}
+        paths = {}
+        for role in ("rollout", "recompute"):
+            path = output / role / f"{case.case_id}.pt"
+            artifact = load_tensor_artifact(path)
+            validate_model_artifact(
+                artifact, worker_request(case, fixture, role=role, identity=identity)
+            )
+            artifacts[role], paths[role] = artifact, str(path)
+        paired = compare_artifacts(artifacts["rollout"], artifacts["recompute"])
+        invariance = {}
+        for role in ("rollout", "recompute"):
+            reference = load_tensor_artifact(
+                output / role / f"{L2Case(case.prompt_length, 4, 'full').case_id}.pt"
+            )
+            invariance[role] = compare_batch_prefill(reference, artifacts[role])
+        baseline_checks = {}
+        if identity.get("weight_version") == "baseline":
+            baseline_checks = {
+                role: compare_batch_prefill(baseline_outputs[str(case.prompt_length)], artifact)
+                for role, artifact in artifacts.items()
+            }
+        passed = (
+            paired["passed"]
+            and all(all(checks.values()) for checks in invariance.values())
+            and all(all(checks.values()) for checks in baseline_checks.values())
+        )
+        records[case.case_id] = {
+            **paired,
+            "passed": passed,
+            "batch_prefill": invariance,
+            "baseline_generation": baseline_checks,
+            "artifacts": {
+                role: {"path": path, "sha256": file_digest(path)} for role, path in paths.items()
+            },
+        }
+        if not passed and first_failure is None:
+            if not paired["passed"]:
+                first_failure = {
+                    "case": case,
+                    "paired": paired,
+                    "contexts": [("rollout", case, False), ("recompute", case, False)],
+                }
+            elif baseline_checks and not all(
+                all(checks.values()) for checks in baseline_checks.values()
+            ):
+                failed_role = next(
+                    role
+                    for role in ("rollout", "recompute")
+                    if not all(baseline_checks[role].values())
+                )
+                reference_case = L2Case(case.prompt_length, 4, "full")
+                reference = baseline_outputs[str(case.prompt_length)]
+                first_failure = {
+                    "case": case,
+                    "paired": {
+                        **paired,
+                        "first_divergent_layer": first_divergent_layer(
+                            reference, artifacts[failed_role]
+                        ),
+                        "first_divergent_node": first_divergent_node(
+                            reference, artifacts[failed_role]
+                        ),
+                    },
+                    "contexts": [("rollout", reference_case, True), (failed_role, case, False)],
+                }
+            else:
+                failed_role = next(
+                    role for role in ("rollout", "recompute") if not all(invariance[role].values())
+                )
+                reference_case = L2Case(case.prompt_length, 4, "full")
+                reference = load_tensor_artifact(
+                    output / failed_role / f"{reference_case.case_id}.pt"
+                )
+                first_failure = {
+                    "case": case,
+                    "paired": {
+                        **paired,
+                        "first_divergent_layer": first_divergent_layer(
+                            reference, artifacts[failed_role]
+                        ),
+                        "first_divergent_node": first_divergent_node(
+                            reference, artifacts[failed_role]
+                        ),
+                    },
+                    "contexts": [(failed_role, reference_case, False), (failed_role, case, False)],
+                }
+    report = {
+        "schema_version": 1,
+        "status": "passed" if first_failure is None else "failed",
+        "scope": "qwen3_next_full_model_l2",
+        "identity": identity,
+        "fixture": {
+            "path": str(fixture_path),
+            "sha256": file_digest(fixture_path),
+            "content_sha256": fixture["content_sha256"],
+        },
+        "case_ids": [case.case_id for case in case_matrix()],
+        "cases": records,
+        "processes": processes,
+        "worker_results": {
+            role: {
+                "path": str(output / role / "worker-result.json"),
+                "sha256": file_digest(output / role / "worker-result.json"),
+            }
+            for role in ("rollout", "recompute")
+        },
+        "raw_bit_comparison": True,
+        "logits_dtype": "bfloat16",
+        "logprobs_dtype": "float32",
+        "response_length": 16,
+        "vocab_size": 151936,
+        "provider_binary": provider_binary,
+    }
+    atomic_json(output / "l2-matrix.json", report)
+    if first_failure:
+        case = first_failure["case"]
+        layer = first_failure["paired"]["first_divergent_layer"]
+        capture = {
+            "case_id": case.case_id,
+            "first_divergent_layer": layer,
+            "first_divergent_node": first_failure["paired"].get("first_divergent_node"),
+            "first_divergent_component": "lm_head_or_logprob" if layer is None else "decoder_layer",
+            "first_difference": first_failure["paired"]["first_difference"],
+            "captures": {},
+        }
+        for label, (role, context_case, generate) in zip(
+            ("reference", "candidate"), first_failure["contexts"]
+        ):
+            directory = output / "first-divergence" / label
+            directory.mkdir(parents=True, exist_ok=True)
+            request = worker_request(
+                context_case,
+                fixture,
+                role=role,
+                identity=identity,
+                trace_layers=[47] if layer is None else [layer],
+                generate=generate,
+            )
+            request_path = directory / "request.json"
+            atomic_json(request_path, request)
+            try:
+                command = [
+                    manifest["python"],
+                    str(Path(__file__)),
+                    "--worker",
+                    role,
+                    "--manifest",
+                    str(manifest_path),
+                    "--output",
+                    str(directory),
+                    "--fixture",
+                    str(fixture_path),
+                    "--debug-request",
+                    str(request_path),
+                    "--provider-binary",
+                    json.dumps(provider_binary),
+                ]
+                run_process(command, log_path=directory / "worker.log")
+                wait_gpu_release(uuids)
+            except (RuntimeError, subprocess.SubprocessError) as exc:
+                capture["captures"][label] = {"status": "failed", "error": str(exc)}
+                break
+            capture["captures"][label] = {
+                "status": "saved",
+                "role": role,
+                "case_id": context_case.case_id,
+                "path": str(directory / "debug.pt"),
+                "sha256": file_digest(directory / "debug.pt"),
+            }
+        atomic_json(output / "first-divergence.json", capture)
+    if report["status"] == "passed":
+        validate_matrix_report(report, expected_identity=identity, artifact_root=output)
+    return report
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--manifest", type=Path)
+    parser.add_argument("--output", type=Path)
+    parser.add_argument("--fixture", type=Path)
+    parser.add_argument("--worker", choices=("rollout", "recompute"))
+    parser.add_argument("--debug-request", type=Path)
+    parser.add_argument("--provider-binary", type=json.loads)
+    parser.add_argument("--check-ready", action="store_true")
+    parser.add_argument("--gpu-probe", action="store_true")
+    args = parser.parse_args()
+    if args.check_ready:
+        try:
+            _, capabilities = check_ready()
+        except (RuntimeError, AttributeError) as exc:
+            print(json.dumps({"status": "not_ready", "error": str(exc)}))
+            return 2
+        print(json.dumps({"status": "ready", "capabilities": capabilities}))
+        return 0
+    if args.gpu_probe:
+        gpu_probe(args.output)
+        return 0
+    if args.manifest is None or args.output is None:
+        parser.error("--manifest and --output are required")
+    manifest = json.loads(args.manifest.read_text())
+    fixture_path = args.fixture or Path(
+        manifest.get("input_fixture", args.manifest.parent / "inputs/frozen.pt")
+    )
+    if args.worker:
+        worker_batch(
+            manifest,
+            args.output,
+            args.worker,
+            fixture_path,
+            debug_request=args.debug_request,
+            provider_binary=args.provider_binary,
+        )
+        return 0
+    args.output.mkdir(parents=True, exist_ok=True)
+    try:
+        if manifest["stage"] != "l2":
+            raise RuntimeError("Standalone L2 runner cannot close training acceptance")
+        from qwen3_next_runtime import require_predecessor
+
+        previous = Path(manifest["previous"])
+        require_predecessor("l2", previous, Path(manifest["checkpoint"]), manifest)
+        predecessor = json.loads((previous / "run.json").read_text())
+        previous_result = json.loads((previous / "result.json").read_text())
+        validate_result(predecessor, previous_result, run_dir=previous)
+        report = run_matrix(args.manifest, args.output, fixture_path)
+        if report["status"] != "passed":
+            raise RuntimeError("Full-model raw-bit L2 failed; first divergence artifacts retained")
+        prior_operators = previous / previous_result["evidence"]["operators"]["path"]
+        shutil.copyfile(prior_operators, args.output / "operators.json")
+        atomic_json(
+            args.output / "predecessor-evidence.json",
+            {
+                "run_id": predecessor["run_id"],
+                "execution_id": predecessor["execution_id"],
+                "result_sha256": file_digest(previous / "result.json"),
+            },
+        )
+        evidence = {
+            name: evidence_record(args.output / filename, args.manifest.parent)
+            for name, filename in {
+                "operators": "operators.json",
+                "environment": "environment.json",
+                "l2_matrix": "l2-matrix.json",
+                "predecessor": "predecessor-evidence.json",
+                "provider_binary": "provider-binary.json",
+            }.items()
+        }
+        result = result_envelope(manifest, os.environ["QWEN3_NEXT_ATTEMPT"], evidence=evidence)
+        result["full_model_l2"] = "passed"
+        validate_result(
+            manifest, result, run_dir=args.manifest.parent, attempt=os.environ["QWEN3_NEXT_ATTEMPT"]
+        )
+        atomic_json(args.output / "result.json", result)
+    except Exception as exc:
+        atomic_json(
+            args.output / "result.json",
+            {
+                "run_id": manifest["run_id"],
+                "attempt": os.environ["QWEN3_NEXT_ATTEMPT"],
+                "status": "failed",
+                "full_model_l2": "not_passed",
+                **failure_fields(exc),
+            },
+        )
+        raise
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/qwen3_next_layer_contract.py b/scripts/qwen3_next_layer_contract.py
new file mode 100644
index 000000000..685e0117e
--- /dev/null
+++ b/scripts/qwen3_next_layer_contract.py
@@ -0,0 +1,123 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Pinned layer component inventory and original tensor scheduler acceptance."""
+
+import hashlib
+import json
+from pathlib import Path
+
+from qwen3_next_runtime import file_digest, identity_digest
+
+
+def layer_requirements(root):
+    root = Path(root)
+    path = root / "rl_engine/testing/qwen3_next_layer_cases.json"
+    registry = json.loads(path.read_text())
+    if registry.get("schema_version") != 1 or set(registry.get("gates", {})) != {
+        "gdn",
+        "attention",
+        "moe",
+        "adapters",
+        "heads",
+    }:
+        raise ValueError("Incomplete layer gate registry")
+    for gate in registry["gates"].values():
+        gate["script_sha256"] = file_digest(root / gate["script"])
+        for cases in gate["groups"].values():
+            if not cases or len(cases) != len(set(cases)):
+                raise ValueError("Mandatory layer case list is empty or duplicated")
+    cases = sorted(
+        f"{name}/{group}/{case}"
+        for name, gate in registry["gates"].items()
+        for group, values in gate["groups"].items()
+        for case in values
+    )
+    return {
+        **registry,
+        "registry_sha256": file_digest(path),
+        "case_ids_per_rank": cases,
+        "case_ids_sha256": hashlib.sha256(("\n".join(cases) + "\n").encode()).hexdigest(),
+    }
+
+
+def validate_rank_result(row, requirement, rank):
+    if any(
+        row.get(name) != value
+        for name, value in {
+            "status": "passed",
+            "rank": rank,
+            "scope": requirement["scope"],
+            "full_model_l2": "not_evaluated",
+            **requirement.get("fields", {}),
+        }.items()
+    ):
+        raise ValueError("Layer rank evidence has a wrong identity, scope or status")
+    if any(row.get(name, 0) for name in ("skipped", "failures", "errors")):
+        raise ValueError("Layer rank evidence includes skipped or failed checks")
+    for field, expected in requirement["groups"].items():
+        value = row
+        for key in field.split("."):
+            value = value.get(key, {}) if isinstance(value, dict) else None
+        if not isinstance(value, list) or sorted(value) != sorted(expected):
+            raise ValueError(f"Layer rank evidence changed mandatory cases: {field}")
+
+
+def checked_record(record, *, root=None):
+    if not isinstance(record, dict) or not {"path", "sha256"} <= record.keys():
+        raise ValueError("Missing artifact path/content digest")
+    path = Path(record["path"])
+    if not path.is_absolute():
+        if root is None:
+            raise ValueError("Artifact path must be absolute without an evidence root")
+        path = Path(root) / path
+    path = path.resolve(strict=True)
+    if root is not None and not path.is_relative_to(Path(root).resolve()):
+        raise ValueError("Artifact escaped its current attempt")
+    if file_digest(path) != record["sha256"]:
+        raise ValueError("Artifact content digest changed")
+    return path
+
+
+def validate_layer_report(report, requirement, identity, *, root):
+    if any(
+        report.get(key) != value
+        for key, value in {
+            "schema_version": 1,
+            "status": "passed",
+            "phase": "complete",
+            "scope": "qwen3_next_layer_and_scheduler_gates",
+            "identity": identity,
+            "requirements_sha256": identity_digest(requirement),
+            "full_model_l2": "not_evaluated",
+        }.items()
+    ):
+        raise ValueError("Layer acceptance identity or completeness mismatch")
+    if set(report.get("gates", {})) != set(requirement["gates"]):
+        raise ValueError("Layer acceptance omitted a mandatory gate")
+    from rl_engine.testing.qwen3_next_binary import validate_binary_record
+
+    binary = validate_binary_record(
+        report.get("provider_binary"), source_tree=identity["source_tree"], artifact_root=root
+    )
+    for name, expected in requirement["gates"].items():
+        gate = report["gates"][name]
+        if gate.get("exit_code") != 0 or gate.get("gpu_release_verified") is not True:
+            raise ValueError("Layer process failed or did not release GPU memory")
+        ranks = gate.get("ranks", {})
+        if set(ranks) != {"0", "1", "2", "3"}:
+            raise ValueError("Layer acceptance requires each of the four TP ranks")
+        for rank, record in ranks.items():
+            row = json.loads(checked_record(record, root=root).read_text())
+            validate_rank_result(row, expected, int(rank))
+            if row.get("provider_binary") != binary:
+                raise ValueError("Layer ranks did not import the archived CUDA provider")
+    scheduler_path = checked_record(report.get("scheduler"), root=root)
+    scheduler = json.loads(scheduler_path.read_text())
+    from rl_engine.testing.qwen3_next_l2 import validate_scheduler_report
+
+    validate_scheduler_report(scheduler, expected_identity=identity, artifact_root=root)
+    if scheduler.get("provider_binary") != report["provider_binary"]:
+        raise ValueError("Layer and scheduler gates used different CUDA providers")
+    if scheduler["case_ids"] != requirement["scheduler_case_ids"]:
+        raise ValueError("Scheduler evidence differs from the pinned layer registry")
diff --git a/scripts/qwen3_next_layers.py b/scripts/qwen3_next_layers.py
new file mode 100644
index 000000000..07a047011
--- /dev/null
+++ b/scripts/qwen3_next_layers.py
@@ -0,0 +1,342 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""TP4 layer gates and actual native scheduler acceptance, before independent L2."""
+
+import argparse
+import importlib
+import json
+import os
+import shutil
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from qwen3_next_contract import (  # noqa: E402
+    atomic_json,
+    checkpoint_identity,
+    evidence_record,
+    result_envelope,
+    validate_result,
+)
+from qwen3_next_l2 import (  # noqa: E402
+    model_identity,
+    run_process,
+    verify_before_model_load,
+    wait_gpu_release,
+    worker_batch,
+)
+from qwen3_next_layer_contract import (  # noqa: E402
+    layer_requirements,
+    validate_layer_report,
+    validate_rank_result,
+)
+from qwen3_next_runtime import failure_fields  # noqa: E402
+from qwen3_next_runtime import (  # noqa: E402
+    file_digest,
+    identity_digest,
+    memory_limits,
+    parse_memory_request,
+    verify_environment,
+)
+
+from rl_engine.testing.qwen3_next_binary import (  # noqa: E402
+    archive_provider_binary,
+    validate_binary_record,
+)
+from rl_engine.testing.qwen3_next_l2 import (  # noqa: E402
+    L2Case,
+    case_matrix,
+    compare_batch_prefill,
+    first_divergent_layer,
+    load_tensor_artifact,
+    validate_fixture,
+    validate_scheduler_report,
+)
+
+SCHEDULER_MODULE = "rl_engine.integrations.qwen3_next_native_rollout"
+
+
+def check_scheduler_ready():
+    try:
+        module = importlib.import_module(SCHEDULER_MODULE)
+        actual = module.capabilities()
+    except (ModuleNotFoundError, AttributeError) as exc:
+        raise RuntimeError(f"native_scheduler_not_ready: {exc}") from exc
+    required = {
+        "rollout": True,
+        "debug_layers": True,
+        "architecture_layers": 48,
+        "tp": 4,
+        "batched_cases": True,
+        "native_scheduler": True,
+    }
+    if any(actual.get(key) != value for key, value in required.items()) or not callable(
+        getattr(module, "run_rollout_cases", None)
+    ):
+        raise RuntimeError(f"native_scheduler_not_ready: required {required}, received {actual}")
+    return module, actual
+
+
+def scheduler_report(manifest, output, fixture_path, process, provider_binary):
+    fixture = load_tensor_artifact(fixture_path)
+    validate_fixture(fixture)
+    records = {}
+    for case in case_matrix():
+        path = output / "rollout" / f"{case.case_id}.pt"
+        candidate = load_tensor_artifact(path)
+        reference = load_tensor_artifact(
+            output / "rollout" / f"{L2Case(case.prompt_length, 4, 'full').case_id}.pt"
+        )
+        baseline = load_tensor_artifact(
+            fixture["baseline_artifacts"][str(case.prompt_length)]["path"]
+        )
+        batch = compare_batch_prefill(reference, candidate)
+        generated = compare_batch_prefill(baseline, candidate)
+        records[case.case_id] = {
+            "passed": all(batch.values()) and all(generated.values()),
+            "batch_prefill": {"rollout": batch},
+            "baseline_generation": {"rollout": generated},
+            "artifacts": {"rollout": {"path": str(path), "sha256": file_digest(path)}},
+        }
+        if not records[case.case_id]["passed"] and not (output / "first-divergence.json").exists():
+            divergent = reference if not all(batch.values()) else baseline
+            atomic_json(
+                output / "first-divergence.json",
+                {
+                    "case_id": case.case_id,
+                    "first_divergent_layer": first_divergent_layer(divergent, candidate),
+                    "candidate_artifact": str(path),
+                    "reference_worker_traces": divergent.get("worker_traces", []),
+                    "candidate_worker_traces": candidate.get("worker_traces", []),
+                    "capture": "all_48_layers_and_owned_states_in_original_worker_traces",
+                },
+            )
+    worker_path = output / "rollout/worker-result.json"
+    report = {
+        "schema_version": 1,
+        "status": "passed" if all(row["passed"] for row in records.values()) else "failed",
+        "scope": "qwen3_next_native_scheduler_only",
+        "full_model_l2": "not_evaluated",
+        "raw_bit_comparison": True,
+        "identity": model_identity(manifest),
+        "case_ids": [case.case_id for case in case_matrix()],
+        "cases": records,
+        "fixture": {
+            "path": str(fixture_path),
+            "sha256": file_digest(fixture_path),
+            "content_sha256": fixture["content_sha256"],
+        },
+        "processes": {"rollout": process},
+        "worker_results": {
+            "rollout": {"path": str(worker_path), "sha256": file_digest(worker_path)}
+        },
+        "provider_binary": provider_binary,
+    }
+    atomic_json(output / "scheduler-report.json", report)
+    validate_scheduler_report(
+        report, expected_identity=model_identity(manifest), artifact_root=output
+    )
+    return report
+
+
+def run_layers(manifest_path, output):
+    from qwen3_next_runtime import require_predecessor
+
+    manifest = json.loads(manifest_path.read_text())
+    if manifest["stage"] != "layers":
+        raise ValueError("Layer runner can only close the layers stage")
+    previous = Path(manifest["previous"])
+    require_predecessor("layers", previous, Path(manifest["checkpoint"]), manifest)
+    verify_environment(manifest["environment"], require_venv=True)
+    requirement = layer_requirements(ROOT)
+    if requirement != manifest["layer_requirements"]:
+        raise ValueError("Layer registry differs from the submitted source snapshot")
+    memory = memory_limits()
+    requested = parse_memory_request(manifest["memory"])
+    if any(
+        value is None or value < requested
+        for value in (memory["effective_bytes"], memory["available_bytes"])
+    ):
+        raise RuntimeError("insufficient_memory: layer allocation cannot satisfy its pinned budget")
+    output.mkdir(parents=True, exist_ok=True)
+    report = {
+        "schema_version": 1,
+        "status": "incomplete",
+        "phase": "build",
+        "scope": "qwen3_next_layer_and_scheduler_gates",
+        "full_model_l2": "not_evaluated",
+        "identity": model_identity(manifest),
+        "requirements_sha256": identity_digest(requirement),
+        "gates": {},
+    }
+
+    def progress(phase):
+        report["phase"] = phase
+        atomic_json(output / "layers.json", report)
+
+    run_process(
+        [manifest["python"], str(ROOT / "setup.py"), "build_ext", "--inplace", "--force"],
+        log_path=output / "build.log",
+        timeout=1800,
+    )
+    provider_binary = archive_provider_binary(ROOT, output, source_tree=manifest["source_tree"])
+    report["provider_binary"] = provider_binary
+    actual_binary = validate_binary_record(provider_binary, source_tree=manifest["source_tree"])
+    progress("checkpoint")
+    verify_before_model_load(manifest, output)
+    probe = output / "gpu-probe.json"
+    run_process(
+        [
+            manifest["python"],
+            str(ROOT / "scripts/qwen3_next_l2.py"),
+            "--gpu-probe",
+            "--output",
+            str(probe),
+        ],
+        log_path=output / "gpu-probe.log",
+        timeout=180,
+    )
+    uuids = json.loads(probe.read_text())["uuids"]
+    wait_gpu_release(uuids)
+    atomic_json(
+        output / "environment.json",
+        {
+            "environment": manifest["environment"],
+            "memory": memory,
+            "gpu_uuids": uuids,
+            "execution_gpus": 4,
+        },
+    )
+    for name, expected in requirement["gates"].items():
+        progress(name)
+        directory = output / name
+        process = run_process(
+            [
+                manifest["python"],
+                "-m",
+                "torch.distributed.run",
+                "--standalone",
+                "--nproc_per_node=4",
+                str(ROOT / expected["script"]),
+                "--checkpoint",
+                manifest["checkpoint"],
+                expected["output_option"],
+                str(directory),
+            ],
+            log_path=output / f"{name}.log",
+        )
+        wait_gpu_release(uuids)
+        ranks = {}
+        for rank in range(4):
+            path = directory / f"rank-{rank}.json"
+            validate_rank_result(json.loads(path.read_text()), expected, rank)
+            if json.loads(path.read_text()).get("provider_binary") != actual_binary:
+                raise ValueError("Layer rank used a different CUDA provider than its build")
+            ranks[str(rank)] = {"path": str(path), "sha256": file_digest(path)}
+        report["gates"][name] = {**process, "gpu_release_verified": True, "ranks": ranks}
+        progress(name + "_passed")
+    progress("native_scheduler")
+    check_scheduler_ready()
+    fixture = output / "inputs/frozen.pt"
+    process = run_process(
+        [
+            manifest["python"],
+            str(Path(__file__)),
+            "--scheduler-worker",
+            "--manifest",
+            str(manifest_path),
+            "--output",
+            str(output / "rollout"),
+            "--fixture",
+            str(fixture),
+            "--provider-binary",
+            json.dumps(provider_binary),
+        ],
+        log_path=output / "scheduler.log",
+    )
+    wait_gpu_release(uuids)
+    process["gpu_release_verified"] = True
+    scheduler_report(manifest, output, fixture, process, provider_binary)
+    report["scheduler"] = {
+        "path": str(output / "scheduler-report.json"),
+        "sha256": file_digest(output / "scheduler-report.json"),
+    }
+    if checkpoint_identity(manifest["checkpoint"]) != manifest["checkpoint_identity"]:
+        raise RuntimeError("checkpoint_mismatch: official snapshot changed during layer execution")
+    report.update(status="passed", phase="complete")
+    validate_layer_report(report, requirement, model_identity(manifest), root=output)
+    atomic_json(output / "layers.json", report)
+    previous_result = json.loads((previous / "result.json").read_text())
+    shutil.copyfile(
+        previous / previous_result["evidence"]["operators"]["path"], output / "operators.json"
+    )
+    names = {
+        "operators": "operators.json",
+        "environment": "environment.json",
+        "layers": "layers.json",
+        "checkpoint": "weight-verification.json",
+        "provider_binary": "provider-binary.json",
+    }
+    evidence = {
+        name: evidence_record(output / filename, manifest_path.parent)
+        for name, filename in names.items()
+    }
+    result = result_envelope(manifest, os.environ["QWEN3_NEXT_ATTEMPT"], evidence=evidence)
+    validate_result(manifest, result, run_dir=manifest_path.parent)
+    atomic_json(output / "result.json", result)
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--manifest", type=Path)
+    parser.add_argument("--output", type=Path)
+    parser.add_argument("--fixture", type=Path)
+    parser.add_argument("--scheduler-worker", action="store_true")
+    parser.add_argument("--check-ready", action="store_true")
+    parser.add_argument("--provider-binary", type=json.loads)
+    args = parser.parse_args()
+    if args.check_ready:
+        _, capabilities = check_scheduler_ready()
+        print(json.dumps({"status": "ready", "capabilities": capabilities}))
+        return 0
+    if args.manifest is None or args.output is None:
+        parser.error("--manifest and --output are required")
+    manifest = json.loads(args.manifest.read_text())
+    if args.scheduler_worker:
+        worker_batch(
+            manifest,
+            args.output,
+            "rollout",
+            args.fixture,
+            native_worker=check_scheduler_ready(),
+            provider_binary=args.provider_binary,
+        )
+        return 0
+    args.output.mkdir(parents=True, exist_ok=True)
+    try:
+        run_layers(args.manifest, args.output)
+    except Exception as exc:
+        kind = str(exc).split(":", 1)[0]
+        atomic_json(
+            args.output / "result.json",
+            {
+                "run_id": manifest["run_id"],
+                "attempt": os.environ["QWEN3_NEXT_ATTEMPT"],
+                "status": "incomplete" if kind == "native_scheduler_not_ready" else "failed",
+                "phase": (
+                    "native_scheduler" if kind == "native_scheduler_not_ready" else "layer_gate"
+                ),
+                **failure_fields(exc),
+                "full_model_l2": "not_evaluated",
+            },
+        )
+        raise
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/qwen3_next_operator_cases.py b/scripts/qwen3_next_operator_cases.py
new file mode 100644
index 000000000..caf139e4c
--- /dev/null
+++ b/scripts/qwen3_next_operator_cases.py
@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Regenerate ``rl_engine/testing/qwen3_next_operator_cases.json`` from pytest collection.
+
+    python scripts/qwen3_next_operator_cases.py          # check, exit 1 if stale
+    python scripts/qwen3_next_operator_cases.py --write
+
+The registry pins the exact test IDs the preflight operator gate must run. The
+``norm`` and ``core`` suites are mandatory and must pass without skips;
+``diagnostics`` holds the approximate cross-provider comparisons, which are
+recorded but never gate. Which file and test belongs to which suite is declared
+below; the IDs themselves come from collection, so a parametrisation change is
+a reviewed registry diff, not a silent change in what the gate runs.
+"""
+
+import argparse
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+REGISTRY = ROOT / "rl_engine/testing/qwen3_next_operator_cases.json"
+NORM_FILES = ("tests/test_qwen3_next_norm.py", "tests/check_qwen3_next_norm_providers.py")
+CORE_FILES = (
+    "tests/check_qwen3_next_attention.py",
+    "tests/check_qwen3_next_batch_invariance.py",
+    "tests/check_qwen3_next_conv_bridge.py",
+    "tests/check_qwen3_next_core_matrix.py",
+    "tests/check_qwen3_next_forward.py",
+    "tests/check_qwen3_next_gdn_bridge.py",
+    "tests/check_qwen3_next_gdn_sequence.py",
+    "tests/check_qwen3_next_paged_attention.py",
+    "tests/check_qwen3_next_shared_core.py",
+)
+DIAGNOSTIC_FILES = ("tests/check_gdn_recurrent_golden.py",)
+# Approximate comparisons against alternative providers, collected from the
+# mandatory files above but recorded as diagnostics.
+DIAGNOSTIC_TESTS = {
+    "tests/check_qwen3_next_norm_providers.py": (
+        "test_ours_tracks_each_vllm_path_within_bounds",
+        "test_vllm_paths_disagree_only_within_bounds",
+    ),
+    "tests/check_qwen3_next_batch_invariance.py": (
+        "test_flashinfer_chunked_prefill_deviation_stays_rounding_scale",
+        "test_triton_chunked_prefill_split_matches_one_pass",
+    ),
+}
+DESCRIPTION = (
+    "Mandatory norm/core contracts; alternative-provider approximate comparisons are "
+    "diagnostics only. Generated by scripts/qwen3_next_operator_cases.py; case changes "
+    "require review before a frozen run."
+)
+
+
+def collect(files):
+    out = subprocess.run(
+        [sys.executable, "-m", "pytest", "--collect-only", "-q", "-p", "no:cacheprovider", *files],
+        cwd=ROOT,
+        capture_output=True,
+        text=True,
+    )
+    ids = [line for line in out.stdout.splitlines() if "::test_" in line]
+    if out.returncode or not ids:
+        raise SystemExit(f"collection failed:\n{out.stdout[-2000:]}\n{out.stderr[-2000:]}")
+    return ids
+
+
+def _diagnostic(case):
+    path, name = case.split("::", 1)
+    return name.split("[", 1)[0] in DIAGNOSTIC_TESTS.get(path, ())
+
+
+def registry():
+    norm, core = collect(NORM_FILES), collect(CORE_FILES)
+    diagnostics = collect(DIAGNOSTIC_FILES) + [c for c in norm + core if _diagnostic(c)]
+    return {
+        "schema_version": 1,
+        "description": DESCRIPTION,
+        "suites": {
+            "norm": sorted(c for c in norm if not _diagnostic(c)),
+            "core": sorted(c for c in core if not _diagnostic(c)),
+            "diagnostics": sorted(diagnostics),
+        },
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--write", action="store_true")
+    args = parser.parse_args()
+    text = json.dumps(registry(), indent=2) + "\n"
+    if args.write:
+        REGISTRY.write_text(text)
+        print(f"wrote {REGISTRY.relative_to(ROOT)}")
+    elif not REGISTRY.is_file() or REGISTRY.read_text() != text:
+        raise SystemExit(f"{REGISTRY.relative_to(ROOT)} is stale; rerun with --write")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_operator_gates.py b/scripts/qwen3_next_operator_gates.py
new file mode 100644
index 000000000..d1c45d558
--- /dev/null
+++ b/scripts/qwen3_next_operator_gates.py
@@ -0,0 +1,151 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Run declared operator acceptance or explicitly separate diagnostic cases."""
+
+import argparse
+import importlib.metadata
+import json
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+from rl_engine.testing.qwen3_next_cases import case_digest, execution_verdict, load_cases
+
+ROOT = Path(__file__).resolve().parents[1]
+MANIFEST = ROOT / "rl_engine/testing/qwen3_next_operator_cases.json"
+
+
+def run_suite(out: Path, suite: str) -> dict:
+    out.mkdir(parents=True, exist_ok=True)
+    cases = load_cases(MANIFEST, suite)
+    diagnostic = suite == "diagnostics"
+    case_path = out / "case-results.json"
+    case_path.unlink(missing_ok=True)
+    env = dict(os.environ)
+    env["QWEN3_NEXT_ROUTING_EVIDENCE"] = str(out / "gdn-step-routing.json")
+    env["QWEN3_NEXT_FLASHINFER_EVIDENCE"] = str(out)
+    proc = subprocess.run(
+        [
+            sys.executable,
+            "-m",
+            "pytest",
+            "-q",
+            "-p",
+            "no:cacheprovider",
+            "-p",
+            "rl_engine.testing.pytest_case_manifest",
+            "--case-manifest",
+            str(MANIFEST),
+            "--case-suite",
+            suite,
+            "--case-report",
+            str(case_path),
+            "--junitxml",
+            str(out / "providers.xml"),
+            *cases,
+        ],
+        cwd=ROOT,
+        env=env,
+        check=False,
+    )
+    try:
+        case_report = json.loads(case_path.read_text()) if case_path.exists() else {}
+    except json.JSONDecodeError:
+        case_report = {}
+    verified = execution_verdict(
+        cases, case_report.get("collected_case_ids", []), case_report.get("outcomes", {})
+    )
+    passed = proc.returncode == 0 and case_report.get("passed") is True
+    passed = passed and verified["passed"] and case_report.get("expected_case_ids") == cases
+    passed = passed and case_report.get("expected_case_ids_sha256") == case_digest(cases)
+    reports = {}
+    if suite in ("norm", "all"):
+        for op in ("qwen3_next_rms_norm", "rms_norm_gated"):
+            for gate in ("forward", "gradient"):
+                name = f"{op}-{gate}.json"
+                with (out / name).open("w") as stream:
+                    norm = subprocess.run(
+                        [
+                            sys.executable,
+                            f"scripts/check_{gate}_invariance.py",
+                            "--manifest",
+                            "rl_engine/testing/qwen3_next_norm_manifest.json",
+                            "--op",
+                            op,
+                            "--candidate",
+                            "cuda",
+                            "--backend-profile",
+                            "cuda_bf16",
+                            "--hidden",
+                            "2048",
+                            "--head-dim",
+                            "128",
+                            "--json",
+                        ],
+                        cwd=ROOT,
+                        env=env,
+                        stdout=stream,
+                        check=False,
+                    )
+                try:
+                    report = json.loads((out / name).read_text())
+                except json.JSONDecodeError:
+                    report = {}
+                valid = all(
+                    (
+                        norm.returncode == 0,
+                        report.get("passed") is True,
+                        report.get("workload", {}).get("scope") == "qwen3_next_norm_operators",
+                    )
+                )
+                reports[name] = {
+                    "passed": valid,
+                    "exit_code": norm.returncode,
+                    "workload": report.get("workload"),
+                }
+                passed = passed and valid
+    status = "passed" if passed else "failed"
+    if diagnostic:
+        status = "diagnostic_completed" if passed else "diagnostic_failed"
+    return {
+        "scope": "qwen3_next_operator_diagnostics" if diagnostic else "qwen3_next_operators_only",
+        "status": status,
+        "suite": suite,
+        "diagnostic_only": diagnostic,
+        "full_model_l2": "not_evaluated",
+        "git_commit": subprocess.check_output(
+            ["git", "rev-parse", "HEAD"], cwd=ROOT, text=True
+        ).strip(),
+        "git_dirty": bool(
+            subprocess.check_output(["git", "status", "--porcelain"], cwd=ROOT, text=True).strip()
+        ),
+        "versions": {
+            name: importlib.metadata.version(name)
+            for name in ("torch", "vllm", "transformers", "triton")
+        },
+        "mandatory_case_ids": [] if diagnostic else cases,
+        "case_ids_sha256": case_digest(cases),
+        "pytest_exit_code": proc.returncode,
+        "provider_tests": verified["counts"],
+        "norm_gates": reports,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--out", type=Path, required=True)
+    parser.add_argument("--suite", choices=("norm", "core", "all", "diagnostics"), default="all")
+    args = parser.parse_args()
+    result = run_suite(args.out.resolve(), args.suite)
+    partial = args.out / "summary.json.partial"
+    partial.write_text(json.dumps(result, indent=2) + "\n")
+    partial.replace(args.out / "summary.json")
+    print(json.dumps(result, indent=2))
+    return 0 if result["status"] in ("passed", "diagnostic_completed") else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/qwen3_next_optimizer_checkpoint.py b/scripts/qwen3_next_optimizer_checkpoint.py
new file mode 100644
index 000000000..df6c1c07e
--- /dev/null
+++ b/scripts/qwen3_next_optimizer_checkpoint.py
@@ -0,0 +1,307 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Bounded native DistributedOptimizer parameter-state I/O for fixed TP4/DP1.
+
+The installed native DP-zero saver packs entire gradient buckets. With CPU Adam
+offload and no communication overlap, a bucket can contain most of the model.
+This adapter uses the native parameter getters/setters and verifies their exact
+DP1 range coverage, while copying at most 64 MiB into each serialized block.
+It never implements an optimizer, changes dtype, or changes native state layout.
+"""
+
+import json
+from pathlib import Path
+
+import torch
+from qwen3_next_contract import atomic_json
+
+from rl_engine.integrations.qwen3_next_training import named_trainable_parameters, state_digest
+
+PARAMETER_KEYS = ("param", "exp_avg", "exp_avg_sq")
+
+
+def _hdo_param_generator(cpu_optimizer):
+    from megatron.core.optimizer.cpu_offloading.hybrid_optimizer import _param_generator
+
+    return _param_generator(cpu_optimizer)
+
+
+def verify_hdo_optimizer_master_aliases(model, leaves):
+    """CPU Adam must update the same FP32 master tensors the checkpoint getter exposes.
+
+    The distributed optimizer keys its HybridDeviceOptimizer by the DP-sharded
+    parameter (``optimizer.param_groups[group][order]`` found through
+    ``model_param_group_index_map``), not by the model parameter, and the
+    checkpoint getter returns that shard's ``state["master_param"]``. The FP32
+    copy CPU Adam steps (``param_to_fp32_param[shard]``) is a separate tensor
+    that must hold the same values, and the CPU optimizer's state entry must be
+    the very dict the HDO keeps for the shard.
+    """
+    try:
+        from megatron.core.optimizer.cpu_offloading.hybrid_optimizer import HybridDeviceOptimizer
+    except ModuleNotFoundError:
+        return
+
+    for record in native_parameter_layout(model, leaves).values():
+        leaf = record["leaf"]
+        hdo = leaf.optimizer
+        if not isinstance(hdo, HybridDeviceOptimizer):
+            continue
+        orig = record["parameter"]
+        group_index, group_order = leaf.model_param_group_index_map[orig]
+        shard = hdo.param_groups[group_index]["params"][group_order]
+        master = leaf._get_main_param_and_optimizer_states(orig)["param"]
+        hdo_state = hdo.state.get(shard)
+        if not isinstance(hdo_state, dict) or hdo_state.get("master_param") is not master:
+            raise ValueError("HDO state master_param is not the checkpoint-visible master")
+        if shard not in hdo.param_to_fp32_param:
+            raise ValueError("Native HDO must keep an FP32 copy for each sharded parameter")
+        fp32_inner = hdo.param_to_fp32_param[shard]
+        if hdo.param_to_inner_param.get(shard) is not fp32_inner:
+            raise ValueError("HDO inner optimizer parameter is not the FP32 master copy")
+        if fp32_inner.dtype != torch.float32 or not torch.equal(master, fp32_inner):
+            raise ValueError("HDO FP32 master copy diverged from checkpoint-visible master weights")
+        linked = False
+        for cpu_optimizer in hdo.cpu_optimizers:
+            for param in _hdo_param_generator(cpu_optimizer):
+                if param is not fp32_inner:
+                    continue
+                if cpu_optimizer.state.get(param) is not hdo_state:
+                    raise ValueError("CPU Adam state is not aliased to the HDO parameter state")
+                linked = True
+        if not linked:
+            raise ValueError("CPU Adam does not own the FP32 master for a model parameter")
+
+
+BLOCK_BYTES = 64 * 1024 * 1024
+
+
+def native_parameter_layout(model, leaves):
+    names = {id(value): name for name, value in named_trainable_parameters(model).items()}
+    records = {}
+    for leaf_index, leaf in enumerate(leaves):
+        covered = set()
+        for buffer_index, dtype_maps in enumerate(leaf.gbuf_ranges):
+            for dtype, buckets in dtype_maps.items():
+                for bucket_index, bucket in enumerate(buckets):
+                    intervals = []
+                    capacity = leaf.buffers[buffer_index].buckets[bucket_index].grad_data.numel()
+                    for parameter, ranges in bucket["param_map"].items():
+                        name = names.get(id(parameter))
+                        if name is None or name in records or parameter in covered:
+                            raise ValueError(
+                                "Native optimizer ranges must partition model parameters"
+                            )
+                        local, relative = ranges["gbuf_local"], ranges["param"]
+                        if (
+                            relative.start != 0
+                            or relative.end != parameter.numel()
+                            or local.end - local.start != parameter.numel()
+                            or not 0 <= local.start < local.end <= capacity
+                        ):
+                            raise ValueError("Native DP1 ranges must cover each complete parameter")
+                        if parameter not in leaf.model_param_group_index_map:
+                            raise ValueError(
+                                "Native bucket parameter is absent from optimizer groups"
+                            )
+                        intervals.append((local.start, local.end))
+                        covered.add(parameter)
+                        records[name] = {
+                            "parameter": parameter,
+                            "leaf": leaf,
+                            "layout": {
+                                "leaf": leaf_index,
+                                "buffer": buffer_index,
+                                "dtype": [str(item) for item in dtype],
+                                "bucket": bucket_index,
+                                "bucket_numel": capacity,
+                                "group_order": list(leaf.model_param_group_index_map[parameter]),
+                                "param_range": [relative.start, relative.end],
+                                "local_range": [local.start, local.end],
+                            },
+                        }
+                    intervals.sort()
+                    if any(right[0] < left[1] for left, right in zip(intervals, intervals[1:])):
+                        raise ValueError("Native optimizer bucket ranges overlap")
+        if covered != leaf.model_param_group_index_map.keys():
+            raise ValueError("Native bucket ranges do not cover all optimizer parameters")
+    if set(records) != set(names.values()):
+        raise ValueError("Native optimizer state does not cover all model parameters")
+    return dict(sorted(records.items()))
+
+
+def native_parameter_states(record):
+    parameter, leaf = record["parameter"], record["leaf"]
+    states = leaf._get_main_param_and_optimizer_states(parameter)
+    if not set(PARAMETER_KEYS).issubset(states) or set(states) - set(PARAMETER_KEYS) - {"step"}:
+        raise ValueError("Native master weights or Adam moments are missing or unsupported")
+    for key in PARAMETER_KEYS:
+        value = states[key]
+        if (
+            not isinstance(value, torch.Tensor)
+            or value.dtype != torch.float32
+            or value.device.type != "cpu"
+            or value.numel() != parameter.numel()
+            or not value.is_contiguous()
+        ):
+            raise ValueError(f"Invalid fully offloaded native {key}")
+    if "step" in states:
+        step = states["step"]
+        if isinstance(step, torch.Tensor):
+            if step.device.type != "cpu" or step.numel() != 1:
+                raise ValueError("Native Adam step must be a CPU scalar")
+        elif type(step) not in (int, float):
+            raise ValueError("Native Adam step must be a numeric scalar")
+    return states
+
+
+def parameter_state_records(model, leaves):
+    records, byte_count = {}, 0
+    for name, record in native_parameter_layout(model, leaves).items():
+        states = native_parameter_states(record)
+        records[name] = {key: state_digest(value) for key, value in states.items()}
+        byte_count += sum(
+            value.numel() * value.element_size()
+            for value in states.values()
+            if isinstance(value, torch.Tensor)
+        )
+    return records, byte_count
+
+
+def save_bounded_parameter_state(model, leaves, rank_dir, save_tensor_file):
+    rank_dir = Path(rank_dir)
+    directory = rank_dir / "optimizer-state"
+    directory.mkdir()
+    index = {
+        "schema_version": 1,
+        "scope": "native_dp1_parameter_state",
+        "block_bytes": BLOCK_BYTES,
+        "parameters": {},
+    }
+    extras = {}
+    width = BLOCK_BYTES // (len(PARAMETER_KEYS) * 4)
+    for parameter_index, (name, record) in enumerate(
+        native_parameter_layout(model, leaves).items()
+    ):
+        states = native_parameter_states(record)
+        entry = {
+            "layout": record["layout"],
+            "shapes": {key: list(states[key].shape) for key in PARAMETER_KEYS},
+            "blocks": [],
+        }
+        count = record["parameter"].numel()
+        for block_index, start in enumerate(range(0, count, width)):
+            end = min(start + width, count)
+            filename = f"optimizer-state/p{parameter_index:05d}-b{block_index:05d}.pt"
+            # Clone bounded views: torch.save otherwise serializes their entire
+            # underlying native storage, which can itself be a whole buffer.
+            block = {key: states[key].reshape(-1)[start:end].clone() for key in PARAMETER_KEYS}
+            save_tensor_file(rank_dir / filename, block)
+            del block
+            entry["blocks"].append({"path": filename, "start": start, "end": end})
+        extras[name] = {
+            key: value.clone() if isinstance(value, torch.Tensor) else value
+            for key, value in states.items()
+            if key not in PARAMETER_KEYS
+        }
+        index["parameters"][name] = entry
+    save_tensor_file(rank_dir / "optimizer-extra.pt", extras)
+    atomic_json(rank_dir / "optimizer-index.json", index)
+    return index
+
+
+def parameter_state_files(rank_dir, index=None):
+    rank_dir = Path(rank_dir)
+    if index is None:
+        index = json.loads((rank_dir / "optimizer-index.json").read_text())
+    if (index.get("schema_version"), index.get("scope"), index.get("block_bytes")) != (
+        1,
+        "native_dp1_parameter_state",
+        BLOCK_BYTES,
+    ):
+        raise ValueError("Unsupported native bounded optimizer checkpoint")
+    files = ["optimizer-header.pt", "optimizer-index.json", "optimizer-extra.pt"]
+    for entry in index["parameters"].values():
+        for block in entry["blocks"]:
+            path = Path(block["path"])
+            if len(path.parts) != 2 or path.parts[0] != "optimizer-state" or path.suffix != ".pt":
+                raise ValueError("Optimizer block path is outside its rank state directory")
+            files.append(str(path))
+    if len(files) != len(set(files)):
+        raise ValueError("Native optimizer blocks cannot alias another parameter's state")
+    return files
+
+
+def load_bounded_parameter_state(model, leaves, rank_dir):
+    rank_dir = Path(rank_dir)
+    index = json.loads((rank_dir / "optimizer-index.json").read_text())
+    parameter_state_files(rank_dir, index)
+    current = native_parameter_layout(model, leaves)
+    if current.keys() != index["parameters"].keys():
+        raise ValueError("Saved native optimizer parameter inventory differs")
+    extras = torch.load(rank_dir / "optimizer-extra.pt", map_location="cpu", weights_only=True)
+    if extras.keys() != current.keys():
+        raise ValueError("Saved native optimizer extra-state inventory differs")
+    width = BLOCK_BYTES // (len(PARAMETER_KEYS) * 4)
+    touched_leaves = []
+    with torch.no_grad():
+        for name, record in current.items():
+            entry = index["parameters"][name]
+            states = native_parameter_states(record)
+            shapes = {key: list(states[key].shape) for key in PARAMETER_KEYS}
+            if entry["layout"] != record["layout"] or entry["shapes"] != shapes:
+                raise ValueError("Saved native optimizer layout or state shapes differ")
+            position = 0
+            for block in entry["blocks"]:
+                start, end = block["start"], block["end"]
+                if (
+                    type(start) is not int
+                    or type(end) is not int
+                    or start != position
+                    or not start < end <= min(start + width, record["parameter"].numel())
+                ):
+                    raise ValueError(
+                        "Native optimizer blocks must cover each parameter exactly once"
+                    )
+                payload = torch.load(
+                    rank_dir / block["path"], map_location="cpu", weights_only=True
+                )
+                if payload.keys() != set(PARAMETER_KEYS):
+                    raise ValueError("Native optimizer block omits masters or moments")
+                for key in PARAMETER_KEYS:
+                    value = payload[key]
+                    if (
+                        value.dtype != torch.float32
+                        or value.shape != (end - start,)
+                        or not torch.isfinite(value).all()
+                    ):
+                        raise ValueError("Native optimizer block shape/dtype/finite check failed")
+                    states[key].reshape(-1)[start:end].copy_(value)
+                del payload
+                position = end
+            if position != record["parameter"].numel():
+                raise ValueError("Native optimizer blocks do not cover the complete parameter")
+            if extras[name].keys() != states.keys() - set(PARAMETER_KEYS):
+                raise ValueError("Native optimizer extra-state schema differs")
+            for key, value in extras[name].items():
+                if isinstance(states[key], torch.Tensor):
+                    if not isinstance(value, torch.Tensor) or value.dtype != states[key].dtype:
+                        raise ValueError("Native Adam scalar dtype differs")
+                    states[key].copy_(value)
+                else:
+                    if type(value) is not type(states[key]):
+                        raise ValueError("Native Adam scalar type differs")
+                    states[key] = value
+            record["leaf"]._set_main_param_and_optimizer_states(record["parameter"], states)
+            if record["leaf"] not in touched_leaves:
+                touched_leaves.append(record["leaf"])
+    try:
+        from megatron.core.optimizer.cpu_offloading.hybrid_optimizer import HybridDeviceOptimizer
+    except ModuleNotFoundError:
+        return
+
+    for leaf in touched_leaves:
+        hdo = leaf.optimizer
+        if isinstance(hdo, HybridDeviceOptimizer):
+            hdo._sync_hdo_state_to_sub_optimizers()
diff --git a/scripts/qwen3_next_preflight.py b/scripts/qwen3_next_preflight.py
new file mode 100644
index 000000000..2b46c266c
--- /dev/null
+++ b/scripts/qwen3_next_preflight.py
@@ -0,0 +1,252 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Required four-GPU infrastructure checks, never full-model L2 acceptance."""
+
+import argparse
+import importlib.metadata
+import json
+import os
+import platform
+import subprocess
+import sys
+from pathlib import Path
+
+from qwen3_next_contract import checkpoint_identity, evidence_record, result_envelope
+from qwen3_next_runtime import (
+    atomic_json,
+    failure_fields,
+    memory_limits,
+    parse_memory_request,
+    subprocess_failure,
+    verify_checkpoint,
+    verify_environment,
+)
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--manifest", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    args = parser.parse_args()
+    manifest = json.loads(args.manifest.read_text())
+    output = args.output
+    output.mkdir(parents=True, exist_ok=True)
+    progress = {
+        "run_id": manifest["run_id"],
+        "attempt": os.environ["QWEN3_NEXT_ATTEMPT"],
+        "status": "running",
+        "phase": "inventory",
+        "full_model_l2": "not_evaluated",
+    }
+
+    def run(name, command, env=None):
+        progress["phase"] = name
+        atomic_json(output / "progress.json", progress)
+        print(f"PREFLIGHT {name}: {command}", flush=True)
+        log_path = output / f"{name}.log"
+        with log_path.open("w") as log:
+            completed = subprocess.run(command, stdout=log, stderr=subprocess.STDOUT, env=env)
+        if completed.returncode:
+            raise subprocess_failure(completed.returncode, log_path)
+
+    try:
+        verify_environment(manifest["environment"])
+        if checkpoint_identity(manifest["checkpoint"]) != manifest["checkpoint_identity"]:
+            raise RuntimeError("checkpoint_mismatch: pinned metadata or shard inventory changed")
+        import torch
+
+        versions = {
+            name: importlib.metadata.version(name)
+            for name in (
+                "torch",
+                "vllm",
+                "triton",
+                "transformers",
+                "flashinfer-python",
+                "safetensors",
+            )
+        }
+        nvcc = subprocess.check_output(
+            [f"{manifest['cuda_home']}/bin/nvcc", "--version"], text=True
+        )
+        inventory = {
+            "source_commit": manifest["source_commit"],
+            "environment_sha256": manifest["environment_sha256"],
+            "visible_gpu_ids": os.environ.get("CUDA_VISIBLE_DEVICES"),
+            "execution_gpus": 4,
+            "python": sys.version,
+            "executable": sys.executable,
+            "platform": platform.platform(),
+            "versions": versions,
+            "torch_cuda": torch.version.cuda,
+            "nvcc": nvcc,
+            "compiler": subprocess.check_output(["g++", "--version"], text=True),
+            "nvidia_smi": subprocess.check_output(["nvidia-smi"], text=True),
+            "gpu_topology": subprocess.check_output(["nvidia-smi", "topo", "-m"], text=True),
+            "visible_gpus": [
+                torch.cuda.get_device_properties(i).__repr__()
+                for i in range(torch.cuda.device_count())
+            ],
+            "memory": memory_limits(),
+            "memory_requested": manifest.get("memory"),
+        }
+        atomic_json(output / "environment.json", inventory)
+        run("environment-lock", [sys.executable, "-m", "pip", "freeze", "--all"])
+        if torch.cuda.device_count() != 4 or any(
+            "B200" not in torch.cuda.get_device_name(i) for i in range(4)
+        ):
+            raise RuntimeError(
+                "topology_mismatch: requires exactly four B200 GPUs in the execution step"
+            )
+        if "V13.0.88" not in nvcc or torch.version.cuda != "13.0":
+            raise RuntimeError(
+                "environment_mismatch: requires CUDA toolkit 13.0.88 and a CUDA 13.0 torch runtime"
+            )
+        for name, required in {
+            "torch": "2.13.0",
+            "vllm": "0.30.0",
+            "triton": "3.7.1",
+            "transformers": "5.17.0",
+        }.items():
+            if versions[name].split("+", 1)[0] != required:
+                raise RuntimeError(
+                    f"environment_mismatch: unverified runtime version: {name}={versions[name]}"
+                )
+        memory = inventory["memory"]
+        quota = memory["effective_bytes"]
+        # Compare against what THIS run requested, recorded by the submitter, so
+        # the budget is not a second constant that has to be kept in sync -- and
+        # so a trimmed allocation fails loudly instead of passing a stale bound.
+        if "memory" not in manifest:
+            raise RuntimeError("Manifest does not record the memory request")
+        requested = parse_memory_request(manifest["memory"])
+        if quota is None or quota < requested:
+            raise RuntimeError(
+                f"insufficient_memory: node granted less memory than this run requested "
+                f"({manifest['memory']} = {requested} bytes): "
+                f"effective={quota}, cgroup={memory['cgroup_bytes']}, "
+                f"declared={memory['declared_bytes']}"
+            )
+        # A grant is not availability on a shared node. Fail in seconds rather
+        # than being OOM-killed minutes into a 162 GB model load.
+        available = memory["available_bytes"]
+        if available is None or available < requested:
+            raise RuntimeError(
+                f"insufficient_memory: node does not currently have the requested memory free "
+                f"({manifest['memory']} = {requested} bytes): "
+                f"MemAvailable={available}. Co-tenants can hold host RAM that an "
+                "allocation nominally grants; use an exclusive allocation or wait "
+                "for the node to drain."
+            )
+        run("build", [sys.executable, "setup.py", "build_ext", "--inplace"])
+        env = {
+            **os.environ,
+            "PY": sys.executable,
+            "QWEN3_NEXT_EVIDENCE_DIR": str(output / "operators"),
+            "QWEN3_NEXT_OPERATOR_SUITE": manifest.get("suite", "all"),
+        }
+        run("operators", ["bash", "ci/run_qwen3_next_operator_gates.sh"], env)
+        if manifest["stage"] == "preflight" or manifest.get("suite") in ("norm", "all"):
+            run(
+                "device-guards",
+                [
+                    sys.executable,
+                    "-m",
+                    "pytest",
+                    "-q",
+                    "tests/check_qwen3_next_multigpu.py",
+                    f"--junitxml={output}/device-guards.xml",
+                ],
+            )
+            from xml.etree import ElementTree
+
+            suites = list(
+                ElementTree.parse(output / "device-guards.xml").getroot().iter("testsuite")
+            )
+            if not suites or any(
+                int(s.get(k, 0)) for s in suites for k in ("skipped", "errors", "failures")
+            ):
+                raise RuntimeError("Device guards must pass without skips")
+        checkpoint = None
+        if manifest["stage"] == "preflight":
+            run(
+                "tp4",
+                [
+                    sys.executable,
+                    "-m",
+                    "torch.distributed.run",
+                    "--standalone",
+                    "--nproc_per_node=4",
+                    "scripts/qwen3_next_tp_probe.py",
+                    "--output",
+                    str(output / "tp4"),
+                ],
+            )
+            ranks = [
+                json.loads((output / "tp4" / f"rank-{rank}.json").read_text()) for rank in range(4)
+            ]
+            if any(
+                row.get("status") != "passed"
+                or row.get("rank") != rank
+                or row.get("world_size") != 4
+                for rank, row in enumerate(ranks)
+            ):
+                raise RuntimeError("All four TP ranks must pass")
+            atomic_json(output / "tp4.json", {"status": "passed", "ranks": ranks})
+            progress["phase"] = "checkpoint-integrity"
+            atomic_json(output / "progress.json", progress)
+            identity = json.loads(
+                Path("rl_engine/testing/qwen3_next_norm_manifest.json").read_text()
+            )
+            checkpoint = verify_checkpoint(Path(manifest["checkpoint"]), identity["model_identity"])
+            atomic_json(output / "checkpoint.json", checkpoint)
+            run(
+                "rollout-smoke",
+                [
+                    sys.executable,
+                    "scripts/qwen3_next_rollout_smoke.py",
+                    "--model",
+                    manifest["checkpoint"],
+                    "--output",
+                    str(output / "rollout-smoke.json"),
+                    "--tensor-parallel-size",
+                    "4",
+                    "--non-strict-smoke",
+                ],
+            )
+        import rl_engine._C
+        from rl_engine.integrations.qwen3_next_gdn import _provider
+
+        provider = _provider()
+        names = {"operators": "operators/summary.json", "environment": "environment.json"}
+        if manifest["stage"] == "preflight" or manifest.get("suite") in ("norm", "all"):
+            names["device_guards"] = "device-guards.xml"
+        if manifest["stage"] == "preflight":
+            names.update(
+                tp4="tp4.json", checkpoint="checkpoint.json", rollout_smoke="rollout-smoke.json"
+            )
+        evidence = {
+            name: evidence_record(output / filename, args.manifest.parent)
+            for name, filename in names.items()
+        }
+        result = result_envelope(manifest, os.environ["QWEN3_NEXT_ATTEMPT"], evidence=evidence)
+        result.update(
+            checkpoint=checkpoint,
+            provider_paths={
+                "norm": rl_engine._C.__file__,
+                "gdn": f"{provider.__module__}.{provider.__name__}",
+            },
+        )
+        atomic_json(output / "result.json", result)
+    except Exception as exc:
+        progress.update(
+            status="failed", attempt=os.environ["QWEN3_NEXT_ATTEMPT"], **failure_fields(exc)
+        )
+        atomic_json(output / "result.json", progress)
+        raise
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_publish_evidence.py b/scripts/qwen3_next_publish_evidence.py
new file mode 100644
index 000000000..1cb6cb2e7
--- /dev/null
+++ b/scripts/qwen3_next_publish_evidence.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Copy a finished Qwen3-Next chain's evidence into the repository, without site details.
+
+    python scripts/qwen3_next_publish_evidence.py --last <train run dir> \\
+        --chain-report chain-report.json --out docs/usage/evidence/qwen3-next-chain-<date>
+
+For every stage run on the chain (preflight -> ... -> the given run) this copies
+``run.json``, ``result.json`` and every evidence file the result names (JSON
+and XML only; tensor artifacts stay with the run), plus the chain report. Each
+absolute root is replaced by a placeholder - ``<CHECKPOINT>``, ``<RUN>``,
+``<PYTHON>``, ``<CUDA_HOME>``, ``<SITE>``, ``<HOME>`` - and host and user names
+by ``<HOST>`` / ``<USER>``. The copy fails if any file still contains a
+site-specific path or name afterwards. ``redaction.json`` records what was
+replaced and ``SHA256SUMS`` the published bytes; ``measured-commit.txt`` names
+the commit the stages ran.
+"""
+
+import argparse
+import getpass
+import hashlib
+import json
+import re
+import shutil
+import socket
+from pathlib import Path
+
+FORBIDDEN = (
+    r"/data/",
+    r"/home/",
+    r"/raid/",
+    r"/scratch",
+    r"\bdgx-",
+    r"SLURM_",
+)
+TEXT_SUFFIXES = (".json", ".xml", ".txt", ".log")
+
+
+def chain_runs(last):
+    runs, run = [], Path(last).resolve()
+    while run is not None:
+        manifest = json.loads((run / "run.json").read_text())
+        runs.append((manifest, run))
+        run = Path(manifest["previous"]) if manifest.get("previous") else None
+    if runs[-1][0]["stage"] != "preflight":
+        raise SystemExit("The chain does not lead back to a preflight run")
+    if len({manifest["source_commit"] for manifest, _ in runs}) != 1:
+        raise SystemExit("The chain mixes source commits")
+    return list(reversed(runs))
+
+
+def replacements(runs):
+    """Longest-first (path, placeholder) pairs for every root the chain recorded."""
+    pairs = {}
+    for manifest, run in runs:
+        pairs[str(run)] = "<RUN>"
+        pairs[str(run.parent)] = "<RUNS>"
+        pairs[manifest["checkpoint"]] = "<CHECKPOINT>"
+        pairs[manifest["cuda_home"]] = "<CUDA_HOME>"
+        pairs[manifest["python"]] = "<PYTHON>"
+        python_root = Path(manifest["python"]).parents[1]
+        pairs[str(python_root)] = "<PYTHON_ENV>"
+        if manifest.get("training_storage"):
+            pairs[manifest["training_storage"]] = "<TRAINING_STORAGE>"
+    pairs[str(Path.home())] = "<HOME>"
+    return sorted(pairs.items(), key=lambda item: -len(item[0]))
+
+
+def redact(text, pairs, counts):
+    for value, placeholder in pairs:
+        if value and value in text:
+            counts[placeholder] = counts.get(placeholder, 0) + text.count(value)
+            text = text.replace(value, placeholder)
+    for pattern, placeholder in (
+        (r"/[^\s\"']*/site-packages", "<SITE>"),
+        (r"\bdgx-[A-Za-z0-9-]+", "<HOST>"),
+        (rf"\b{re.escape(socket.gethostname())}\b", "<HOST>"),
+        (rf"\b{re.escape(getpass.getuser())}\b", "<USER>"),
+    ):
+        text, count = re.subn(pattern, placeholder, text)
+        if count:
+            counts[placeholder] = counts.get(placeholder, 0) + count
+    return text
+
+
+def leftovers(text):
+    return [pattern for pattern in FORBIDDEN if re.search(pattern, text)]
+
+
+def publish(runs, chain_report, out):
+    out.mkdir(parents=True, exist_ok=False)
+    pairs, counts, files = replacements(runs), {}, []
+    for manifest, run in runs:
+        stage_dir = out / manifest["stage"]
+        stage_dir.mkdir()
+        result = json.loads((run / "result.json").read_text())
+        sources = {"run.json": run / "run.json", "result.json": run / "result.json"}
+        for name, record in result.get("evidence", {}).items():
+            path = run / record["path"]
+            if path.suffix in TEXT_SUFFIXES and path.is_file():
+                sources[f"{name}{path.suffix}"] = path
+        for name, source in sources.items():
+            target = stage_dir / name
+            target.write_text(redact(source.read_text(), pairs, counts))
+            files.append(target)
+    if chain_report is not None:
+        target = out / "chain-report.json"
+        target.write_text(redact(Path(chain_report).read_text(), pairs, counts))
+        files.append(target)
+    (out / "measured-commit.txt").write_text(runs[0][0]["source_commit"] + "\n")
+    (out / "redaction.json").write_text(
+        json.dumps({"placeholders": dict(sorted(counts.items()))}, indent=2) + "\n"
+    )
+    files += [out / "measured-commit.txt", out / "redaction.json"]
+    dirty = {str(f.relative_to(out)): leftovers(f.read_text()) for f in files}
+    dirty = {name: found for name, found in dirty.items() if found}
+    if dirty:
+        shutil.rmtree(out)
+        raise SystemExit(f"Site-specific text survived redaction: {dirty}")
+    sums = [
+        f"{hashlib.sha256(f.read_bytes()).hexdigest()}  {f.relative_to(out)}" for f in sorted(files)
+    ]
+    (out / "SHA256SUMS").write_text("\n".join(sums) + "\n")
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--last", type=Path, required=True)
+    parser.add_argument("--chain-report", type=Path)
+    parser.add_argument("--out", type=Path, required=True)
+    args = parser.parse_args()
+    runs = chain_runs(args.last)
+    if any(not (run / "result.json").is_file() for _, run in runs):
+        raise SystemExit("Every stage on the chain must have finished")
+    publish(runs, args.chain_report, args.out)
+    print(f"published {len(runs)} stages to {args.out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_rollout_smoke.py b/scripts/qwen3_next_rollout_smoke.py
new file mode 100644
index 000000000..8ebb72c84
--- /dev/null
+++ b/scripts/qwen3_next_rollout_smoke.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Real-checkpoint rollout diagnostic. This does not perform VIME or L2 validation."""
+
+import argparse
+import json
+import os
+from pathlib import Path
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--model", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--tensor-parallel-size", type=int, default=1)
+    parser.add_argument("--non-strict-smoke", action="store_true")
+    args = parser.parse_args()
+    config = json.loads((args.model / "config.json").read_text())
+    if config.get("model_type") != "qwen3_next" or config.get("num_hidden_layers") != 48:
+        raise SystemExit("Expected the full Qwen3-Next checkpoint")
+    os.environ["VLLM_BATCH_INVARIANT"] = "0" if args.non_strict_smoke else "1"
+    os.environ["VLLM_PLUGINS"] = ""
+    from vllm import LLM, SamplingParams
+
+    model = LLM(
+        model=str(args.model.resolve()),
+        dtype="bfloat16",
+        tensor_parallel_size=args.tensor_parallel_size,
+        gpu_memory_utilization=0.93,
+        max_model_len=512,
+        max_num_seqs=2,
+        enforce_eager=True,
+        enable_prefix_caching=False,
+        seed=1234,
+        mamba_cache_dtype="bfloat16",
+        mamba_ssm_cache_dtype="float32",
+        kernel_config={"moe_backend": "triton", "enable_flashinfer_autotune": False},
+    )
+    outputs = model.generate(
+        ["The capital of France is", "Calculate 7 plus 5. The result is"],
+        SamplingParams(temperature=0.0, top_p=1.0, max_tokens=8, logprobs=1),
+    )
+    results = []
+    for output in outputs:
+        response = output.outputs[0]
+        results.append(
+            {
+                "prompt_token_ids": output.prompt_token_ids,
+                "response_token_ids": list(response.token_ids),
+                "selected_logprobs": [
+                    row[token].logprob for row, token in zip(response.logprobs, response.token_ids)
+                ],
+                "text": response.text,
+            }
+        )
+    payload = {
+        "scope": "rollout_smoke_only",
+        "full_model_l2": "not_evaluated",
+        "batch_invariant_requested": not args.non_strict_smoke,
+        "moe_backend": "triton",
+        "model": str(args.model.resolve()),
+        "tensor_parallel_size": args.tensor_parallel_size,
+        "results": results,
+    }
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(json.dumps(payload, indent=2) + "\n")
+    print("REAL_CHECKPOINT_ROLLOUT_SMOKE_COMPLETE", flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_run.py b/scripts/qwen3_next_run.py
new file mode 100644
index 000000000..db8133d94
--- /dev/null
+++ b/scripts/qwen3_next_run.py
@@ -0,0 +1,337 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Run the Qwen3-Next full-model chain inside any allocation with four B200 GPUs.
+
+    python scripts/qwen3_next_run.py --stage all \\
+        --runs-root <dir> --checkpoint <Qwen3-Next-80B-A3B-Instruct> --cuda-home <cuda-13.0>
+
+Stages run in order, each in its own run directory and its own clean clone of
+the current commit; ``--from``/``--until`` select a contiguous part. The stages
+are ``preflight`` (environment, operator gates, device guards, TP4 probe,
+checkpoint hashes, rollout smoke), ``layers`` (per-layer and
+scheduler gates, which certify the shared provider), ``l2`` (full-model
+train/prefill/decode raw-bit matrix), then ``train1``, ``train2`` and ``train``
+(two optimizer updates, export, L2 on the updated weights, reload and
+resumption). A single stage takes ``--previous <run dir>`` of its predecessor.
+
+Every run directory holds ``run.json`` (the immutable manifest, identified by a
+fresh ``execution_id``), ``attempts/<n>/`` (the stage's output) and, once the
+stage passes, ``result.json``. The launcher refuses a dirty tree and never
+talks to a batch scheduler; wrap it in whatever allocation command the cluster
+uses.
+"""
+
+import argparse
+import json
+import os
+import signal
+import subprocess
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from uuid import uuid4
+
+from qwen3_next_contract import (
+    CONTRACT_VERSION,
+    checkpoint_identity,
+    operator_requirements,
+    validate_result,
+)
+from qwen3_next_layer_contract import layer_requirements
+from qwen3_next_runtime import (
+    STAGES,
+    atomic_json,
+    failure_fields,
+    file_digest,
+    interpreter_path,
+    probe_environment,
+    require_predecessor,
+    verify_environment,
+    wait_owned_child,
+)
+
+ROOT = Path(__file__).resolve().parents[1]
+ORDER = ("preflight", "layers", "l2", "train1", "train2", "train")
+PROVIDER_SOURCES = (
+    "rl_engine/integrations/qwen3_next_provider.py",
+    "rl_engine/integrations/qwen3_next_conv.py",
+    "rl_engine/integrations/qwen3_next_gdn.py",
+    "rl_engine/integrations/qwen3_next_engine_weights.py",
+    "rl_engine/integrations/qwen3_next_profile.py",
+    "rl_engine/integrations/qwen3_next_tp.py",
+    "rl_engine/integrations/qwen3_next_tp_gdn.py",
+    "rl_engine/integrations/qwen3_next_tp_blocks.py",
+    "rl_engine/integrations/qwen3_next_forward.py",
+    "rl_engine/integrations/qwen3_next_embedding.py",
+    "rl_engine/integrations/qwen3_next_logprob.py",
+    "rl_engine/integrations/qwen3_next_vime.py",
+    "rl_engine/integrations/qwen3_next_vllm.py",
+    "rl_engine/integrations/qwen3_next_vllm_attention.py",
+    "rl_engine/integrations/qwen3_next_vllm_model.py",
+    "rl_engine/kernels/ops/cuda/norm/rmsnorm.py",
+    "csrc/cuda/rmsnorm.cu",
+)
+
+
+def _git(*args, cwd=ROOT):
+    return subprocess.check_output(["git", *args], cwd=cwd, text=True).strip()
+
+
+def provider_config():
+    return {
+        "profile": "qwen3_next_tp4_shared_eager_v1",
+        "scope": "shared_full_forward_implementation",
+        "scheduler_batch_invariance_certified": False,
+        "sources": {name: file_digest(ROOT / name) for name in PROVIDER_SOURCES},
+        "weights": "bfloat16",
+        "recurrent_state": "float32",
+        "mtp": False,
+        "prefix_reuse": False,
+        "dropout": False,
+        "dynamic_tuning": False,
+        "cuda_graph": False,
+        "sequence_parallel": False,
+        "fp32_residual_connection": False,
+        "vocab_size": 151936,
+        "padded_vocab_size": 151936,
+        "expert_tensor_parallel_size": 4,
+        # Large enough that the batch-invariance matrix (up to 64 concurrent
+        # requests) runs under the same certified provider identity.
+        "native_rollout_kv_cache_memory_bytes": 4 << 30,
+        "runtime_env": {"TRITON_F32_DEFAULT": "ieee", "VLLM_USE_V2_MODEL_RUNNER": "0"},
+    }
+
+
+def prepare(stage, previous, args):
+    """Create the run directory, its clean source clone and its immutable manifest."""
+    if _git("status", "--porcelain", "--untracked-files=no"):
+        raise SystemExit("Commit the source before starting an immutable run")
+    commit = _git("rev-parse", "HEAD")
+    checkpoint = args.checkpoint.resolve(strict=True)
+    stamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
+    execution_id = uuid4().hex
+    run_id = f"{stamp}-{stage}-{commit[:8]}-{execution_id[:8]}"
+    run_dir = args.runs_root.resolve() / run_id
+    run_dir.mkdir(parents=True)
+    source = run_dir / "source"
+    subprocess.run(
+        ["git", "clone", "-q", "--shared", "--no-checkout", str(ROOT), str(source)], check=True
+    )
+    subprocess.run(["git", "checkout", "-q", "--detach", commit], cwd=source, check=True)
+    python = interpreter_path(args.python)
+    manifest = {
+        "run_id": run_id,
+        "execution_id": execution_id,
+        "contract_version": CONTRACT_VERSION,
+        "suite": "all",
+        "execution_gpus": 4,
+        "source_tree": _git("rev-parse", "HEAD^{tree}"),
+        "stage": stage,
+        "source_commit": commit,
+        "source": str(source),
+        "python": python,
+        "cuda_home": str(args.cuda_home.resolve(strict=True)),
+        "checkpoint": str(checkpoint),
+        "memory": args.memory or STAGES[stage][0],
+        "previous": str(previous) if previous else None,
+        "status": "prepared",
+        "full_model_l2": "not_evaluated",
+    }
+    if args.training_storage_root and stage == "train1":
+        manifest["training_storage"] = str(args.training_storage_root.resolve(strict=True) / run_id)
+    environment = probe_environment(python)
+    manifest["environment"] = environment
+    manifest["environment_sha256"] = environment["sha256"]
+    manifest["cuda_identity"] = {
+        "home": manifest["cuda_home"],
+        "nvcc_sha256": file_digest(Path(manifest["cuda_home"]) / "bin/nvcc"),
+    }
+    manifest["checkpoint_identity"] = checkpoint_identity(checkpoint)
+    manifest["provider_config"] = provider_config()
+    manifest["operator_requirements"] = operator_requirements(ROOT, "all")
+    manifest["layer_requirements"] = layer_requirements(ROOT)
+    manifest["topology"] = {"tp": 4, "pp": 1, "cp": 1, "ep": 1}
+    require_predecessor(stage, previous, checkpoint, manifest)
+    if stage == "l2":
+        certificate = previous / "result.json"
+        manifest["scheduler_profile_certificate"] = {
+            "path": str(certificate),
+            "sha256": file_digest(certificate),
+        }
+    elif stage in ("train1", "train2", "train"):
+        manifest["scheduler_profile_certificate"] = json.loads((previous / "run.json").read_text())[
+            "scheduler_profile_certificate"
+        ]
+    atomic_json(run_dir / "run.json", manifest)
+    return run_dir, manifest
+
+
+def stage_environment(manifest, run_dir, attempt):
+    env = os.environ.copy()
+    env.pop("PYTHONHOME", None)
+    env.update(
+        CUDA_HOME=manifest["cuda_home"],
+        PYTHONUNBUFFERED="1",
+        VLLM_PLUGINS="",
+        MAX_JOBS="4",
+        KERNEL_ALIGN_DEV_RPATH="1",
+        QWEN3_NEXT_ATTEMPT=attempt,
+        QWEN3_NEXT_ALLOCATED_MEMORY=manifest["memory"],
+        # Four native ranks share the host. Left unpinned, each rank spins a
+        # thread per core on the ~50k small per-tensor loads of the HF import.
+        OMP_NUM_THREADS="16",
+        MKL_NUM_THREADS="16",
+    )
+    # Layer gates qualify the providers without vLLM's aten overrides; the
+    # certified full-model stages run with them.
+    if manifest["stage"] in ("preflight", "layers"):
+        env["VLLM_BATCH_INVARIANT"] = "0"
+    else:
+        env["VLLM_BATCH_INVARIANT"] = "1"
+        env["QWEN_SHARED_PROFILE_RESULT"] = manifest["scheduler_profile_certificate"]["path"]
+    env.update(manifest["provider_config"]["runtime_env"])
+    base_bin = Path(manifest["environment"]["identity"]["base_prefix"]) / "bin"
+    env["PATH"] = (
+        f"{Path(manifest['python']).parent}:{manifest['cuda_home']}/bin:{base_bin}:{env['PATH']}"
+    )
+    env["PYTHONPATH"] = manifest["source"]
+    env["TRITON_CACHE_DIR"] = str(run_dir / "cache/triton")
+    env["TORCH_EXTENSIONS_DIR"] = str(run_dir / "cache/torch")
+    return env
+
+
+def execute(run_dir, manifest):
+    """Run one stage in its clone; on success write and validate ``result.json``."""
+    attempt = "0"
+    output = run_dir / "attempts" / attempt
+    output.mkdir(parents=True)
+    status = {
+        "run_id": manifest["run_id"],
+        "execution_id": manifest["execution_id"],
+        "stage": manifest["stage"],
+        "attempt": attempt,
+        "status": "running",
+        "full_model_l2": "not_evaluated",
+    }
+    atomic_json(output / "status.json", status)
+    (run_dir / "RUNNING").touch()
+    received = None
+
+    def stop(signum, frame):
+        nonlocal received
+        received = received or signum
+
+    for sig in (signal.SIGUSR1, signal.SIGTERM, signal.SIGINT):
+        signal.signal(sig, stop)
+    child = None
+    try:
+        verify_environment(manifest["environment"], require_venv=manifest["stage"] != "preflight")
+        if manifest["environment"]["identity"]["build_tools"]["ninja"] is None:
+            raise RuntimeError("environment_mismatch: locked Ninja build tool is missing")
+        source = manifest["source"]
+        if _git("rev-parse", "HEAD^{tree}", cwd=source) != manifest["source_tree"] or _git(
+            "status", "--porcelain", "--untracked-files=no", cwd=source
+        ):
+            raise RuntimeError("source_mismatch: frozen tracked source changed")
+        if (
+            file_digest(Path(manifest["cuda_home"]) / "bin/nvcc")
+            != manifest["cuda_identity"]["nvcc_sha256"]
+        ):
+            raise RuntimeError("environment_mismatch: CUDA toolkit changed")
+        atomic_json(output / "runtime-identity.json", manifest["environment"])
+        command = [
+            manifest["python"],
+            str(Path(source) / STAGES[manifest["stage"]][3]),
+            "--manifest",
+            str(run_dir / "run.json"),
+            "--output",
+            str(output),
+        ]
+        child = subprocess.Popen(
+            command,
+            cwd=source,
+            env=stage_environment(manifest, run_dir, attempt),
+            start_new_session=True,
+        )
+        code = wait_owned_child(child, lambda: received is not None)
+        if received:
+            status.update(status="interrupted", signal=received, failure_kind="unknown")
+        elif code:
+            status.update(
+                status="failed",
+                exit_code=code,
+                **failure_fields(RuntimeError(f"Stage exited with code {code}")),
+            )
+            failure = output / "result.json"
+            if failure.is_file():
+                record = json.loads(failure.read_text())
+                status.update({k: record[k] for k in ("failure_kind", "error") if k in record})
+        else:
+            result = json.loads((output / "result.json").read_text())
+            validate_result(manifest, result, run_dir=run_dir, attempt=attempt)
+            status.update(result)
+            atomic_json(run_dir / "result.json", result)
+    except Exception as exc:
+        status.update(status="interrupted" if received else "failed", **failure_fields(exc))
+        raise
+    finally:
+        if child is not None and child.poll() is None:
+            wait_owned_child(child, lambda: True)
+        atomic_json(output / "status.json", status)
+        (run_dir / "RUNNING").unlink(missing_ok=True)
+    return status
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("--stage", choices=(*ORDER, "all"), required=True)
+    parser.add_argument("--runs-root", type=Path, required=True)
+    parser.add_argument("--checkpoint", type=Path, required=True)
+    parser.add_argument("--cuda-home", type=Path, required=True)
+    parser.add_argument("--python", type=Path, default=Path(sys.executable))
+    parser.add_argument("--previous", type=Path, help="passed run directory of the predecessor")
+    parser.add_argument(
+        "--from",
+        dest="first",
+        choices=ORDER,
+        default="preflight",
+        help="with --stage all: first stage to run (later than preflight needs --previous)",
+    )
+    parser.add_argument(
+        "--until", choices=ORDER, default="train", help="with --stage all: last stage to run"
+    )
+    parser.add_argument(
+        "--memory",
+        help="host memory this allocation grants (e.g. 1280G); default: the stage's budget",
+    )
+    parser.add_argument(
+        "--training-storage-root",
+        type=Path,
+        help="shared directory for training checkpoints and exports (default: in the run)",
+    )
+    args = parser.parse_args()
+    if args.stage == "all":
+        stages = ORDER[ORDER.index(args.first) : ORDER.index(args.until) + 1]
+        if not stages:
+            raise SystemExit("--from must not come after --until")
+    else:
+        stages = (args.stage,)
+    if (stages[0] == "preflight") != (args.previous is None):
+        raise SystemExit("Every stage after preflight needs --previous <passed predecessor run>")
+    previous = args.previous.resolve(strict=True) if args.previous else None
+    for stage in stages:
+        run_dir, manifest = prepare(stage, previous, args)
+        print(f"[qwen3-next] {stage}: {run_dir}", flush=True)
+        status = execute(run_dir, manifest)
+        print(f"[qwen3-next] {stage}: {status['status']}", flush=True)
+        if status["status"] != "passed":
+            raise SystemExit(1)
+        previous = run_dir
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_runtime.py b/scripts/qwen3_next_runtime.py
new file mode 100644
index 000000000..321597ac5
--- /dev/null
+++ b/scripts/qwen3_next_runtime.py
@@ -0,0 +1,772 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Scheduler-independent runtime for the Qwen3-Next stage runners.
+
+Environment identity, failure classification, memory preconditions, owned child
+processes, checkpoint verification, stage ordering and crash-recoverable
+training storage. Nothing here talks to a batch scheduler: a run is identified
+by the ``execution_id`` that ``scripts/qwen3_next_run.py`` writes into
+``run.json``, and any allocation that provides four GPUs can host it.
+"""
+
+import errno
+import hashlib
+import importlib.metadata
+import json
+import os
+import re
+import shutil
+import signal
+import site
+import subprocess
+import sys
+import sysconfig
+import time
+from pathlib import Path
+from urllib.parse import unquote, urlparse
+
+# --------------------------------------------------------------------------- #
+# Environment identity
+# --------------------------------------------------------------------------- #
+
+
+def file_digest(path):
+    with Path(path).open("rb") as stream:
+        return hashlib.file_digest(stream, "sha256").hexdigest()
+
+
+def identity_digest(value):
+    encoded = json.dumps(value, sort_keys=True, separators=(",", ":")).encode()
+    return hashlib.sha256(encoded).hexdigest()
+
+
+def interpreter_path(path):
+    path = Path(os.path.abspath(os.path.expanduser(str(path))))
+    if not path.is_file() or not os.access(path, os.X_OK):
+        raise ValueError(f"Python interpreter is not executable: {path}")
+    return str(path)
+
+
+def source_identity(path):
+    path = Path(path)
+    result = subprocess.run(
+        ["git", "-C", str(path), "rev-parse", "--show-toplevel"],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    if result.returncode:
+        raise ValueError(f"Editable Python source has no auditable Git identity: {path}")
+    root = result.stdout.strip()
+    tree = subprocess.check_output(
+        ["git", "-C", root, "rev-parse", "HEAD^{tree}"], text=True
+    ).strip()
+    patch = subprocess.check_output(["git", "-C", root, "diff", "--binary", "HEAD"])
+    return {"path": root, "tree": tree, "patch_sha256": hashlib.sha256(patch).hexdigest()}
+
+
+def installed_provider_sources(distributions):
+    """Pin provider code inside a wheel too; unchanged metadata is insufficient.
+
+    Editable frameworks are separately identified by Git tree and patch. vLLM
+    is an installed wheel in the shared environment, and local patches to its
+    numerical kernels or actual scheduler must invalidate the environment lock.
+    No framework import or GPU initialization is needed for this inventory.
+    """
+    prefixes = (
+        "vllm/model_executor/determinism/",
+        "vllm/model_executor/layers/mamba/",
+        "vllm/model_executor/layers/fused_moe/",
+        "vllm/model_executor/models/qwen3_next.py",
+        "vllm/third_party/flash_linear_attention/",
+        "vllm/v1/attention/",
+        "vllm/v1/core/sched/",
+        "vllm/v1/worker/",
+        "vllm/v1/sample/",
+        "vllm/config/",
+        "vllm/compilation/",
+        "vllm/distributed/",
+    )
+    records = {}
+    for dist in distributions:
+        if dist.metadata.get("Name", "").lower().replace("_", "-") != "vllm":
+            continue
+        files = dist.files
+        if not files:
+            raise ValueError("Installed vLLM has no auditable provider file inventory")
+        selected = sorted(
+            str(path)
+            for path in files
+            if str(path).endswith(".py") and str(path).startswith(prefixes)
+        )
+        required = "vllm/model_executor/determinism/batch_invariant.py"
+        if required not in selected:
+            raise ValueError("Installed vLLM omits the pinned shared GEMM provider")
+        for name in selected:
+            path = Path(dist.locate_file(name))
+            records[name] = {"path": str(path.absolute()), "sha256": file_digest(path)}
+    return records
+
+
+def installation_roots():
+    """The interpreter's site directories, independent of what has been imported.
+
+    ``sys.path`` grows at runtime: importing setuptools appends its ``_vendor``
+    directory, after which ``importlib.metadata.distributions()`` also lists the
+    vendored packages. An identity read through ``sys.path`` therefore depends on
+    import order, and a worker that imports the model before verifying would
+    disagree with the manifest written by a fresh interpreter. Enumerate the fixed
+    site roots instead; in a fresh interpreter they are exactly the ``site-packages``
+    entries of ``sys.path``.
+    """
+    roots = {Path(sysconfig.get_path("purelib")), Path(sysconfig.get_path("platlib"))}
+    roots.update(Path(p) for p in site.getsitepackages())
+    if site.ENABLE_USER_SITE:
+        roots.add(Path(site.getusersitepackages()))
+    return sorted(root for root in roots if root.is_dir())
+
+
+def current_identity():
+    distributions = []
+    sources = {}
+    roots = installation_roots()
+    installed = list(importlib.metadata.distributions(path=[str(root) for root in roots]))
+    for dist in installed:
+        if dist.metadata.get("Name", "").lower().replace("_", "-") in {"rl-engine", "rl-kernel"}:
+            continue
+        direct = dist.read_text("direct_url.json")
+        if direct:
+            direct_value = json.loads(direct)
+            if direct_value.get("dir_info", {}).get("editable"):
+                location = Path(unquote(urlparse(direct_value["url"]).path))
+                # This project is loaded from the frozen source snapshot instead.
+                if dist.metadata.get("Name", "").lower().replace("_", "-") != "rl-engine":
+                    sources[str(location)] = source_identity(location)
+        distributions.append(
+            {
+                "name": dist.metadata.get("Name"),
+                "version": dist.version,
+                "metadata_sha256": identity_digest(dist.read_text("METADATA")),
+                "direct_url": direct,
+            }
+        )
+    pth = {}
+    for root in roots:
+        for path in sorted(root.glob("*.pth")):
+            pth[str(path)] = file_digest(path)
+            for line in path.read_text().splitlines():
+                location = Path(line)
+                if (
+                    line
+                    and not line.startswith(("#", "import "))
+                    and location.is_absolute()
+                    and location.is_dir()
+                ):
+                    if (location / ".git").exists():
+                        sources[str(location)] = source_identity(location)
+    config = Path(sys.prefix) / "pyvenv.cfg"
+    ninja = next(
+        (
+            Path(prefix) / "bin/ninja"
+            for prefix in (sys.prefix, sys.base_prefix)
+            if (Path(prefix) / "bin/ninja").is_file()
+        ),
+        None,
+    )
+    build_tools = {"ninja": {"path": str(ninja), "sha256": file_digest(ninja)} if ninja else None}
+    identity = {
+        "executable": sys.executable,
+        "binary": str(Path(sys.executable).resolve()),
+        "binary_sha256": file_digest(sys.executable),
+        "prefix": sys.prefix,
+        "base_prefix": sys.base_prefix,
+        "version": sys.version,
+        "pyvenv_cfg_sha256": file_digest(config) if config.is_file() else None,
+        "distributions": sorted(
+            distributions, key=lambda item: (item["name"] or "", item["version"])
+        ),
+        "build_tools": build_tools,
+        "pth": pth,
+        "sources": sources,
+        "installed_provider_sources": installed_provider_sources(installed),
+    }
+    return {"sha256": identity_digest(identity), "identity": identity}
+
+
+def probe_environment(python):
+    command = [interpreter_path(python), str(Path(__file__).absolute()), "--json"]
+    env = os.environ.copy()
+    env.pop("PYTHONHOME", None)
+    env.pop("PYTHONPATH", None)
+    output = subprocess.check_output(command, text=True, env=env, timeout=180)
+    return json.loads(output)
+
+
+def verify_environment(expected, *, require_venv=False):
+    actual = current_identity()
+    if actual != expected:
+        raise RuntimeError(
+            "environment_mismatch: interpreter, dependencies or editable source changed"
+        )
+    if require_venv and sys.prefix == sys.base_prefix:
+        raise RuntimeError("environment_mismatch: training/recompute requires the isolated venv")
+    return actual
+
+
+if __name__ == "__main__":
+    print(json.dumps(current_identity(), sort_keys=True))
+
+
+# --------------------------------------------------------------------------- #
+# Failure classification
+# --------------------------------------------------------------------------- #
+
+
+FAILURE_KINDS = frozenset(
+    {
+        "environment_mismatch",
+        "source_mismatch",
+        "checkpoint_mismatch",
+        "topology_mismatch",
+        "insufficient_memory",
+        "insufficient_disk",
+        "out_of_memory",
+        "time_limit",
+        "unknown",
+    }
+)
+
+
+class ProcessFailure(RuntimeError):
+    def __init__(self, message, *, failure_kind):
+        super().__init__(message)
+        self.failure_kind = failure_kind
+
+
+def failure_kind(error=None, *, text=""):
+    explicit = getattr(error, "failure_kind", None)
+    if explicit in FAILURE_KINDS:
+        return explicit
+    if isinstance(error, (TimeoutError, subprocess.TimeoutExpired)):
+        return "time_limit"
+    if isinstance(error, OSError) and error.errno == errno.ENOSPC:
+        return "insufficient_disk"
+    if type(error).__name__ == "OutOfMemoryError":
+        return "out_of_memory"
+    if isinstance(error, (ImportError, ModuleNotFoundError)):
+        return "environment_mismatch"
+    detail = f"{type(error).__name__}: {error}\n{text}" if error is not None else text
+    for kind in sorted(FAILURE_KINDS - {"unknown", "out_of_memory", "time_limit"}):
+        if re.search(rf"\b{kind}\s*:", detail):
+            return kind
+    if re.search(r"\bOutOfMemoryError\s*:|CUDA out of memory", detail, re.IGNORECASE):
+        return "out_of_memory"
+    if re.search(r"\bENOSPC\b|\[Errno 28\]|No space left on device", detail):
+        return "insufficient_disk"
+    if re.search(r"\b(?:ModuleNotFoundError|ImportError)\s*:", detail):
+        return "environment_mismatch"
+    if re.search(r"\b(?:TimeoutExpired|TimeoutError|time_limit)\s*:", detail):
+        return "time_limit"
+    # Signals, exit 137, MemoryError and a bare 'Killed' are not proof of CUDA OOM.
+    return "unknown"
+
+
+def failure_fields(error):
+    return {"failure_kind": failure_kind(error), "error": f"{type(error).__name__}: {error}"}
+
+
+def subprocess_failure(code, log_path):
+    with Path(log_path).open("rb") as stream:
+        stream.seek(0, 2)
+        stream.seek(max(0, stream.tell() - 65536))
+        tail = stream.read().decode("utf-8", errors="replace")
+    return ProcessFailure(
+        f"Native model process failed with exit {code}; see {log_path}\n{tail}",
+        failure_kind=failure_kind(text=tail),
+    )
+
+
+# --------------------------------------------------------------------------- #
+# Memory, checkpoint and atomic files
+# --------------------------------------------------------------------------- #
+
+
+def atomic_json(path, payload):
+    path = Path(path)
+    temporary = path.with_suffix(path.suffix + ".partial")
+    with temporary.open("w") as stream:
+        stream.write(json.dumps(payload, indent=2) + "\n")
+        stream.flush()
+        os.fsync(stream.fileno())
+    temporary.replace(path)
+    fd = os.open(path.parent, os.O_RDONLY | os.O_DIRECTORY)
+    try:
+        os.fsync(fd)
+    finally:
+        os.close(fd)
+
+
+def digest(path):
+    with path.open("rb") as stream:
+        return hashlib.file_digest(stream, "sha256").hexdigest()
+
+
+_MEMORY_SUFFIXES = {"K": 1024, "M": 1024**2, "G": 1024**3, "T": 1024**4}
+
+
+def parse_memory_request(text):
+    """A memory size (e.g. ``1280G``) in bytes; a bare number is MB."""
+    text = str(text).strip()
+    if not text:
+        raise RuntimeError("Empty memory request")
+    scale = _MEMORY_SUFFIXES.get(text[-1].upper())
+    digits = text[:-1] if scale else text
+    if not digits.isdigit():
+        raise RuntimeError(f"Unparseable memory request: {text!r}")
+    return int(digits) * (scale if scale else 1024**2)
+
+
+def available_memory_bytes():
+    """MemAvailable, i.e. what the kernel thinks can be had without swapping."""
+    for line in Path("/proc/meminfo").read_text().splitlines():
+        if line.startswith("MemAvailable:"):
+            return int(line.split()[1]) * 1024
+    return None
+
+
+def memory_limits(environ=None):
+    """Every known memory ceiling, and what is free right now.
+
+    ``effective_bytes`` is the smallest of the cgroup limit and the allocation
+    the launcher declares in ``QWEN3_NEXT_ALLOCATED_MEMORY`` (``--memory``).
+    It is None only when neither is known, which the stages treat as a hard
+    failure: host free memory alone is never taken as a grant.
+    ``available_bytes`` is a snapshot of MemAvailable; on a shared node a grant
+    is not an availability guarantee, so both are checked.
+    """
+    environ = os.environ if environ is None else environ
+    records = {}
+    for line in Path("/proc/self/cgroup").read_text().splitlines():
+        _, controllers, relative = line.split(":", 2)
+        if controllers == "":
+            root, name = Path("/sys/fs/cgroup"), "memory.max"
+        elif "memory" in controllers.split(","):
+            root, name = Path("/sys/fs/cgroup/memory"), "memory.limit_in_bytes"
+        else:
+            continue
+        current = root / relative.lstrip("/")
+        while current.is_relative_to(root):
+            path = current / name
+            if path.is_file():
+                records[str(path)] = path.read_text().strip()
+            if current == root:
+                break
+            current = current.parent
+    finite = [int(value) for value in records.values() if value.isdigit() and int(value) < 2**60]
+    declared = environ.get("QWEN3_NEXT_ALLOCATED_MEMORY")
+    sources = {
+        "cgroup_bytes": min(finite) if finite else None,
+        "declared_bytes": parse_memory_request(declared) if declared else None,
+    }
+    known = [value for value in sources.values() if value is not None]
+    return {
+        "limits": records,
+        **sources,
+        "effective_bytes": min(known) if known else None,
+        "available_bytes": available_memory_bytes(),
+    }
+
+
+def verify_checkpoint(checkpoint, identity):
+    if checkpoint.name != identity["revision"]:
+        raise RuntimeError("Checkpoint revision does not match the pinned manifest")
+    config = json.loads((checkpoint / "config.json").read_text())
+    for key, value in identity["config_fingerprint"].items():
+        if config.get(key) != value:
+            raise RuntimeError(f"Checkpoint architecture mismatch: {key}")
+    snapshot = identity["weight_snapshot"]
+    if digest(checkpoint / snapshot["index_file"]) != snapshot["index_sha256"]:
+        raise RuntimeError("Checkpoint index checksum mismatch")
+    for shard in snapshot["shards"]:
+        path = checkpoint / shard["filename"]
+        if path.stat().st_size != shard["size_bytes"] or digest(path) != shard["sha256"]:
+            raise RuntimeError(f"Checkpoint shard checksum mismatch: {path.name}")
+    return {
+        "model_id": identity["model_id"],
+        "revision": identity["revision"],
+        "content_hash": snapshot["content_hash"],
+        "shards_verified": len(snapshot["shards"]),
+        "metadata_sha256": {p.name: digest(p) for p in sorted(checkpoint.glob("*.json"))},
+    }
+
+
+# --------------------------------------------------------------------------- #
+# Owned child processes
+# --------------------------------------------------------------------------- #
+
+
+def signal_group(child, signum):
+    try:
+        os.killpg(child.pid, signum)
+    except ProcessLookupError:
+        pass
+
+
+def wait_owned_child(child, interrupted, *, grace_seconds=10):
+    deadline = None
+    while True:
+        if interrupted() and deadline is None:
+            signal_group(child, signal.SIGTERM)
+            deadline = time.monotonic() + grace_seconds
+        if deadline is not None and time.monotonic() >= deadline:
+            # Kill the group even if its leader exited, so grandchildren cannot linger.
+            signal_group(child, signal.SIGKILL)
+            return child.wait(timeout=10)
+        code = child.poll()
+        if code is not None and deadline is None:
+            return code
+        time.sleep(0.05)
+
+
+# --------------------------------------------------------------------------- #
+# Stage order
+# --------------------------------------------------------------------------- #
+
+STAGES = {
+    "preflight": ("256G", "01:00:00", None, "scripts/qwen3_next_preflight.py"),
+    "layers": ("512G", "04:00:00", "preflight", "scripts/qwen3_next_layers.py"),
+    "l2": ("512G", "04:00:00", "layers", "scripts/qwen3_next_l2.py"),
+    # The training acceptance is split so no single job holds a node for a day:
+    # train1 = update1 + export + L2, train2 = update2 + export + L2 (continues
+    # train1's durable state), train = reload + L2 + continuous/resumed controls.
+    "train1": ("1280G", "06:00:00", "l2", "scripts/qwen3_next_training.py"),
+    "train2": ("1280G", "07:00:00", "train1", "scripts/qwen3_next_training.py"),
+    "train": ("1280G", "09:00:00", "train2", "scripts/qwen3_next_training.py"),
+}
+
+
+def require_predecessor(stage, previous, checkpoint, expected=None):
+    """The previous stage's run passed, finished, and pinned the same identity."""
+    from qwen3_next_contract import acceptance_identity, checkpoint_identity, validate_result
+
+    predecessor = STAGES[stage][2]
+    if predecessor is None:
+        return
+    if previous is None:
+        raise ValueError(f"{stage} requires a passed {predecessor} run")
+    manifest = json.loads((previous / "run.json").read_text())
+    result = json.loads((previous / "result.json").read_text())
+    if manifest["stage"] != predecessor or result.get("status") != "passed":
+        raise ValueError("predecessor stage has not passed")
+    if (previous / "RUNNING").exists():
+        raise ValueError("predecessor run is still executing")
+    validate_result(manifest, result, run_dir=previous)
+    if manifest["checkpoint_identity"] != checkpoint_identity(checkpoint):
+        raise ValueError("predecessor checkpoint identity mismatch")
+    if expected is not None and acceptance_identity(manifest) != acceptance_identity(expected):
+        raise ValueError("predecessor source/environment/provider/topology identity mismatch")
+
+
+# --------------------------------------------------------------------------- #
+# Training storage
+# --------------------------------------------------------------------------- #
+
+
+def _boundary(name):
+    """CPU fault-injection seam; production deliberately performs no action."""
+
+
+def _sync(path):
+    descriptor = os.open(path, os.O_RDONLY | os.O_DIRECTORY)
+    try:
+        os.fsync(descriptor)
+    finally:
+        os.close(descriptor)
+
+
+def _read(path):
+    if path.is_symlink() or not path.is_file():
+        raise ValueError(f"Missing or external storage ownership record: {path}")
+    return json.loads(path.read_text())
+
+
+def _write(path, value):
+    if path.is_symlink() or path.with_suffix(path.suffix + ".partial").is_symlink():
+        raise ValueError("Storage intent cannot follow a symlink")
+    atomic_json(path, value)
+
+
+def _root_record(root, identity):
+    return {
+        "schema_version": 1,
+        "scope": "qwen3_next_owned_storage",
+        "root": str(root),
+        "identity": identity,
+    }
+
+
+def owned_root(root, identity):
+    root = Path(root).absolute()
+    if root.is_symlink():
+        raise ValueError("Training storage root cannot be a symlink")
+    root = root.resolve()
+    root.parent.mkdir(parents=True, exist_ok=True)
+    parent_owner = root.parent / f".{root.name}.owner.json"
+    expected = _root_record(root, identity)
+    if parent_owner.exists():
+        if _read(parent_owner) != expected:
+            raise ValueError("Training storage belongs to another run")
+    else:
+        # Existing deployments can migrate only with their original owner.
+        # An interrupted new creation has no directory until parent proof lands.
+        if root.exists() and any(root.iterdir()):
+            if _read(root / "RUN_OWNER.json") != identity:
+                raise ValueError("Existing training files lack a matching run owner")
+        _write(parent_owner, expected)
+    _boundary("root_owner_persisted")
+    root.mkdir(exist_ok=True)
+    _sync(root.parent)
+    _boundary("root_created")
+    owner = root / "RUN_OWNER.json"
+    if owner.exists() and _read(owner) != identity:
+        raise ValueError("Internal training owner disagrees with durable parent proof")
+    _write(owner, identity)
+    return root
+
+
+def require_owner(root, identity):
+    root = Path(root).absolute()
+    if root.is_symlink() or not root.is_dir():
+        raise ValueError("Training storage must be a direct owned directory")
+    root = root.resolve()
+    if _read(root.parent / f".{root.name}.owner.json") != _root_record(root, identity):
+        raise ValueError("Training storage lacks matching durable parent ownership")
+    return root
+
+
+def _child(root, name):
+    if not isinstance(name, str) or not name or Path(name).name != name or name in (".", ".."):
+        raise ValueError("Storage intent must name one direct child")
+    path = root / name
+    if path.is_symlink():
+        raise ValueError("Storage intents cannot follow target symlinks")
+    return path
+
+
+def create_staging(root, destination_name, metadata):
+    if metadata.get("status") != "staging":
+        raise ValueError("Staging creation needs an explicit unpublished status")
+    root = require_owner(root, metadata["identity"])
+    destination = _child(root, destination_name)
+    if destination.name.startswith(".") or destination.suffix:
+        raise ValueError("Published storage names must remain visible and have no suffix")
+    staging = destination.with_suffix(".partial")
+    if destination.exists() or staging.exists():
+        raise ValueError("A staging intent cannot adopt an existing directory")
+    intent = root / f".{destination.name}.staging.json"
+    value = {
+        "schema_version": 1,
+        "scope": "qwen3_next_staging_intent",
+        "identity": metadata["identity"],
+        "staging": staging.name,
+        "published": destination.name,
+        "metadata": metadata,
+    }
+    if intent.exists():
+        raise ValueError("Staging names cannot reuse an earlier durable intent")
+    _write(intent, value)
+    _boundary("staging_intent_persisted")
+    staging.mkdir()
+    _sync(root)
+    _boundary("staging_created")
+    _write(staging / "STAGING.json", metadata)
+    _boundary("staging_marker_persisted")
+    return staging, destination
+
+
+def _finish_retirement(root, intent, record, *, recovering=False):
+    target, tombstone = _child(root, record["target"]), _child(root, record["tombstone"])
+    if target.exists() and tombstone.exists():
+        raise ValueError("Retirement has conflicting original and tombstone directories")
+    if record["status"] == "retired":
+        if target.exists() or tombstone.exists():
+            raise ValueError("Retired storage name was unexpectedly reused")
+        return record
+    if "successor" in record["proof"]:
+        successor = Path(record["proof"]["successor"])
+        if successor.parent != root or successor.is_symlink() or successor == target:
+            raise ValueError("Retirement successor escaped its owning run")
+        if recovering:
+            from qwen3_next_checkpoint import validate_checkpoint
+
+            if (
+                validate_checkpoint(successor, record["identity"])
+                != record["proof"]["successor_complete"]
+            ):
+                raise ValueError("Retirement successor no longer matches its verified publication")
+    if target.exists():
+        marker, expected, optional = _retirement_marker(record)
+        marker = target / marker
+        if marker.exists() or marker.is_symlink() or not optional:
+            if _read(marker) != expected:
+                raise ValueError("Retirement target differs from its durable ownership proof")
+        target.rename(tombstone)
+        _sync(root)
+        _boundary("retirement_renamed")
+    if tombstone.exists():
+        shutil.rmtree(tombstone)
+        _sync(root)
+        _boundary("retirement_removed")
+    record = {**record, "status": "retired"}
+    _write(intent, record)
+    _boundary("retirement_committed")
+    return record
+
+
+def retire_directory(path, root, identity, *, proof):
+    root = require_owner(root, identity)
+    path = Path(path).absolute()
+    if path.is_symlink():
+        raise ValueError("Retirement cannot follow a target symlink")
+    path = path.resolve()
+    if path.parent != root or path.name.startswith("."):
+        raise ValueError("Retirement target must be a visible direct child of this run")
+    target = _child(root, path.name)
+    intent = root / f".{target.name}.delete.json"
+    recovering = intent.exists()
+    if recovering:
+        record = _read(intent)
+        _validate_retirement(record, target.name, identity)
+        if record["proof"] != proof:
+            raise ValueError("Retirement retry changed its original ownership proof")
+    else:
+        if not target.is_dir():
+            raise ValueError("Cannot authorize retirement of an unknown directory")
+        record = {
+            "schema_version": 1,
+            "scope": "qwen3_next_retirement",
+            "identity": identity,
+            "target": target.name,
+            "tombstone": f".{target.name}.deleting",
+            "status": "retiring",
+            "proof": proof,
+        }
+        _validate_retirement(record, target.name, identity)
+        _write(intent, record)
+        _boundary("retirement_intent_persisted")
+    return _finish_retirement(root, intent, record, recovering=recovering)
+
+
+def _validate_retirement(record, target, identity):
+    if (
+        record.get("schema_version") != 1
+        or record.get("scope") != "qwen3_next_retirement"
+        or record.get("identity") != identity
+        or record.get("target") != target
+        or target.startswith(".")
+        or record.get("tombstone") != f".{target}.deleting"
+        or record.get("status") not in ("retiring", "retired")
+        or not isinstance(record.get("proof"), dict)
+    ):
+        raise ValueError("Retirement intent belongs to another target/run or is malformed")
+    _retirement_marker(record)
+
+
+def _retirement_marker(record):
+    """Bind a destructive intent to one of the supported ownership proofs."""
+    proof, identity = record["proof"], record["identity"]
+    if proof.get("scope") == "qwen3_next_staging_intent":
+        metadata = proof.get("metadata", {})
+        published = proof.get("published")
+        if (
+            proof.get("schema_version") != 1
+            or proof.get("identity") != identity
+            or proof.get("staging") != record["target"]
+            or not isinstance(published, str)
+            or Path(published).name != published
+            or published.startswith(".")
+            or Path(published).suffix
+            or str(Path(published).with_suffix(".partial")) != record["target"]
+            or metadata.get("identity") != identity
+            or metadata.get("status") != "staging"
+        ):
+            raise ValueError("Staging retirement proof does not own its target")
+        return "STAGING.json", metadata, True
+    if proof.get("status") == "staging" and proof.get("identity") == identity:
+        if Path(record["target"]).suffix != ".partial":
+            raise ValueError("Legacy staging proof cannot authorize a published directory")
+        return "STAGING.json", proof, False
+    complete, successor = proof.get("complete", {}), proof.get("successor_complete", {})
+    if (
+        complete.get("identity") == identity
+        and complete.get("status") == "complete"
+        and successor.get("identity") == identity
+        and successor.get("status") == "complete"
+        and type(complete.get("step")) is int
+        and type(successor.get("step")) is int
+        and complete["step"] < successor["step"]
+        and isinstance(proof.get("successor"), str)
+    ):
+        return "COMPLETE.json", complete, False
+    exported = proof.get("export", {})
+    if exported.get("training_identity") == identity and exported.get("status") == "complete":
+        return "EXPORT_COMPLETE.json", exported, False
+    raise ValueError("Retirement lacks a supported same-run ownership proof")
+
+
+def recover_storage(root, identity):
+    root = require_owner(root, identity)
+    recovered = []
+    for intent in sorted(root.glob(".*.delete.json")):
+        record = _read(intent)
+        target = record.get("target")
+        _child(root, target)
+        _validate_retirement(record, target, identity)
+        if intent.name != f".{target}.delete.json":
+            raise ValueError("Retirement filename contradicts its target")
+        if record["status"] == "retiring":
+            recovered.append(_finish_retirement(root, intent, record, recovering=True))
+        else:
+            _finish_retirement(root, intent, record)
+    for intent in sorted(root.glob(".*.staging.json")):
+        record = _read(intent)
+        if (
+            record.get("schema_version") != 1
+            or record.get("scope") != "qwen3_next_staging_intent"
+            or record.get("identity") != identity
+            or record.get("metadata", {}).get("identity") != identity
+            or record.get("metadata", {}).get("status") != "staging"
+        ):
+            raise ValueError("Staging intent belongs to another run or is malformed")
+        staging, published = _child(root, record["staging"]), _child(root, record["published"])
+        if (
+            published.name.startswith(".")
+            or published.suffix
+            or staging != published.with_suffix(".partial")
+            or intent.name != f".{published.name}.staging.json"
+        ):
+            raise ValueError("Staging intent paths do not describe one publication")
+        if staging.exists() and published.exists():
+            raise ValueError("Staging and published copies cannot coexist")
+        if staging.exists():
+            marker = staging / "STAGING.json"
+            if marker.exists() and _read(marker) != record["metadata"]:
+                raise ValueError("Staging marker differs from its durable parent intent")
+            recovered.append(retire_directory(staging, root, identity, proof=record))
+    # A marker from the earlier protocol can prove ownership, but an arbitrary
+    # directory with neither marker nor parent intent must never be deleted.
+    for staging in sorted(root.glob("*.partial")):
+        if not staging.is_dir():
+            continue
+        metadata = _read(staging / "STAGING.json")
+        if metadata.get("identity") != identity or metadata.get("status") != "staging":
+            raise ValueError("Interrupted state belongs to another run or lacks a matching owner")
+        recovered.append(retire_directory(staging, root, identity, proof=metadata))
+    return recovered
+
+
+def training_storage_root(manifest, run_root):
+    """The run's checkpoint and export root: ``--training-storage`` or ``<run>/training-state``."""
+    shared = manifest.get("training_storage")
+    return Path(shared) if shared else Path(run_root) / "training-state"
diff --git a/scripts/qwen3_next_tp_head_check.py b/scripts/qwen3_next_tp_head_check.py
new file mode 100644
index 000000000..6e65b6292
--- /dev/null
+++ b/scripts/qwen3_next_tp_head_check.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Real-weight TP4 embedding, final norm, LM-head and logprob component gate."""
+
+import argparse
+import json
+import os
+from datetime import timedelta
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+from qwen3_next_contract import atomic_json
+from safetensors import safe_open
+
+from rl_engine.integrations.qwen3_next_embedding import shared_embedding
+from rl_engine.integrations.qwen3_next_forward import shared_linear, shared_logprob
+from rl_engine.integrations.qwen3_next_logprob import qwen_logprob_contract
+from rl_engine.integrations.qwen3_next_tp import _CopyToTP
+from rl_engine.kernels.ops.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+REQUIRED_CASES = [
+    "hf-embedding-roundtrip",
+    "hf-lm-head-roundtrip",
+    "replicated-norm-gradient",
+    "nonzero-finite-embedding-head-gradient",
+    "embedding-repeat-100",
+    *[f"batch-invariant-prompt{length}" for length in (8, 64, 256, 1024)],
+    "logprob-original-and-promoted-logits",
+    "entropy-interface",
+]
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--checkpoint", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    args = parser.parse_args()
+    rank = int(os.environ["LOCAL_RANK"])
+    if int(os.environ["WORLD_SIZE"]) != 4 or torch.cuda.device_count() != 4:
+        raise RuntimeError("Head acceptance requires four ranks and four visible GPUs")
+    torch.cuda.set_device(rank)
+    dist.init_process_group(
+        "nccl", timeout=timedelta(minutes=10), device_id=torch.device("cuda", rank)
+    )
+    group = dist.group.WORLD
+    try:
+        index = json.loads((args.checkpoint / "model.safetensors.index.json").read_text())[
+            "weight_map"
+        ]
+
+        def load(name):
+            with safe_open(args.checkpoint / index[name], framework="pt", device="cpu") as reader:
+                value = reader.get_tensor(name)
+            if value.dtype != torch.bfloat16:
+                raise ValueError("Official component weight must be BF16")
+            return value
+
+        weights = {}
+        for name in ("model.embed_tokens.weight", "lm_head.weight"):
+            original = load(name)
+            local = torch.nn.Parameter(original.chunk(4)[rank].cuda().contiguous())
+            peers = [torch.empty_like(local) for _ in range(4)]
+            dist.all_gather(peers, local.detach())
+            exact(torch.cat(peers).cpu(), original, name=f"loaded {name} HF roundtrip")
+            weights[name] = local
+            del original, peers
+        embedding, head = weights["model.embed_tokens.weight"], weights["lm_head.weight"]
+        norm = torch.nn.Parameter(load("model.norm.weight").cuda())
+        norm_op = Qwen3NextRMSNormCudaOp()
+
+        def forward(tokens):
+            hidden = shared_embedding(tokens, embedding, group)
+            normalized = norm_op(hidden, norm, eps=1e-6)
+            return shared_linear(_CopyToTP.apply(normalized, group), head)
+
+        with torch.no_grad():
+            for length in (8, 64, 256, 1024):
+                tokens = torch.arange(4 * (length + 16), device="cuda", dtype=torch.int64)
+                tokens = ((tokens * 2399 + 1) % 151936).reshape(4, length + 16)
+                batch = forward(tokens)
+                singleton = forward(tokens[:1])
+                exact(batch[:1], singleton, name=f"embedding/norm/LM batch {length}")
+            repeated = torch.tensor([1, 37985, 75969, 113953, 1, 75969], device="cuda")
+            expected = shared_embedding(repeated, embedding, group)
+            for _ in range(100):
+                exact(shared_embedding(repeated, embedding, group), expected)
+
+        tokens = torch.tensor([1, 37985, 75969, 113953] * 4, device="cuda")
+        logits = forward(tokens)
+        contract = qwen_logprob_contract(logits, rank=rank, world_size=4)
+        logp, lse, entropy = shared_logprob(logits, tokens, contract=contract, tp_group=group)
+        promoted = logits.float()
+        promoted_logp, _, _ = shared_logprob(
+            promoted,
+            tokens,
+            contract=qwen_logprob_contract(promoted, rank=rank, world_size=4),
+            tp_group=group,
+        )
+        exact(logp, promoted_logp, name="native VIME loss input promotion")
+        without_entropy, _, no_entropy = shared_logprob(
+            logits, tokens, contract=contract, tp_group=group, with_entropy=False
+        )
+        exact(logp, without_entropy, name="entropy request logprob identity")
+        if no_entropy is not None or not all(torch.isfinite(t).all() for t in (entropy, lse)):
+            raise AssertionError("Entropy/LSE interface is invalid")
+        (-logp.mean()).backward()
+        for name, parameter in (("embedding", embedding), ("lm_head", head), ("norm", norm)):
+            gradient = parameter.grad
+            valid = gradient is not None
+            valid = valid and bool(torch.isfinite(gradient).all() and gradient.count_nonzero())
+            if not valid:
+                raise AssertionError(f"Missing finite nonzero {name} gradient")
+        peers = [torch.empty_like(norm.grad) for _ in range(4)]
+        dist.all_gather(peers, norm.grad)
+        for peer in peers:
+            exact(peer, norm.grad, name="replicated final norm gradient")
+        from rl_engine.integrations.qwen3_next_binary import imported_provider_identity
+
+        args.output.mkdir(parents=True, exist_ok=True)
+        atomic_json(
+            args.output / f"rank-{rank}.json",
+            {
+                "status": "passed",
+                "scope": "tp4_embedding_lm_head_only",
+                "rank": rank,
+                "provider_binary": imported_provider_identity(),
+                "full_model_l2": "not_evaluated",
+                "cases": sorted(REQUIRED_CASES),
+            },
+        )
+        dist.barrier()
+    finally:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_tp_probe.py b/scripts/qwen3_next_tp_probe.py
new file mode 100644
index 000000000..fd55d6d56
--- /dev/null
+++ b/scripts/qwen3_next_tp_probe.py
@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Four independent NCCL ranks must communicate exactly before model loading."""
+
+import argparse
+import os
+from datetime import timedelta
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+from qwen3_next_runtime import atomic_json
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--output", type=Path, required=True)
+    args = parser.parse_args()
+    rank = int(os.environ["LOCAL_RANK"])
+    if int(os.environ["WORLD_SIZE"]) != 4:
+        raise RuntimeError("This probe requires TP=4")
+    torch.cuda.set_device(rank)
+    dist.init_process_group(
+        "nccl", timeout=timedelta(minutes=3), device_id=torch.device("cuda", rank)
+    )
+    try:
+        value = torch.arange(1024, device="cuda", dtype=torch.int64) + rank
+        gathered = [torch.empty_like(value) for _ in range(4)]
+        dist.all_gather(gathered, value)
+        for peer, actual in enumerate(gathered):
+            assert torch.equal(actual, torch.arange(1024, device="cuda") + peer)
+        dist.all_reduce(value)
+        assert torch.equal(value, 4 * torch.arange(1024, device="cuda") + 6)
+        torch.cuda.synchronize()
+        args.output.mkdir(parents=True, exist_ok=True)
+        atomic_json(
+            args.output / f"rank-{rank}.json",
+            {
+                "status": "passed",
+                "rank": rank,
+                "world_size": 4,
+                "device": torch.cuda.get_device_name(),
+                "nccl": torch.cuda.nccl.version(),
+            },
+        )
+        dist.barrier()
+    finally:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_training.py b/scripts/qwen3_next_training.py
new file mode 100644
index 000000000..21c52025a
--- /dev/null
+++ b/scripts/qwen3_next_training.py
@@ -0,0 +1,1077 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Native RL update, full-model L2 and atomic restart acceptance on four GPUs."""
+
+import argparse
+import json
+import math
+import os
+import re
+import shutil
+import signal
+import subprocess
+import sys
+import time
+from pathlib import Path
+from uuid import uuid4
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from qwen3_next_checkpoint import (  # noqa: E402
+    newest_complete_checkpoint,
+    publish_checkpoint,
+    resume_interrupted_publications,
+    validate_checkpoint,
+    write_rank_manifest,
+)
+from qwen3_next_contract import (  # noqa: E402
+    SCOPES,
+    atomic_json,
+    evidence_record,
+    result_envelope,
+    validate_result,
+)
+from qwen3_next_l2 import check_ready as check_l2_ready  # noqa: E402
+from qwen3_next_l2 import (  # noqa: E402
+    model_identity,
+    run_matrix,
+    run_process,
+    verify_before_model_load,
+    wait_gpu_release,
+)
+from qwen3_next_runtime import failure_fields  # noqa: E402
+from qwen3_next_runtime import (  # noqa: E402
+    file_digest,
+    owned_root,
+    recover_storage,
+    retire_directory,
+    training_storage_root,
+    verify_environment,
+)
+
+from rl_engine.integrations.qwen3_next_native_training import (  # noqa: E402
+    policy_fixture,
+    run_training_phase,
+    validate_active_parameter_names,
+)
+from rl_engine.integrations.qwen3_next_training import state_digest  # noqa: E402
+from rl_engine.testing.qwen3_next_binary import (  # noqa: E402
+    archive_provider_binary,
+    validate_binary_record,
+)
+from rl_engine.testing.qwen3_next_l2 import (  # noqa: E402
+    case_matrix,
+    checked_artifact_path,
+    compare_artifacts,
+    json_digest,
+    load_tensor_artifact,
+    validate_matrix_report,
+)
+
+SECTIONS = (
+    "baseline",
+    "update1",
+    "update2",
+    "reload",
+    "continuous_step3",
+    "resumed_step3",
+    "checkpoint",
+    "fault_injection",
+)
+FAULTS = ("interrupted_writer", "missing_rank", "hash_error", "unpublished_load")
+PHASES = ("update1", "update2", "reload", "continuous", "resumed")
+# Each training stage runs the phases up to its last one and leaves the durable
+# state (checkpoints, journal) for the next stage to continue from.
+STAGE_LAST_PHASE = {"train1": "update1", "train2": "update2", "train": "resumed"}
+
+
+def stage_phases(stage):
+    """Phases a training stage executes, in order."""
+    last = STAGE_LAST_PHASE[stage]
+    return PHASES[: PHASES.index(last) + 1]
+
+
+def continued_training_storage(previous):
+    """Durable state a continuing stage resumes: the train1 run's, however many stages back.
+
+    train2 continues train1's storage instead of creating its own, so the final
+    stage must follow the predecessor chain back to the run that owns the state.
+    """
+    run = Path(previous)
+    for _ in range(len(STAGE_LAST_PHASE)):
+        manifest = json.loads((run / "run.json").read_text())
+        if manifest.get("stage") == "train1":
+            return training_storage_root(manifest, run)
+        if manifest.get("stage") not in STAGE_LAST_PHASE or not manifest.get("previous"):
+            break
+        run = Path(manifest["previous"])
+    raise ValueError("Training continuation does not lead back to a train1 run")
+
+
+def continuation_identity_matches(journal_identity, model_identity_value, fixture_sha256):
+    """A later training stage continues a journal only for the same model/env/fixture."""
+    expected = {**model_identity_value, "fixture_sha256": fixture_sha256}
+    actual = {k: v for k, v in journal_identity.items() if k not in ("run_id", "execution_id")}
+    return actual == expected
+
+
+EVENTS = (
+    "l2:baseline",
+    "worker:update1",
+    "l2:update1",
+    "worker:update2",
+    "l2:update2",
+    "worker:reload",
+    "l2:reload",
+    "worker:continuous",
+    "worker:resumed",
+)
+
+
+def record(path):
+    path = Path(path).absolute()
+    return {"path": str(path), "sha256": file_digest(path)}
+
+
+def read_record(value):
+    path = Path(value["path"])
+    if (
+        not path.is_absolute()
+        or path.is_symlink()
+        or not path.is_file()
+        or file_digest(path) != value["sha256"]
+    ):
+        raise ValueError(f"Training evidence changed or is missing: {path}")
+    return json.loads(path.read_text())
+
+
+def require_digest(value):
+    if (
+        not isinstance(value, str)
+        or len(value) != 64
+        or any(char not in "0123456789abcdef" for char in value)
+    ):
+        raise ValueError("Training state contains an invalid original-byte digest")
+
+
+def validate_state(state, *, rank, step, identity, parameters):
+    expected = {
+        "schema_version": 1,
+        "scope": "native_vime_training_state",
+        "rank": rank,
+        "identity": identity,
+        "step": step,
+        "sample_position": step * 4,
+        "consumed_train_samples": step * 4,
+        "fixture_sha256": identity["fixture_sha256"],
+    }
+    hash_fields = {
+        "model_sha256",
+        "optimizer_parameter_sha256",
+        "optimizer_header_sha256",
+        "scheduler_sha256",
+        "rng_sha256",
+        "state_sha256",
+    }
+    if set(state) != set(expected) | hash_fields or any(
+        state.get(key) != value for key, value in expected.items()
+    ):
+        raise ValueError("Native state omits or changes rank/step/sample/fixture identity")
+    if (
+        state_digest({key: value for key, value in state.items() if key != "state_sha256"})
+        != state["state_sha256"]
+    ):
+        raise ValueError("Native state digest does not bind the recorded recovery state")
+    if not isinstance(state["model_sha256"], list) or len(state["model_sha256"]) != 1:
+        raise ValueError("Native state requires the complete single PP1 model chunk")
+    for value in state["model_sha256"] + [
+        state[key] for key in ("optimizer_header_sha256", "scheduler_sha256", "rng_sha256")
+    ]:
+        require_digest(value)
+    moments = state["optimizer_parameter_sha256"]
+    if set(moments) != set(parameters):
+        raise ValueError("Native optimizer state does not cover every trainable parameter")
+    for values in moments.values():
+        if not {"param", "exp_avg", "exp_avg_sq"}.issubset(values):
+            raise ValueError("Native state omitted FP32 masters or Adam moments")
+        for value in values.values():
+            require_digest(value)
+
+
+def validate_journal(journal, identity, *, verify_files=True):
+    if journal.get("schema_version") != 1 or journal.get("identity") != identity:
+        raise ValueError("Training journal belongs to different source/environment/fixture")
+    events = journal.get("events", [])
+    if events != list(EVENTS[: len(events)]) or not events:
+        raise ValueError("Training journal did not preserve update/L2/reload/control order")
+    if set(journal.get("workers", {})) != {
+        event.split(":")[1] for event in events if event.startswith("worker:")
+    }:
+        raise ValueError("Training journal worker records disagree with durable phase order")
+    if set(journal.get("l2_reports", {})) != {
+        event.split(":")[1] for event in events if event.startswith("l2:")
+    }:
+        raise ValueError("Training journal L2 records disagree with durable phase order")
+    if verify_files:
+        for value in journal["workers"].values():
+            read_record(value)
+        for value in journal["l2_reports"].values():
+            validate_matrix_report(read_record(value))
+
+
+def repeat_reload_in_current_attempt(journal, attempt):
+    """Restore durable updates, but never pass with an older attempt's final reload."""
+    if "reload" not in journal["workers"]:
+        return False
+    original = read_record(journal["workers"]["reload"])
+    if original.get("attempt") == str(attempt):
+        return False
+    if not str(original.get("attempt", "")).isdigit():
+        raise ValueError("Recovery source has no valid attempt identity")
+    cutoff = journal["events"].index("worker:reload")
+    retired = {
+        "reason": "repeat_final_reload_and_controls_in_current_attempt",
+        "from_attempt": original["attempt"],
+        "to_attempt": str(attempt),
+        "events": journal["events"][cutoff:],
+        "workers": {
+            phase: journal["workers"].pop(phase)
+            for phase in ("reload", "continuous", "resumed")
+            if phase in journal["workers"]
+        },
+        "l2_report": journal["l2_reports"].pop("reload", None),
+    }
+    journal["events"] = journal["events"][:cutoff]
+    journal.setdefault("recovery", []).append(retired)
+    return True
+
+
+def validate_update(audit, *, step, fixture):
+    if (audit.get("step"), audit.get("sample_position"), audit.get("fixture")) != (
+        step,
+        step * 4,
+        fixture,
+    ):
+        raise ValueError("Native policy update progress/fixture mismatch")
+    norm = audit.get("grad_norm", float("nan"))
+    if audit.get("policy_loss_calls") != 4 or not math.isfinite(norm) or norm <= 0:
+        raise ValueError("Native policy update needs four losses and a finite nonzero norm")
+    before, after = (
+        audit.get("parameter_hashes_before", {}),
+        audit.get("parameter_hashes_after", {}),
+    )
+    if not before or before.keys() != after.keys():
+        raise ValueError("Native update must hash every trainable parameter before and after")
+    validate_active_parameter_names(before)
+    for value in [*before.values(), *after.values()]:
+        require_digest(value)
+    if audit.get("progress") != {
+        "consumed_before": (step - 1) * 4,
+        "consumed_after": step * 4,
+        "scheduler_before": (step - 1) * 4,
+        "scheduler_after": step * 4,
+        "sample_counter_owner": "qwen3_next_native_training.policy_step",
+    }:
+        raise ValueError("Native update did not commit matching sample/scheduler progress")
+    changed = [name for name in before if before[name] != after[name]]
+    if not changed or sorted(changed) != sorted(audit.get("changed_parameters", [])):
+        raise ValueError("Native optimizer did not prove an actual parameter change")
+    gradients = audit.get("gradients", {})
+    parameters, groups = gradients.get("parameters", {}), gradients.get("active_groups", {})
+    if parameters.keys() != before.keys() or groups.keys() != before.keys():
+        raise ValueError("Every active parameter tensor must have predeclared gradient evidence")
+    for name in before:
+        if (
+            parameters[name].get("present") is not True
+            or parameters[name].get("nonzero") is not True
+        ):
+            raise ValueError(f"Active parameter tensor has no valid gradient: {name}")
+        if parameters[name].get("dtype") not in ("torch.float32", "torch.bfloat16"):
+            raise ValueError("Active gradient evidence omitted or changed its storage dtype")
+        if groups[name] != [name]:
+            raise ValueError("Active gradient group was substituted after execution")
+    metrics = audit.get("metrics", {})
+    if not metrics or any(
+        type(value) not in (int, float) or not math.isfinite(value) for value in metrics.values()
+    ):
+        raise ValueError("Native policy loss must retain finite scalar training metrics")
+    return True
+
+
+def validate_worker(value, *, phase, identity):
+    if (
+        value.get("schema_version") != 1
+        or value.get("status") != "passed"
+        or value.get("phase") != phase
+        or value.get("identity") != identity
+        or value.get("all_rank_processes_exited") is not True
+        or value.get("gpu_release_verified") is not True
+        or [rank.get("rank") for rank in value.get("ranks", [])] != list(range(4))
+    ):
+        raise ValueError("Incomplete native four-rank worker evidence")
+    pids = [rank.get("pid") for rank in value["ranks"]]
+    if any(type(pid) is not int or pid <= 0 for pid in pids) or len(set(pids)) != 4:
+        raise ValueError("Native worker requires four distinct rank processes")
+    binary = validate_binary_record(
+        value.get("provider_binary"), source_tree=identity["source_tree"]
+    )
+    for rank, row in enumerate(value["ranks"]):
+        if row.get("provider_binary") != binary:
+            raise ValueError("Native training rank differs from its archived CUDA provider")
+        if row.get("identity") != identity or row.get("phase") != phase:
+            raise ValueError("Native rank identity mismatch")
+        if row.get("native_entrypoints") != [
+            "vime.backends.megatron_utils.model.setup_model_and_optimizer",
+            "vime.backends.megatron_utils.model.train_one_step",
+            "vime.backends.megatron_utils.loss.policy_loss_function",
+        ]:
+            raise ValueError("Worker did not identify the required native training entrypoints")
+        if phase in ("continuous", "resumed"):
+            controls = row.get("controls", {})
+            if set(controls) != ({"1", "2", "3"} if phase == "continuous" else {"3"}):
+                raise ValueError("Native recovery controls omitted or added an update")
+            updates = [(int(step), control) for step, control in controls.items()]
+        elif phase == "reload":
+            restored = row.get("restored", {})
+            audit = restored.get("rank_evidence", {}).get("update_evidence", {})
+            updates = [(2, {"update": audit, "state": row.get("state", {})})]
+        else:
+            updates = [(1 if phase == "update1" else 2, row)]
+        for step, control in updates:
+            audit = control["update"]
+            validate_update(audit, step=step, fixture=identity["fixture_sha256"])
+            validate_state(
+                control["state"],
+                rank=rank,
+                step=step,
+                identity=identity,
+                parameters=audit["parameter_hashes_after"],
+            )
+
+
+def validate_export_evidence(worker, identity):
+    from qwen3_next_training_export import validate_training_export_record
+
+    evidence = worker["export_evidence"]
+    complete = read_record(evidence["complete"])
+    checkpoint_identity = validate_training_export_record(
+        complete, worker["export"]["checkpoint_identity"]
+    )
+    if checkpoint_identity != worker["export"]["checkpoint_identity"]:
+        raise ValueError("Archived export proof differs from the weights actually tested by L2")
+    if complete["training_identity"] != identity:
+        raise ValueError("Archived export belongs to another native training identity")
+    read_record(evidence["index"])
+    # The original completion record binds the exact index bytes, including HF
+    # tensor-to-shard mapping. Historical proof never authorizes loading a file.
+    if complete["files"]["model.safetensors.index.json"]["sha256"] != evidence["index"]["sha256"]:
+        raise ValueError("Archived export index differs from its completion manifest")
+    audits = [
+        (
+            row["restored"]["rank_evidence"]["update_evidence"]
+            if worker["phase"] == "reload"
+            else row["update"]
+        )
+        for row in worker["ranks"]
+    ]
+    if complete.get("model_identity_method") != "native_parameter_hashes_after" or complete[
+        "model_rank_sha256"
+    ] != [state_digest(audit["parameter_hashes_after"]) for audit in audits]:
+        raise ValueError("HF export bytes do not belong to the actual optimizer-updated actor")
+
+
+def archive_export(worker, directory, identity):
+    from qwen3_next_training_export import validate_training_export
+
+    export = worker["export"]
+    root = Path(export["checkpoint"])
+    validate_training_export(root, export["checkpoint_identity"])
+    evidence = {}
+    for kind, name in (
+        ("complete", "EXPORT_COMPLETE.json"),
+        ("index", "model.safetensors.index.json"),
+    ):
+        destination = Path(directory) / name
+        # Atomic JSON preserves the parsed content, while recording the index's
+        # original bytes is required for the completion manifest hash binding.
+        with destination.open("xb") as output, (root / name).open("rb") as source:
+            shutil.copyfileobj(source, output)
+            output.flush()
+            os.fsync(output.fileno())
+        evidence[kind] = record(destination)
+    worker["export_evidence"] = evidence
+    validate_export_evidence(worker, identity)
+
+
+def compare_reload_matrices(updated, reloaded):
+    """Reopen every original phase artifact; a saved passed boolean is insufficient."""
+    ids = [case.case_id for case in case_matrix()]
+    if any(
+        matrix.get("case_ids") != ids or set(matrix.get("cases", {})) != set(ids)
+        for matrix in (updated, reloaded)
+    ):
+        raise ValueError("Reload comparison requires both complete sixteen-case matrices")
+    result = {"status": "passed", "raw_bit_comparison": True, "case_ids": ids, "cases": {}}
+    for case in ids:
+        result["cases"][case] = {}
+        for role in ("rollout", "recompute"):
+            left = load_tensor_artifact(
+                checked_artifact_path(updated["cases"][case]["artifacts"][role])
+            )
+            right = load_tensor_artifact(
+                checked_artifact_path(reloaded["cases"][case]["artifacts"][role])
+            )
+            compared = compare_artifacts(left, right)
+            if not compared["passed"]:
+                raise ValueError(
+                    f"Reload changed original {role} tensor bits/state at {case}: {compared}"
+                )
+            result["cases"][case][role] = compared["comparisons"]
+    return result
+
+
+def validate_training_report(report, *, verify_files=True):
+    """No partial matrix, optimizer header or stale worker can close training."""
+    if (
+        report.get("schema_version") != 1
+        or report.get("status") != "passed"
+        or report.get("scope") != "qwen3_next_training_and_reload"
+        or report.get("required_sections") != list(SECTIONS)
+    ):
+        raise ValueError("Incomplete/unsupported native training acceptance")
+    if not verify_files:
+        raise ValueError("Training acceptance requires verification of original evidence files")
+    if report.get("events") != list(EVENTS):
+        raise ValueError("Training acceptance omitted or reordered a required phase")
+    l2 = report.get("l2_reports", {})
+    workers = report.get("workers", {})
+    if set(l2) != {"baseline", "update1", "update2", "reload"} or set(workers) != {
+        "update1",
+        "update2",
+        "reload",
+        "continuous",
+        "resumed",
+    }:
+        raise ValueError("Training omitted an update, full L2 matrix or recovery control")
+    matrices = {}
+    versions = {
+        "baseline": "baseline",
+        "update1": "update-1",
+        "update2": "update-2",
+        "reload": "reload-2",
+    }
+    for phase, item in l2.items():
+        matrices[phase] = matrix = read_record(item)
+        validate_matrix_report(matrix)
+        identity = matrix["identity"]
+        if identity.get("weight_version") != versions[phase]:
+            raise ValueError("Training phase used the wrong model weight version")
+        immutable = set(report["identity"]) - {
+            "checkpoint",
+            "checkpoint_identity",
+            "weight_version",
+            "base_checkpoint",
+            "base_checkpoint_identity",
+        }
+        if any(identity.get(key) != report["identity"][key] for key in immutable):
+            raise ValueError("Training phase changed source, environment, provider or topology")
+        if phase == "baseline":
+            if identity != report["identity"]:
+                raise ValueError("Training baseline must retain the exact original model identity")
+        elif (identity.get("base_checkpoint"), identity.get("base_checkpoint_identity")) != (
+            report["identity"]["checkpoint"],
+            report["identity"]["checkpoint_identity"],
+        ):
+            raise ValueError("Updated actor export changed its official base checkpoint identity")
+        if matrix["fixture"]["content_sha256"] != report["l2_fixture_sha256"]:
+            raise ValueError("Training L2 changed its frozen input tokens")
+    identity = report["training_identity"]
+    if {key: identity.get(key) for key in report["identity"]} != report["identity"]:
+        raise ValueError("Training checkpoint is detached from the baseline model identity")
+    if report.get("reload_matches_update2") != compare_reload_matrices(
+        matrices["update2"], matrices["reload"]
+    ):
+        raise ValueError("Reload report does not match the reopened original phase artifacts")
+    phases = {}
+    for phase, item in workers.items():
+        value = phases[phase] = read_record(item)
+        validate_worker(value, phase=phase, identity=identity)
+        if phase in ("update1", "update2", "reload"):
+            validate_export_evidence(value, identity)
+            export = value["export"]
+            if any(rank["export"] != export for rank in value["ranks"]):
+                raise ValueError("Native ranks disagree on exported weights")
+            for key in ("checkpoint", "checkpoint_identity", "weight_version"):
+                if export[key] != matrices[phase]["identity"][key]:
+                    raise ValueError("L2 did not test the actual updated/reloaded actor export")
+        if phase in ("update1", "update2"):
+            step = 1 if phase == "update1" else 2
+            for rank in value["ranks"]:
+                validate_update(rank["update"], step=step, fixture=identity["fixture_sha256"])
+    for rank in range(4):
+        if (
+            phases["update2"]["ranks"][rank]["update"]["parameter_hashes_before"]
+            != phases["update1"]["ranks"][rank]["update"]["parameter_hashes_after"]
+        ):
+            raise ValueError("Step2 did not start from the actual completed step1 parameters")
+        if phases["update2"]["ranks"][rank]["state"] != phases["reload"]["ranks"][rank]["state"]:
+            raise ValueError("Step2 model/optimizer/scheduler/RNG/progress changed on restart")
+        continuous = phases["continuous"]["ranks"][rank]["controls"]
+        resumed = phases["resumed"]["ranks"][rank]["controls"]
+        if set(continuous) != {"1", "2", "3"} or set(resumed) != {"3"}:
+            raise ValueError("Continuous/restarted controls omitted a required update")
+        for step in (1, 2, 3):
+            validate_update(
+                continuous[str(step)]["update"], step=step, fixture=identity["fixture_sha256"]
+            )
+            if (
+                step < 3
+                and continuous[str(step)]["state"]
+                != phases[f"update{step}"]["ranks"][rank]["state"]
+            ):
+                raise ValueError("Continuous reference changed an earlier update")
+        validate_update(resumed["3"]["update"], step=3, fixture=identity["fixture_sha256"])
+        if continuous["3"]["state"] != resumed["3"]["state"]:
+            raise ValueError("Continuous and restarted step3 states are not exactly equal")
+    checkpoint = report["checkpoint"]
+    complete = validate_checkpoint(checkpoint["path"], identity, verify_files=False)
+    if (
+        complete["step"] != 2
+        or file_digest(Path(checkpoint["path"]) / "COMPLETE.json") != checkpoint["sha256"]
+    ):
+        raise ValueError("Training acceptance requires the verified complete step2 checkpoint")
+    for rank in range(4):
+        progress = json.loads(
+            (Path(checkpoint["path"]) / f"rank-{rank}" / "progress.json").read_text()
+        )
+        state = phases["update2"]["ranks"][rank]["state"]
+        for key in state.keys() - {"scope", "state_sha256"}:
+            if progress.get(key) != state[key]:
+                raise ValueError(
+                    "Published checkpoint bytes contradict the step2 recovery identity"
+                )
+        if progress.get("update_evidence") != phases["update2"]["ranks"][rank]["update"]:
+            raise ValueError("Published checkpoint detached the committed optimizer update")
+    validate_fault_evidence(read_record(report["fault_injection"]), identity)
+    return json_digest(report)
+
+
+def validate_fault_evidence(faults, identity):
+    from qwen3_next_checkpoint import _validate_ranks
+
+    if faults.get("identity") != identity or set(faults.get("cases", {})) != set(FAULTS):
+        raise ValueError("Checkpoint publication fault matrix is incomplete")
+    if any(value.get("passed") is not True for value in faults["cases"].values()):
+        raise ValueError("An interrupted/corrupted checkpoint was not rejected")
+    interrupted = faults["cases"]["interrupted_writer"]
+    if interrupted.get("exit_code") != -signal.SIGTERM:
+        raise ValueError("Interrupted writer did not record its actual termination")
+    checked_artifact_path(interrupted["writer_file"])
+    for case in FAULTS:
+        path = Path(faults["cases"][case]["checkpoint"])
+        if path.is_symlink() or path.suffix != ".partial" or not path.is_dir():
+            raise ValueError("Fault evidence did not preserve its original unpublished directory")
+        try:
+            if case in ("missing_rank", "hash_error"):
+                _validate_ranks(path, world_size=4, identity=identity)
+            else:
+                validate_checkpoint(path, identity)
+        except (OSError, ValueError) as exc:
+            if case == "missing_rank" and not isinstance(exc, FileNotFoundError):
+                raise ValueError("Missing-rank fixture failed for an unrelated reason") from exc
+            if case == "hash_error" and not re.search("checksum mismatch|size mismatch", str(exc)):
+                raise ValueError("Corruption fixture failed for an unrelated reason") from exc
+        else:
+            raise ValueError(f"Fault fixture no longer reproduces its rejection: {case}")
+
+
+def checkpoint_faults(output, identity):
+    """Kill a real writer and corrupt protocol files; never touch model checkpoints."""
+    output = Path(output)
+    output.mkdir(parents=True, exist_ok=False)
+    partial = output / "interrupted.partial"
+    partial.mkdir()
+    code = (
+        "import pathlib,sys,time; p=pathlib.Path(sys.argv[1]); "
+        "p.write_bytes(b'incomplete checkpoint'); time.sleep(300)"
+    )
+    child = subprocess.Popen(
+        [sys.executable, "-c", code, str(partial / "writing")], start_new_session=True
+    )
+    deadline = time.monotonic() + 10
+    try:
+        while not (partial / "writing").exists():
+            if child.poll() is not None or time.monotonic() > deadline:
+                raise RuntimeError("Fault injection writer did not start")
+            time.sleep(0.01)
+        os.killpg(child.pid, signal.SIGTERM)
+        code = child.wait(timeout=10)
+    finally:
+        if child.poll() is None:
+            os.killpg(child.pid, signal.SIGKILL)
+            child.wait(timeout=10)
+    recovery = newest_complete_checkpoint(output, identity)
+    cases = {
+        "interrupted_writer": {
+            "passed": code != 0 and recovery["checkpoint"] is None,
+            "exit_code": code,
+            "checkpoint": str(partial),
+            "writer_file": record(partial / "writing"),
+        }
+    }
+    try:
+        validate_checkpoint(partial, identity)
+    except (OSError, ValueError) as exc:
+        cases["unpublished_load"] = {"passed": True, "error": str(exc), "checkpoint": str(partial)}
+    partial = output / "missing.partial"
+    partial.mkdir()
+    for rank in range(4):
+        files = {}
+        for role in ("model", "optimizer", "rng", "progress"):
+            path = partial / f"rank-{rank}-{role}.bin"
+            path.write_bytes(f"{rank}:{role}".encode())
+            files[role] = [path.name]
+        write_rank_manifest(
+            partial,
+            rank=rank,
+            world_size=4,
+            identity=identity,
+            step=2,
+            sample_position=8,
+            files=files,
+        )
+    hidden = partial / "rank-3.manifest.json"
+    corrupted = output / "corrupted.partial"
+    shutil.copytree(partial, corrupted)
+    hidden.unlink()
+    try:
+        publish_checkpoint(partial, output / "missing", world_size=4, identity=identity)
+    except (OSError, ValueError) as exc:
+        cases["missing_rank"] = {"passed": True, "error": str(exc), "checkpoint": str(partial)}
+    (corrupted / "rank-2-optimizer.bin").write_bytes(b"corrupt optimizer bytes")
+    try:
+        publish_checkpoint(corrupted, output / "corrupted", world_size=4, identity=identity)
+    except (OSError, ValueError) as exc:
+        cases["hash_error"] = {"passed": True, "error": str(exc), "checkpoint": str(corrupted)}
+    result = {
+        "identity": identity,
+        "scope": "actual_checkpoint_publication_protocol",
+        "cases": cases,
+    }
+    if set(cases) != set(FAULTS) or not all(value["passed"] for value in cases.values()):
+        raise ValueError("Checkpoint fault injection accepted incomplete state")
+    validate_fault_evidence(result, identity)
+    atomic_json(output / "faults.json", result)
+    return record(output / "faults.json")
+
+
+def _owned_remove(path, root, identity=None, *, proof=None):
+    path, root = Path(path), Path(root)
+    if path.is_symlink() or root.is_symlink() or not path.is_dir():
+        raise ValueError("Cleanup target is not a directory owned by this training run")
+    path, root = path.resolve(), root.resolve()
+    if path.parent != root:
+        raise ValueError("Cleanup target is not a direct child of this training run")
+    if identity is None or proof is None:
+        raise ValueError("Cleanup requires explicit ownership and retirement proof")
+    return retire_directory(path, root, identity, proof=proof)
+
+
+def remove_interrupted(root, identity):
+    return recover_storage(root, identity)
+
+
+def retire_export(worker, root, identity):
+    validate_export_evidence(worker, identity)
+    path = Path(worker["export"]["checkpoint"])
+    if not path.exists():
+        return  # A prior attempt may have retired it after durable L2 publication.
+    complete = path / "EXPORT_COMPLETE.json"
+    if (
+        complete.is_symlink()
+        or json.loads(complete.read_text()) != worker["export"]["checkpoint_identity"]
+    ):
+        raise ValueError("Cleanup export is not the artifact whose complete L2 was retained")
+    _owned_remove(
+        path,
+        root,
+        identity,
+        proof={
+            "export": worker["export"]["checkpoint_identity"],
+            "evidence": worker["export_evidence"],
+        },
+    )
+
+
+def remove_uncommitted_exports(root, identity, journal):
+    """A crash after export publication cannot retain an unbounded extra copy."""
+    from qwen3_next_training_export import validate_training_export_record
+
+    keep = {
+        str(Path(read_record(item)["export"]["checkpoint"]).resolve())
+        for phase, item in journal["workers"].items()
+        if phase in ("update1", "update2", "reload") and phase not in journal["l2_reports"]
+    }
+    removed = []
+    for path in Path(root).iterdir():
+        if (
+            not path.is_dir()
+            or path.suffix == ".partial"
+            or path.name.startswith(".")
+            or str(path.resolve()) in keep
+        ):
+            continue
+        marker = path / "EXPORT_COMPLETE.json"
+        if path.is_symlink() or marker.is_symlink() or not marker.is_file():
+            raise ValueError("Completed export cleanup has no safe ownership proof")
+        complete = json.loads(marker.read_text())
+        validate_training_export_record(complete, complete)
+        if complete["training_identity"] != identity:
+            raise ValueError("Completed export belongs to another training run")
+        removed.append(
+            {"path": str(path.absolute()), "complete": complete, "action": "retire_unneeded_export"}
+        )
+        _owned_remove(path, root, identity, proof={"export": complete})
+    return removed
+
+
+def execute(manifest_path, output):
+    from qwen3_next_runtime import require_predecessor
+
+    manifest_path, output = Path(manifest_path).resolve(), Path(output).resolve()
+    manifest = json.loads(manifest_path.read_text())
+    run_root = manifest_path.parent
+    stage = manifest["stage"]
+    if stage not in STAGE_LAST_PHASE:
+        raise ValueError("Native training runner requires a training stage")
+    previous = Path(manifest["previous"])
+    require_predecessor(stage, previous, Path(manifest["checkpoint"]), manifest)
+    prior_result = json.loads((previous / "result.json").read_text())
+    continuation = stage != "train1"
+    if continuation:
+        # Continue the predecessor training run's durable state under its own identity.
+        if manifest.get("training_storage"):
+            raise ValueError("A continuing training stage uses its predecessor's storage")
+        durable = continued_training_storage(previous)
+        journal_path = durable / "journal.json"
+        journal = json.loads(journal_path.read_text())
+        baseline_path = Path(journal["l2_reports"]["baseline"]["path"])
+    else:
+        baseline_path = previous / prior_result["evidence"]["l2_matrix"]["path"]
+    baseline = json.loads(baseline_path.read_text())
+    validate_matrix_report(baseline, expected_identity=model_identity(manifest))
+    fixture_path = Path(baseline["fixture"]["path"])
+    if file_digest(fixture_path) != baseline["fixture"]["sha256"]:
+        raise ValueError("Baseline L2 fixture was changed")
+    fixture = policy_fixture(fixture_path)
+    if continuation:
+        identity = journal["identity"]
+        if not continuation_identity_matches(
+            identity, model_identity(manifest), fixture.content_sha256
+        ):
+            raise ValueError("Training continuation identity differs from the journal")
+        if journal["l2_reports"]["baseline"] != record(baseline_path):
+            raise ValueError("Training continuation baseline L2 changed")
+    else:
+        identity = {
+            **model_identity(manifest),
+            "run_id": manifest["run_id"],
+            "execution_id": manifest["execution_id"],
+            "fixture_sha256": fixture.content_sha256,
+        }
+        durable = training_storage_root(manifest, run_root)
+    if not durable.is_absolute():
+        raise ValueError("Training storage must be an absolute path")
+    durable.mkdir(parents=True, exist_ok=True)
+    checkpoint_root, export_root = durable / "checkpoints", durable / "exports"
+    owned_root(checkpoint_root, identity)
+    owned_root(export_root, identity)
+    journal_path = durable / "journal.json"
+    journal = {
+        "schema_version": 1,
+        "identity": identity,
+        "workers": {},
+        "l2_reports": {"baseline": record(baseline_path)},
+        "events": ["l2:baseline"],
+        "recovery": [],
+    }
+    if journal_path.exists():
+        journal = json.loads(journal_path.read_text())
+    validate_journal(journal, identity)
+    for phase, value in journal["workers"].items():
+        worker = read_record(value)
+        validate_worker(worker, phase=phase, identity=identity)
+        if phase in ("update1", "update2", "reload"):
+            validate_export_evidence(worker, identity)
+    if repeat_reload_in_current_attempt(journal, os.environ["QWEN3_NEXT_ATTEMPT"]):
+        validate_journal(journal, identity, verify_files=False)
+        atomic_json(journal_path, journal)
+    verify_environment(manifest["environment"], require_venv=True)
+    check_l2_ready()
+    run_process(
+        [manifest["python"], str(ROOT / "setup.py"), "build_ext", "--inplace"],
+        log_path=output / "build.log",
+        timeout=1800,
+    )
+    provider_binary = archive_provider_binary(ROOT, output, source_tree=manifest["source_tree"])
+    probe = output / "gpu-probe.json"
+    run_process(
+        [
+            manifest["python"],
+            str(ROOT / "scripts/qwen3_next_l2.py"),
+            "--gpu-probe",
+            "--output",
+            str(probe),
+        ],
+        log_path=output / "gpu-probe.log",
+        timeout=180,
+    )
+    uuids = json.loads(probe.read_text())["uuids"]
+    wait_gpu_release(uuids)
+    # A preempted attempt may have written a whole checkpoint without publishing it:
+    # publish it (full validation) before interrupted state is retired.
+    journal["recovery"].extend(resume_interrupted_publications(checkpoint_root, identity))
+    for root in (checkpoint_root, export_root):
+        journal["recovery"].extend(remove_interrupted(root, identity))
+    journal["recovery"].extend(remove_uncommitted_exports(export_root, identity, journal))
+    atomic_json(journal_path, journal)
+    export_bytes = sum(
+        value["size"] for value in manifest["checkpoint_identity"]["shards"].values()
+    )
+    for phase in stage_phases(stage):
+        if phase not in journal["workers"]:
+            recovery = newest_complete_checkpoint(checkpoint_root, identity, verify_files=False)
+            checkpoint = None if phase == "continuous" else recovery["checkpoint"]
+            directory = output / f"{phase}-{uuid4().hex}"
+            directory.mkdir()
+            request = {
+                "manifest": manifest,
+                "phase": phase,
+                "identity": identity,
+                "fixture": str(fixture_path),
+                "checkpoint": checkpoint,
+                "checkpoint_root": str(checkpoint_root),
+                "export_root": str(export_root),
+                "export_bytes": export_bytes,
+                "provider_binary": provider_binary,
+                "expected_states": {
+                    str(step): [
+                        rank["state"]
+                        for rank in read_record(journal["workers"][f"update{step}"])["ranks"]
+                    ]
+                    for step in (1, 2)
+                    if f"update{step}" in journal["workers"]
+                },
+            }
+            request_path, worker_path = directory / "request.json", directory / "worker.json"
+            atomic_json(request_path, request)
+            process = run_process(
+                [
+                    manifest["python"],
+                    str(Path(__file__)),
+                    "--worker-request",
+                    str(request_path),
+                    "--output",
+                    str(worker_path),
+                ],
+                log_path=directory / "worker.log",
+                timeout=8 * 3600,
+            )
+            wait_gpu_release(uuids)
+            worker = json.loads(worker_path.read_text())
+            worker.update(
+                {
+                    "gpu_release_verified": True,
+                    "process": process,
+                    "attempt": os.environ["QWEN3_NEXT_ATTEMPT"],
+                }
+            )
+            validate_worker(worker, phase=phase, identity=identity)
+            if phase in ("update1", "update2", "reload"):
+                archive_export(worker, directory, identity)
+            atomic_json(worker_path, worker)
+            journal["workers"][phase] = record(worker_path)
+            journal["events"].append(f"worker:{phase}")
+            validate_journal(journal, identity, verify_files=False)
+            atomic_json(journal_path, journal)
+        worker = read_record(journal["workers"][phase])
+        if phase in ("update1", "update2", "reload") and phase not in journal["l2_reports"]:
+            directory = output / f"{phase}-l2-{uuid4().hex}"
+            directory.mkdir()
+            phase_manifest = {
+                **manifest,
+                **worker["export"],
+                "memory": "512G",
+                "input_fixture": str(fixture_path),
+                "input_fixture_sha256": file_digest(fixture_path),
+                "prebuilt_provider_binary": provider_binary,
+            }
+            phase_manifest_path = directory / "manifest.json"
+            atomic_json(phase_manifest_path, phase_manifest)
+            matrix = run_matrix(phase_manifest_path, directory, fixture_path)
+            validate_matrix_report(matrix, expected_identity=model_identity(phase_manifest))
+            journal["l2_reports"][phase] = record(directory / "l2-matrix.json")
+            journal["events"].append(f"l2:{phase}")
+            validate_journal(journal, identity, verify_files=False)
+            atomic_json(journal_path, journal)
+        if phase == "reload":
+            journal["reload_matches_update2"] = compare_reload_matrices(
+                read_record(journal["l2_reports"]["update2"]),
+                read_record(journal["l2_reports"]["reload"]),
+            )
+            atomic_json(journal_path, journal)
+        # Native workers retire the previous full checkpoint immediately after
+        # verified successor publication; export retirement waits for durable L2.
+        if phase in ("update1", "update2", "reload"):
+            retire_export(worker, export_root, identity)
+    if stage != "train":
+        _close_partial_training_stage(
+            manifest, output, run_root, previous, prior_result, journal, identity, baseline, uuids
+        )
+        return
+    last = read_record(journal["workers"]["reload"])["checkpoint"]
+    report = {
+        "schema_version": 1,
+        "status": "passed",
+        "scope": "qwen3_next_training_and_reload",
+        "identity": model_identity(manifest),
+        "training_identity": identity,
+        "required_sections": list(SECTIONS),
+        "l2_fixture_sha256": baseline["fixture"]["content_sha256"],
+        "l2_reports": journal["l2_reports"],
+        "workers": journal["workers"],
+        "events": journal["events"],
+        "recovery": journal["recovery"],
+        "reload_matches_update2": journal["reload_matches_update2"],
+        "checkpoint": {"path": last, "sha256": file_digest(Path(last) / "COMPLETE.json")},
+        "fault_injection": checkpoint_faults(output / "checkpoint-faults", identity),
+        "attempt": os.environ["QWEN3_NEXT_ATTEMPT"],
+    }
+    validate_training_report(report)
+    atomic_json(output / "training-report.json", report)
+    atomic_json(output / "reload.json", report)
+    shutil.copyfile(journal["l2_reports"]["reload"]["path"], output / "l2-matrix.json")
+    shutil.copyfile(
+        previous / prior_result["evidence"]["operators"]["path"], output / "operators.json"
+    )
+    atomic_json(
+        output / "environment.json",
+        {"environment": manifest["environment"], "gpu_uuids": uuids, "execution_gpus": 4},
+    )
+    evidence = {
+        name: evidence_record(output / filename, run_root)
+        for name, filename in {
+            "updates": "training-report.json",
+            "reload": "reload.json",
+            "l2_matrix": "l2-matrix.json",
+            "operators": "operators.json",
+            "environment": "environment.json",
+        }.items()
+    }
+    result = result_envelope(manifest, os.environ["QWEN3_NEXT_ATTEMPT"], evidence=evidence)
+    result.update(full_model_l2="passed", training_and_reload="passed")
+    validate_result(manifest, result, run_dir=run_root, attempt=os.environ["QWEN3_NEXT_ATTEMPT"])
+    atomic_json(output / "result.json", result)
+
+
+def _close_partial_training_stage(
+    manifest, output, run_root, previous, prior_result, journal, identity, baseline, uuids
+):
+    """Record a train1/train2 result: its phases, L2 matrix and the checkpoint left behind."""
+    stage = manifest["stage"]
+    last_phase = STAGE_LAST_PHASE[stage]
+    checkpoint = read_record(journal["workers"][last_phase])["checkpoint"]
+    phases = list(stage_phases(stage))
+    if any(
+        phase not in journal["workers"] or phase not in journal["l2_reports"] for phase in phases
+    ):
+        raise ValueError("Partial training stage closed before its phases completed")
+    progress = {
+        "schema_version": 1,
+        "status": "passed",
+        "scope": SCOPES[stage],
+        "identity": model_identity(manifest),
+        "training_identity": identity,
+        "phases": phases,
+        "l2_fixture_sha256": baseline["fixture"]["content_sha256"],
+        "l2_reports": journal["l2_reports"],
+        "workers": journal["workers"],
+        "events": journal["events"],
+        "recovery": journal["recovery"],
+        "checkpoint": {
+            "path": checkpoint,
+            "sha256": file_digest(Path(checkpoint) / "COMPLETE.json"),
+        },
+        "attempt": os.environ["QWEN3_NEXT_ATTEMPT"],
+    }
+    atomic_json(output / "training-progress.json", progress)
+    shutil.copyfile(journal["l2_reports"][last_phase]["path"], output / "l2-matrix.json")
+    shutil.copyfile(
+        previous / prior_result["evidence"]["operators"]["path"], output / "operators.json"
+    )
+    atomic_json(
+        output / "environment.json",
+        {"environment": manifest["environment"], "gpu_uuids": uuids, "execution_gpus": 4},
+    )
+    evidence = {
+        name: evidence_record(output / filename, run_root)
+        for name, filename in {
+            "updates": "training-progress.json",
+            "l2_matrix": "l2-matrix.json",
+            "operators": "operators.json",
+            "environment": "environment.json",
+        }.items()
+    }
+    result = result_envelope(manifest, os.environ["QWEN3_NEXT_ATTEMPT"], evidence=evidence)
+    result.update(full_model_l2="passed", training_phases=phases)
+    validate_result(manifest, result, run_dir=run_root, attempt=os.environ["QWEN3_NEXT_ATTEMPT"])
+    atomic_json(output / "result.json", result)
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--manifest", type=Path)
+    parser.add_argument("--output", type=Path)
+    parser.add_argument("--worker-request", type=Path)
+    parser.add_argument("--check-ready", action="store_true")
+    args = parser.parse_args()
+    if args.check_ready:
+        check_l2_ready()
+        from qwen3_next_training_checkpoint import save_training_checkpoint  # noqa: F401
+        from qwen3_next_training_export import export_training_weights  # noqa: F401
+
+        print(json.dumps({"status": "ready", "native_training": True}))
+        return 0
+    if args.output is None:
+        parser.error("--output is required")
+    if args.worker_request:
+        request = json.loads(args.worker_request.read_text())
+        verify_environment(request["manifest"]["environment"], require_venv=True)
+        verify_before_model_load(request["manifest"], args.output.parent)
+        run_training_phase(request, args.output)
+        return 0
+    if args.manifest is None:
+        parser.error("--manifest is required")
+    args.output.mkdir(parents=True, exist_ok=True)
+    try:
+        execute(args.manifest, args.output)
+    except Exception as exc:
+        atomic_json(
+            args.output / "result.json",
+            {
+                "run_id": json.loads(args.manifest.read_text())["run_id"],
+                "attempt": os.environ["QWEN3_NEXT_ATTEMPT"],
+                "status": "failed",
+                "training_and_reload": "not_passed",
+                **failure_fields(exc),
+            },
+        )
+        raise
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/qwen3_next_training_checkpoint.py b/scripts/qwen3_next_training_checkpoint.py
new file mode 100644
index 000000000..75347aba4
--- /dev/null
+++ b/scripts/qwen3_next_training_checkpoint.py
@@ -0,0 +1,602 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Serialize the real native VIME/Megatron training state, one TP rank at a time.
+
+DistributedOptimizer.state_dict() deliberately omits master weights and Adam
+moments. The bounded native parameter getter/setter adapter preserves all of
+these states without allocating whole-bucket CPU packing copies. Publication
+and recovery use the all-rank atomic protocol.
+"""
+
+import gc
+import json
+import os
+import uuid
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+from qwen3_next_checkpoint import (
+    newest_complete_checkpoint,
+    publish_checkpoint,
+    require_checkpoint_disk_space,
+    validate_checkpoint,
+    write_rank_manifest,
+)
+from qwen3_next_contract import atomic_json
+from qwen3_next_optimizer_checkpoint import (
+    load_bounded_parameter_state,
+    native_parameter_layout,
+    native_parameter_states,
+    parameter_state_files,
+)
+from qwen3_next_optimizer_checkpoint import parameter_state_records as _optimizer_parameter_records
+from qwen3_next_optimizer_checkpoint import (
+    save_bounded_parameter_state,
+    verify_hdo_optimizer_master_aliases,
+)
+from qwen3_next_runtime import create_staging, owned_root
+
+from rl_engine.integrations.qwen3_next_training import (
+    capture_rng_state,
+    named_trainable_parameters,
+    restore_rng_state,
+    state_digest,
+)
+from rl_engine.testing.qwen3_next_rl_fixture import PolicyFixture, require_policy_configuration
+
+SCHEMA_VERSION = 1
+CLASS_TAG = "__qwen3_next_native_optimizer_class__"
+
+
+def _native_leaves(args, model, optimizer, tracker):
+    """Reject an alternate optimizer/topology before any checkpoint work starts."""
+    require_policy_configuration(args)
+    if tracker is None or not tracker.get_states():
+        raise ValueError("Native checkpoint must include the initialized Megatron RNG tracker")
+    if type(getattr(args, "consumed_train_samples", None)) is not int:
+        raise ValueError("Native training sample progress must be explicit")
+    from megatron.core import parallel_state
+    from megatron.core.optimizer.cpu_offloading.hybrid_optimizer import HybridDeviceOptimizer
+    from megatron.core.optimizer.distrib_optimizer import DistributedOptimizer
+    from megatron.core.optimizer.optimizer import ChainedOptimizer
+
+    if not dist.is_initialized() or dist.get_world_size() != 4:
+        raise ValueError("Native checkpoint requires an initialized four-rank process group")
+    topology = (
+        parallel_state.get_tensor_model_parallel_world_size(),
+        parallel_state.get_pipeline_model_parallel_world_size(),
+        parallel_state.get_context_parallel_world_size(),
+        parallel_state.get_expert_model_parallel_world_size(),
+        parallel_state.get_data_parallel_world_size(),
+    )
+    if topology != (4, 1, 1, 1, 1):
+        raise ValueError("Native checkpoint requires TP4 and PP/CP/EP/DP1")
+    if parallel_state.get_tensor_model_parallel_rank() != dist.get_rank():
+        raise ValueError("Checkpoint rank order must equal tensor-parallel rank order")
+    leaves = (
+        list(optimizer.chained_optimizers)
+        if isinstance(optimizer, ChainedOptimizer)
+        else [optimizer]
+    )
+    if not leaves or any(not isinstance(leaf, DistributedOptimizer) for leaf in leaves):
+        raise TypeError("Checkpoint requires actual native DistributedOptimizer leaves")
+    for leaf in leaves:
+        if getattr(leaf, "is_stub_optimizer", False):
+            raise ValueError("Stub optimizers cannot provide complete training state")
+        if leaf.ddp_config.use_megatron_fsdp:
+            raise ValueError("The fixed TP4 checkpoint path does not use FSDP")
+        for group in (leaf.data_parallel_group, leaf.data_parallel_group_gloo):
+            if group is None or dist.get_world_size(group) != 1 or dist.get_rank(group) != 0:
+                raise ValueError("Serial native optimizer saving requires singleton DP groups")
+        native = leaf.optimizer
+        if not isinstance(native, HybridDeviceOptimizer):
+            raise TypeError("Native checkpoint requires Megatron CPU optimizer offload")
+        if (
+            native.offload_fraction != 1.0
+            or not native.param_update_in_fp32
+            or native.overlap_cpu_optimizer_d2h_h2d
+            or native.gpu_optimizer is not None
+        ):
+            raise ValueError("Native optimizer must fully offload FP32 masters without overlap")
+    parameters = named_trainable_parameters(model)
+    if any(value.dtype != torch.bfloat16 or not value.is_cuda for value in parameters.values()):
+        raise ValueError("The complete native model must retain BF16 CUDA parameters")
+    return leaves
+
+
+def _all_rank_call(action, label):
+    """Propagate a Python failure before another rank enters a later collective."""
+    value, error = None, None
+    try:
+        value = action()
+    except Exception as exc:
+        error = f"{type(exc).__name__}: {exc}"
+    errors = [None] * 4
+    dist.all_gather_object(errors, error)
+    if any(errors):
+        raise RuntimeError(f"{label} failed across ranks: {errors}")
+    return value
+
+
+def _rank_zero_call(action, label):
+    value = _all_rank_call(lambda: action() if dist.get_rank() == 0 else None, label)
+    values = [value]
+    dist.broadcast_object_list(values, src=0)
+    return values[0]
+
+
+def _serial_rank_call(action, label):
+    result = None
+    for owner in range(4):
+        dist.barrier()
+        current = _all_rank_call(
+            lambda: action() if dist.get_rank() == owner else None, f"{label} rank {owner}"
+        )
+        if dist.get_rank() == owner:
+            result = current
+            gc.collect()
+    return result
+
+
+def _native_class_allowlist(leaves):
+    allowed = {}
+    for leaf in leaves:
+        for name in ("cpu_optimizer_cls", "gpu_optimizer_cls"):
+            cls = getattr(leaf.optimizer, name)
+            if cls is not None:
+                key = f"{cls.__module__}.{cls.__qualname__}"
+                if not isinstance(cls, type) or (key in allowed and allowed[key] is not cls):
+                    raise ValueError("Ambiguous native optimizer class identity")
+                allowed[key] = cls
+    return allowed
+
+
+def _encode_header(value, allowed):
+    """Preserve native class-valued param-group defaults without unsafe unpickling."""
+    if isinstance(value, type):
+        key = f"{value.__module__}.{value.__qualname__}"
+        if allowed.get(key) is not value:
+            raise TypeError(f"Unpinned class in native optimizer header: {key}")
+        return {CLASS_TAG: key}
+    if isinstance(value, dict):
+        if CLASS_TAG in value:
+            raise ValueError("Reserved native class tag in optimizer header")
+        return {key: _encode_header(item, allowed) for key, item in value.items()}
+    if isinstance(value, (list, tuple)):
+        return type(value)(_encode_header(item, allowed) for item in value)
+    if value is None or isinstance(value, torch.Tensor) or type(value) in (str, bool, int, float):
+        return value
+    raise TypeError(f"Unsupported native optimizer header value: {type(value)!r}")
+
+
+def _decode_header(value, allowed):
+    if isinstance(value, dict):
+        if CLASS_TAG in value:
+            if set(value) != {CLASS_TAG} or value[CLASS_TAG] not in allowed:
+                raise ValueError(
+                    "Saved optimizer class is absent from the running native optimizer"
+                )
+            return allowed[value[CLASS_TAG]]
+        return {key: _decode_header(item, allowed) for key, item in value.items()}
+    if isinstance(value, (list, tuple)):
+        return type(value)(_decode_header(item, allowed) for item in value)
+    return value
+
+
+def _model_states(model):
+    if not model:
+        raise ValueError("Cannot save an empty native model")
+    return [chunk.state_dict_for_save_checkpoint() for chunk in model]
+
+
+def _state_signature(value):
+    if isinstance(value, torch.Tensor):
+        return {"dtype": str(value.dtype), "shape": list(value.shape)}
+    if isinstance(value, dict):
+        return {key: _state_signature(item) for key, item in value.items()}
+    if isinstance(value, (list, tuple)):
+        return type(value)(_state_signature(item) for item in value)
+    if value is None or type(value) in (str, bool, int, float):
+        return type(value).__name__
+    raise TypeError(f"Unsupported native model state type: {type(value)!r}")
+
+
+def _tensor_bytes(value, seen=None):
+    if seen is None:
+        seen = set()
+    if isinstance(value, torch.Tensor):
+        # A serialized view retains its underlying storage, including TP padding.
+        storage = value.untyped_storage()
+        key = (value.device, storage.data_ptr(), storage.nbytes())
+        if key in seen:
+            return 0
+        seen.add(key)
+        return storage.nbytes()
+    if isinstance(value, dict):
+        return sum(_tensor_bytes(item, seen) for item in value.values())
+    if isinstance(value, (list, tuple)):
+        return sum(_tensor_bytes(item, seen) for item in value)
+    return 0
+
+
+def _encode_rng(state):
+    numpy_state = state["numpy"]
+    return {
+        **state,
+        "numpy": (
+            numpy_state[0],
+            torch.from_numpy(numpy_state[1].copy()),
+            *numpy_state[2:],
+        ),
+    }
+
+
+def _decode_rng(state):
+    numpy_state = state["numpy"]
+    if numpy_state[1].dtype != torch.uint32 or numpy_state[1].device.type != "cpu":
+        raise ValueError("Invalid saved NumPy RNG state")
+    return {**state, "numpy": (numpy_state[0], numpy_state[1].numpy().copy(), *numpy_state[2:])}
+
+
+def _save_tensor_file(path, payload):
+    with Path(path).open("xb") as stream:
+        torch.save(payload, stream)
+        stream.flush()
+        os.fsync(stream.fileno())
+
+
+def _rank_files(rank, chunk_count, rank_dir):
+    return {
+        "model": [f"rank-{rank}/model-{index}.pt" for index in range(chunk_count)],
+        "optimizer": [f"rank-{rank}/{name}" for name in parameter_state_files(rank_dir)],
+        "rng": [f"rank-{rank}/rng.pt"],
+        "progress": [
+            f"rank-{rank}/{name}" for name in ("progress.json", "scheduler.pt", "fixture.pt")
+        ],
+    }
+
+
+def _validate_native_checkpoint(checkpoint, identity, chunk_count):
+    # Sizes and manifests only: _load_rank compares every loaded tensor's digest.
+    complete = validate_checkpoint(checkpoint, identity, verify_files=False)
+    for rank in range(4):
+        manifest = json.loads((checkpoint / f"rank-{rank}.manifest.json").read_text())
+        files = {
+            role: sorted(record["path"] for record in records)
+            for role, records in manifest["files"].items()
+        }
+        expected = {
+            role: sorted(paths)
+            for role, paths in _rank_files(rank, chunk_count, checkpoint / f"rank-{rank}").items()
+        }
+        if files != expected:
+            raise ValueError(
+                "Native checkpoint manifest omits or substitutes a required state file"
+            )
+    return complete
+
+
+def _save_rank(
+    model,
+    optimizer,
+    scheduler,
+    fixture,
+    staging,
+    *,
+    leaves,
+    identity,
+    step,
+    sample_position,
+    tracker,
+    native_args,
+    update_evidence,
+):
+    rank = dist.get_rank()
+    rank_dir = staging / f"rank-{rank}"
+    rank_dir.mkdir()
+    torch.cuda.synchronize()
+    rng = capture_rng_state(tracker=tracker)
+    states = _model_states(model)
+    model_hashes = [state_digest(state) for state in states]
+    native_records, _ = _optimizer_parameter_records(model, leaves)
+    header = _encode_header(optimizer.state_dict(), _native_class_allowlist(leaves))
+    schedule = scheduler.state_dict()
+    for index, state in enumerate(states):
+        _save_tensor_file(rank_dir / f"model-{index}.pt", state)
+    del states
+    _save_tensor_file(rank_dir / "optimizer-header.pt", header)
+    save_bounded_parameter_state(model, leaves, rank_dir, _save_tensor_file)
+    verify_hdo_optimizer_master_aliases(model, leaves)
+    _save_tensor_file(rank_dir / "rng.pt", _encode_rng(rng))
+    _save_tensor_file(rank_dir / "scheduler.pt", schedule)
+    _save_tensor_file(
+        rank_dir / "fixture.pt",
+        {
+            "tokens": fixture.tokens,
+            "old_logprobs": fixture.old_logprobs,
+            "baseline_identity": fixture.baseline_identity,
+            "content_sha256": fixture.content_sha256,
+        },
+    )
+    progress = {
+        "schema_version": SCHEMA_VERSION,
+        "scope": "native_vime_training_checkpoint",
+        "rank": rank,
+        "world_size": 4,
+        "identity": identity,
+        "step": step,
+        "sample_position": sample_position,
+        "consumed_train_samples": native_args.consumed_train_samples,
+        "update_evidence": update_evidence,
+        "fixture_sha256": fixture.content_sha256,
+        "model_sha256": model_hashes,
+        "optimizer_parameter_sha256": native_records,
+        "optimizer_header_sha256": state_digest(header),
+        "scheduler_sha256": state_digest(schedule),
+        "rng_sha256": state_digest(rng),
+        "serialization": "native_parameter_views_bounded_serial_tp_ranks",
+    }
+    atomic_json(rank_dir / "progress.json", progress)
+    if state_digest(capture_rng_state(tracker=tracker)) != progress["rng_sha256"]:
+        raise ValueError("Checkpoint serialization unexpectedly changed training RNG state")
+    write_rank_manifest(
+        staging,
+        rank=rank,
+        world_size=4,
+        identity=identity,
+        step=step,
+        sample_position=sample_position,
+        files=_rank_files(rank, len(model), rank_dir),
+    )
+    return progress
+
+
+def _load_rank(
+    model,
+    optimizer,
+    scheduler,
+    fixture,
+    checkpoint,
+    *,
+    leaves,
+    identity,
+    complete,
+    tracker,
+    native_args,
+):
+    rank = dist.get_rank()
+    rank_dir = checkpoint / f"rank-{rank}"
+    progress = json.loads((rank_dir / "progress.json").read_text())
+    expected = {
+        "schema_version": SCHEMA_VERSION,
+        "scope": "native_vime_training_checkpoint",
+        "rank": rank,
+        "world_size": 4,
+        "identity": identity,
+        "step": complete["step"],
+        "sample_position": complete["sample_position"],
+        "fixture_sha256": fixture.content_sha256,
+        "serialization": "native_parameter_views_bounded_serial_tp_ranks",
+    }
+    if any(progress.get(key) != value for key, value in expected.items()):
+        raise ValueError("Saved native training progress or fixture identity differs")
+    saved_fixture = PolicyFixture(**torch.load(rank_dir / "fixture.pt", weights_only=True))
+    saved_fixture.validate()
+    if saved_fixture.content_sha256 != fixture.content_sha256:
+        raise ValueError("Recovery must reuse the frozen baseline policy samples/logprobs")
+    header = torch.load(rank_dir / "optimizer-header.pt", map_location="cpu", weights_only=True)
+    if state_digest(header) != progress["optimizer_header_sha256"]:
+        raise ValueError("Native optimizer header content mismatch")
+    # Native load_state_dict may initialize state with a dummy step. Load the
+    # saved BF16 model afterwards and restore RNG only after every other load.
+    optimizer.load_state_dict(_decode_header(header, _native_class_allowlist(leaves)))
+    load_bounded_parameter_state(model, leaves, rank_dir)
+    verify_hdo_optimizer_master_aliases(model, leaves)
+    if len(progress["model_sha256"]) != len(model):
+        raise ValueError("Saved model chunk count differs")
+    for index, chunk in enumerate(model):
+        saved = torch.load(
+            rank_dir / f"model-{index}.pt", map_location="cpu", weights_only=True, mmap=True
+        )
+        current = chunk.state_dict_for_save_checkpoint()
+        if _state_signature(saved) != _state_signature(current):
+            raise ValueError("Model checkpoint would change tensor dtypes, shapes or state keys")
+        if state_digest(saved) != progress["model_sha256"][index]:
+            raise ValueError("Saved native model content mismatch")
+        del current
+        chunk.load_state_dict(saved, strict=True)
+        del saved
+    schedule = torch.load(rank_dir / "scheduler.pt", map_location="cpu", weights_only=True)
+    scheduler.load_state_dict(schedule)
+    torch.cuda.synchronize()
+    if [state_digest(value) for value in _model_states(model)] != progress["model_sha256"]:
+        raise ValueError("Native model reload changed original tensor bits")
+    restored, _ = _optimizer_parameter_records(model, leaves)
+    if restored != progress["optimizer_parameter_sha256"]:
+        raise ValueError("Native optimizer reload changed FP32 master weights or Adam moments")
+    current_header = _encode_header(optimizer.state_dict(), _native_class_allowlist(leaves))
+    if state_digest(current_header) != progress["optimizer_header_sha256"]:
+        raise ValueError("Native optimizer reload changed optimizer metadata or step")
+    if state_digest(scheduler.state_dict()) != progress["scheduler_sha256"]:
+        raise ValueError("Native scheduler reload changed schedule progress")
+    if type(progress.get("consumed_train_samples")) is not int:
+        raise ValueError("Saved native consumed sample progress is absent")
+    native_args.consumed_train_samples = progress["consumed_train_samples"]
+    rng = _decode_rng(torch.load(rank_dir / "rng.pt", map_location="cpu", weights_only=True))
+    if state_digest(rng) != progress["rng_sha256"]:
+        raise ValueError("Saved RNG content mismatch")
+    restore_rng_state(rng, tracker=tracker)
+    if state_digest(capture_rng_state(tracker=tracker)) != progress["rng_sha256"]:
+        raise ValueError("Native RNG restore changed original state")
+    return progress
+
+
+def save_training_checkpoint(
+    args,
+    model,
+    optimizer,
+    scheduler,
+    fixture,
+    root,
+    *,
+    step,
+    sample_position,
+    identity,
+    tracker,
+    export_bytes,
+    update_evidence=None,
+):
+    """Collectively save a complete native checkpoint; call on all four TP ranks."""
+    if (
+        type(step) is not int
+        or step not in (1, 2, 3)
+        or type(sample_position) is not int
+        or sample_position != step * 4
+    ):
+        raise ValueError("Checkpoint progress must match complete four-sample native updates")
+    fixture.validate()
+    leaves = _all_rank_call(
+        lambda: _native_leaves(args, model, optimizer, tracker), "native topology"
+    )
+    root = Path(root).absolute()
+
+    def estimate():
+        optimizer_bytes = sum(
+            value.numel() * value.element_size()
+            for record in native_parameter_layout(model, leaves).values()
+            for states in [native_parameter_states(record)]
+            for value in states.values()
+            if isinstance(value, torch.Tensor)
+        )
+        # Four-rank allgather carries only byte counts, never parameter tensors.
+        model_bytes = sum(_tensor_bytes(state) for state in _model_states(model))
+        # Native DP packing retains buffer padding, rather than only model numels.
+        return model_bytes + (optimizer_bytes * 105 // 100) + 64 * 1024**2
+
+    local_bytes = _all_rank_call(estimate, "checkpoint disk estimate")
+    estimates = [None] * 4
+    dist.all_gather_object(estimates, local_bytes)
+
+    def prepare():
+        owned_root(root, identity)
+        recovery = newest_complete_checkpoint(root, identity, verify_files=False)
+        retained = 0
+        if recovery["checkpoint"]:
+            retained = sum(
+                path.stat().st_size
+                for path in Path(recovery["checkpoint"]).rglob("*")
+                if path.is_file()
+            )
+        require_checkpoint_disk_space(
+            root,
+            checkpoint_bytes=sum(estimates),
+            export_bytes=export_bytes,
+            retained_complete_bytes=retained,
+        )
+        staging, destination = create_staging(
+            root,
+            f"step-{step:08d}-{uuid.uuid4().hex}",
+            {
+                "schema_version": 1,
+                "scope": "native_vime_training_checkpoint",
+                "status": "staging",
+                "identity": identity,
+                "step": step,
+                "sample_position": sample_position,
+            },
+        )
+        return {"staging": str(staging), "destination": str(destination), "recovery": recovery}
+
+    paths = _rank_zero_call(prepare, "checkpoint disk budget and staging")
+    staging, destination = Path(paths["staging"]), Path(paths["destination"])
+    _serial_rank_call(
+        lambda: _save_rank(
+            model,
+            optimizer,
+            scheduler,
+            fixture,
+            staging,
+            leaves=leaves,
+            identity=identity,
+            step=step,
+            sample_position=sample_position,
+            tracker=tracker,
+            native_args=args,
+            update_evidence=update_evidence,
+        ),
+        "native checkpoint save",
+    )
+    complete = _rank_zero_call(
+        lambda: publish_checkpoint(staging, destination, world_size=4, identity=identity),
+        "checkpoint all-file validation and publication",
+    )
+    return {"checkpoint": str(destination), "complete": complete, "recovery": paths["recovery"]}
+
+
+def load_training_checkpoint(
+    args, model, optimizer, scheduler, fixture, checkpoint, *, identity, tracker
+):
+    """Verify every rank's file before any load, then restore native state serially."""
+    fixture.validate()
+    leaves = _all_rank_call(
+        lambda: _native_leaves(args, model, optimizer, tracker), "native topology"
+    )
+    checkpoint = Path(checkpoint).resolve()
+    complete = _rank_zero_call(
+        lambda: _validate_native_checkpoint(checkpoint, identity, len(model)),
+        "checkpoint pre-load all-file validation",
+    )
+    if complete["step"] not in (1, 2, 3) or complete["sample_position"] != complete["step"] * 4:
+        raise ValueError("Checkpoint progress differs from complete fixed native updates")
+    progress = _serial_rank_call(
+        lambda: _load_rank(
+            model,
+            optimizer,
+            scheduler,
+            fixture,
+            checkpoint,
+            leaves=leaves,
+            identity=identity,
+            complete=complete,
+            tracker=tracker,
+            native_args=args,
+        ),
+        "native checkpoint load",
+    )
+    return {
+        "checkpoint": str(checkpoint),
+        "step": complete["step"],
+        "sample_position": complete["sample_position"],
+        "rank_evidence": progress,
+    }
+
+
+def training_state_identity(
+    args, model, optimizer, scheduler, fixture, *, step, sample_position, identity, tracker
+):
+    """Exact per-rank control/recovery state identity, with no PID or path fields."""
+    fixture.validate()
+    if type(step) is not int or step not in (1, 2, 3) or sample_position != step * 4:
+        raise ValueError("State audit requires complete fixed native updates")
+    leaves = _native_leaves(args, model, optimizer, tracker)
+    torch.cuda.synchronize()
+    records, _ = _optimizer_parameter_records(model, leaves)
+    header = _encode_header(optimizer.state_dict(), _native_class_allowlist(leaves))
+    result = {
+        "schema_version": SCHEMA_VERSION,
+        "scope": "native_vime_training_state",
+        "rank": dist.get_rank(),
+        "identity": identity,
+        "step": step,
+        "sample_position": sample_position,
+        "consumed_train_samples": args.consumed_train_samples,
+        "fixture_sha256": fixture.content_sha256,
+        "model_sha256": [state_digest(value) for value in _model_states(model)],
+        "optimizer_parameter_sha256": records,
+        "optimizer_header_sha256": state_digest(header),
+        "scheduler_sha256": state_digest(scheduler.state_dict()),
+        "rng_sha256": state_digest(capture_rng_state(tracker=tracker)),
+    }
+    return {**result, "state_sha256": state_digest(result)}
diff --git a/scripts/qwen3_next_training_export.py b/scripts/qwen3_next_training_export.py
new file mode 100644
index 000000000..60b43906a
--- /dev/null
+++ b/scripts/qwen3_next_training_export.py
@@ -0,0 +1,503 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Publish fresh BF16 HF weights through the installed native VIME exporter.
+
+An export completion record preserves provenance after historical weights are
+retired. Only validate_training_export, which rehashes every current file, can
+authorize loading an export. A retained record alone cannot authorize a load.
+"""
+
+import hashlib
+import json
+import math
+import os
+import re
+import uuid
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+from qwen3_next_checkpoint import require_checkpoint_disk_space
+from qwen3_next_contract import atomic_json, checkpoint_identity
+from qwen3_next_runtime import create_staging, file_digest, owned_root
+from qwen3_next_training_checkpoint import _all_rank_call, _rank_zero_call
+
+from rl_engine.integrations.qwen3_next_training import (
+    capture_rng_state,
+    named_trainable_parameters,
+    state_digest,
+    tensor_digest,
+)
+
+COMPLETE_FILE = "EXPORT_COMPLETE.json"
+INDEX_FILE = "model.safetensors.index.json"
+KIND = "qwen3_next_native_hf_export"
+# Official tensors the native trainer neither loads nor updates: the multi-token-prediction head.
+# VIME's HF saver writes only Megatron-owned parameters, so these are carried from the pinned base.
+PASSTHROUGH_PREFIXES = ("mtp.",)
+PASSTHROUGH_SHARD = "model-base-passthrough-{idx:05d}-of-{total:05d}.safetensors"
+IDENTITY_KEYS = (
+    "source_commit",
+    "source_tree",
+    "environment_sha256",
+    "provider_config",
+    "topology",
+)
+
+
+def _json_digest(value):
+    return hashlib.sha256(
+        json.dumps(value, sort_keys=True, separators=(",", ":")).encode()
+    ).hexdigest()
+
+
+def _reference():
+    path = Path(__file__).resolve().parents[1] / "rl_engine/testing/qwen3_next_norm_manifest.json"
+    return json.loads(path.read_text())["model_identity"]
+
+
+def _schema_reference():
+    path = Path(__file__).resolve().parents[1] / "rl_engine/testing/qwen3_next_export_schema.json"
+    record = json.loads(path.read_text())
+    if (
+        record.get("schema_version") != 1
+        or record.get("checkpoint_revision") != _reference()["revision"]
+    ):
+        raise ValueError("Pinned export schema must identify the official checkpoint revision")
+    return record
+
+
+def _safe_file(root, name):
+    path = Path(name)
+    if len(path.parts) != 1 or path.is_absolute() or path.name in (".", ".."):
+        raise ValueError("HF export file must be directly inside its export directory")
+    candidate = root / path
+    if candidate.is_symlink() or not candidate.is_file():
+        raise ValueError(f"Missing or external HF export file: {name}")
+    return candidate
+
+
+def _weight_schema(root, *, check_finite):
+    from safetensors import safe_open
+
+    root = Path(root)
+    index = json.loads((root / INDEX_FILE).read_text())
+    weight_map = index["weight_map"]
+    if not weight_map or any(not isinstance(key, str) for key in weight_map):
+        raise ValueError("An HF export must contain the complete named model")
+    by_shard = {}
+    for name, shard in weight_map.items():
+        if Path(shard).name != shard or not shard.endswith(".safetensors"):
+            raise ValueError("HF tensor index points outside safetensors shards")
+        by_shard.setdefault(shard, set()).add(name)
+    schema = {}
+    for shard, names in sorted(by_shard.items()):
+        with safe_open(root / shard, framework="pt", device="cpu") as reader:
+            if set(reader.keys()) != names:
+                raise ValueError("HF shard tensor inventory differs from its weight index")
+            for name in sorted(names):
+                view = reader.get_slice(name)
+                shape, dtype = view.get_shape(), view.get_dtype()
+                if dtype != "BF16" or not shape or any(value <= 0 for value in shape):
+                    raise ValueError(f"HF export must preserve nonempty BF16 weights: {name}")
+                schema[name] = {"dtype": dtype, "shape": shape}
+                if check_finite:
+                    # Keep verification memory bounded, including embedding/LM head.
+                    row_size = max(1, math.prod(shape[1:]))
+                    rows = max(1, (32 * 1024**2) // (row_size * 2))
+                    for start in range(0, shape[0], rows):
+                        if not torch.isfinite(view[start : start + rows]).all():
+                            raise ValueError(f"Nonfinite exported model weight: {name}")
+    layers = {
+        int(match.group(1))
+        for name in schema
+        if (match := re.match(r"model\.layers\.(\d+)\.", name))
+    }
+    if layers != set(range(48)):
+        raise ValueError("HF export must retain all 48 real decoder layers")
+    if index.get("metadata", {}).get("total_size") != sum(
+        math.prod(record["shape"]) * 2 for record in schema.values()
+    ):
+        raise ValueError("HF tensor index total byte size differs from its tensors")
+    return schema
+
+
+def validate_training_export_record(record, expected_identity):
+    """Validate a durable history record; this deliberately does not authorize loading."""
+    if record != expected_identity:
+        raise ValueError("Published HF export identity differs from the requested weights")
+    if (record.get("schema_version"), record.get("kind"), record.get("status")) != (
+        1,
+        KIND,
+        "complete",
+    ):
+        raise ValueError("HF export has no supported completion record")
+    if (record.get("architecture_layers"), record.get("weights_dtype")) != (48, "bfloat16"):
+        raise ValueError("HF export architecture or precision differs")
+    expected_versions = {"update-1": 1, "update-2": 2, "reload-2": 2}
+    if (
+        record.get("weight_version") not in expected_versions
+        or type(record.get("step")) is not int
+        or record.get("step") != expected_versions[record["weight_version"]]
+    ):
+        raise ValueError("HF export does not identify an accepted native update/reload phase")
+    training, base = record["training_identity"], record["base_checkpoint_identity"]
+    if (
+        training.get("checkpoint") != record["base_checkpoint"]
+        or training.get("checkpoint_identity") != base
+        or any(key not in training for key in IDENTITY_KEYS)
+    ):
+        raise ValueError(
+            "Updated weights require explicit fixed baseline/source/environment provenance"
+        )
+    if base.get("revision") != _reference()["revision"]:
+        raise ValueError("HF export base revision differs from the official pinned checkpoint")
+    hashes = record.get("model_rank_sha256")
+    if record.get("model_identity_method") != "native_parameter_hashes_after":
+        raise ValueError("HF export must bind directly to the native update parameter audit")
+    if not isinstance(hashes, list) or len(hashes) != 4:
+        raise ValueError("HF export requires four actual native model state hashes")
+    passthrough = record.get("base_passthrough")
+    if (
+        not isinstance(passthrough, dict)
+        or passthrough.get("prefixes") != list(PASSTHROUGH_PREFIXES)
+        or type(passthrough.get("tensor_count")) is not int
+        or passthrough["tensor_count"] < 0
+        or not isinstance(passthrough.get("shards"), dict)
+    ):
+        raise ValueError("HF export must state which untrained official tensors came from the base")
+    if any(
+        not isinstance(value, str) or not re.fullmatch("[0-9a-f]{64}", value) for value in hashes
+    ):
+        raise ValueError("HF export has an invalid native model state hash")
+    files = record.get("files", {})
+    shards = {name: value for name, value in files.items() if name.endswith(".safetensors")}
+    if not shards or INDEX_FILE not in files or "config.json" not in files:
+        raise ValueError("HF export is missing model shards, index or configuration")
+    for name, item in files.items():
+        if (
+            Path(name).name != name
+            or type(item.get("size")) is not int
+            or item["size"] <= 0
+            or not isinstance(item.get("sha256"), str)
+            or not re.fullmatch("[0-9a-f]{64}", item["sha256"])
+        ):
+            raise ValueError("Invalid file identity in HF export record")
+    if record.get("weights_content_sha256") != _json_digest(shards):
+        raise ValueError("HF export shard content proof differs")
+    if record.get("metadata") != {
+        name: value["sha256"]
+        for name, value in files.items()
+        if not name.endswith(".safetensors") and name != "STAGING.json"
+    }:
+        raise ValueError("HF export metadata proof differs")
+    reference = _schema_reference()
+    if (
+        record.get("weight_schema_sha256") != reference["weight_schema_sha256"]
+        or record.get("tensor_count") != reference["tensor_count"]
+    ):
+        raise ValueError("HF export omits the pinned complete official tensor schema proof")
+    return record
+
+
+def validate_training_export(checkpoint, expected_identity):
+    """Rehash every current export file and exact full-model schema before loading."""
+    checkpoint = Path(checkpoint)
+    if checkpoint.suffix == ".partial" or checkpoint.name.startswith("."):
+        raise ValueError("An unpublished HF export cannot be loaded")
+    record = json.loads((checkpoint / COMPLETE_FILE).read_text())
+    validate_training_export_record(record, expected_identity)
+    present = {path.name for path in checkpoint.iterdir() if path.name != COMPLETE_FILE}
+    if present != record["files"].keys():
+        raise ValueError("HF export file inventory changed after publication")
+    for name, expected in record["files"].items():
+        path = _safe_file(checkpoint, name)
+        if path.stat().st_size != expected["size"] or file_digest(path) != expected["sha256"]:
+            raise ValueError(f"HF export file checksum mismatch: {name}")
+    base = Path(record["base_checkpoint"])
+    if checkpoint_identity(base) != record["base_checkpoint_identity"]:
+        raise ValueError("HF export official base metadata or shard identity changed")
+    schema = _weight_schema(checkpoint, check_finite=True)
+    if (
+        schema != _weight_schema(base, check_finite=False)
+        or _json_digest(schema) != record["weight_schema_sha256"]
+        or len(schema) != record["tensor_count"]
+    ):
+        raise ValueError("HF export differs from the complete official model tensor schema")
+    return record
+
+
+def _carry_base_passthrough(staging, base_checkpoint):
+    """Copy the official tensors the native model does not own from the pinned base, byte for byte.
+
+    Returns the passthrough record for publication. Any missing tensor outside
+    ``PASSTHROUGH_PREFIXES`` is a real omission by the saver and is rejected; the saver must
+    also not have written part of the passthrough head itself.
+    """
+    from safetensors import safe_open
+    from safetensors.torch import save_file
+
+    staging, base = Path(staging), Path(base_checkpoint)
+    index = json.loads((staging / INDEX_FILE).read_text())
+    staged = index["weight_map"]
+    official = json.loads((base / INDEX_FILE).read_text())["weight_map"]
+    extra = sorted(set(staged) - set(official))
+    if extra:
+        raise ValueError(f"Native HF saver wrote tensors outside the official schema: {extra[:5]}")
+    missing = sorted(set(official) - set(staged))
+    foreign = [name for name in missing if not name.startswith(PASSTHROUGH_PREFIXES)]
+    if foreign:
+        raise ValueError(f"Native HF saver omitted trained official tensors: {foreign[:5]}")
+    if any(name.startswith(PASSTHROUGH_PREFIXES) for name in staged):
+        raise ValueError("Native HF saver wrote part of the base passthrough head itself")
+    record = {"prefixes": list(PASSTHROUGH_PREFIXES), "tensor_count": len(missing), "shards": {}}
+    if not missing:
+        return record
+    by_shard = {}
+    for name in missing:
+        by_shard.setdefault(official[name], []).append(name)
+    added = 0
+    for idx, source in enumerate(sorted(by_shard), start=1):
+        if Path(source).name != source or not source.endswith(".safetensors"):
+            raise ValueError("Official tensor index points outside safetensors shards")
+        filename = PASSTHROUGH_SHARD.format(idx=idx, total=len(by_shard))
+        if (staging / filename).exists():
+            raise ValueError(f"Passthrough shard already present in the staging export: {filename}")
+        with safe_open(base / source, framework="pt", device="cpu") as reader:
+            tensors = {name: reader.get_tensor(name) for name in by_shard[source]}
+        for name, value in tensors.items():
+            if (
+                value.dtype != torch.bfloat16
+                or value.numel() == 0
+                or not torch.isfinite(value).all()
+            ):
+                raise ValueError(f"Official passthrough tensor is not finite BF16: {name}")
+        save_file(tensors, staging / filename, metadata={"format": "pt"})
+        with safe_open(staging / filename, framework="pt", device="cpu") as reader:
+            if set(reader.keys()) != set(tensors) or any(
+                not torch.equal(reader.get_tensor(name), value) for name, value in tensors.items()
+            ):
+                raise ValueError(
+                    f"Passthrough shard does not reproduce the base tensors: {filename}"
+                )
+        for name, value in tensors.items():
+            staged[name] = filename
+            added += value.numel() * value.element_size()
+        record["shards"][filename] = {"source": source, "tensor_count": len(tensors)}
+    metadata = index.setdefault("metadata", {})
+    metadata["total_size"] = int(metadata.get("total_size", 0)) + added
+    atomic_json(staging / INDEX_FILE, index)
+    record["names_sha256"] = _json_digest(missing)
+    return record
+
+
+def _publish_export(
+    staging,
+    destination,
+    *,
+    step,
+    weight_version,
+    identity,
+    base_checkpoint,
+    base_checkpoint_identity,
+    model_hashes,
+):
+    staging, destination, base_checkpoint = Path(staging), Path(destination), Path(base_checkpoint)
+    if staging.suffix != ".partial" or staging.parent != destination.parent or destination.exists():
+        raise ValueError("HF export publication requires a new sibling destination")
+    if checkpoint_identity(base_checkpoint) != base_checkpoint_identity:
+        raise ValueError("HF export official base identity changed")
+    passthrough = _carry_base_passthrough(staging, base_checkpoint)
+    schema = _weight_schema(staging, check_finite=True)
+    if schema != _weight_schema(base_checkpoint, check_finite=False):
+        raise ValueError("Native HF saver did not preserve the complete official tensor schema")
+    expected_assets = {
+        name: digest
+        for name, digest in base_checkpoint_identity["metadata"].items()
+        if name != INDEX_FILE
+    }
+    actual_assets = {
+        path.name: file_digest(path)
+        for path in staging.iterdir()
+        if path.name not in (INDEX_FILE, "STAGING.json") and path.suffix != ".safetensors"
+    }
+    if expected_assets != actual_assets:
+        raise ValueError("Native HF export changed tokenizer/configuration assets")
+    files = {}
+    for path in sorted(staging.iterdir()):
+        _safe_file(staging, path.name)
+        with path.open("rb") as stream:
+            os.fsync(stream.fileno())
+        files[path.name] = {"size": path.stat().st_size, "sha256": file_digest(path)}
+    record = {
+        "schema_version": 1,
+        "kind": KIND,
+        "status": "complete",
+        "weight_version": weight_version,
+        "step": step,
+        "export_id": destination.name,
+        "base_checkpoint": str(base_checkpoint),
+        "base_checkpoint_identity": base_checkpoint_identity,
+        "training_identity": identity,
+        "architecture_layers": 48,
+        "weights_dtype": "bfloat16",
+        "model_rank_sha256": model_hashes,
+        "model_identity_method": "native_parameter_hashes_after",
+        "base_passthrough": passthrough,
+        "files": files,
+        "metadata": {
+            name: value["sha256"]
+            for name, value in files.items()
+            if not name.endswith(".safetensors") and name != "STAGING.json"
+        },
+        "weight_schema_sha256": _json_digest(schema),
+        "tensor_count": len(schema),
+        "weights_content_sha256": _json_digest(
+            {name: value for name, value in files.items() if name.endswith(".safetensors")}
+        ),
+    }
+    validate_training_export_record(record, record)
+    atomic_json(staging / COMPLETE_FILE, record)
+    staging.rename(destination)
+    fd = os.open(destination.parent, os.O_RDONLY | os.O_DIRECTORY)
+    try:
+        os.fsync(fd)
+    finally:
+        os.close(fd)
+    return record
+
+
+def export_training_weights(
+    args,
+    model,
+    root,
+    *,
+    step,
+    weight_version,
+    identity,
+    base_checkpoint,
+    base_checkpoint_identity,
+    checkpoint_bytes,
+    retained_complete_bytes=0,
+):
+    """Collectively export the current native model; call on all four TP ranks."""
+    from megatron.core import parallel_state
+    from megatron.core.tensor_parallel.random import get_cuda_rng_tracker
+    from vime.backends.megatron_utils.hf_checkpoint_saver import save_hf_model_to_path
+
+    from rl_engine.integrations.qwen3_next_vime import install_vime_gdn_weight_hooks
+
+    if (
+        type(step) is not int
+        or {"update-1": 1, "update-2": 2, "reload-2": 2}.get(weight_version) != step
+    ):
+        raise ValueError("Native HF export requires an explicit accepted update/reload phase")
+    if (
+        identity.get("checkpoint") != str(base_checkpoint)
+        or identity.get("checkpoint_identity") != base_checkpoint_identity
+    ):
+        raise ValueError("Native HF export provenance must retain the fixed official baseline")
+    if not dist.is_initialized() or dist.get_world_size() != 4:
+        raise ValueError("Native HF export requires four actual distributed ranks")
+    if (
+        parallel_state.get_tensor_model_parallel_world_size(),
+        parallel_state.get_expert_tensor_parallel_world_size(),
+        parallel_state.get_pipeline_model_parallel_world_size(),
+        parallel_state.get_context_parallel_world_size(),
+        parallel_state.get_expert_model_parallel_world_size(),
+    ) != (4, 4, 1, 1, 1):
+        raise ValueError("Native HF export requires TP/ETP4 and PP/CP/EP1")
+    if Path(args.hf_checkpoint).resolve() != Path(base_checkpoint).resolve():
+        raise ValueError("Native HF conversion metadata must come from the pinned official base")
+    if (
+        getattr(args, "actor_num_nodes", None) != 1
+        or getattr(args, "actor_num_gpus_per_node", None) != 4
+    ):
+        raise ValueError("Native HF export requires one node writer for its four GPUs")
+    if not 0 < args.update_weight_buffer_size <= 4 * 1024**3:
+        raise ValueError("Native HF export must use a pinned bounded parameter gather size")
+    if save_hf_model_to_path.__module__ != "vime.backends.megatron_utils.hf_checkpoint_saver":
+        raise ValueError("Native VIME HF exporter was replaced")
+    install_vime_gdn_weight_hooks()
+    tracker = get_cuda_rng_tracker()
+    parameters = named_trainable_parameters(model)
+    if any(value.dtype != torch.bfloat16 for value in parameters.values()):
+        raise ValueError("Native HF export must retain BF16 model parameters")
+    torch.cuda.synchronize()
+    rng_before = state_digest(capture_rng_state(tracker=tracker))
+
+    def parameter_identity():
+        return state_digest({name: tensor_digest(value) for name, value in parameters.items()})
+
+    model_before = _all_rank_call(parameter_identity, "native export model identity")
+    model_hashes = [None] * 4
+    dist.all_gather_object(model_hashes, model_before)
+    root = Path(root).absolute()
+
+    def prepare():
+        owned_root(root, identity)
+        if checkpoint_identity(base_checkpoint) != base_checkpoint_identity:
+            raise ValueError("Native HF export official base identity changed before conversion")
+        export_bytes = sum(item["size"] for item in base_checkpoint_identity["shards"].values())
+        require_checkpoint_disk_space(
+            root,
+            checkpoint_bytes=checkpoint_bytes,
+            export_bytes=export_bytes,
+            retained_complete_bytes=retained_complete_bytes,
+        )
+        staging, destination = create_staging(
+            root,
+            f"{weight_version}-{uuid.uuid4().hex}",
+            {
+                "schema_version": 1,
+                "scope": KIND,
+                "status": "staging",
+                "identity": identity,
+                "step": step,
+                "weight_version": weight_version,
+                "base_checkpoint": str(base_checkpoint),
+                "base_checkpoint_identity": base_checkpoint_identity,
+            },
+        )
+        return {"staging": str(staging), "destination": str(destination)}
+
+    paths = _rank_zero_call(prepare, "native HF export disk budget and staging")
+    save_hf_model_to_path(
+        args,
+        paths["staging"],
+        model,
+        model_name="qwen3_next",
+        quantization_config=None,
+        progress_desc=f"Native HF export {weight_version}",
+    )
+    torch.cuda.synchronize()
+
+    def verify_immutable():
+        if (
+            parameter_identity() != model_before
+            or state_digest(capture_rng_state(tracker=tracker)) != rng_before
+        ):
+            raise ValueError("Native HF export unexpectedly changed model parameters or RNG")
+
+    _all_rank_call(verify_immutable, "native export model/RNG immutability")
+    record = _rank_zero_call(
+        lambda: _publish_export(
+            paths["staging"],
+            paths["destination"],
+            step=step,
+            weight_version=weight_version,
+            identity=identity,
+            base_checkpoint=base_checkpoint,
+            base_checkpoint_identity=base_checkpoint_identity,
+            model_hashes=model_hashes,
+        ),
+        "native HF all-file publication",
+    )
+    return {
+        "checkpoint": paths["destination"],
+        "checkpoint_identity": record,
+        "weight_version": weight_version,
+        "base_checkpoint": str(base_checkpoint),
+        "base_checkpoint_identity": base_checkpoint_identity,
+    }
diff --git a/setup.py b/setup.py
index 9831c7022..59be67c20 100644
--- a/setup.py
+++ b/setup.py
@@ -472,7 +472,7 @@ def build_extension(self, ext):
         "tabulate",
         "numpy",
         "accelerate",
-        "transformers==5.13.1",
+        "transformers>=5.13.1,<=5.17.0",
     ],
     ext_modules=get_extensions(),
     cmdclass=get_cmdclass(),
@@ -480,6 +480,13 @@ def build_extension(self, ext):
         "cuda": ["flashinfer"],
         "rocm": ["aiter"],
         "vllm": ["vllm>=0.6.0"],
+        "qwen3-next": [
+            "torch==2.13.0",
+            "transformers==5.17.0",
+            "vllm==0.30.0",
+            "triton==3.7.1",
+            "flashinfer-python==0.6.18.post1",
+        ],
         "drift-viewer": ["Pillow>=10", "PySide6>=6.6"],
     },
     entry_points={
diff --git a/tests/check_qwen3_next_batch_invariance.py b/tests/check_qwen3_next_batch_invariance.py
new file mode 100644
index 000000000..7611da505
--- /dev/null
+++ b/tests/check_qwen3_next_batch_invariance.py
@@ -0,0 +1,453 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Which GDN components are batch-invariant, per component, measured.
+
+vLLM refuses strict mode for this model at engine init:
+
+    VLLM batch_invariant mode is not supported for GDN_ATTN.
+
+The guard is a capability declaration -- `selector.py:235` raises when
+`VLLM_BATCH_INVARIANT` is set and the mamba backend's
+`supports_batch_invariance()` is False, which `gdn_attn` does not override. So the
+question that decides whether full-model L2 is reachable is not "can the guard be
+bypassed" (it must not be) but "are the components on the strict path actually
+batch-invariant, and if not, which one is not".
+
+Measured on B200 (sm_100), vLLM 0.30.0, per component:
+
+=========================================  ======================================
+component                                  batch invariance
+=========================================  ======================================
+`causal_conv1d_update`                     bitwise (8 seeds x 4 positions,
+                                           fp32 and bf16 cache)
+`fused_recurrent_..._packed_decode`        bitwise (12 seeds x 4 positions,
+                                           fp32 and bf16 state)
+FLA Triton `chunk_gated_delta_rule`        bitwise w.r.t. batch composition
+FlashInfer GDN prefill                     **not** bitwise
+=========================================  ======================================
+
+FlashInfer is the *default* prefill backend on SM100 (`_resolve_gdn_prefill_backend`
+returns "flashinfer" for compute capability 100 with `head_k_dim == 128` and CUDA
+>= 13), so the default configuration is genuinely not batch-invariant and the
+guard is correct rather than conservative. The deviation is rounding-scale, not
+structural: adding a companion sequence moved 12149 of 262144 output elements,
+`max|diff| = 2.44e-04` against a tensor of scale 7.18e-02 (3.4e-03 relative), and
+left the final state within 5.7e-03 relative while almost no bit patterns matched
+-- the signature of a pervasive small change in reduction order, consistent with
+chunk tiling that depends on the batch's token count.
+
+**The actionable consequence**: pinning `gdn_prefill_backend="triton"` makes every
+component measured here bitwise batch-invariant. That is the configuration a
+full-model L2 attempt should use. It does not by itself justify flipping
+`supports_batch_invariance()`, which is a statement about the whole backend --
+metadata construction, chunked-prefill splitting, CUDA graphs and the speculative
+paths are outside this file's scope and remain unmeasured.
+
+These assertions therefore state what is true (the three invariant components) and
+bound the deviation of the fourth. They deliberately do NOT assert that FlashInfer
+*differs*: that would be asserting a defect persists, and would fail the day
+upstream fixes it, which is the opposite of what we want to learn.
+
+Named ``check_`` rather than ``test_`` because it imports real vLLM; see
+``tests/distributed/check_*.py``. Run via ``ci/run_qwen3_next_operator_gates.sh``
+or explicitly:
+
+    CUDA_VISIBLE_DEVICES=1 pytest tests/check_qwen3_next_batch_invariance.py -v
+"""
+
+from __future__ import annotations
+
+from functools import partial
+
+import pytest
+import torch
+
+from rl_engine.testing.tensor_identity import tensor_bitwise_equal
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required")
+
+# Qwen3-Next-80B-A3B-Instruct GDN dimensions.
+_H, _HV, _K, _V = 16, 32, 128, 128
+_SCALE = _K**-0.5
+_CONV_DIM, _CONV_WIDTH = 8192, 4
+_PACKED_DIM = _H * _K * 2 + _HV * _V
+
+#: Positions within the batch whose target sequence is re-run alone.
+_POSITIONS = (0, 1, 31, 63)
+_BATCH = 64
+
+
+def _randn(generator, *shape, dt=torch.bfloat16):
+    return torch.randn(*shape, device="cuda", dtype=dt, generator=generator)
+
+
+def _l2_normalize(t: torch.Tensor) -> torch.Tensor:
+    """``t / sqrt(sum(t*t) + 1e-6)`` over the last dim.
+
+    q/k arrive L2-normalized in the real path: prefill runs
+    ``fused_post_conv_prep(apply_l2norm=True)`` and then passes
+    ``use_qk_l2norm_in_kernel=False``. Feeding raw randn with that flag off leaves
+    them unnormalized and the delta-rule update diverges to NaN -- a property of
+    the inputs, not of the kernel. The division is by the norm cast to ``t``'s
+    dtype, which is what the measurements recorded in the execution doc used.
+    """
+    norm = torch.sqrt((t.float() * t.float()).sum(-1, keepdim=True) + 1e-6)
+    return t / norm.to(t.dtype)
+
+
+def _gen(seed: int) -> torch.Generator:
+    return torch.Generator(device="cuda").manual_seed(seed)
+
+
+def _decay(hv: int, tokens: int, g: torch.Generator) -> torch.Tensor:
+    """``g = -exp(A_log) * softplus(a)``, which is always <= 0.
+
+    Feeding a sign-free random tensor instead lets ``exp(g) > 1`` and the
+    recurrence diverges to NaN, which looks like a kernel fault and is not one.
+    """
+    a_log = torch.randn(hv, device="cuda", dtype=torch.float32, generator=g)
+    a = torch.randn(1, tokens, hv, device="cuda", dtype=torch.float32, generator=g)
+    return -torch.exp(a_log) * torch.nn.functional.softplus(a)
+
+
+# --------------------------------------------------------------------------- #
+# 1. Causal conv1d state update -- bitwise
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize("state_dtype", [torch.float32, torch.bfloat16])
+def test_causal_conv1d_update_is_batch_invariant(state_dtype):
+    pytest.importorskip("vllm")
+    from vllm.model_executor.layers.mamba.ops.causal_conv1d import causal_conv1d_update
+
+    for seed in range(8):
+        g = _gen(seed)
+        x = torch.randn(_BATCH, _CONV_DIM, device="cuda", dtype=torch.bfloat16, generator=g)
+        # One cache line per sequence plus the null block.
+        state = torch.randn(
+            _BATCH + 2, _CONV_DIM, _CONV_WIDTH - 1, device="cuda", dtype=state_dtype, generator=g
+        )
+        weight = torch.randn(
+            _CONV_DIM, _CONV_WIDTH, device="cuda", dtype=torch.bfloat16, generator=g
+        )
+        bias = torch.randn(_CONV_DIM, device="cuda", dtype=torch.bfloat16, generator=g)
+        indices = torch.arange(1, _BATCH + 1, device="cuda", dtype=torch.int32)
+
+        full_state, full_out = state.clone(), torch.empty_like(x)
+        causal_conv1d_update(
+            x, full_state, weight, bias, "silu", conv_state_indices=indices, out=full_out
+        )
+
+        for row in _POSITIONS:
+            lone_state = state.clone()
+            lone_out = torch.empty_like(x[row : row + 1])
+            causal_conv1d_update(
+                x[row : row + 1],
+                lone_state,
+                weight,
+                bias,
+                "silu",
+                conv_state_indices=indices[row : row + 1].clone(),
+                out=lone_out,
+            )
+            block = int(indices[row])
+            assert tensor_bitwise_equal(lone_out[0], full_out[row]), f"out seed={seed} row={row}"
+            assert tensor_bitwise_equal(lone_state[block], full_state[block]), f"state row={row}"
+
+
+# --------------------------------------------------------------------------- #
+# 2. Packed recurrent decode -- bitwise. This is the rollout decode provider.
+# --------------------------------------------------------------------------- #
+@pytest.mark.parametrize("state_dtype", [torch.float32, torch.bfloat16])
+def test_packed_recurrent_decode_is_batch_invariant(state_dtype):
+    pytest.importorskip("vllm")
+    from vllm.third_party.flash_linear_attention.ops import (
+        fused_recurrent_gated_delta_rule_packed_decode as decode,
+    )
+
+    def run(mixed, a, b, a_log, dt_bias, state, indices):
+        working = state.clone()
+        out = torch.empty(mixed.shape[0], 1, _HV, _V, device="cuda", dtype=mixed.dtype)
+        decode(
+            mixed,
+            a,
+            b,
+            a_log,
+            dt_bias,
+            _SCALE,
+            working,
+            out,
+            indices,
+            use_qk_l2norm_in_kernel=True,
+        )
+        return out, working
+
+    for seed in range(12):
+        g = _gen(seed)
+        mk = partial(_randn, g)
+        mixed, a, b = mk(_BATCH, _PACKED_DIM), mk(_BATCH, _HV), mk(_BATCH, _HV)
+        a_log, dt_bias = mk(_HV, dt=torch.float32), mk(_HV, dt=torch.float32)
+        state = mk(_BATCH + 2, _HV, _V, _K, dt=state_dtype) * 0.1
+        indices = torch.arange(1, _BATCH + 1, device="cuda", dtype=torch.int32)
+
+        full_out, full_state = run(mixed, a, b, a_log, dt_bias, state, indices)
+        for row in _POSITIONS:
+            lone_out, lone_state = run(
+                mixed[row : row + 1],
+                a[row : row + 1],
+                b[row : row + 1],
+                a_log,
+                dt_bias,
+                state,
+                indices[row : row + 1].clone(),
+            )
+            block = int(indices[row])
+            assert tensor_bitwise_equal(lone_out[0], full_out[row]), f"out seed={seed} row={row}"
+            assert tensor_bitwise_equal(lone_state[block], full_state[block]), f"state row={row}"
+
+
+# --------------------------------------------------------------------------- #
+# 3 & 4. Chunked prefill, per backend
+# --------------------------------------------------------------------------- #
+def _prefill_inputs(seqs, seed):
+    """Inputs for ``seqs`` packed into one varlen batch.
+
+    Each sequence draws from its own generator (``seed + i``), so the first
+    sequence's tensors are identical whatever follows it. One generator shared
+    across the batch would make them depend on the total token count: the CUDA
+    RNG offset advances in rounded blocks, which hides that for short batches
+    and stops hiding it for longer ones, silently comparing different data.
+    """
+    a_log = torch.randn(_HV, device="cuda", dtype=torch.float32, generator=_gen(seed + 10_000))
+    parts = {name: [] for name in ("q", "k", "v", "g", "beta")}
+    for index, tokens in enumerate(seqs):
+        g = _gen(seed + index)
+        mk = partial(_randn, g)
+        parts["q"].append(_l2_normalize(mk(1, tokens, _HV, _K)))
+        parts["k"].append(_l2_normalize(mk(1, tokens, _HV, _K)))
+        parts["v"].append(mk(1, tokens, _HV, _V))
+        a = torch.randn(1, tokens, _HV, device="cuda", dtype=torch.float32, generator=g)
+        parts["g"].append(-torch.exp(a_log) * torch.nn.functional.softplus(a))
+        parts["beta"].append(
+            torch.rand(1, tokens, _HV, device="cuda", dtype=torch.float32, generator=g)
+        )
+    cu = torch.tensor(
+        [0, *torch.cumsum(torch.tensor(seqs), 0).tolist()], device="cuda", dtype=torch.int32
+    )
+    return {
+        **{name: torch.cat(tensors, dim=1) for name, tensors in parts.items()},
+        "cu_seqlens": cu,
+        "initial_state": torch.zeros(len(seqs), _HV, _V, _K, device="cuda", dtype=torch.float32),
+    }
+
+
+#: The target sequence, then the companions appended after it. The long ones are
+#: what a shared generator used to get wrong; see `_prefill_inputs`.
+_COMPANIONS = ([], [64], [17, 95], [3], [1], [64, 64, 64, 64], [2048])
+_TARGET = 64
+
+
+def test_target_inputs_do_not_depend_on_companions():
+    """Guards the comparisons below: they must compare the same target data."""
+    alone = _prefill_inputs([_TARGET], seed=0)
+    for companions in _COMPANIONS:
+        batch = _prefill_inputs([_TARGET, *companions], seed=0)
+        for name in ("q", "k", "v", "g", "beta"):
+            assert tensor_bitwise_equal(batch[name][:, :_TARGET], alone[name]), (name, companions)
+
+
+def test_triton_chunked_prefill_is_batch_invariant():
+    """Supplementary native batch probe; shared full/chunk/decode uses packed recurrence."""
+    pytest.importorskip("vllm")
+    from vllm.third_party.flash_linear_attention.ops import chunk_gated_delta_rule
+
+    reference = None
+    for companions in _COMPANIONS:
+        inp = _prefill_inputs([_TARGET, *companions], seed=0)
+        out, final_state = chunk_gated_delta_rule(
+            inp["q"],
+            inp["k"],
+            inp["v"],
+            inp["g"],
+            inp["beta"],
+            scale=_SCALE,
+            initial_state=None,
+            output_final_state=True,
+            cu_seqlens=inp["cu_seqlens"],
+            use_qk_l2norm_in_kernel=False,
+        )
+        target_out, target_state = out[0, :_TARGET], final_state[0]
+        assert not torch.isnan(target_out).any(), f"NaN with companions={companions}"
+        if reference is None:
+            reference = (target_out.clone(), target_state.clone())
+            continue
+        assert tensor_bitwise_equal(target_out, reference[0]), f"out, companions={companions}"
+        assert tensor_bitwise_equal(target_state, reference[1]), f"state, companions={companions}"
+
+
+def test_flashinfer_chunked_prefill_deviation_stays_rounding_scale():
+    """The DEFAULT prefill backend on SM100, which is not batch-invariant.
+
+    Only the magnitude is asserted. Asserting that it *differs* would be asserting
+    a defect persists, and would start failing the day upstream makes it
+    invariant -- which is the outcome we want. A structural regression (a
+    deviation well past rounding scale) still fails here.
+    """
+    pytest.importorskip("vllm")
+    pytest.importorskip("flashinfer")
+    from vllm.model_executor.layers.mamba.gdn.qwen_gdn_linear_attn import fi_chunk_gated_delta_rule
+
+    reference = None
+    for companions in _COMPANIONS:
+        inp = _prefill_inputs([_TARGET, *companions], seed=0)
+        out, final_state = fi_chunk_gated_delta_rule(
+            inp["q"],
+            inp["k"],
+            inp["v"],
+            inp["g"],
+            inp["beta"],
+            inp["initial_state"],
+            True,
+            cu_seqlens=inp["cu_seqlens"],
+            use_qk_l2norm_in_kernel=True,
+        )
+        target_out, target_state = out[0, :_TARGET], final_state[0]
+        assert not torch.isnan(target_out).any(), f"NaN with companions={companions}"
+        if reference is None:
+            reference = (target_out.clone(), target_state.clone())
+            continue
+        for name, got, want in (
+            ("out", target_out, reference[0]),
+            ("state", target_state, reference[1]),
+        ):
+            scale = max(want.float().abs().max().item(), 1e-9)
+            relative = (got.float() - want.float()).abs().max().item() / scale
+            assert relative <= 5e-2, (
+                f"{name} deviation {relative:.3e} relative with companions="
+                f"{companions} is past rounding scale; measured baseline was "
+                "3.4e-03 (out) and 5.7e-03 (state)"
+            )
+
+
+# --------------------------------------------------------------------------- #
+# 5. Metadata construction
+#
+# `GDNMetadataBuilder` derives the chunk partition from `cu_seqlens` via
+# `prepare_chunk_indices` / `prepare_chunk_offsets`. Per-sequence chunk counts
+# are `cdiv(own_length, FLA_CHUNK_SIZE)`, so a sequence's partition cannot depend
+# on its companions -- only its sequence id and its offset into the flat chunk
+# list shift. This is pure index arithmetic, so it is checked exactly, on CPU.
+# --------------------------------------------------------------------------- #
+def _chunk_metadata(seqs):
+    from vllm.third_party.flash_linear_attention.ops.index import (
+        prepare_chunk_indices,
+        prepare_chunk_offsets,
+    )
+    from vllm.third_party.flash_linear_attention.ops.utils import FLA_CHUNK_SIZE
+
+    cu = torch.tensor([0, *torch.cumsum(torch.tensor(seqs), 0).tolist()], dtype=torch.int32)
+    return (
+        prepare_chunk_indices(cu, FLA_CHUNK_SIZE),
+        prepare_chunk_offsets(cu, FLA_CHUNK_SIZE),
+        FLA_CHUNK_SIZE,
+    )
+
+
+@pytest.mark.parametrize("position", [0, 1, 2])
+def test_chunk_metadata_partition_is_batch_invariant(position):
+    """A sequence's own chunk partition must not move when the batch changes.
+
+    The sequence id and the flat offset legitimately shift with position; what
+    must not change is how many chunks the sequence is cut into and which of its
+    own tokens each chunk covers.
+    """
+    pytest.importorskip("vllm")
+    target = 150  # deliberately not a multiple of the chunk size
+    reference = None
+    for others in ([], [64], [17, 95], [3], [1, 1, 1]):
+        seqs = list(others)
+        seqs.insert(min(position, len(seqs)), target)
+        seq_id = min(position, len(others))
+
+        indices, offsets, chunk_size = _chunk_metadata(seqs)
+        mine = indices[indices[:, 0] == seq_id]
+        # Chunk ids within the sequence, and the flat span it occupies.
+        partition = (
+            mine[:, 1].tolist(),
+            int(offsets[seq_id + 1] - offsets[seq_id]),
+        )
+        expected_chunks = -(-target // chunk_size)  # ceil div
+        assert partition[0] == list(range(expected_chunks)), f"others={others}"
+        assert partition[1] == expected_chunks, f"offset span, others={others}"
+        if reference is None:
+            reference = partition
+        else:
+            assert partition == reference, f"others={others}"
+
+
+# --------------------------------------------------------------------------- #
+# 6. Chunked-prefill splitting
+#
+# vLLM may process one prompt over several forward passes, handing the recurrent
+# state between them. Whether that equals a single pass is the prefill analogue
+# of the prefill-vs-decode question, and it is what decides L2 for prompts longer
+# than `max_num_batched_tokens`.
+# --------------------------------------------------------------------------- #
+def _run_triton_prefill(inp, tokens, initial_state):
+    from vllm.third_party.flash_linear_attention.ops import chunk_gated_delta_rule
+
+    lo, hi = tokens
+    cu = torch.tensor([0, hi - lo], device="cuda", dtype=torch.int32)
+    return chunk_gated_delta_rule(
+        inp["q"][:, lo:hi],
+        inp["k"][:, lo:hi],
+        inp["v"][:, lo:hi],
+        inp["g"][:, lo:hi],
+        inp["beta"][:, lo:hi],
+        scale=_SCALE,
+        initial_state=initial_state,
+        output_final_state=True,
+        cu_seqlens=cu,
+        use_qk_l2norm_in_kernel=False,
+    )
+
+
+@pytest.mark.parametrize("split", [128, 100, 64, 192])
+def test_triton_chunked_prefill_split_matches_one_pass(split):
+    """Splitting a prompt and handing over the state, versus one pass.
+
+    `split=128` and `split=64` are multiples of FLA_CHUNK_SIZE; `split=100` and
+    `split=192` are not, which is the realistic case because an engine-step
+    boundary is set by the token budget, not by the kernel's chunk size.
+    """
+    pytest.importorskip("vllm")
+    total = 256
+    inp = _prefill_inputs([total], seed=0)
+
+    zero = torch.zeros(1, _HV, _V, _K, device="cuda", dtype=torch.float32)
+    whole_out, whole_state = _run_triton_prefill(inp, (0, total), zero.clone())
+    first_out, first_state = _run_triton_prefill(inp, (0, split), zero.clone())
+    second_out, second_state = _run_triton_prefill(inp, (split, total), first_state)
+
+    joined = torch.cat([first_out[0], second_out[0]], dim=0)
+    aligned = split % 64 == 0
+    assert not torch.isnan(whole_out).any(), "degenerate inputs, not a kernel fault"
+
+    out_rel = (
+        (joined.float() - whole_out[0].float()).abs().max()
+        / max(whole_out[0].float().abs().max().item(), 1e-9)
+    ).item()
+    state_rel = (
+        (second_state.float() - whole_state.float()).abs().max()
+        / max(whole_state.float().abs().max().item(), 1e-9)
+    ).item()
+
+    if aligned:
+        # A chunk-aligned split must be bitwise: the kernel's own chunk
+        # boundaries already fall there, so no reduction order changes.
+        assert tensor_bitwise_equal(joined, whole_out[0]), f"split={split} out, rel={out_rel:.3e}"
+        assert tensor_bitwise_equal(second_state, whole_state), f"split={split} state"
+    else:
+        # An unaligned split re-cuts the sequence, so the reduction order moves.
+        # Bound it; do not assert equality, and do not assert inequality either.
+        assert out_rel <= 5e-2, f"split={split} out deviation {out_rel:.3e}"
+        assert state_rel <= 5e-2, f"split={split} state deviation {state_rel:.3e}"
diff --git a/tests/check_qwen3_next_engine_metadata.py b/tests/check_qwen3_next_engine_metadata.py
new file mode 100644
index 000000000..05c91d121
--- /dev/null
+++ b/tests/check_qwen3_next_engine_metadata.py
@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Real vLLM builder semantics; no GPU kernel or capability override is needed."""
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+from vllm.config import CompilationConfig
+from vllm.config.compilation import CompilationMode, CUDAGraphMode
+from vllm.v1.attention.backend import CommonAttentionMetadata
+from vllm.v1.kv_cache_interface import MambaSpec
+
+from rl_engine.integrations.qwen3_next_vllm import (
+    SharedGDNBackend,
+    SharedGDNMetadataBuilder,
+    metadata_sequences,
+)
+
+
+def build_metadata(lengths, computed, slots):
+    config = SimpleNamespace(
+        additional_config={"gdn_prefill_backend": "triton"},
+        speculative_config=None,
+        compilation_config=CompilationConfig(
+            mode=CompilationMode.NONE, cudagraph_mode=CUDAGraphMode.NONE
+        ),
+        scheduler_config=SimpleNamespace(max_num_seqs=4),
+        parallel_config=SimpleNamespace(decode_context_parallel_size=1),
+        cache_config=SimpleNamespace(mamba_cache_mode="none"),
+        model_config=SimpleNamespace(hf_text_config=SimpleNamespace(linear_key_head_dim=128)),
+    )
+    spec = MambaSpec(
+        block_size=64,
+        shapes=((3, 2048), (8, 128, 128)),
+        dtypes=(torch.bfloat16, torch.float32),
+        mamba_cache_mode="none",
+    )
+    builder = SharedGDNMetadataBuilder(
+        spec, ["model.layers.0.linear_attn"], config, torch.device("cpu")
+    )
+    cu = torch.tensor([0, *torch.tensor(lengths).cumsum(0).tolist()], dtype=torch.int32)
+    seq = torch.tensor(lengths, dtype=torch.int32) + torch.tensor(computed, dtype=torch.int32)
+    common = CommonAttentionMetadata(
+        query_start_loc=cu,
+        query_start_loc_cpu=cu,
+        seq_lens=seq,
+        num_reqs=len(lengths),
+        num_actual_tokens=sum(lengths),
+        max_query_len=max(lengths),
+        max_seq_len=int(seq.max()),
+        block_table_tensor=torch.tensor(slots, dtype=torch.int32)[:, None],
+        slot_mapping=torch.zeros(sum(lengths), dtype=torch.int64),
+    )
+    return builder.build(0, common)
+
+
+def test_fresh_single_token_prompt_resets_reused_slot():
+    metadata = build_metadata([1], [0], [3])
+    slots, cu, reset = metadata_sequences(metadata, 1)
+    assert slots.tolist() == [3] and cu.tolist() == [0, 1] and reset.tolist() == [3]
+
+
+def test_mixed_step_resets_only_new_prefill_and_preserves_request_order():
+    metadata = build_metadata([1, 8, 3], [64, 0, 7], [7, 3, 5])
+    slots, cu, reset = metadata_sequences(metadata, 12)
+    assert slots.tolist() == [7, 3, 5]
+    assert cu.tolist() == [0, 1, 9, 12]
+    assert reset.tolist() == [3]
+
+
+def test_null_padding_has_no_state_owner():
+    metadata = build_metadata([1, 0], [64, 0], [7, 0])
+    slots, cu, reset = metadata_sequences(metadata, 1)
+    assert slots.tolist() == [7] and cu.tolist() == [0, 1] and reset.numel() == 0
+
+
+@pytest.mark.parametrize("slots", [[0], [-1], [3, 3]])
+def test_active_null_negative_or_duplicate_slots_are_rejected(slots):
+    metadata = build_metadata([1] * len(slots), [2] * len(slots), slots)
+    with pytest.raises(ValueError, match="distinct positive"):
+        metadata_sequences(metadata, len(slots))
+
+
+def test_custom_backend_cannot_bypass_the_batch_invariance_guard():
+    from vllm.v1.attention.backends.gdn_attn import GDNAttentionBackend
+
+    assert SharedGDNBackend.supports_batch_invariance() is False
+    assert GDNAttentionBackend.supports_batch_invariance() is False
+    assert SharedGDNBackend.get_name() != GDNAttentionBackend.get_name()
+
+
+def test_real_selector_rejects_the_unaccepted_custom_backend(monkeypatch):
+    from vllm import envs
+    from vllm.v1.attention.backends.registry import MambaAttentionBackendEnum, register_backend
+    from vllm.v1.attention.selector import _cached_get_mamba_attn_backend, get_mamba_attn_backend
+
+    from rl_engine.integrations.qwen3_next_vllm import _register_backend
+
+    try:
+        previous = MambaAttentionBackendEnum.CUSTOM.get_path()
+    except ValueError:
+        previous = None
+    monkeypatch.setattr(envs, "VLLM_BATCH_INVARIANT", True)
+    _cached_get_mamba_attn_backend.cache_clear()
+    try:
+        _register_backend()
+        with pytest.raises(RuntimeError, match="not supported for RL_KERNEL_QWEN3_NEXT_GDN"):
+            get_mamba_attn_backend(MambaAttentionBackendEnum.CUSTOM)
+        with pytest.raises(RuntimeError, match="not supported for GDN_ATTN"):
+            get_mamba_attn_backend(MambaAttentionBackendEnum.GDN_ATTN)
+    finally:
+        if previous is None:
+            MambaAttentionBackendEnum.CUSTOM.clear_override()
+        else:
+            register_backend(MambaAttentionBackendEnum.CUSTOM, previous, is_mamba=True)
+        _cached_get_mamba_attn_backend.cache_clear()
diff --git a/tests/check_qwen3_next_forward.py b/tests/check_qwen3_next_forward.py
index ce4beb1f7..5df5b9a9a 100644
--- a/tests/check_qwen3_next_forward.py
+++ b/tests/check_qwen3_next_forward.py
@@ -1,7 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-"""Mandatory shared CUDA GEMM and MoE checks; these do not claim model L2."""
+"""Mandatory shared CUDA GEMM/MoE/logprob checks; these do not claim model L2."""
+
+from dataclasses import replace
 
 import pytest
 import torch
@@ -10,9 +12,18 @@
 from rl_engine.integrations.qwen3_next_forward import (
     combine_routes,
     shared_linear,
+    shared_logprob,
     shared_moe,
     stable_top10_routes,
 )
+from rl_engine.kernels.logprob_contract import (
+    LogprobContract,
+    LogprobDType,
+    LogprobRole,
+    MaskSpec,
+    ReductionSpec,
+    ShardingSpec,
+)
 from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
 
 
@@ -157,6 +168,48 @@ def test_moe_vjp_matches_independent_double_formula():
         assert (value.grad.double() - reference.grad).norm() / reference.grad.norm() < 0.016
 
 
+def logprob_contract(tokens):
+    return LogprobContract(
+        role=LogprobRole.TRAIN,
+        dtype=LogprobDType.FP32,
+        mask=MaskSpec(tokens, (True,) * tokens),
+        sharding=ShardingSpec(0, 1, ((0, 151936),), 151936, 151936),
+        reduction=ReductionSpec(),
+    )
+
+
+def test_full_vocabulary_logprob_entropy_modes_and_gradient():
+    logits = random((4, 151936), dtype=torch.float32, grad=True)
+    targets = torch.tensor([0, 37984, 75968, 151935], device="cuda")
+    contract = logprob_contract(4)
+    logp, lse, entropy = shared_logprob(logits, targets, contract=contract)
+    infer_logp, infer_lse, empty = shared_logprob(
+        logits, targets, contract=replace(contract, role=LogprobRole.INFER), with_entropy=False
+    )
+    exact(logp, infer_logp)
+    exact(lse, infer_lse)
+    assert empty is None
+    row_logp, _, row_entropy = shared_logprob(logits[:1], targets[:1], contract=logprob_contract(1))
+    exact(row_logp, logp[:1])
+    exact(row_entropy, entropy[:1])
+    (-logp.sum() + 0.03 * entropy.sum()).backward()
+    reference = logits.detach().double().requires_grad_()
+    log_softmax = reference.log_softmax(-1)
+    expected_logp = log_softmax.gather(1, targets[:, None]).squeeze(1)
+    expected_entropy = -(log_softmax.exp() * log_softmax).sum(-1)
+    (-expected_logp.sum() + 0.03 * expected_entropy.sum()).backward()
+    torch.testing.assert_close(logp.double(), expected_logp, atol=0.00002, rtol=0)
+    torch.testing.assert_close(entropy.double(), expected_entropy, atol=0.00002, rtol=0)
+    torch.testing.assert_close(logits.grad.double(), reference.grad, atol=0.000002, rtol=0)
+
+
+def test_logprob_nan_rejected_before_comparison():
+    logits = random((1, 151936), dtype=torch.float32)
+    logits[0, 0] = torch.nan
+    with pytest.raises(ValueError, match="finite"):
+        shared_logprob(logits, torch.tensor([0], device="cuda"), contract=logprob_contract(1))
+
+
 @torch.no_grad()
 def test_route_weights_are_batch_invariant():
     logits = random((32, 512), 805, dtype=torch.float32).mul_(100)
diff --git a/tests/check_qwen3_next_multigpu.py b/tests/check_qwen3_next_multigpu.py
new file mode 100644
index 000000000..ede9df2f7
--- /dev/null
+++ b/tests/check_qwen3_next_multigpu.py
@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Required allocation-only checks. Missing GPUs fail instead of skipping."""
+
+import pytest
+import torch
+
+
+@pytest.mark.parametrize("device", [1, 2, 3])
+@pytest.mark.parametrize("gated", [False, True])
+def test_norm_guard_restores_current_device_and_uses_input_stream(device, gated):
+    from rl_engine import _C
+
+    assert torch.cuda.device_count() == 4, "Four allocated GPUs are required"
+    torch.cuda.set_device(device)
+    x = torch.randn(32, 128, device=f"cuda:{device}", dtype=torch.bfloat16)
+    weight = torch.randn(128, device=x.device, dtype=x.dtype)
+    gate = torch.randn_like(x)
+    dy = torch.randn_like(x)
+
+    def invoke(w=weight):
+        if gated:
+            out, rstd = _C.rmsnorm_gated_forward(x, w, gate, 1e-6)
+            dx = _C.rmsnorm_gated_backward_dx(dy, x, w, gate, rstd)
+        else:
+            out, rstd = _C.rmsnorm_forward(x, w, 1e-6, 1.0)
+            dx = _C.rmsnorm_backward_dx(dy, x, w, rstd, 1.0)
+        return out, rstd, dx
+
+    expected = invoke()
+    stream = torch.cuda.Stream(device=device)
+    stream.wait_stream(torch.cuda.current_stream(device))
+    # set_stream changes the current device: deliberately switch back before C++ entry.
+    torch.cuda.set_stream(stream)
+    torch.cuda.set_device(0)
+    actual = invoke()
+    assert torch.cuda.current_device() == 0
+    stream.synchronize()
+    for got, want in zip(actual, expected):
+        assert got.device == x.device
+        assert torch.isfinite(got).all()
+        assert torch.equal(got.contiguous().view(torch.uint8), want.contiguous().view(torch.uint8))
+    with pytest.raises(RuntimeError, match="device"):
+        invoke(weight.to("cuda:0"))
+    assert torch.cuda.current_device() == 0
diff --git a/tests/check_qwen3_next_native_rollout.py b/tests/check_qwen3_next_native_rollout.py
new file mode 100644
index 000000000..05ba3cdad
--- /dev/null
+++ b/tests/check_qwen3_next_native_rollout.py
@@ -0,0 +1,318 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Native scheduler/sampler plumbing tests; these are not GPU model evidence."""
+
+from types import SimpleNamespace as Namespace
+
+import pytest
+import torch
+
+from rl_engine.integrations import qwen3_next_native_rollout as rollout
+from rl_engine.testing.qwen3_next_l2 import L2Case, json_digest
+
+
+def request(batch=4, length=8, chunked=False, frozen=False):
+    chunks = L2Case(length, batch, "chunked" if chunked else "full").chunks
+    prompts = [[row + 1] * length for row in range(batch)]
+    responses = [list(range(16)) for _ in range(batch)] if frozen else None
+    result = {
+        "schema_version": 1,
+        "role": "rollout",
+        "case_id": "cpu-plumbing",
+        "identity": {
+            "architecture_layers": 48,
+            "topology": {"tp": 4, "pp": 1, "cp": 1, "ep": 1},
+            "checkpoint": "/explicit/test-only-checkpoint",
+            "provider_config": {"native_rollout_kv_cache_memory_bytes": 4 << 30},
+        },
+        "prompt_tokens": prompts,
+        "response_tokens": responses,
+        "sample_ids": list(range(batch)),
+        "prefill_chunks": chunks,
+        "execution": {
+            "rollout_mode": "autoregressive",
+            "prefill_chunks": chunks,
+            "response_mode": "teacher_forced" if frozen else "generate",
+        },
+        "response_length": 16,
+        "vocab_size": 151936,
+        "logits_dtype": "bfloat16",
+        "logprobs_dtype": "float32",
+        "prediction_positions": list(range(length - 1, length + 15)),
+        "state_position": length + 14,
+        "consumed_response_tokens": 15,
+        "required_layer_digests": list(range(48)),
+        "trace_layers": [],
+        "consumed_input_tokens": (
+            None
+            if responses is None
+            else [left + right[:-1] for left, right in zip(prompts, responses)]
+        ),
+        "generation": {
+            "temperature": 0.0,
+            "top_p": 1.0,
+            "top_k": -1,
+            "seed": 1234,
+            "ignore_eos": True,
+        },
+    }
+    result["request_sha256"] = json_digest(result)
+    return result
+
+
+def seal(value):
+    value["request_sha256"] = json_digest(
+        {key: item for key, item in value.items() if key != "request_sha256"}
+    )
+    return value
+
+
+@pytest.mark.parametrize("length", [8, 64, 256, 1024])
+@pytest.mark.parametrize("batch", [1, 4])
+@pytest.mark.parametrize("chunked", [False, True])
+def test_exact_boundaries_cover_every_prefill_and_fifteen_decode_inputs(length, batch, chunked):
+    value = request(batch, length, chunked, frozen=True)
+    assert rollout.validate_request(value) == (batch, length)
+    computed = 0
+    for count in value["prefill_chunks"] + [1] * 15:
+        assert rollout.next_scheduled_tokens(computed, length, value["prefill_chunks"]) == count
+        computed += count
+    assert computed == length + 15
+    with pytest.raises(ValueError, match="exact requested"):
+        rollout.next_scheduled_tokens(computed, length, value["prefill_chunks"])
+
+
+@pytest.mark.parametrize(
+    "field,value",
+    [
+        ("prediction_positions", list(range(8, 24))),
+        ("consumed_response_tokens", 16),
+        ("prefill_chunks", [7]),
+        ("logits_dtype", "float32"),
+        ("trace_layers", [48]),
+    ],
+)
+def test_worker_rejects_even_resigned_requests_that_change_the_contract(field, value):
+    item = request()
+    item[field] = value
+    with pytest.raises(ValueError):
+        rollout.validate_request(seal(item))
+
+
+def scheduled_request(sample, computed=0, chunks=(1, 63), batch=4):
+    descriptor = {
+        "request_sha256": "same-case",
+        "sample_id": sample,
+        "batch_size": batch,
+        "prompt_length": sum(chunks),
+        "prefill_chunks": list(chunks),
+    }
+    return Namespace(
+        request_id=str(sample),
+        sampling_params=Namespace(extra_args={"shared_l2": descriptor}),
+        num_computed_tokens=computed,
+        is_finished=lambda: False,
+    )
+
+
+def scheduler(requests):
+    value = object.__new__(rollout.ExactPrefillScheduler)
+    value.requests = {row.request_id: row for row in requests}
+    value.scheduler_config = Namespace(long_prefill_token_threshold=0)
+    value._pause_state = rollout.PauseState.UNPAUSED
+    return value
+
+
+def test_native_scheduler_barrier_waits_for_all_four_requests(monkeypatch):
+    item = scheduler([scheduled_request(0)])
+    calls = []
+
+    def native(self, throttle):
+        calls.append(self._pause_state)
+        return Namespace(total_num_scheduled_tokens=0)
+
+    monkeypatch.setattr(rollout.Scheduler, "schedule", native)
+    item.schedule()
+    assert calls == [rollout.PauseState.PAUSED_ALL]
+    assert item._pause_state == rollout.PauseState.UNPAUSED
+
+
+def test_native_scheduler_caps_real_decisions_and_rejects_partial_batches(monkeypatch):
+    item = scheduler([scheduled_request(rank, computed=1) for rank in range(4)])
+
+    def native(self, throttle):
+        assert self.scheduler_config.long_prefill_token_threshold == 63
+        return Namespace(
+            num_scheduled_tokens={str(rank): 63 for rank in range(4)}, preempted_req_ids=set()
+        )
+
+    monkeypatch.setattr(rollout.Scheduler, "schedule", native)
+    assert item.schedule().num_scheduled_tokens == {str(rank): 63 for rank in range(4)}
+    monkeypatch.setattr(
+        rollout.Scheduler,
+        "schedule",
+        lambda self, throttle: Namespace(num_scheduled_tokens={"0": 63}, preempted_req_ids=set()),
+    )
+    with pytest.raises(RuntimeError, match="exact_scheduler_mismatch"):
+        item.schedule()
+
+
+def test_native_scheduler_rejects_desynchronized_request_offsets():
+    item = scheduler([scheduled_request(rank, computed=rank) for rank in range(4)])
+    with pytest.raises(RuntimeError, match="synchronized"):
+        item.schedule()
+
+
+def test_native_logits_processor_forces_frozen_tokens_without_mutating_raw_logits():
+    processor = rollout.ForceFrozenTokens(None, torch.device("cpu"), False)
+    assert processor.is_argmax_invariant() is False
+    original = torch.arange(151936, dtype=torch.float32)
+    frozen = list(range(16))
+    apply = processor.new_req_logits_processor(
+        Namespace(extra_args={"shared_l2": {"response_tokens": frozen}})
+    )
+    forced = apply(frozen[:7], original)
+    assert int(forced.argmax()) == 7
+    assert int(original.argmax()) == 151935
+    assert forced.data_ptr() != original.data_ptr()
+    assert (
+        processor.new_req_logits_processor(
+            Namespace(extra_args={"shared_l2": {"response_tokens": None}})
+        )
+        is None
+    )
+
+
+def test_runtime_profile_reads_actual_config_and_rejects_changed_cache_budget():
+    expected = rollout.NATIVE_ROLLOUT_PROFILE
+    config = Namespace(
+        scheduler_config=Namespace(
+            scheduler_cls=rollout.ExactPrefillScheduler,
+            max_num_seqs=64,
+            max_num_batched_tokens=16384,
+            async_scheduling=False,
+        ),
+        model_config=Namespace(max_model_len=1040, dtype=torch.bfloat16),
+        cache_config=Namespace(
+            kv_cache_memory_bytes=4 << 30,
+            enable_prefix_caching=False,
+            mamba_ssm_cache_dtype="float32",
+        ),
+        use_v2_model_runner=False,
+    )
+    assert rollout.runtime_profile(config) == expected
+    config.cache_config.kv_cache_memory_bytes *= 2
+    with pytest.raises(ValueError, match="changed the explicit"):
+        rollout.runtime_profile(config)
+
+
+def test_implementation_readiness_does_not_claim_scheduler_or_model_acceptance():
+    capabilities = rollout.capabilities()
+    assert capabilities["rollout"] and capabilities["native_scheduler"]
+    assert not capabilities["scheduler_batch_invariance_verified"]
+    assert not capabilities["full_model_l2_verified"]
+
+
+def test_engine_arguments_are_accepted_by_the_installed_native_api(monkeypatch):
+    import huggingface_hub.constants
+    from vllm.engine.arg_utils import EngineArgs
+
+    monkeypatch.setattr(huggingface_hub.constants, "HF_HUB_OFFLINE", False)
+    value = EngineArgs(**rollout.engine_arguments(request()["identity"], "ExplicitTestModel"))
+    assert value.tensor_parallel_size == 4
+    assert value.kv_cache_memory_bytes == 4 << 30
+    assert value.scheduler_cls.endswith(".ExactPrefillScheduler")
+    assert value.attention_config.backend.name == "CUSTOM"
+    assert value.compilation_config.mode == 0
+
+
+def trace_for_cpu_bookkeeping():
+    value = object.__new__(rollout.RolloutTrace)
+    value.request = request(frozen=True)
+    value.worker = Namespace(model_runner=Namespace(input_batch=Namespace(req_ids=["r2", "r0"])))
+    value.step = {"r0": (0, 7, 1), "r2": (2, 7, 1)}
+    value.responses = torch.tensor(value.request["response_tokens"], dtype=torch.int64)
+    value.progress = {"r0": 7, "r2": 7}
+    value.scheduled = Namespace(total_num_scheduled_tokens=2)
+    value.schedule = []
+    return value
+
+
+def test_trace_uses_actual_native_request_order_and_absolute_positions():
+    value = trace_for_cpu_bookkeeping()
+    value.begin_forward(torch.tensor([3, 1]), torch.tensor([7, 7]))
+    assert value.current == [(2, 0, 1, 7), (0, 1, 2, 7)]
+    assert value.progress == {"r0": 8, "r2": 8}
+    assert value.schedule == [
+        {"samples": [2, 0], "computed_before": [7, 7], "token_counts": [1, 1]}
+    ]
+    value.step = {"r0": (0, 8, 1), "r2": (2, 8, 1)}
+    value.begin_forward(torch.tensor([0, 0]), torch.tensor([8, 8]))
+    assert value.progress == {"r0": 9, "r2": 9}
+
+
+@pytest.mark.parametrize("tokens,positions", [([2, 1], [7, 7]), ([3, 1], [8, 7])])
+def test_trace_rejects_wrong_consumed_tokens_or_prediction_offsets(tokens, positions):
+    value = trace_for_cpu_bookkeeping()
+    with pytest.raises(ValueError):
+        value.begin_forward(torch.tensor(tokens), torch.tensor(positions))
+
+
+@pytest.mark.parametrize("field,replacement", [("sha256", "b" * 64), ("path", "/other/_C.so")])
+def test_rollout_rejects_provider_binary_changed_during_case(monkeypatch, field, replacement):
+    value = object.__new__(rollout.RolloutTrace)
+    value.provider_binary = {"module": "rl_engine._C", "path": "/actual/_C.so", "sha256": "a" * 64}
+    changed = {**value.provider_binary, field: replacement}
+    monkeypatch.setattr(rollout, "imported_provider_identity", lambda: changed)
+    # This must fail before examining numerical observations or publishing a trace.
+    with pytest.raises(RuntimeError, match="binary changed during"):
+        value.finish()
+
+
+def test_rollout_rejects_provider_replaced_between_worker_initialization_and_case(monkeypatch):
+    provider = {"module": "rl_engine._C", "path": "/actual/_C.so", "sha256": "a" * 64}
+    worker = Namespace(shared_provider_binary=provider)
+    monkeypatch.setattr(
+        rollout, "imported_provider_identity", lambda: {**provider, "sha256": "b" * 64}
+    )
+    with pytest.raises(RuntimeError, match="binary changed after"):
+        rollout.RolloutTrace(worker, request(frozen=True), "/unused-cpu-test")
+
+
+@pytest.mark.parametrize("tamper", ["missing", "module", "path", "sha256"])
+def test_assemble_rejects_different_binary_in_one_rehashed_rank_trace(tmp_path, tamper):
+    value = request(frozen=True)
+    provider = {"module": "rl_engine._C", "path": "/actual/_C.so", "sha256": "a" * 64}
+    records = []
+    for rank in range(4):
+        observed = dict(provider)
+        if rank == 3:
+            if tamper == "missing":
+                observed = None
+            else:
+                observed[tamper] = {
+                    "module": "fallback",
+                    "path": "/other/_C.so",
+                    "sha256": "b" * 64,
+                }[tamper]
+        path = tmp_path / f"rank-{rank}.pt"
+        rollout.save_tensor_artifact(
+            path,
+            {
+                "rank": rank,
+                "request_sha256": value["request_sha256"],
+                "provider_binary": observed,
+            },
+        )
+        records.append(
+            {
+                "rank": rank,
+                "path": str(path),
+                "sha256": rollout.file_digest(path),
+                "request_sha256": value["request_sha256"],
+            }
+        )
+    # File SHA is valid: per-rank provenance must still reject the rewritten identity.
+    with pytest.raises(ValueError, match="binary evidence|different provider binaries"):
+        rollout.assemble_trace(value, records, value["response_tokens"])
diff --git a/tests/check_qwen3_next_paged_attention.py b/tests/check_qwen3_next_paged_attention.py
new file mode 100644
index 000000000..89d0dd148
--- /dev/null
+++ b/tests/check_qwen3_next_paged_attention.py
@@ -0,0 +1,83 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Paged-KV component tests; a real vLLM scheduler gate is still required."""
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_vllm_attention import (
+    SharedAttentionImpl,
+    SharedAttentionMetadata,
+)
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
+
+
+@pytest.mark.parametrize("batch", [1, 4])
+@pytest.mark.parametrize("length", [8, 65, 1024])
+def test_paged_attention_full_chunk_reorder_and_cache_bits(batch, length):
+    torch.manual_seed(777)
+    q = torch.randn(batch, length, 4, 256, device="cuda", dtype=torch.bfloat16)
+    k = torch.randn(batch, length, 1, 256, device="cuda", dtype=torch.bfloat16)
+    v = torch.randn_like(k)
+    block = 64
+    pages = (length + block - 1) // block
+    tables = torch.arange(1, batch * pages + 1, device="cuda").reshape(batch, pages)
+    full_cache = torch.zeros(batch * pages + 1, 1, block, 512, device="cuda", dtype=torch.bfloat16)
+    chunk_cache = full_cache.clone()
+    impl = SharedAttentionImpl(4, 256, 1 / 16, 1)
+
+    def invoke(cache, start, end, order):
+        positions = torch.arange(start, end, device="cuda")
+        table = tables[order]
+        slots = (table[:, positions // block] * block + positions % block).reshape(-1)
+        metadata = SharedAttentionMetadata(
+            tuple(index * (end - start) for index in range(batch + 1)),
+            (end,) * batch,
+            table,
+            slots,
+            batch * (end - start),
+        )
+        query = q[order, start:end].reshape(-1, 4, 256)
+        out = torch.empty_like(query)
+        impl.forward(
+            None,
+            query,
+            k[order, start:end].reshape(-1, 1, 256),
+            v[order, start:end].reshape(-1, 1, 256),
+            cache,
+            metadata,
+            out,
+        )
+        return out.reshape(batch, end - start, 4, 256)
+
+    expected = invoke(full_cache, 0, length, list(range(batch)))
+    actual = torch.empty_like(expected)
+    start = 0
+    for end in sorted(
+        {1, length, *(point for point in (63, 64, 65, 100, 528, 576) if point < length)}
+    ):
+        order = list(reversed(range(batch))) if start else list(range(batch))
+        result = invoke(chunk_cache, start, end, order)
+        actual[order, start:end] = result
+        start = end
+    assert_tensor_bitwise_equal(actual, expected, name="paged full/chunk/reorder outputs")
+    assert_tensor_bitwise_equal(chunk_cache, full_cache, name="complete scheduler-owned KV cache")
+
+
+@pytest.mark.parametrize("bad_slots", [[-1, 1], [1, 1], [1, 99], [1, 0]])
+def test_paged_attention_invalid_indices_do_not_mutate_cache(bad_slots):
+    cache = torch.zeros(2, 1, 4, 512, device="cuda", dtype=torch.bfloat16)
+    original = cache.clone()
+    query = torch.zeros(2, 4, 256, device="cuda", dtype=torch.bfloat16)
+    key = torch.zeros(2, 1, 256, device="cuda", dtype=torch.bfloat16)
+    metadata = SharedAttentionMetadata(
+        (0, 2), (2,), torch.tensor([[0]], device="cuda"), torch.tensor(bad_slots, device="cuda"), 2
+    )
+    with pytest.raises(ValueError):
+        SharedAttentionImpl(4, 256, 1 / 16, 1).forward(
+            None, query, key, key, cache, metadata, query.clone()
+        )
+    assert_tensor_bitwise_equal(cache, original)
diff --git a/tests/check_qwen3_next_vime_weight_hooks.py b/tests/check_qwen3_next_vime_weight_hooks.py
new file mode 100644
index 000000000..f8851eddc
--- /dev/null
+++ b/tests/check_qwen3_next_vime_weight_hooks.py
@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""CPU checks through VIME's real parameter shard and HF export entrypoints."""
+
+from types import SimpleNamespace
+
+import torch
+from megatron.core import mpu
+from vime.backends.megatron_utils import hf_to_megatron as load_api
+from vime.backends.megatron_utils import megatron_to_hf as export_api
+from vime.backends.megatron_utils.hf_to_megatron.common import shard_mcore_tensor
+
+from rl_engine.integrations.qwen3_next_vime import SharedInputNorm, install_vime_gdn_weight_hooks
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+class Reader(dict):
+    def get_tensor(self, key):
+        return self[key]
+
+
+def test_vime_hf_load_shard_gather_export_roundtrip(monkeypatch):
+    monkeypatch.setitem(load_api._LOADERS, "qwen3_next", load_api._LOADERS["qwen3_next"])
+    monkeypatch.setattr(export_api, "convert_qwen3_next_to_hf", export_api.convert_qwen3_next_to_hf)
+    monkeypatch.setattr(mpu, "get_tensor_model_parallel_world_size", lambda: 4)
+    install_vime_gdn_weight_hooks()
+    generator = torch.Generator().manual_seed(42)
+    weight = torch.randn((8192, 1, 4), generator=generator).bfloat16()
+    name = "module.module.decoder.layers.0.self_attention.linear_attn.conv1d.weight"
+    reader = Reader({"model.layers.0.linear_attn.conv1d.weight": weight})
+    packed = load_api._LOADERS["qwen3_next"](name, reader, SimpleNamespace())
+    parameter = torch.nn.Parameter(torch.empty(2048, 1, 4, dtype=torch.bfloat16))
+    parameter.tensor_model_parallel = True
+    parameter.partition_dim = 0
+    parameter.partition_stride = 1
+    local = []
+    for rank in range(4):
+        monkeypatch.setattr(mpu, "get_tensor_model_parallel_rank", lambda rank=rank: rank)
+        shard = shard_mcore_tensor(name, packed, parameter)
+        exact(shard, torch.cat([part.chunk(4)[rank] for part in weight.split((2048, 2048, 4096))]))
+        local.append(shard)
+    args = SimpleNamespace(
+        hidden_size=2048, num_attention_heads=16, num_query_groups=2, kv_channels=256
+    )
+    converted = export_api.convert_qwen3_next_to_hf(args, name, torch.cat(local))
+    assert converted[0][0] == "model.layers.0.linear_attn.conv1d.weight"
+    exact(converted[0][1], weight)
+
+
+def test_shared_norm_keeps_existing_parameter_name_and_identity():
+    weight = torch.nn.Parameter(torch.zeros(2048, dtype=torch.bfloat16))
+    norm = SharedInputNorm(1e-6, torch.device("cpu"), weight=weight)
+    assert dict(norm.named_parameters()) == {"weight": weight}
+    assert norm.weight is weight
+    assert norm.weight.tensor_model_parallel is False
+
+
+def test_vime_kv_load_shard_export_uses_two_replica_pairs(monkeypatch):
+    monkeypatch.setattr(mpu, "get_tensor_model_parallel_world_size", lambda: 4)
+    install_vime_gdn_weight_hooks()
+    weight = torch.randn(512, 2048).bfloat16()
+    name = "module.module.decoder.layers.3.self_attention.self_attn.k_proj.weight"
+    reader = Reader({"model.layers.3.self_attn.k_proj.weight": weight})
+    packed = load_api._LOADERS["qwen3_next"](name, reader, SimpleNamespace())
+    parameter = torch.nn.Parameter(torch.empty(256, 2048, dtype=torch.bfloat16))
+    parameter.tensor_model_parallel = True
+    parameter.partition_dim = 0
+    parameter.partition_stride = 1
+    shards = []
+    for rank in range(4):
+        monkeypatch.setattr(mpu, "get_tensor_model_parallel_rank", lambda rank=rank: rank)
+        shard = shard_mcore_tensor(name, packed, parameter)
+        exact(shard, weight.chunk(2)[rank // 2])
+        shards.append(shard)
+    args = SimpleNamespace(
+        hidden_size=2048, num_attention_heads=16, num_query_groups=2, kv_channels=256
+    )
+    outputs = export_api.convert_qwen3_next_to_hf(args, name, torch.cat(shards))
+    assert outputs[0][0] == "model.layers.3.self_attn.k_proj.weight"
+    exact(outputs[0][1], weight)
+
+
+def test_vime_shared_expert_direct_names_roundtrip(monkeypatch):
+    install_vime_gdn_weight_hooks()
+    weight = torch.randn(512, 2048).bfloat16()
+    hf_name = "model.layers.0.mlp.shared_expert.gate_proj.weight"
+    name = "module.module.decoder.layers.0.mlp.shared_expert.gate_proj.weight"
+    loaded = load_api._LOADERS["qwen3_next"](name, Reader({hf_name: weight}), SimpleNamespace())
+    outputs = export_api.convert_qwen3_next_to_hf(SimpleNamespace(), name, loaded)
+    assert outputs[0][0] == hf_name
+    exact(outputs[0][1], weight)
diff --git a/tests/check_qwen3_next_vllm_model_contract.py b/tests/check_qwen3_next_vllm_model_contract.py
new file mode 100644
index 000000000..e5ece2808
--- /dev/null
+++ b/tests/check_qwen3_next_vllm_model_contract.py
@@ -0,0 +1,281 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Actual vLLM imports/loaders and constructor contracts, without GPU execution."""
+
+from types import SimpleNamespace as Namespace
+
+import pytest
+import torch
+from vllm.model_executor import parameter
+from vllm.model_executor.layers import linear
+
+from rl_engine.integrations import qwen3_next_vllm_model as adapter
+from rl_engine.integrations.qwen3_next_tp_blocks import shard_attention_weights
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
+
+
+def profile():
+    config = Namespace(
+        hidden_size=2048,
+        num_hidden_layers=48,
+        num_attention_heads=16,
+        num_key_value_heads=2,
+        head_dim=256,
+        vocab_size=151936,
+        num_experts=512,
+        num_experts_per_tok=10,
+        moe_intermediate_size=512,
+        shared_expert_intermediate_size=512,
+        rms_norm_eps=1e-6,
+        max_position_embeddings=262144,
+        layer_types=[
+            "full_attention" if index % 4 == 3 else "linear_attention" for index in range(48)
+        ],
+        rope_parameters={
+            "rope_theta": 10000000,
+            "partial_rotary_factor": 0.25,
+            "rope_type": "default",
+        },
+    )
+    return Namespace(
+        model_config=Namespace(dtype=torch.bfloat16, enforce_eager=True, hf_text_config=config),
+        parallel_config=Namespace(
+            tensor_parallel_size=4,
+            pipeline_parallel_size=1,
+            enable_expert_parallel=False,
+            enable_eplb=False,
+            use_sequence_parallel_moe=False,
+            enable_dbo=False,
+        ),
+        quant_config=None,
+        speculative_config=None,
+        lora_config=None,
+        cache_config=Namespace(enable_prefix_caching=False),
+        kernel_config=Namespace(moe_backend="triton"),
+        attention_config=Namespace(backend=adapter.AttentionBackendEnum.CUSTOM),
+    )
+
+
+def test_profile_keeps_engine_and_model_acceptance_closed():
+    adapter.validate_profile(profile())
+    assert adapter.profile_capabilities()["scheduler_batch_invariance_verified"] is False
+    assert adapter.profile_capabilities()["full_model_l2_verified"] is False
+
+
+@pytest.mark.parametrize(
+    "scope,name,value",
+    [
+        ("model_config", "dtype", torch.float32),
+        ("model_config", "enforce_eager", False),
+        ("parallel_config", "tensor_parallel_size", 2),
+        ("parallel_config", "enable_expert_parallel", True),
+        ("cache_config", "enable_prefix_caching", True),
+        ("kernel_config", "moe_backend", "auto"),
+        ("attention_config", "backend", None),
+    ],
+)
+def test_real_wrapper_constructor_rejects_drift_before_allocating_model(scope, name, value):
+    config = profile()
+    setattr(getattr(config, scope), name, value)
+    with pytest.raises(ValueError):
+        adapter.RLKernelQwen3NextForCausalLM(vllm_config=config)
+
+
+@pytest.mark.parametrize("rank", range(4))
+def test_actual_native_qkv_loader_preserves_gate_layout_and_kv_replication(monkeypatch, rank):
+    for module in (linear, parameter):
+        monkeypatch.setattr(module, "get_tensor_model_parallel_rank", lambda: rank)
+        monkeypatch.setattr(module, "get_tensor_model_parallel_world_size", lambda: 4)
+    # Construct the real native layer, then use its unchanged HF shard loader.
+    layer = linear.QKVParallelLinear(
+        2048,
+        256,
+        32,
+        2,
+        bias=False,
+        params_dtype=torch.bfloat16,
+    )
+    weights = {}
+    for name, rows in (("q_proj.weight", 8192), ("k_proj.weight", 512), ("v_proj.weight", 512)):
+        weights[name] = (
+            torch.arange(rows, dtype=torch.int32)
+            .remainder(251)
+            .to(torch.bfloat16)[:, None]
+            .expand(rows, 2048)
+            .contiguous()
+        )
+    weights.update(
+        {
+            "o_proj.weight": torch.zeros(2048, 4096, dtype=torch.bfloat16),
+            "q_norm.weight": torch.zeros(256, dtype=torch.bfloat16),
+            "k_norm.weight": torch.zeros(256, dtype=torch.bfloat16),
+        }
+    )
+    expected = shard_attention_weights(weights, rank)
+    for key, shard in (("q_proj.weight", "q"), ("k_proj.weight", "k"), ("v_proj.weight", "v")):
+        layer.weight.weight_loader(layer.weight, weights[key], shard)
+    for actual, key in zip(
+        layer.weight.split((2048, 256, 256)), ("q_proj.weight", "k_proj.weight", "v_proj.weight")
+    ):
+        exact(actual, expected[key], name=f"native rank{rank} {key}")
+
+
+def test_shared_moe_postload_preserves_parameter_identity_and_loader(monkeypatch):
+    # Meta tensors verify 512-expert allocation shapes without allocating an
+    # unnecessary CPU model. Real data movement is covered by the TP4 GPU gate.
+    gate = torch.nn.Parameter(torch.empty(512, 256, 2048, device="meta", dtype=torch.bfloat16))
+    down = torch.nn.Parameter(torch.empty(512, 2048, 128, device="meta", dtype=torch.bfloat16))
+    loader = object()
+    gate.weight_loader = down.weight_loader = loader
+    layer = Namespace(w13_weight=gate, w2_weight=down)
+    method = object.__new__(adapter.SharedPlainBF16MoEMethod)
+    monkeypatch.setattr(
+        adapter.SharedPlainBF16MoEMethod,
+        "get_fused_moe_quant_config",
+        lambda self, layer: "plain-bf16",
+    )
+    method.process_weights_after_loading(layer)
+    assert layer.w13_weight is gate and layer.w2_weight is down
+    assert gate.weight_loader is loader and down.weight_loader is loader
+    assert method.moe_quant_config == "plain-bf16"
+    with pytest.raises(RuntimeError, match="native MoE arithmetic"):
+        method.apply()
+
+
+def test_shared_model_rejects_rope_or_layer_layout_changes():
+    config = profile()
+    config.model_config.hf_text_config.rope_parameters["rope_theta"] = 10000
+    with pytest.raises(ValueError, match="rotary dimension"):
+        adapter.validate_profile(config)
+    config = profile()
+    config.model_config.hf_text_config.layer_types[0] = "full_attention"
+    with pytest.raises(ValueError, match="36 GDN"):
+        adapter.validate_profile(config)
+
+
+@pytest.mark.parametrize(
+    "decoder_class", [adapter.Qwen3NextDecoderLayer, adapter.SharedVLLMDecoderLayer]
+)
+def test_real_decoder_invokes_gdn_with_single_input_and_consumes_returned_tensor(
+    monkeypatch, decoder_class
+):
+    from rl_engine.integrations.qwen3_next_vllm import SharedQwenGDN
+
+    class ResidualNorm(torch.nn.Module):
+        def forward(self, hidden, residual=None):
+            if residual is None:
+                return hidden
+            return hidden + residual, hidden + residual
+
+    gdn = object.__new__(SharedQwenGDN)
+    torch.nn.Module.__init__(gdn)
+    calls = []
+
+    def into(self, hidden, output):
+        assert output is not hidden and output.data_ptr() != hidden.data_ptr()
+        assert output.dtype == hidden.dtype and output.shape == hidden.shape
+        output.copy_(hidden + 2)
+        calls.append(output)
+
+    monkeypatch.setattr(SharedQwenGDN, "_forward_into", into)
+    monkeypatch.setattr(adapter, "_rms", lambda value, module: value)
+    decoder = object.__new__(decoder_class)
+    torch.nn.Module.__init__(decoder)
+    decoder.layer_type = "linear_attention"
+    decoder.layer_scale = decoder.use_attn_reduce_scatter_for_moe = False
+    decoder.linear_attn = gdn
+    decoder.input_layernorm = decoder.post_attention_layernorm = ResidualNorm()
+    decoder.mlp = torch.nn.Identity()
+    hidden = torch.ones((2, 2048), dtype=torch.bfloat16)
+    output, residual = decoder(hidden_states=hidden, residual=None, positions=torch.arange(2))
+    assert len(calls) == 1
+    exact(calls[0], torch.full_like(hidden, 3), name="public GDN return")
+    exact(output, torch.full_like(hidden, 4), name="decoder consumed GDN output")
+    exact(residual, torch.full_like(hidden, 4), name="decoder residual")
+
+
+def test_binding_all48_layers_preserves_parameter_identity_and_native_loaders():
+    from rl_engine.integrations.qwen3_next_vllm import SharedQwenGDN
+    from rl_engine.integrations.qwen3_next_vllm_attention import SharedAttentionBackend
+
+    def instance(cls):
+        value = object.__new__(cls)
+        torch.nn.Module.__init__(value)
+        return value
+
+    loader = object()
+
+    def weight_module(shape):
+        module = torch.nn.Module()
+        module.weight = torch.nn.Parameter(torch.empty(shape, device="meta", dtype=torch.bfloat16))
+        module.weight.weight_loader = loader
+        return module
+
+    # The binding operation does not run a native constructor or GPU kernel.
+    # Meta tensors make its 48-layer ownership contract affordable to test.
+    model = instance(adapter.Qwen3NextModel)
+    model.start_layer, model.end_layer = 0, 48
+    model.embed_tokens = weight_module((37984, 2048))
+    model.norm = weight_module((2048,))
+    layers = []
+    for index in range(48):
+        layer = instance(adapter.Qwen3NextDecoderLayer)
+        layer.layer_type = "full_attention" if index % 4 == 3 else "linear_attention"
+        if layer.layer_type == "linear_attention":
+            layer.linear_attn = instance(SharedQwenGDN)
+        else:
+            layer.self_attn = instance(adapter.Qwen3NextAttention)
+            layer.self_attn.attn = Namespace(attn_backend=SharedAttentionBackend)
+            layer.self_attn.qkv_proj = weight_module((2560, 2048))
+            layer.self_attn.o_proj = weight_module((2048, 1024))
+        layer.mlp = instance(adapter.Qwen3NextSparseMoeBlock)
+        layer.mlp.is_fused_shared_expert_enabled = False
+        layer.mlp.replicate_shared_expert = False
+        layer.mlp.shared_expert = torch.nn.Module()
+        layer.mlp.shared_expert.gate_up_proj = weight_module((256, 2048))
+        layer.mlp.shared_expert.down_proj = weight_module((2048, 128))
+        routed = torch.nn.Module()
+        routed.quant_method = instance(adapter.UnquantizedFusedMoEMethod)
+        routed.w13_weight = weight_module((512, 256, 2048)).weight
+        routed.w2_weight = weight_module((512, 2048, 128)).weight
+        layer.mlp.experts = torch.nn.Module()
+        layer.mlp.experts.routed_experts = routed
+        layers.append(layer)
+    model.layers = torch.nn.ModuleList(layers)
+    before = {name: id(value) for name, value in model.named_parameters()}
+    adapter._bind_native_model(model, object())
+    assert isinstance(model, adapter.SharedVLLMModel)
+    assert {name: id(value) for name, value in model.named_parameters()} == before
+    assert all(value.weight_loader is loader for value in model.parameters())
+    assert all(isinstance(layer, adapter.SharedVLLMDecoderLayer) for layer in model.layers)
+    assert all(isinstance(layer.mlp, adapter.SharedVLLMMoE) for layer in model.layers)
+
+
+def test_spawn_worker_registers_model_before_native_worker_initialization(monkeypatch):
+    from rl_engine.integrations import qwen3_next_vllm_worker as worker
+
+    calls = []
+    monkeypatch.setattr(worker, "register_shared_model", lambda: calls.append("registered"))
+    monkeypatch.setattr(
+        worker,
+        "imported_provider_identity",
+        lambda: calls.append("native-binary") or {"test": True},
+    )
+    monkeypatch.setattr(
+        worker.Worker, "__init__", lambda self, *args, **kwargs: calls.append("native-init")
+    )
+    worker.SharedQwenWorker()
+    assert calls == ["registered", "native-binary", "native-init"]
+
+
+def test_architecture_registration_is_idempotent_and_rejects_another_model(monkeypatch):
+    from vllm.model_executor.models import ModelRegistry
+
+    monkeypatch.setattr(ModelRegistry, "models", dict(ModelRegistry.models))
+    ModelRegistry.models.pop(adapter.ARCHITECTURE, None)
+    assert adapter.register_shared_model() == adapter.ARCHITECTURE
+    assert adapter.register_shared_model() == adapter.ARCHITECTURE
+    ModelRegistry.models[adapter.ARCHITECTURE] = Namespace(load_model_cls=lambda: torch.nn.Linear)
+    with pytest.raises(ValueError, match="different model"):
+        adapter.register_shared_model()
diff --git a/tests/test_qwen3_next_binary_evidence.py b/tests/test_qwen3_next_binary_evidence.py
new file mode 100644
index 000000000..a86976fe3
--- /dev/null
+++ b/tests/test_qwen3_next_binary_evidence.py
@@ -0,0 +1,68 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Loaded provider identity must match immutable build evidence, including on reload."""
+
+import json
+import sys
+from types import SimpleNamespace
+
+import pytest
+
+from rl_engine.testing.qwen3_next_binary import (
+    archive_provider_binary,
+    validate_binary_record,
+    verify_imported_provider,
+)
+
+
+@pytest.fixture
+def archived(tmp_path):
+    source, output = tmp_path / "source", tmp_path / "attempt"
+    (source / "rl_engine").mkdir(parents=True)
+    output.mkdir()
+    binary = source / "rl_engine/_C.cpython-test.so"
+    binary.write_bytes(b"built CUDA extension")
+    return binary, archive_provider_binary(source, output, source_tree="original-tree")
+
+
+def test_archive_survives_later_live_binary_rebuild(archived):
+    binary, record = archived
+    actual = validate_binary_record(record, source_tree="original-tree")
+    binary.write_bytes(b"later build")
+    assert validate_binary_record(record) == actual
+    with pytest.raises(ValueError, match="source tree"):
+        validate_binary_record(record, source_tree="different-tree")
+
+
+def test_imported_binary_cannot_substitute_another_provider(archived, monkeypatch):
+    _, record = archived
+    expected = validate_binary_record(record)
+    loaded = dict(expected)
+    monkeypatch.setitem(
+        sys.modules,
+        "rl_engine.integrations.qwen3_next_binary",
+        SimpleNamespace(imported_provider_identity=lambda: loaded),
+    )
+    assert verify_imported_provider(record, source_tree="original-tree") == expected
+    loaded["sha256"] = "0" * 64
+    with pytest.raises(ValueError, match="different CUDA binary"):
+        verify_imported_provider(record, source_tree="original-tree")
+
+
+def test_archived_binary_corruption_and_attempt_escape_are_rejected(archived, tmp_path):
+    from pathlib import Path
+
+    _, record = archived
+    with pytest.raises(ValueError, match="escaped"):
+        validate_binary_record(record, artifact_root=tmp_path / "different-attempt")
+    metadata = json.loads(Path(record["path"]).read_text())
+    Path(metadata["archive"]["path"]).write_bytes(b"corrupted original binary")
+    with pytest.raises(ValueError, match="content changed"):
+        validate_binary_record(record)
+
+
+@pytest.mark.parametrize("record", [None, {}, {"path": "/tmp/unpinned"}])
+def test_missing_binary_record_cannot_close_native_acceptance(record):
+    with pytest.raises(ValueError, match="pinned CUDA binary"):
+        validate_binary_record(record)
diff --git a/tests/test_qwen3_next_case_manifest.py b/tests/test_qwen3_next_case_manifest.py
new file mode 100644
index 000000000..9834c76a7
--- /dev/null
+++ b/tests/test_qwen3_next_case_manifest.py
@@ -0,0 +1,106 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Mandatory case completeness must not be inferred from an aggregate count."""
+
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+from rl_engine.testing.qwen3_next_cases import case_digest, execution_verdict, load_cases
+
+CASE = "tests/example.py::test_required"
+SUCCESS = {"setup": "passed", "call": "passed", "teardown": "passed"}
+
+
+@pytest.mark.parametrize(
+    "fault", ["missing", "extra", "duplicate", "skipped", "teardown", "incomplete"]
+)
+def test_declared_case_must_complete_all_phases(fault):
+    collected = [CASE]
+    outcomes = {CASE: dict(SUCCESS)}
+    if fault == "missing":
+        collected = []
+    elif fault == "extra":
+        collected.append("tests/example.py::test_undeclared")
+    elif fault == "duplicate":
+        collected.append(CASE)
+    elif fault == "skipped":
+        outcomes[CASE]["call"] = "skipped"
+    elif fault == "teardown":
+        outcomes[CASE]["teardown"] = "failed"
+    else:
+        del outcomes[CASE]["call"]
+    assert execution_verdict([CASE], collected, outcomes)["passed"] is False
+
+
+def test_complete_report_records_exact_identity():
+    result = execution_verdict([CASE], [CASE], {CASE: SUCCESS})
+    assert result["passed"] is True
+    assert result["counts"] == {"tests": 1, "failures": 0, "errors": 0, "skipped": 0}
+    assert result["expected_case_ids_sha256"] == case_digest([CASE])
+
+
+def test_diagnostics_cannot_enter_required_case_set(tmp_path):
+    path = tmp_path / "cases.json"
+    path.write_text(
+        json.dumps(
+            {
+                "schema_version": 1,
+                "suites": {
+                    "norm": [CASE],
+                    "core": ["tests/example.py::test_core"],
+                    "diagnostics": ["tests/example.py::test_approximate"],
+                },
+            }
+        )
+    )
+    assert load_cases(path, "all") == sorted([CASE, "tests/example.py::test_core"])
+
+
+@pytest.mark.parametrize("outcome", ["pass", "skip", "xfail", "deselected"])
+def test_plugin_enforces_real_pytest_outcomes(tmp_path, outcome):
+    test_file = tmp_path / "test_acceptance.py"
+    body = "pass"
+    if outcome == "skip":
+        body = "pytest.skip('missing CUDA')"
+    elif outcome == "xfail":
+        body = "pytest.xfail('unimplemented')"
+    test_file.write_text(f"import pytest\ndef test_required():\n    {body}\n")
+    case = "test_acceptance.py::test_required"
+    manifest = tmp_path / "cases.json"
+    manifest.write_text(json.dumps({"schema_version": 1, "suites": {"core": [case]}}))
+    report_path = tmp_path / "report.json"
+    args = [
+        sys.executable,
+        "-m",
+        "pytest",
+        "-q",
+        "-p",
+        "no:cacheprovider",
+        "-p",
+        "rl_engine.testing.pytest_case_manifest",
+        "--case-manifest",
+        str(manifest),
+        "--case-suite",
+        "core",
+        "--case-report",
+        str(report_path),
+        str(test_file),
+    ]
+    if outcome == "deselected":
+        args.extend(["-k", "not required"])
+    # Use this source tree even when a different RL-Kernel checkout is installed.
+    import os
+
+    env = dict(os.environ, PYTHONPATH=str(Path(__file__).resolve().parents[1]))
+    result = subprocess.run(args, cwd=tmp_path, env=env, capture_output=True, text=True)
+    assert result.returncode == (
+        0 if outcome == "pass" else (5 if outcome == "deselected" else 1)
+    ), (result.stdout + result.stderr)
+    report = json.loads(report_path.read_text())
+    assert report["passed"] is (outcome == "pass")
+    assert report["expected_case_ids"] == [case]
diff --git a/tests/test_qwen3_next_chain_report.py b/tests/test_qwen3_next_chain_report.py
new file mode 100644
index 000000000..71b0eacde
--- /dev/null
+++ b/tests/test_qwen3_next_chain_report.py
@@ -0,0 +1,149 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""The Qwen3-Next chain report keeps the WS1 chain-gate shape and attributes first drift."""
+
+import dataclasses
+
+import pytest
+import torch
+
+from rl_engine.kernels.gtest import qwen3_next_chain_report as chain
+from rl_engine.kernels.gtest.chain_gate import ChainGateReport
+from rl_engine.kernels.gtest.tolerance import load_contract
+
+CASES = ("p0008-b1-full", "p0008-b1-chunked")
+
+
+def artifact(seed):
+    g = torch.Generator().manual_seed(seed)
+    return {
+        "logprobs": -torch.rand((1, 16), generator=g),
+        "logits": torch.randn((1, 16, 64), generator=g).to(torch.bfloat16),
+        "embedding_digest": {"sha256": "a" * 64, "sample_sha256": ["a" * 64]},
+        "layer_digests": [{"layer": layer, "output_sha256": "b" * 64} for layer in range(48)],
+    }
+
+
+def record(passed=True, node=None, layer=None, tensor=None):
+    return {
+        "passed": passed,
+        "first_divergent_node": None if node is None else {"layer": layer, "node": node},
+        "first_difference": None if tensor is None else {"tensor": tensor},
+        "artifacts": {},
+    }
+
+
+def inputs(records=None, recompute_seed=1):
+    matrices, artifacts = {}, {}
+    for version in ("baseline", "update1"):
+        matrices[version] = {
+            "status": "passed",
+            "case_ids": list(CASES),
+            "cases": {case: record() for case in CASES},
+            "fixture": {"content_sha256": "f" * 64},
+        }
+        artifacts[version] = {
+            case: {"rollout": artifact(1), "recompute": artifact(recompute_seed)} for case in CASES
+        }
+    for (version, case), value in (records or {}).items():
+        matrices[version]["cases"][case] = value
+        matrices[version]["status"] = "failed"
+    return matrices, artifacts
+
+
+def build(matrices, artifacts):
+    return chain.build_report(
+        matrices,
+        artifacts,
+        contract=load_contract(),
+        measured_commit="c" * 40,
+        weight_source="Qwen/Qwen3-Next-80B-A3B-Instruct@rev",
+        weight_hash="h" * 64,
+        device="cuda",
+        gpu_name="NVIDIA B200",
+        compute_capability="10.0",
+    )
+
+
+def test_report_is_a_chain_gate_report_superset_and_passes_when_bitwise():
+    report = build(*inputs())
+    fields = {field.name for field in dataclasses.fields(ChainGateReport)}
+    assert fields <= set(report)
+    assert report["schema_version"] == "qwen3-next-chain-v1"
+    assert report["compatible_schema"] == "ws1-c10-c11-v5"
+    assert report["passed"] and report["first_drift"] is None
+    assert report["first_drift_family"] is None
+    assert len(report["cells"]) == 4 and len(report["invariance"]) == 8
+    assert all(item["atol"] == 0 and item["passed"] for item in report["invariance"])
+    assert report["weight_versions"] == ["baseline", "update1"]
+    assert [entry["case_id"] for entry in report["decode_prefill"]] == [
+        "baseline/p0008-b1-chunked",
+        "update1/p0008-b1-chunked",
+    ]
+
+
+def test_a_single_bit_in_the_replay_fails_the_report():
+    matrices, artifacts = inputs()
+    replay = artifacts["update1"]["p0008-b1-full"]["recompute"]
+    replay["logprobs"] = replay["logprobs"].clone()
+    replay["logprobs"][0, 3] = torch.nextafter(replay["logprobs"][0, 3], torch.tensor(0.0))
+    report = build(matrices, artifacts)
+    assert not report["passed"]
+    failing = [item for item in report["invariance"] if not item["passed"]]
+    assert [item["config_pair"][0] for item in failing] == ["update1/p0008-b1-full/rollout"]
+
+
+@pytest.mark.parametrize(
+    "node,layer,family,label",
+    [
+        ("embedding", 0, "embedding", "embedding"),
+        ("input_layernorm", 0, "norm", "layer0.input_layernorm"),
+        ("post_attention_layernorm", 5, "norm", "layer5.post_attention_layernorm"),
+        ("mixer", 2, "gdn", "layer2.mixer"),
+        ("state", 7, "full_attention", "layer7.state"),
+        ("mixer", 47, "full_attention", "layer47.mixer"),
+        ("router", 9, "moe", "layer9.router"),
+        ("mlp", 10, "moe", "layer10.mlp"),
+    ],
+)
+def test_fabricated_drift_resolves_to_its_layer_family(node, layer, family, label):
+    failing = record(passed=False, node=node, layer=layer)
+    report = build(*inputs({("update1", "p0008-b1-chunked"): failing}))
+    assert not report["passed"]
+    assert report["first_drift"] == f"{label}:rollout->recompute"
+    assert report["first_drift_family"] == family
+    assert report["first_drift_layer"] == layer
+    assert report["first_drift_cell"] == "update1/p0008-b1-chunked"
+
+
+def test_drift_after_the_last_layer_is_the_lm_head_or_logprob():
+    failing = record(passed=False, tensor="logprobs")
+    report = build(*inputs({("baseline", "p0008-b1-full"): failing}))
+    assert report["first_drift"] == "logprobs:rollout->recompute"
+    assert report["first_drift_family"] == "lm_head_logprob"
+    assert report["first_drift_layer"] is None
+
+
+def test_the_earliest_weight_version_and_case_names_the_first_drift():
+    report = build(
+        *inputs(
+            {
+                ("update1", "p0008-b1-full"): record(False, "router", 3),
+                ("baseline", "p0008-b1-chunked"): record(False, "mixer", 11),
+            }
+        )
+    )
+    assert report["first_drift_cell"] == "baseline/p0008-b1-chunked"
+    assert report["first_drift_family"] == "full_attention"
+
+
+def test_unknown_nodes_and_versions_are_rejected():
+    with pytest.raises(ValueError, match="Unknown divergence node"):
+        chain.family_of(3, "gate")
+    with pytest.raises(ValueError, match="needs a decoder layer"):
+        chain.family_of(None, "mixer")
+    matrices, artifacts = inputs()
+    matrices["update3"] = matrices["update1"]
+    with pytest.raises(ValueError, match="subset"):
+        build(matrices, artifacts)
diff --git a/tests/test_qwen3_next_checkpoint.py b/tests/test_qwen3_next_checkpoint.py
new file mode 100644
index 000000000..54113ea66
--- /dev/null
+++ b/tests/test_qwen3_next_checkpoint.py
@@ -0,0 +1,222 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Crash boundaries for four-rank atomic checkpoint publication."""
+
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+from qwen3_next_checkpoint import (  # noqa: E402
+    newest_complete_checkpoint,
+    publish_checkpoint,
+    resume_interrupted_publications,
+    validate_checkpoint,
+    write_rank_manifest,
+)
+
+IDENTITY = {"provider": "explicit", "tp": 4, "environment": "pinned", "weight_revision": "official"}
+
+
+def staged(root, step=1):
+    directory = root / f"step-{step}.partial"
+    directory.mkdir()
+    for rank in range(4):
+        rank_dir = directory / f"rank-{rank}"
+        rank_dir.mkdir()
+        files = {}
+        for role in ("model", "optimizer", "rng", "progress"):
+            path = rank_dir / f"{role}.json"
+            path.write_text(json.dumps({"rank": rank, "role": role, "step": step}))
+            files[role] = [str(path.relative_to(directory))]
+        write_rank_manifest(
+            directory,
+            rank=rank,
+            world_size=4,
+            identity=IDENTITY,
+            step=step,
+            sample_position=step * 4,
+            files=files,
+        )
+    return directory
+
+
+def test_complete_publication_and_recovery(tmp_path):
+    partial = staged(tmp_path)
+    destination = tmp_path / "step-1"
+    publish_checkpoint(partial, destination, world_size=4, identity=IDENTITY)
+    assert not partial.exists()
+    assert validate_checkpoint(destination, IDENTITY)["step"] == 1
+    assert json.loads((tmp_path / "LATEST.json").read_text())["path"] == "step-1"
+    recovery = newest_complete_checkpoint(tmp_path, IDENTITY)
+    assert recovery["checkpoint"] == str(destination)
+    assert recovery["sample_position"] == 4
+
+
+def test_missing_rank_prevents_completion_marker(tmp_path):
+    partial = staged(tmp_path)
+    (partial / "rank-3.manifest.json").unlink()
+    with pytest.raises(FileNotFoundError):
+        publish_checkpoint(partial, tmp_path / "step-1", world_size=4, identity=IDENTITY)
+    assert not (partial / "COMPLETE.json").exists()
+    assert not (tmp_path / "step-1").exists()
+
+
+def test_corruption_prevents_publication(tmp_path):
+    partial = staged(tmp_path)
+    (partial / "rank-2/optimizer.json").write_text("damaged")
+    with pytest.raises(ValueError, match="checksum|size mismatch"):
+        publish_checkpoint(partial, tmp_path / "step-1", world_size=4, identity=IDENTITY)
+    assert not (partial / "COMPLETE.json").exists()
+
+
+def test_only_completed_checkpoints_can_load(tmp_path):
+    partial = staged(tmp_path)
+    with pytest.raises(ValueError, match="Unpublished"):
+        validate_checkpoint(partial, IDENTITY)
+    assert newest_complete_checkpoint(tmp_path, IDENTITY)["checkpoint"] is None
+
+
+def test_corrupt_latest_falls_back_and_records_reason(tmp_path):
+    for step in (1, 2):
+        partial = staged(tmp_path, step)
+        publish_checkpoint(partial, tmp_path / f"step-{step}", world_size=4, identity=IDENTITY)
+    (tmp_path / "step-2/rank-0/rng.json").write_text("corrupt")
+    staged(tmp_path, 3)
+    recovery = newest_complete_checkpoint(tmp_path, IDENTITY)
+    assert recovery["step"] == 1
+    assert recovery["rejected"][0]["path"] == "step-2"
+    assert recovery["interrupted"] == ["step-3.partial"]
+
+
+def test_identity_mismatch_never_resumes_fresh_silently(tmp_path):
+    partial = staged(tmp_path)
+    publish_checkpoint(partial, tmp_path / "step-1", world_size=4, identity=IDENTITY)
+    with pytest.raises(ValueError, match="No valid"):
+        newest_complete_checkpoint(tmp_path, {**IDENTITY, "tp": 2})
+
+
+def test_rank_progress_mismatch_prevents_publication(tmp_path):
+    partial = staged(tmp_path)
+    path = partial / "rank-3.manifest.json"
+    payload = json.loads(path.read_text())
+    payload["step"] = 2
+    path.write_text(json.dumps(payload))
+    with pytest.raises(ValueError, match="different progress"):
+        publish_checkpoint(partial, tmp_path / "step-1", world_size=4, identity=IDENTITY)
+
+
+def test_rank_cannot_omit_optimizer_or_reuse_rng_as_model(tmp_path):
+    partial = tmp_path / "new.partial"
+    partial.mkdir()
+    (partial / "state").write_text("state")
+    with pytest.raises(ValueError, match="Every rank"):
+        write_rank_manifest(
+            partial,
+            rank=0,
+            world_size=4,
+            identity=IDENTITY,
+            step=1,
+            sample_position=4,
+            files={"model": ["state"]},
+        )
+    with pytest.raises(ValueError, match="multiple state roles"):
+        write_rank_manifest(
+            partial,
+            rank=0,
+            world_size=4,
+            identity=IDENTITY,
+            step=1,
+            sample_position=4,
+            files={role: ["state"] for role in ("model", "optimizer", "rng", "progress")},
+        )
+
+
+def test_crash_after_marker_before_rename_stays_unloadable(tmp_path, monkeypatch):
+    partial = staged(tmp_path)
+
+    def crash(*args, **kwargs):
+        raise OSError("simulated interrupted rename")
+
+    monkeypatch.setattr(Path, "rename", crash)
+    with pytest.raises(OSError):
+        publish_checkpoint(partial, tmp_path / "step-1", world_size=4, identity=IDENTITY)
+    assert (partial / "COMPLETE.json").exists()
+    assert newest_complete_checkpoint(tmp_path, IDENTITY)["checkpoint"] is None
+
+
+def test_published_directory_cannot_be_overwritten(tmp_path):
+    partial = staged(tmp_path)
+    destination = tmp_path / "step-1"
+    destination.mkdir()
+    with pytest.raises(ValueError, match="new sibling"):
+        publish_checkpoint(partial, destination, world_size=4, identity=IDENTITY)
+
+
+def test_disk_budget_counts_last_next_exports_and_records_failure(tmp_path, monkeypatch):
+    from collections import namedtuple
+
+    import qwen3_next_checkpoint as checkpoint
+
+    usage = namedtuple("Usage", "total used free")
+    monkeypatch.setattr(checkpoint.shutil, "disk_usage", lambda path: usage(1000, 750, 250))
+    with pytest.raises(RuntimeError, match="insufficient_disk"):
+        checkpoint.require_checkpoint_disk_space(
+            tmp_path, checkpoint_bytes=100, export_bytes=40, headroom_bytes=20
+        )
+    record = json.loads((tmp_path / "disk-budget.json").read_text())
+    assert record["required_free_bytes"] == 260 and record["status"] == "insufficient_disk"
+    record = checkpoint.require_checkpoint_disk_space(
+        tmp_path,
+        checkpoint_bytes=100,
+        export_bytes=40,
+        retained_complete_bytes=100,
+        headroom_bytes=20,
+    )
+    assert record["required_free_bytes"] == 160 and record["status"] == "passed"
+
+
+def test_discovery_without_hashing_checks_sizes_and_defers_content_to_the_loader(tmp_path):
+    partial = staged(tmp_path)
+    publish_checkpoint(partial, tmp_path / "step-1", world_size=4, identity=IDENTITY)
+    target = tmp_path / "step-1/rank-0/rng.json"
+    original = target.read_text()
+    target.write_text("X" * len(original))  # same size, different bytes
+    fast = newest_complete_checkpoint(tmp_path, IDENTITY, verify_files=False)
+    assert fast["checkpoint"] == str(tmp_path / "step-1") and fast["rejected"] == []
+    with pytest.raises(ValueError, match="checksum mismatch"):
+        newest_complete_checkpoint(tmp_path, IDENTITY)  # hashed discovery still rejects it
+    target.write_text(original + "!")  # size change is still caught without hashing
+    with pytest.raises(ValueError, match="size mismatch"):
+        newest_complete_checkpoint(tmp_path, IDENTITY, verify_files=False)
+
+
+def test_interrupted_but_complete_partial_is_published_on_resume(tmp_path):
+    partial = staged(tmp_path, 1)  # all four ranks and manifests written, never published
+    (partial / "STAGING.json").write_text(json.dumps({"identity": IDENTITY, "status": "staging"}))
+    records = resume_interrupted_publications(tmp_path, IDENTITY)
+    assert records == [
+        {"reason": "interrupted_publication_resumed", "checkpoint": "step-1", "step": 1}
+    ]
+    assert not partial.exists()
+    assert newest_complete_checkpoint(tmp_path, IDENTITY)["checkpoint"] == str(tmp_path / "step-1")
+
+
+def test_incomplete_or_corrupt_partial_is_not_resumed(tmp_path):
+    missing = staged(tmp_path, 1)
+    (missing / "rank-3.manifest.json").unlink()
+    corrupt = staged(tmp_path, 2)
+    (corrupt / "rank-0/rng.json").write_text("corrupt")
+    for partial in (missing, corrupt):
+        (partial / "STAGING.json").write_text(
+            json.dumps({"identity": IDENTITY, "status": "staging"})
+        )
+    records = resume_interrupted_publications(tmp_path, IDENTITY)
+    assert [r["reason"] for r in records] == ["interrupted_publication_not_resumable"]
+    assert records[0]["staging"] == "step-2.partial"
+    assert missing.exists() and corrupt.exists()
+    assert not (corrupt / "COMPLETE.json").exists()
+    assert newest_complete_checkpoint(tmp_path, IDENTITY)["checkpoint"] is None
diff --git a/tests/test_qwen3_next_engine_weights.py b/tests/test_qwen3_next_engine_weights.py
new file mode 100644
index 000000000..862d8a15e
--- /dev/null
+++ b/tests/test_qwen3_next_engine_weights.py
@@ -0,0 +1,81 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Segmented convolution must survive the engines' actual rank-major TP layout."""
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_engine_weights import (
+    conv_hf_to_rank_major,
+    conv_rank_major_to_hf,
+    expert_hf_to_rank_major,
+    expert_rank_major_to_hf,
+    kv_hf_to_rank_major,
+    kv_rank_major_to_hf,
+    validate_training_topology,
+)
+from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal
+
+
+def test_conv_rank_major_shards_preserve_q_k_v_segments():
+    generator = torch.Generator().manual_seed(27)
+    weight = torch.randn((8192, 1, 4), generator=generator).bfloat16()
+    rank_major = conv_hf_to_rank_major(weight)
+    segments = weight.split((2048, 2048, 4096))
+    for rank, shard in enumerate(rank_major.chunk(4)):
+        assert_tensor_bitwise_equal(shard, torch.cat([part.chunk(4)[rank] for part in segments]))
+    assert_tensor_bitwise_equal(conv_rank_major_to_hf(rank_major), weight)
+
+
+@pytest.mark.parametrize(
+    "field",
+    [
+        "tensor_model_parallel_size",
+        "context_parallel_size",
+        "pipeline_model_parallel_size",
+        "expert_model_parallel_size",
+        "sequence_parallel",
+        "fp32_residual_connection",
+        "hidden_dropout",
+        "mtp_num_layers",
+    ],
+)
+def test_training_adapter_rejects_unsupported_topology(field):
+    values = dict(
+        tensor_model_parallel_size=4,
+        context_parallel_size=1,
+        pipeline_model_parallel_size=1,
+        expert_model_parallel_size=1,
+        sequence_parallel=False,
+        fp32_residual_connection=False,
+        hidden_dropout=0,
+        mtp_num_layers=0,
+    )
+    values[field] = 2 if field.endswith("size") else 1
+    with pytest.raises(ValueError):
+        validate_training_topology(
+            SimpleNamespace(**values), SimpleNamespace(sequence_parallel=False)
+        )
+
+
+def test_kv_export_rejects_diverged_replica():
+    weight = torch.randn(512, 2048).bfloat16()
+    packed = kv_hf_to_rank_major(weight)
+    assert_tensor_bitwise_equal(kv_rank_major_to_hf(packed), weight)
+    packed[256, 0] = packed[256, 0] + 1
+    with pytest.raises(AssertionError, match="KV replica pair 0"):
+        kv_rank_major_to_hf(packed)
+
+
+def test_expert_gather_restores_separate_hf_gate_and_up():
+    gate, up = (torch.randn(512, 2048).bfloat16() for _ in range(2))
+    packed = expert_hf_to_rank_major(gate, up)
+    for rank, shard in enumerate(packed.chunk(4)):
+        assert_tensor_bitwise_equal(shard[:128], gate.chunk(4)[rank])
+        assert_tensor_bitwise_equal(shard[128:], up.chunk(4)[rank])
+    restored = expert_rank_major_to_hf(packed)
+    assert_tensor_bitwise_equal(restored[0], gate)
+    assert_tensor_bitwise_equal(restored[1], up)
diff --git a/tests/test_qwen3_next_environment_identity.py b/tests/test_qwen3_next_environment_identity.py
new file mode 100644
index 000000000..35e4606ef
--- /dev/null
+++ b/tests/test_qwen3_next_environment_identity.py
@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+"""The environment identity must not depend on what the process has imported."""
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+
+from qwen3_next_runtime import current_identity, installation_roots  # noqa: E402
+
+
+def _fake_distribution(root: Path, name: str, version: str) -> None:
+    info = root / f"{name}-{version}.dist-info"
+    info.mkdir(parents=True)
+    (info / "METADATA").write_text(f"Metadata-Version: 2.1\nName: {name}\nVersion: {version}\n")
+
+
+def test_identity_ignores_sys_path_entries_added_after_startup(tmp_path, monkeypatch):
+    before = current_identity()
+    extra = tmp_path / "late-site-packages"
+    _fake_distribution(extra, "late_arrival", "1.0")
+    monkeypatch.syspath_prepend(str(extra))
+    after = current_identity()
+    assert after["sha256"] == before["sha256"]
+    assert all(item["name"] != "late_arrival" for item in after["identity"]["distributions"])
+
+
+def test_identity_survives_importing_setuptools():
+    before = current_identity()
+    import setuptools  # noqa: F401  # appends setuptools/_vendor to sys.path
+
+    after = current_identity()
+    assert after["sha256"] == before["sha256"]
+    assert all(item["name"] != "typeguard" for item in after["identity"]["distributions"])
+
+
+def test_installation_roots_are_site_directories_only():
+    roots = installation_roots()
+    assert roots
+    assert all(root.is_dir() for root in roots)
+    assert not any("_vendor" in str(root) for root in roots)
diff --git a/tests/test_qwen3_next_evidence_redaction.py b/tests/test_qwen3_next_evidence_redaction.py
new file mode 100644
index 000000000..5f045e8ab
--- /dev/null
+++ b/tests/test_qwen3_next_evidence_redaction.py
@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Published Qwen3-Next evidence carries no site paths, hosts, users or scheduler details."""
+
+import json
+import re
+import sys
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "scripts"))
+
+import qwen3_next_publish_evidence as publisher  # noqa: E402
+
+PATTERNS = (
+    r"/data/",
+    r"/home/",
+    r"/raid/",
+    r"/scratch",
+    r"\bdgx-",
+    r"SLURM_",
+    r"\bsbatch\b",
+    r"\bsrun\b",
+    r"\bwrd\b",
+    r"\boverflow\b",
+    r"\bjob_id\b",
+)
+
+
+def published_files():
+    roots = [
+        *ROOT.glob("docs/usage/evidence/qwen3-next-*"),
+        *ROOT.glob("docs/operators/qwen3-next-*.md"),
+        *ROOT.glob("docs/usage/qwen3-next-*.md"),
+    ]
+    for root in roots:
+        paths = [root] if root.is_file() else root.rglob("*")
+        for path in paths:
+            if path.is_file() and path.suffix in (".md", ".json", ".txt", ".xml", ""):
+                yield path
+
+
+@pytest.mark.parametrize("path", sorted(published_files()), ids=lambda p: str(p.relative_to(ROOT)))
+def test_published_qwen3_next_evidence_is_redacted(path):
+    text = path.read_text(errors="replace")
+    assert not [pattern for pattern in PATTERNS if re.search(pattern, text)]
+
+
+def chain(tmp_path):
+    runs_root = tmp_path / "data" / "runs"
+    checkpoint = tmp_path / "data" / "ckpt" / "rev"
+    previous = None
+    for stage in ("preflight", "layers"):
+        run = runs_root / f"20261009T000000Z-{stage}"
+        (run / "attempts" / "0").mkdir(parents=True)
+        manifest = {
+            "stage": stage,
+            "source_commit": "c" * 40,
+            "checkpoint": str(checkpoint),
+            "cuda_home": "/usr/local/cuda-13.0",
+            "python": str(tmp_path / "data" / "env" / "bin" / "python"),
+            "previous": str(previous) if previous else None,
+        }
+        (run / "run.json").write_text(json.dumps(manifest))
+        evidence = run / "attempts" / "0" / "environment.json"
+        evidence.write_text(
+            json.dumps(
+                {
+                    "checkpoint": str(checkpoint / "config.json"),
+                    "executable": manifest["python"],
+                    "host": "dgx-1a-02-03",
+                    "site": "/usr/lib/python3/site-packages/vllm/__init__.py",
+                }
+            )
+        )
+        result = {
+            "status": "passed",
+            "evidence": {"environment": {"path": "attempts/0/environment.json"}},
+        }
+        (run / "result.json").write_text(json.dumps(result))
+        previous = run
+    return previous
+
+
+def test_publisher_replaces_every_recorded_root(tmp_path):
+    out = tmp_path / "published"
+    publisher.publish(publisher.chain_runs(chain(tmp_path)), None, out)
+    text = (out / "layers" / "environment.json").read_text()
+    assert "<CHECKPOINT>/config.json" in text and "<PYTHON>" in text
+    assert "<HOST>" in text and "<SITE>" in text and str(tmp_path) not in text
+    assert (out / "measured-commit.txt").read_text() == "c" * 40 + "\n"
+    sums = (out / "SHA256SUMS").read_text().splitlines()
+    assert any(line.endswith("preflight/run.json") for line in sums)
+
+
+def test_publisher_refuses_text_that_survives_redaction(tmp_path):
+    last = chain(tmp_path)
+    leak = last / "attempts" / "0" / "environment.json"
+    leak.write_text(json.dumps({"note": "copied from /scratch/elsewhere"}))
+    with pytest.raises(SystemExit, match="survived redaction"):
+        publisher.publish(publisher.chain_runs(last), None, tmp_path / "published")
+    assert not (tmp_path / "published").exists()
diff --git a/tests/test_qwen3_next_failure.py b/tests/test_qwen3_next_failure.py
new file mode 100644
index 000000000..235f68606
--- /dev/null
+++ b/tests/test_qwen3_next_failure.py
@@ -0,0 +1,136 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Failure classification must retain evidence and never guess from signals."""
+
+import errno
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+from qwen3_next_runtime import failure_kind, subprocess_failure  # noqa: E402
+
+
+@pytest.mark.parametrize(
+    "text,expected",
+    [
+        ("Killed", "unknown"),
+        ("exit 137", "unknown"),
+        ("MemoryError: allocation failed", "unknown"),
+        ("torch.OutOfMemoryError: allocation failed", "out_of_memory"),
+        ("RuntimeError: CUDA out of memory", "out_of_memory"),
+        ("OSError: [Errno 28] No space left on device", "insufficient_disk"),
+        ("RuntimeError: insufficient_disk: budget", "insufficient_disk"),
+        ("ModuleNotFoundError: No module named 'vime'", "environment_mismatch"),
+        ("RuntimeError: environment_mismatch: changed", "environment_mismatch"),
+        ("RuntimeError: source_mismatch: changed", "source_mismatch"),
+        ("RuntimeError: checkpoint_mismatch: changed", "checkpoint_mismatch"),
+        ("subprocess.TimeoutExpired: command exceeded timeout", "time_limit"),
+    ],
+)
+def test_tail_classification_is_explicit(tmp_path, text, expected):
+    log = tmp_path / "worker.log"
+    log.write_text(text)
+    failure = subprocess_failure(-9, log)
+    assert failure_kind(failure) == expected
+    assert text in str(failure)
+    assert str(log) in str(failure)
+
+
+def test_exception_classification_and_actual_sigkill(tmp_path):
+    from qwen3_next_l2 import run_process
+
+    assert failure_kind(OSError(errno.ENOSPC, "disk full")) == "insufficient_disk"
+    assert failure_kind(subprocess.TimeoutExpired("worker", 1)) == "time_limit"
+    with pytest.raises(RuntimeError) as caught:
+        run_process(
+            [sys.executable, "-c", "import os, signal; os.kill(os.getpid(), signal.SIGKILL)"],
+            log_path=tmp_path / "killed.log",
+        )
+    assert failure_kind(caught.value) == "unknown"
+    with pytest.raises(subprocess.TimeoutExpired) as caught:
+        run_process(
+            [sys.executable, "-c", "import time; time.sleep(30)"],
+            log_path=tmp_path / "timeout.log",
+            timeout=0.02,
+            cleanup_grace_seconds=0.05,
+        )
+    assert failure_kind(caught.value) == "time_limit"
+
+
+def test_timeout_kills_ignoring_grandchild_even_after_leader_exits(tmp_path):
+    import os
+    import signal
+    import time
+
+    from qwen3_next_l2 import run_process
+
+    pid_file = tmp_path / "grandchild.pid"
+    grandchild = (
+        "import os,pathlib,signal,time; "
+        "signal.signal(signal.SIGTERM, signal.SIG_IGN); "
+        f"pathlib.Path({str(pid_file)!r}).write_text(str(os.getpid())); time.sleep(60)"
+    )
+    leader = (
+        "import subprocess,sys,time; "
+        f"subprocess.Popen([sys.executable, '-c', {grandchild!r}]); time.sleep(60)"
+    )
+    pid = None
+    try:
+        with pytest.raises(subprocess.TimeoutExpired) as caught:
+            run_process(
+                [sys.executable, "-c", leader],
+                log_path=tmp_path / "family.log",
+                timeout=0.5,
+                cleanup_grace_seconds=0.15,
+            )
+        assert failure_kind(caught.value) == "time_limit"
+        pid = int(pid_file.read_text())
+        stat = Path(f"/proc/{pid}/stat")
+        # An orphan may briefly remain as a zombie until init reaps it, but must
+        # exit once the kernel schedules its pending SIGKILL. We cannot waitpid
+        # an orphan that has already been adopted by init.
+        deadline = time.monotonic() + 2
+        while stat.exists() and stat.read_text().split(") ", 1)[1].split()[0] != "Z":
+            if time.monotonic() >= deadline:
+                break
+            time.sleep(0.01)
+        assert not stat.exists() or stat.read_text().split(") ", 1)[1].split()[0] == "Z"
+    finally:
+        if pid is None and pid_file.exists():
+            pid = int(pid_file.read_text())
+        if pid is not None:
+            try:
+                os.kill(pid, signal.SIGKILL)
+            except ProcessLookupError:
+                pass
+
+
+def test_training_failure_binds_run_attempt_and_error(tmp_path, monkeypatch):
+    import qwen3_next_training as training
+
+    manifest = tmp_path / "run.json"
+    manifest.write_text(json.dumps({"run_id": "controlled-run"}))
+    output = tmp_path / "attempts/2"
+    monkeypatch.setenv("QWEN3_NEXT_ATTEMPT", "2")
+    monkeypatch.setattr(
+        sys, "argv", ["training", "--manifest", str(manifest), "--output", str(output)]
+    )
+
+    def fail(*args):
+        raise OSError(errno.ENOSPC, "controlled full disk")
+
+    monkeypatch.setattr(training, "execute", fail)
+    with pytest.raises(OSError):
+        training.main()
+    result = json.loads((output / "result.json").read_text())
+    assert (result["run_id"], result["attempt"], result["failure_kind"]) == (
+        "controlled-run",
+        "2",
+        "insufficient_disk",
+    )
+    assert "controlled full disk" in result["error"]
diff --git a/tests/test_qwen3_next_forward_contract.py b/tests/test_qwen3_next_forward_contract.py
index 56d681b21..62ea492f9 100644
--- a/tests/test_qwen3_next_forward_contract.py
+++ b/tests/test_qwen3_next_forward_contract.py
@@ -16,6 +16,9 @@
         lambda: provider.stable_top10_routes(torch.zeros(1, 512)),
         lambda: provider.combine_routes(torch.zeros(1, 10, 8), torch.zeros(1, 10)),
         lambda: provider.shared_attention(*(torch.zeros(1, 4, 8, 256) for _ in range(3))),
+        lambda: provider.shared_logprob(
+            torch.zeros(1, 151936), torch.zeros(1, dtype=torch.long), contract=None
+        ),
     ],
 )
 def test_required_cuda_primitives_reject_cpu(call):
diff --git a/tests/test_qwen3_next_imported_binary.py b/tests/test_qwen3_next_imported_binary.py
new file mode 100644
index 000000000..9ac370fc2
--- /dev/null
+++ b/tests/test_qwen3_next_imported_binary.py
@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""CPU integrity checks; stub loader metadata does not claim CUDA execution."""
+
+import hashlib
+import importlib.machinery
+from types import SimpleNamespace as Namespace
+
+import pytest
+
+from rl_engine.integrations import qwen3_next_binary as binary
+
+
+def extension_metadata(tmp_path, monkeypatch):
+    path = tmp_path / ("_C" + importlib.machinery.EXTENSION_SUFFIXES[0])
+    path.write_bytes(b"controlled-native-loader-metadata")
+    loader = importlib.machinery.ExtensionFileLoader("rl_engine._C", str(path))
+    module = Namespace(
+        __name__="rl_engine._C",
+        __file__=str(path),
+        __spec__=Namespace(loader=loader, origin=str(path)),
+    )
+    imports = []
+
+    def import_module(name):
+        imports.append(name)
+        return module
+
+    monkeypatch.setattr(binary.importlib, "import_module", import_module)
+    return path, module, imports
+
+
+def test_import_is_required_and_content_is_rehashed_after_same_size_replacement(
+    tmp_path, monkeypatch
+):
+    path, _module, imports = extension_metadata(tmp_path, monkeypatch)
+    first = binary.imported_provider_identity()
+    assert first == {
+        "module": "rl_engine._C",
+        "path": str(path.resolve()),
+        "sha256": hashlib.sha256(path.read_bytes()).hexdigest(),
+    }
+    path.write_bytes(b"X" * path.stat().st_size)
+    second = binary.imported_provider_identity()
+    assert first["sha256"] != second["sha256"]
+    assert imports == ["rl_engine._C", "rl_engine._C"]
+
+
+def test_extension_import_failure_does_not_use_python_fallback(monkeypatch):
+    def unavailable(name):
+        raise ImportError("controlled missing extension")
+
+    monkeypatch.setattr(binary.importlib, "import_module", unavailable)
+    with pytest.raises(RuntimeError, match="cannot be imported"):
+        binary.imported_provider_identity()
+
+
+@pytest.mark.parametrize("drift", ["name", "loader", "suffix", "origin"])
+def test_native_identity_rejects_fallback_or_disagreeing_import_metadata(
+    tmp_path, monkeypatch, drift
+):
+    path, module, _imports = extension_metadata(tmp_path, monkeypatch)
+    if drift == "name":
+        module.__name__ = "fallback"
+    elif drift == "loader":
+        module.__spec__.loader = importlib.machinery.SourceFileLoader("rl_engine._C", str(path))
+    elif drift == "suffix":
+        module.__file__ = str(tmp_path / "fallback.py")
+    else:
+        other = tmp_path / "other.so"
+        other.write_bytes(path.read_bytes())
+        module.__spec__.origin = str(other)
+    with pytest.raises(RuntimeError, match="native extension|import origin"):
+        binary.imported_provider_identity()
+
+
+def test_file_write_during_hash_is_rejected(tmp_path, monkeypatch):
+    path, _module, _imports = extension_metadata(tmp_path, monkeypatch)
+    original = binary.os.fstat
+    calls = 0
+
+    def modified_fstat(fd):
+        nonlocal calls
+        calls += 1
+        if calls == 2:
+            path.write_bytes(b"changed-during-read")
+        return original(fd)
+
+    monkeypatch.setattr(binary.os, "fstat", modified_fstat)
+    with pytest.raises(RuntimeError, match="changed while"):
+        binary.imported_provider_identity()
+
+
+@pytest.mark.parametrize(
+    "value",
+    [
+        None,
+        {},
+        {"module": "fallback", "path": "/a.so", "sha256": "a" * 64},
+        {"module": "rl_engine._C", "path": "relative.so", "sha256": "a" * 64},
+        {"module": "rl_engine._C", "path": "/a.so", "sha256": "unknown"},
+    ],
+)
+def test_serialized_binary_identity_is_mandatory_and_complete(value):
+    with pytest.raises(ValueError, match="binary evidence"):
+        binary.validate_provider_identity(value)
diff --git a/tests/test_qwen3_next_installed_provider_identity.py b/tests/test_qwen3_next_installed_provider_identity.py
new file mode 100644
index 000000000..5b37860ce
--- /dev/null
+++ b/tests/test_qwen3_next_installed_provider_identity.py
@@ -0,0 +1,45 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Wheel metadata alone cannot detect a locally patched numerical provider."""
+
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+from qwen3_next_runtime import installed_provider_sources  # noqa: E402
+
+
+def test_same_version_provider_patch_changes_content_identity(tmp_path):
+    name = "vllm/model_executor/determinism/batch_invariant.py"
+    path = tmp_path / name
+    path.parent.mkdir(parents=True)
+    path.write_text("KERNEL = 'original'\n")
+    dist = SimpleNamespace(
+        metadata={"Name": "vllm"}, files=[name], locate_file=lambda name: tmp_path / name
+    )
+    first = installed_provider_sources([dist])
+    path.write_text("KERNEL = 'patched'\n")
+    second = installed_provider_sources([dist])
+    assert first[name]["sha256"] != second[name]["sha256"]
+    assert first[name]["path"] == second[name]["path"]
+
+
+@pytest.mark.parametrize("files", [None, [], ["vllm/unrelated.py"]])
+def test_missing_provider_inventory_is_rejected(files):
+    dist = SimpleNamespace(metadata={"Name": "vllm"}, files=files)
+    with pytest.raises(ValueError):
+        installed_provider_sources([dist])
+
+
+def test_provider_file_deleted_after_install_is_rejected(tmp_path):
+    dist = SimpleNamespace(
+        metadata={"Name": "vllm"},
+        files=["vllm/model_executor/determinism/batch_invariant.py"],
+        locate_file=lambda name: tmp_path / name,
+    )
+    with pytest.raises(FileNotFoundError):
+        installed_provider_sources([dist])
diff --git a/tests/test_qwen3_next_l2.py b/tests/test_qwen3_next_l2.py
new file mode 100644
index 000000000..de4b29119
--- /dev/null
+++ b/tests/test_qwen3_next_l2.py
@@ -0,0 +1,719 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""CPU artifact and matrix contracts, never real-model acceptance evidence."""
+
+import copy
+import sys
+from pathlib import Path
+
+import pytest
+import torch
+
+from rl_engine.testing.qwen3_next_l2 import (
+    PROMPT_LENGTHS,
+    RESPONSE_LENGTH,
+    VOCAB_SIZE,
+    L2Case,
+    case_matrix,
+    compare_artifacts,
+    compare_batch_prefill,
+    embedding_digest,
+    first_divergent_layer,
+    freeze_responses,
+    layer_digest,
+    layer_state_names,
+    load_tensor_artifact,
+    prompt_fixture,
+    save_tensor_artifact,
+    validate_fixture,
+    validate_model_artifact,
+    worker_request,
+)
+
+
+class UnitTokenizer:
+    def encode(self, text, *, add_special_tokens):
+        assert not add_special_tokens
+        return [ord(char) for char in text]
+
+
+@pytest.fixture(scope="module")
+def frozen():
+    prompts = prompt_fixture(UnitTokenizer(), {"tokenizer.json": "test-tokenizer-hash"})
+    baseline = {
+        str(length): {
+            "response_tokens": torch.arange(64, dtype=torch.int64).view(4, 16),
+            "logprobs": torch.full((4, 16), -1.0),
+        }
+        for length in PROMPT_LENGTHS
+    }
+    return freeze_responses(
+        prompts,
+        baseline,
+        baseline_identity="unit-test-only",
+        baseline_records={
+            str(length): {"path": f"/unit-test-only/{length}.pt", "sha256": "1" * 64}
+            for length in PROMPT_LENGTHS
+        },
+    )
+
+
+@pytest.fixture(scope="module")
+def small_artifact(frozen):
+    request = worker_request(
+        L2Case(8, 1, "full"), frozen, role="rollout", identity={"test_only": True}
+    )
+    return request, make_artifact(request)
+
+
+def subnode_values(batch):
+    from rl_engine.integrations.qwen3_next_subnode import SUBNODES, subnode_layout
+
+    return {
+        name: torch.zeros((batch, 16, subnode_layout(name)[0]), dtype=subnode_layout(name)[1])
+        for name in SUBNODES
+    }
+
+
+def make_artifact(request):
+    batch = len(request["prompt_tokens"])
+    output = torch.zeros((batch, 16, 2048), dtype=torch.bfloat16)
+
+    def states(layer):
+        return {
+            name: torch.ones(
+                (batch, 1, 2, 2),
+                dtype=torch.float32 if "recurrent_state" in name else torch.bfloat16,
+            )
+            for name in layer_state_names(layer)
+        }
+
+    return {
+        "metadata": {
+            key: request[key]
+            for key in ("schema_version", "case_id", "role", "identity", "request_sha256")
+        },
+        "execution": request["execution"],
+        "prompt_tokens": torch.tensor(request["prompt_tokens"], dtype=torch.int64),
+        "response_tokens": torch.tensor(request["response_tokens"], dtype=torch.int64),
+        "prediction_positions": torch.tensor(request["prediction_positions"], dtype=torch.int64),
+        "logits": torch.zeros((batch, RESPONSE_LENGTH, VOCAB_SIZE), dtype=torch.bfloat16),
+        "logprobs": torch.full((batch, RESPONSE_LENGTH), -1.0, dtype=torch.float32),
+        "layer_digests": [
+            layer_digest(layer, output, states(layer), subnodes=subnode_values(batch))
+            for layer in range(48)
+        ],
+        "embedding_digest": embedding_digest(torch.zeros((batch, 16, 2048), dtype=torch.bfloat16)),
+    }
+
+
+def test_exact_sixteen_case_matrix():
+    cases = case_matrix()
+    assert len(cases) == 16 and len({case.case_id for case in cases}) == 16
+    assert {case.prompt_length for case in cases} == {8, 64, 256, 1024}
+    assert {case.batch_size for case in cases} == {1, 4}
+    assert {case.prefill for case in cases} == {"full", "chunked"}
+    assert all(
+        sum(case.chunks) == case.prompt_length and all(size > 0 for size in case.chunks)
+        for case in cases
+    )
+    assert L2Case(1024, 4, "chunked").chunks[:2] == [1, 63]
+
+
+def test_offsets_and_batch_one_reuse_frozen_first_sample(frozen):
+    validate_fixture(frozen)
+    one = worker_request(L2Case(64, 1, "chunked"), frozen, role="recompute", identity={})
+    four = worker_request(L2Case(64, 4, "full"), frozen, role="rollout", identity={})
+    assert one["prompt_tokens"][0] == four["prompt_tokens"][0]
+    assert one["response_tokens"][0] == four["response_tokens"][0]
+    assert one["prediction_positions"] == list(range(63, 79))
+    assert one["consumed_response_tokens"] == 15
+    assert len(one["consumed_input_tokens"][0]) == 79
+    assert one["state_position"] == 78
+    assert one["execution"] == {"recompute_mode": "full_from_tokens", "input_sequence_length": 79}
+    assert one["prefill_chunks"] is None
+    assert one["rollout_prefill_chunks"] == [1, 63]
+    assert not any(key in one for key in ("hidden_states", "recurrent_state", "rollout_state"))
+
+
+def test_frozen_tokens_and_old_logprobs_cannot_change(frozen):
+    for field in ("prompts", "responses", "old_logprobs"):
+        changed = copy.deepcopy(frozen)
+        changed[field]["64"][0, 0] += 1
+        with pytest.raises(ValueError):
+            validate_fixture(changed)
+
+
+def test_weights_only_tensor_serialization_roundtrip(tmp_path, frozen):
+    path = tmp_path / "input.pt"
+    save_tensor_artifact(path, frozen)
+    restored = load_tensor_artifact(path)
+    validate_fixture(restored)
+    assert restored["content_sha256"] == frozen["content_sha256"]
+    assert not path.with_suffix(".pt.partial").exists()
+
+
+def test_valid_original_bf16_artifact(small_artifact):
+    request, artifact = small_artifact
+    validate_model_artifact(artifact, request)
+    report = compare_artifacts(artifact, artifact)
+    assert report["passed"] and report["first_divergent_layer"] is None
+
+
+@pytest.mark.parametrize("field", ["logits", "logprobs"])
+def test_signed_zero_is_a_real_full_model_difference(small_artifact, field):
+    _, artifact = small_artifact
+    candidate = {**artifact, field: artifact[field].clone()}
+    reference = {**artifact, field: artifact[field].clone()}
+    reference[field].reshape(-1)[0] = 0.0
+    candidate[field].reshape(-1)[0] = -0.0
+    report = compare_artifacts(reference, candidate)
+    assert not report["passed"]
+    assert report["first_difference"]["tensor"] == field
+    assert report["first_difference"]["flat_element"] == 0
+
+
+@pytest.mark.parametrize("value", [float("nan"), float("inf"), float("-inf")])
+def test_nonfinite_logits_never_pass(small_artifact, value):
+    request, artifact = small_artifact
+    changed = {**artifact, "logits": artifact["logits"].clone()}
+    changed["logits"][0, 0, 0] = value
+    with pytest.raises(ValueError, match="nonfinite"):
+        validate_model_artifact(changed, request)
+
+
+def test_widening_logits_or_truncating_vocab_is_rejected(small_artifact):
+    request, artifact = small_artifact
+    for logits in (artifact["logits"].float(), artifact["logits"][..., :1024]):
+        with pytest.raises(ValueError, match="original storage"):
+            validate_model_artifact({**artifact, "logits": logits}, request)
+
+
+def test_wrong_response_offset_and_changed_tokens_are_rejected(small_artifact):
+    request, artifact = small_artifact
+    with pytest.raises(ValueError, match="offsets"):
+        validate_model_artifact(
+            {**artifact, "prediction_positions": artifact["prediction_positions"] + 1}, request
+        )
+    with pytest.raises(ValueError, match="frozen response"):
+        validate_model_artifact(
+            {**artifact, "response_tokens": artifact["response_tokens"] + 1}, request
+        )
+
+
+def test_missing_layers_and_placeholder_hashes_are_rejected(small_artifact):
+    request, artifact = small_artifact
+    with pytest.raises(ValueError, match="48-layer"):
+        validate_model_artifact(
+            {**artifact, "layer_digests": artifact["layer_digests"][:-1]}, request
+        )
+    changed = copy.deepcopy(artifact["layer_digests"])
+    changed[0]["sample_output_sha256"] = []
+    with pytest.raises(ValueError, match="Every layer"):
+        validate_model_artifact({**artifact, "layer_digests": changed}, request)
+    changed = copy.deepcopy(artifact["layer_digests"])
+    changed[0]["output_sha256"] = "0" * 64
+    with pytest.raises(ValueError, match="bound"):
+        validate_model_artifact({**artifact, "layer_digests": changed}, request)
+
+
+def test_first_divergence_tracks_state_even_when_final_logits_match(small_artifact):
+    _, artifact = small_artifact
+    changed = copy.deepcopy(artifact["layer_digests"])
+    changed[11]["sample_state_sha256"] = ["e" * 64]
+    candidate = {**artifact, "layer_digests": changed}
+    result = compare_artifacts(artifact, candidate)
+    assert not result["passed"] and result["first_divergent_layer"] == 11
+
+
+@pytest.mark.parametrize(
+    "node", ["input_layernorm", "mixer", "post_attention_layernorm", "router", "mlp"]
+)
+def test_first_divergent_node_names_the_sub_node(small_artifact, node):
+    from rl_engine.testing.qwen3_next_l2 import first_divergent_node
+
+    _, artifact = small_artifact
+    changed = copy.deepcopy(artifact["layer_digests"])
+    changed[23]["sample_subnode_sha256"][node] = ["e" * 64]
+    changed[30]["sample_output_sha256"] = ["e" * 64]
+    candidate = {**artifact, "layer_digests": changed}
+    assert first_divergent_node(artifact, candidate) == {"layer": 23, "node": node}
+    assert compare_artifacts(artifact, candidate)["first_divergent_node"]["node"] == node
+
+
+def test_first_divergent_node_starts_at_the_embedding(small_artifact):
+    from rl_engine.testing.qwen3_next_l2 import first_divergent_node
+
+    _, artifact = small_artifact
+    changed = copy.deepcopy(artifact["layer_digests"])
+    changed[0]["sample_subnode_sha256"]["input_layernorm"] = ["e" * 64]
+    candidate = {
+        **artifact,
+        "layer_digests": changed,
+        "embedding_digest": {"sample_sha256": ["e" * 64], "sha256": "x"},
+    }
+    assert first_divergent_node(artifact, candidate) == {"layer": 0, "node": "embedding"}
+
+
+def test_first_divergent_node_falls_back_to_layer_output_and_state(small_artifact):
+    from rl_engine.testing.qwen3_next_l2 import first_divergent_node
+
+    _, artifact = small_artifact
+    assert first_divergent_node(artifact, artifact) is None
+    changed = copy.deepcopy(artifact["layer_digests"])
+    changed[5]["sample_state_sha256"] = ["e" * 64]
+    assert first_divergent_node(artifact, {**artifact, "layer_digests": changed}) == {
+        "layer": 5,
+        "node": "state",
+    }
+
+
+def test_layer_digest_requires_every_sub_node_in_order(small_artifact):
+    request, artifact = small_artifact
+    output = torch.zeros((1, 16, 2048), dtype=torch.bfloat16)
+    states = {
+        name: torch.ones(
+            (1, 1, 2, 2), dtype=torch.float32 if "recurrent_state" in name else torch.bfloat16
+        )
+        for name in layer_state_names(0)
+    }
+    nodes = subnode_values(1)
+    with pytest.raises(ValueError, match="sub-nodes"):
+        layer_digest(0, output, states, subnodes=dict(reversed(list(nodes.items()))))
+    nodes["router"] = nodes["router"].int()
+    with pytest.raises(ValueError, match="router"):
+        layer_digest(0, output, states, subnodes=nodes)
+
+
+def test_batch_invariance_selects_first_samples(frozen, small_artifact):
+    _, candidate = small_artifact
+    request = worker_request(
+        L2Case(8, 4, "full"), frozen, role="rollout", identity={"test_only": True}
+    )
+    reference = make_artifact(request)
+    assert all(compare_batch_prefill(reference, candidate).values())
+    reference["logits"][3, 0, 0] = 3.0
+    assert all(compare_batch_prefill(reference, candidate).values())
+    reference["logits"][0, 0, 0] = 3.0
+    assert not compare_batch_prefill(reference, candidate)["logits"]
+    reference["layer_digests"][7]["sample_state_sha256"][0] = "f" * 64
+    assert first_divergent_layer(reference, candidate) == 7
+
+
+def test_only_initial_rollout_can_generate_fixture(frozen):
+    for role, case in (
+        ("recompute", L2Case(64, 4, "full")),
+        ("rollout", L2Case(64, 1, "full")),
+        ("rollout", L2Case(64, 4, "chunked")),
+    ):
+        with pytest.raises(ValueError, match="Only initial"):
+            worker_request(case, frozen, role=role, identity={}, generate=True)
+
+
+def test_native_worker_absence_fails_readiness(monkeypatch):
+    sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+    import qwen3_next_l2 as runner
+
+    def absent(name):
+        raise ModuleNotFoundError(name)
+
+    monkeypatch.setattr(runner.importlib, "import_module", absent)
+    with pytest.raises(RuntimeError, match="native_workers_not_ready"):
+        runner.check_ready()
+
+
+def test_gpu_release_never_cancels_an_unrelated_process(monkeypatch):
+    sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+    import qwen3_next_l2 as runner
+
+    commands = []
+
+    def output(command, **kwargs):
+        commands.append(command)
+        return "99, GPU-owned, 10\\n123, GPU-other, 20\\n".replace("\\n", "\n")
+
+    monkeypatch.setattr(runner.subprocess, "check_output", output)
+    with pytest.raises(RuntimeError, match="gpu_memory_not_released"):
+        runner.wait_gpu_release(["GPU-owned"], timeout=0)
+    runner.wait_gpu_release(["GPU-free"], timeout=0)
+    assert all(command[0] == "nvidia-smi" for command in commands)
+
+
+def test_batch_callback_loads_once_and_freezes_before_recompute_cases(tmp_path, monkeypatch):
+    import json
+    from types import SimpleNamespace
+
+    sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+    import qwen3_next_l2 as runner
+
+    calls = []
+
+    def native_batch(cases):
+        calls.append([])
+        for request, path in cases:
+            calls[-1].append(request)
+            responses = request["response_tokens"]
+            batch = len(request["prompt_tokens"])
+            if responses is None:
+                responses = torch.arange(batch * 16).view(batch, 16).tolist()
+            save_tensor_artifact(
+                path,
+                {
+                    "response_tokens": torch.tensor(responses),
+                    "logprobs": torch.full((batch, 16), -1.0),
+                },
+            )
+
+    def eager_recompute(cases):
+        # The actual recompute worker must collect requests before spawning its
+        # single actor lifetime; none of these outputs exist during collection.
+        requests = list(cases)
+        assert len(requests) == 16
+        assert all(not path.exists() for _, path in requests)
+        native_batch(requests)
+
+    module = SimpleNamespace(run_rollout_cases=native_batch, run_recompute_cases=eager_recompute)
+    monkeypatch.setattr(runner, "check_ready", lambda: (module, {"batched_cases": True}))
+    monkeypatch.setattr(runner, "verify_environment", lambda *a, **k: None)
+    monkeypatch.setattr(runner, "verify_before_model_load", lambda *a, **k: None)
+    monkeypatch.setattr(runner, "validate_model_artifact", lambda *a: None)
+    monkeypatch.setitem(
+        sys.modules,
+        "transformers",
+        SimpleNamespace(
+            AutoTokenizer=SimpleNamespace(from_pretrained=lambda *a, **k: UnitTokenizer())
+        ),
+    )
+    checkpoint = {"metadata": {"tokenizer": "pinned"}}
+    manifest = {
+        "source_commit": "unit",
+        "source_tree": "unit",
+        "environment_sha256": "unit",
+        "checkpoint": "/unit",
+        "checkpoint_identity": checkpoint,
+        "provider_config": {},
+        "topology": {"tp": 4, "pp": 1, "cp": 1, "ep": 1},
+        "environment": {},
+    }
+    monkeypatch.setattr(runner, "checkpoint_identity", lambda path: checkpoint)
+    fixture = tmp_path / "inputs/frozen.pt"
+    runner.worker_batch(manifest, tmp_path / "rollout", "rollout", fixture)
+    assert len(calls) == 1 and len(calls[0]) == 20
+    assert all(row["response_tokens"] is None for row in calls[0][:4])
+    assert all(row["response_tokens"] is not None for row in calls[0][4:])
+    frozen_inputs = load_tensor_artifact(fixture)
+    validate_fixture(frozen_inputs)
+    runner.worker_batch(manifest, tmp_path / "recompute", "recompute", fixture)
+    assert len(calls) == 2 and len(calls[1]) == 16
+    assert all(row["response_tokens"] is not None for row in calls[1])
+    result = json.loads((tmp_path / "recompute/worker-result.json").read_text())
+    assert len(result["cases"]) == 16
+    updated = {
+        **manifest,
+        "weight_version": "update-1",
+        "base_checkpoint": "/unit",
+        "base_checkpoint_identity": checkpoint,
+        "checkpoint": "/updated-unit",
+        "checkpoint_identity": {"metadata": {"tokenizer": "pinned", "index": "new"}},
+    }
+    monkeypatch.setattr(runner, "verify_updated_weights", lambda manifest: {})
+    runner.worker_batch(updated, tmp_path / "updated", "rollout", fixture)
+    assert len(calls) == 3 and len(calls[2]) == 16
+    assert all(row["response_tokens"] is not None for row in calls[2])
+
+
+def test_passed_flag_cannot_replace_full_matrix():
+    from rl_engine.testing.qwen3_next_l2 import validate_matrix_report
+
+    with pytest.raises(ValueError, match="sixteen-case"):
+        validate_matrix_report({"status": "passed", "raw_bit_comparison": True})
+
+
+def test_updated_weights_require_existing_baseline_fixture_before_worker(tmp_path, monkeypatch):
+    import qwen3_next_l2 as runner
+
+    def unexpected():
+        raise AssertionError("A missing frozen fixture must fail before loading native workers")
+
+    monkeypatch.setattr(runner, "check_ready", unexpected)
+    with pytest.raises(RuntimeError, match="already frozen baseline"):
+        runner.worker_batch(
+            {"weight_version": "update-1"}, tmp_path / "out", "rollout", tmp_path / "missing.pt"
+        )
+
+
+def test_updated_preload_binds_actual_export_to_baseline_source_and_version(tmp_path, monkeypatch):
+    import qwen3_next_l2 as runner
+    import qwen3_next_training_export as export
+
+    identity = {
+        "source_commit": "source",
+        "source_tree": "tree",
+        "environment_sha256": "env",
+        "provider_config": {"provider": "strict"},
+        "topology": {"tp": 4},
+    }
+    record = {
+        "weight_version": "update-1",
+        "base_checkpoint": "/official",
+        "base_checkpoint_identity": {"revision": "pinned"},
+        "training_identity": identity,
+    }
+    manifest = {
+        **identity,
+        **{
+            key: record[key]
+            for key in ("weight_version", "base_checkpoint", "base_checkpoint_identity")
+        },
+        "checkpoint": "/updated",
+        "checkpoint_identity": record,
+    }
+    calls = []
+
+    def deep_verify(path, expected):
+        calls.append((path, expected))
+        return record
+
+    monkeypatch.setattr(export, "validate_training_export", deep_verify)
+    assert runner.verify_before_model_load(manifest, tmp_path) == record
+    assert calls == [("/updated", record)]
+    manifest["source_commit"] = "different"
+    with pytest.raises(RuntimeError, match="changed source_commit"):
+        runner.verify_updated_weights(manifest)
+
+
+def test_debug_capture_must_contain_matching_layer_state(small_artifact):
+    from rl_engine.testing.qwen3_next_l2 import validate_debug_capture
+
+    request, artifact = small_artifact
+    request = {**request, "trace_layers": [2]}
+    with pytest.raises(ValueError, match="missing"):
+        validate_debug_capture(artifact, request)
+    capture = {
+        "layer.2.output": torch.zeros((1, 16, 2048), dtype=torch.bfloat16),
+        **{
+            f"layer.2.{name}": torch.ones(
+                (1, 1, 2, 2), dtype=torch.float32 if "recurrent_state" in name else torch.bfloat16
+            )
+            for name in layer_state_names(2)
+        },
+        **{f"layer.2.subnode.{name}": value for name, value in subnode_values(1).items()},
+    }
+    validate_debug_capture({**artifact, "debug_tensors": capture}, request)
+    capture["layer.2.tp0.recurrent_state"].zero_()
+    with pytest.raises(ValueError, match="differ"):
+        validate_debug_capture({**artifact, "debug_tensors": capture}, request)
+
+
+def test_model_artifact_hash_is_rechecked_before_deserialization(tmp_path):
+    from qwen3_next_runtime import file_digest
+
+    from rl_engine.testing.qwen3_next_l2 import checked_artifact_path
+
+    artifact = tmp_path / "output.pt"
+    save_tensor_artifact(artifact, {"logits": torch.zeros(2, dtype=torch.bfloat16)})
+    record = {"path": str(artifact), "sha256": file_digest(artifact)}
+    checked_artifact_path(record, artifact_root=tmp_path)
+    save_tensor_artifact(artifact, {"logits": torch.ones(2, dtype=torch.bfloat16)})
+    with pytest.raises(ValueError, match="digest changed"):
+        checked_artifact_path(record, artifact_root=tmp_path)
+
+
+def scheduler_request_and_audit(frozen):
+    identity = {
+        "weight_version": "baseline",
+        "provider_config": {
+            "profile": "qwen3_next_tp4_shared_eager_v1",
+            "native_rollout_kv_cache_memory_bytes": 4 << 30,
+        },
+    }
+    request = worker_request(L2Case(64, 4, "chunked"), frozen, role="rollout", identity=identity)
+    computed = 0
+    steps = []
+    for count in [*request["prefill_chunks"], *([1] * 15)]:
+        steps.append(
+            {
+                "samples": [3, 1, 0, 2],
+                "computed_before": [computed] * 4,
+                "token_counts": [count] * 4,
+            }
+        )
+        computed += count
+    return request, {
+        "provider_execution_mode": "qualification",
+        "native_runtime_profile": {
+            "kv_cache_memory_bytes_per_gpu": 4 << 30,
+            "max_model_len": 1040,
+            "max_num_seqs": 64,
+            "max_num_batched_tokens": 16384,
+            "scheduler": "rl_engine.integrations.qwen3_next_native_rollout.ExactPrefillScheduler",
+            "async_scheduling": False,
+            "prefix_caching": False,
+            "weights_dtype": "bfloat16",
+            "recurrent_state_dtype": "float32",
+            "model_runner": "v1",
+        },
+        "native_scheduler_steps": steps,
+    }
+
+
+def test_actual_scheduler_must_follow_requested_chunks_and_offsets(frozen):
+    from rl_engine.testing.qwen3_next_l2 import validate_native_scheduler_execution
+
+    request, artifact = scheduler_request_and_audit(frozen)
+    validate_native_scheduler_execution(artifact, request)
+    for key in ("token_counts", "computed_before", "samples"):
+        changed = copy.deepcopy(artifact)
+        changed["native_scheduler_steps"][0][key][0] += 1
+        with pytest.raises(ValueError, match="sample/chunk/offset"):
+            validate_native_scheduler_execution(changed, request)
+    changed = {**artifact, "native_scheduler_steps": artifact["native_scheduler_steps"][:-1]}
+    with pytest.raises(ValueError, match="boundary"):
+        validate_native_scheduler_execution(changed, request)
+
+
+def test_scheduler_cannot_shrink_cache_or_claim_certified_execution(frozen):
+    from rl_engine.testing.qwen3_next_l2 import validate_native_scheduler_execution
+
+    request, artifact = scheduler_request_and_audit(frozen)
+    artifact["native_runtime_profile"]["kv_cache_memory_bytes_per_gpu"] //= 2
+    with pytest.raises(ValueError, match="runtime"):
+        validate_native_scheduler_execution(artifact, request)
+    request, artifact = scheduler_request_and_audit(frozen)
+    artifact["provider_execution_mode"] = "certified"
+    with pytest.raises(ValueError, match="certificate identity"):
+        validate_native_scheduler_execution(artifact, request)
+
+
+def test_layer_hashes_cannot_replace_four_original_worker_traces(frozen):
+    from rl_engine.testing.qwen3_next_l2 import validate_native_worker_traces
+
+    request, artifact = scheduler_request_and_audit(frozen)
+    with pytest.raises(ValueError, match="each TP rank"):
+        validate_native_worker_traces(artifact, request)
+
+
+def test_matrix_recomputes_tensor_comparisons_even_after_reports_are_rehashed(tmp_path, frozen):
+    import json
+
+    from qwen3_next_runtime import file_digest
+
+    from rl_engine.testing.qwen3_next_binary import archive_provider_binary, validate_binary_record
+    from rl_engine.testing.qwen3_next_l2 import json_digest, validate_matrix_report
+
+    source = tmp_path / "source"
+    (source / "rl_engine").mkdir(parents=True)
+    (source / "rl_engine/_C.cpython-unit.so").write_bytes(b"unit native binary")
+    provider_binary = archive_provider_binary(source, tmp_path, source_tree="unit")
+    actual_binary = validate_binary_record(provider_binary)
+
+    identity = {
+        "architecture_layers": 48,
+        "weight_version": "baseline",
+        "topology": {"tp": 4, "pp": 1, "cp": 1, "ep": 1},
+        "test_only": True,
+        "source_tree": "unit",
+    }
+
+    def record(path):
+        return {"path": str(path), "sha256": file_digest(path)}
+
+    def compact_artifact(request):
+        result = make_artifact(request)
+        result["provider_binary"] = actual_binary
+        result["logits"] = torch.zeros((), dtype=torch.bfloat16).expand(result["logits"].shape)
+        return result
+
+    baselines, baseline_records = {}, {}
+    for length in PROMPT_LENGTHS:
+        request = worker_request(
+            L2Case(length, 4, "full"), frozen, role="rollout", identity=identity, generate=True
+        )
+        value = compact_artifact(
+            {**request, "response_tokens": frozen["responses"][str(length)].tolist()}
+        )
+        path = tmp_path / f"baseline-{length}.pt"
+        save_tensor_artifact(path, value)
+        baselines[str(length)] = value
+        baseline_records[str(length)] = record(path)
+    fixture = freeze_responses(
+        frozen,
+        baselines,
+        baseline_identity=json_digest(identity),
+        baseline_records=baseline_records,
+    )
+    fixture_path = tmp_path / "fixture.pt"
+    save_tensor_artifact(fixture_path, fixture)
+    checks = {name: True for name in ("response_tokens", "logits", "logprobs", "layer_states")}
+    processes = {
+        role: {"exit_code": 0, "pid": index + 1, "gpu_release_verified": True}
+        for index, role in enumerate(("rollout", "recompute"))
+    }
+    workers = {
+        role: {
+            "status": "passed",
+            "role": role,
+            "identity": identity,
+            "fixture_sha256": fixture["content_sha256"],
+            "pid": processes[role]["pid"],
+            "cases": {},
+            "provider_binary": actual_binary,
+        }
+        for role in processes
+    }
+    cases = {}
+    for case in case_matrix():
+        records = {}
+        for role in processes:
+            request = worker_request(case, fixture, role=role, identity=identity)
+            path = tmp_path / f"{role}-{case.case_id}.pt"
+            save_tensor_artifact(path, compact_artifact(request))
+            records[role] = record(path)
+            workers[role]["cases"][case.case_id] = {
+                **records[role],
+                "request_sha256": request["request_sha256"],
+            }
+        cases[case.case_id] = {
+            "passed": True,
+            "comparisons": checks,
+            "batch_prefill": {role: checks for role in processes},
+            "baseline_generation": {role: checks for role in processes},
+            "artifacts": records,
+        }
+    worker_records = {}
+    for role in processes:
+        path = tmp_path / f"{role}-worker.json"
+        path.write_text(json.dumps(workers[role]))
+        worker_records[role] = record(path)
+    report = {
+        "schema_version": 1,
+        "status": "passed",
+        "scope": "qwen3_next_full_model_l2",
+        "raw_bit_comparison": True,
+        "logits_dtype": "bfloat16",
+        "logprobs_dtype": "float32",
+        "response_length": 16,
+        "vocab_size": 151936,
+        "identity": identity,
+        "case_ids": [case.case_id for case in case_matrix()],
+        "cases": cases,
+        "processes": processes,
+        "worker_results": worker_records,
+        "fixture": {**record(fixture_path), "content_sha256": fixture["content_sha256"]},
+        "provider_binary": provider_binary,
+    }
+    validate_matrix_report(report, expected_identity=identity, artifact_root=tmp_path)
+    case_id = L2Case(8, 1, "full").case_id
+    path = Path(cases[case_id]["artifacts"]["recompute"]["path"])
+    changed = load_tensor_artifact(path)
+    changed["logits"] = changed["logits"].clone()
+    changed["logits"][0, 0, 0] = -0.0
+    save_tensor_artifact(path, changed)
+    cases[case_id]["artifacts"]["recompute"] = record(path)
+    workers["recompute"]["cases"][case_id].update(record(path))
+    worker_path = Path(worker_records["recompute"]["path"])
+    worker_path.write_text(json.dumps(workers["recompute"]))
+    report["worker_results"]["recompute"] = record(worker_path)
+    with pytest.raises(ValueError, match="tensor bytes contradict"):
+        validate_matrix_report(report, expected_identity=identity, artifact_root=tmp_path)
diff --git a/tests/test_qwen3_next_layers.py b/tests/test_qwen3_next_layers.py
new file mode 100644
index 000000000..7f9cec000
--- /dev/null
+++ b/tests/test_qwen3_next_layers.py
@@ -0,0 +1,135 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""CPU failure-injection tests; none constitutes native model evidence."""
+
+import copy
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "scripts"))
+
+from qwen3_next_layer_contract import (  # noqa: E402
+    checked_record,
+    layer_requirements,
+    validate_layer_report,
+    validate_rank_result,
+)
+from qwen3_next_runtime import STAGES  # noqa: E402
+from qwen3_next_runtime import identity_digest  # noqa: E402
+
+
+def rank_row(requirement, rank):
+    row = {
+        "status": "passed",
+        "rank": rank,
+        "scope": requirement["scope"],
+        "full_model_l2": "not_evaluated",
+        **requirement.get("fields", {}),
+    }
+    for path, cases in requirement["groups"].items():
+        target = row
+        parts = path.split(".")
+        for part in parts[:-1]:
+            target = target.setdefault(part, {})
+        target[parts[-1]] = list(cases)
+    return row
+
+
+def test_layers_are_required_between_preflight_and_l2():
+    assert STAGES["layers"][:3] == ("512G", "04:00:00", "preflight")
+    assert STAGES["l2"][2] == "layers"
+
+
+@pytest.mark.parametrize("gate", ["gdn", "attention", "moe", "adapters", "heads"])
+def test_rank_gate_requires_complete_unduplicated_inventory(gate):
+    requirement = layer_requirements(ROOT)["gates"][gate]
+    row = rank_row(requirement, 2)
+    validate_rank_result(row, requirement, 2)
+    for field in ("rank", "scope", "status", "full_model_l2"):
+        altered = {**row, field: "changed"}
+        with pytest.raises(ValueError, match="identity"):
+            validate_rank_result(altered, requirement, 2)
+    for skipped in (1, 3):
+        with pytest.raises(ValueError, match="skipped"):
+            validate_rank_result({**row, "skipped": skipped}, requirement, 2)
+    for path in requirement["groups"]:
+        for duplicate in (False, True):
+            changed = copy.deepcopy(row)
+            value = changed
+            for part in path.split("."):
+                value = value[part]
+            value.append(value[0]) if duplicate else value.pop()
+            with pytest.raises(ValueError, match="mandatory"):
+                validate_rank_result(changed, requirement, 2)
+
+
+def test_artifact_hash_and_attempt_escape_are_rejected(tmp_path):
+    from qwen3_next_runtime import file_digest
+
+    attempt = tmp_path / "attempt"
+    attempt.mkdir()
+    path = attempt / "rank.json"
+    path.write_text("{}")
+    record = {"path": str(path), "sha256": file_digest(path)}
+    assert checked_record(record, root=attempt) == path
+    path.write_text('{"changed": true}')
+    with pytest.raises(ValueError, match="digest"):
+        checked_record(record, root=attempt)
+    record["sha256"] = file_digest(path)
+    with pytest.raises(ValueError, match="escaped"):
+        checked_record(record, root=attempt / "different")
+
+
+def test_complete_component_flags_cannot_replace_native_scheduler(tmp_path):
+    from rl_engine.testing.qwen3_next_binary import archive_provider_binary, validate_binary_record
+
+    source = tmp_path / "source"
+    (source / "rl_engine").mkdir(parents=True)
+    (source / "rl_engine/_C.cpython-unit.so").write_bytes(b"unit native binary")
+    provider_binary = archive_provider_binary(source, tmp_path, source_tree="unit")
+    actual_binary = validate_binary_record(provider_binary)
+    identity = {"source_tree": "unit"}
+    requirement = layer_requirements(ROOT)
+    report = {
+        "schema_version": 1,
+        "status": "passed",
+        "phase": "complete",
+        "scope": "qwen3_next_layer_and_scheduler_gates",
+        "identity": identity,
+        "requirements_sha256": identity_digest(requirement),
+        "full_model_l2": "not_evaluated",
+        "gates": {},
+        "provider_binary": provider_binary,
+    }
+    from qwen3_next_runtime import file_digest
+
+    for name, expected in requirement["gates"].items():
+        records = {}
+        for rank in range(4):
+            path = tmp_path / f"{name}-{rank}.json"
+            path.write_text(
+                json.dumps({**rank_row(expected, rank), "provider_binary": actual_binary})
+            )
+            records[str(rank)] = {"path": str(path), "sha256": file_digest(path)}
+        report["gates"][name] = {"exit_code": 0, "gpu_release_verified": True, "ranks": records}
+    with pytest.raises(ValueError, match="artifact path"):
+        validate_layer_report(report, requirement, identity, root=tmp_path)
+    report["phase"] = "native_scheduler"
+    with pytest.raises(ValueError, match="completeness"):
+        validate_layer_report(report, requirement, identity, root=tmp_path)
+
+
+def test_missing_native_scheduler_is_explicitly_incomplete(monkeypatch):
+    import qwen3_next_layers as runner
+
+    def missing(_):
+        raise ModuleNotFoundError("native scheduler missing")
+
+    monkeypatch.setattr(runner.importlib, "import_module", missing)
+    with pytest.raises(RuntimeError, match="native_scheduler_not_ready"):
+        runner.check_scheduler_ready()
diff --git a/tests/test_qwen3_next_logprob_adapter.py b/tests/test_qwen3_next_logprob_adapter.py
new file mode 100644
index 000000000..74236b7cd
--- /dev/null
+++ b/tests/test_qwen3_next_logprob_adapter.py
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_logprob import (
+    calculate_log_probs_and_entropy,
+    qwen_logprob_contract,
+)
+
+
+def test_contract_pins_complete_tp4_vocab_and_original_dtype():
+    for rank in range(4):
+        contract = qwen_logprob_contract(
+            torch.empty(2, 37984, dtype=torch.bfloat16), rank=rank, world_size=4
+        )
+        assert contract.sharding.vocab_shard_bounds[rank] == (rank * 37984, (rank + 1) * 37984)
+        assert contract.sharding.padded_vocab_size == 151936
+        assert contract.sharding.cp_world_size == 1
+        assert contract.dtype.value == "bf16"
+
+
+@pytest.mark.parametrize(
+    "rank,world,width,dtype",
+    [
+        (0, 1, 151936, torch.bfloat16),
+        (4, 4, 37984, torch.bfloat16),
+        (0, 4, 38016, torch.bfloat16),
+        (0, 4, 37984, torch.float16),
+    ],
+)
+def test_contract_rejects_profile_drift(rank, world, width, dtype):
+    with pytest.raises(ValueError):
+        qwen_logprob_contract(torch.empty(0, width, dtype=dtype), rank=rank, world_size=world)
+
+
+def test_numerical_callback_has_no_fallback_for_mask_or_absent_group():
+    logits, tokens = torch.empty(0, 37984), torch.empty(0, dtype=torch.long)
+    with pytest.raises(ValueError, match="top-p"):
+        calculate_log_probs_and_entropy(logits, tokens, None, log_prob_keep_mask=torch.empty(0))
+    with pytest.raises(ValueError, match="explicit initialized"):
+        calculate_log_probs_and_entropy(logits, tokens, None)
diff --git a/tests/test_qwen3_next_native_recompute.py b/tests/test_qwen3_next_native_recompute.py
new file mode 100644
index 000000000..bde75df74
--- /dev/null
+++ b/tests/test_qwen3_next_native_recompute.py
@@ -0,0 +1,232 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from pathlib import Path
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_native_recompute import response_rows
+from rl_engine.integrations.qwen3_next_subnode import SUBNODES, subnode_layout
+from rl_engine.integrations.qwen3_next_vime_runtime import runtime_argv
+
+
+@pytest.mark.parametrize("batch", [1, 4])
+def test_response_rows_start_at_last_prompt_and_do_not_consume_last_target(batch):
+    request = {
+        "prompt_tokens": [[1] * 8 for _ in range(batch)],
+        "consumed_input_tokens": [[1] * 23 for _ in range(batch)],
+        "prediction_positions": list(range(7, 23)),
+    }
+    hidden = torch.arange(batch * 23 * 2).reshape(batch * 23, 1, 2).to(torch.bfloat16)
+    selected = response_rows(hidden, request)
+    assert selected.dtype == torch.bfloat16 and selected.shape == (batch, 16, 2)
+    for index in range(batch):
+        assert torch.equal(selected[index], hidden[index * 23 + 7 : (index + 1) * 23, 0])
+    with pytest.raises(ValueError, match="BF16"):
+        response_rows(hidden.float(), request)
+    request["prediction_positions"] = list(range(8, 24))
+    with pytest.raises(ValueError, match="last target"):
+        response_rows(hidden, request)
+
+
+def test_native_cli_has_full_model_vocabulary_topology_and_no_precision_override():
+    argv = runtime_argv("/checkpoint", training=True)
+    for name, value in {
+        "num-layers": 48,
+        "tensor-model-parallel-size": 4,
+        "expert-tensor-parallel-size": 4,
+        "pipeline-model-parallel-size": 1,
+        "context-parallel-size": 1,
+        "padded-vocab-size": 151936,
+    }.items():
+        assert argv[argv.index("--" + name) + 1] == str(value)
+    assert "--optimization-dtype" not in argv and "--reduce-dtype" not in argv
+    assert "--sequence-parallel" not in argv
+    assert "--use-rollout-logprobs" in argv
+
+
+def test_recompute_batch_loads_once_and_keeps_verifiable_original_rank_traces(
+    tmp_path, monkeypatch
+):
+    import copy
+    import hashlib
+
+    from rl_engine.integrations import qwen3_next_native_recompute as recompute
+    from rl_engine.testing.qwen3_next_l2 import (
+        load_tensor_artifact,
+        save_tensor_artifact,
+        validate_native_worker_traces,
+    )
+
+    request = {
+        "schema_version": 1,
+        "case_id": "cpu-contract-only",
+        "role": "recompute",
+        "identity": {
+            "provider_config": {"profile": "qwen3_next_tp4_shared_eager_v1"},
+            "weight_version": "baseline",
+        },
+        "request_sha256": "a" * 64,
+        "execution": {"recompute_mode": "full_from_tokens", "input_sequence_length": 23},
+        "prompt_tokens": [[1] * 8],
+        "response_tokens": [[2] * 16],
+        "prediction_positions": list(range(7, 23)),
+        "sample_ids": [0],
+        "consumed_response_tokens": 15,
+        "state_position": 22,
+        "trace_layers": [],
+    }
+    second = {**request, "case_id": "second-cpu-case", "request_sha256": "b" * 64}
+    calls = []
+
+    def spawn(target, *, args, nprocs, join):
+        cases, staging = args
+        calls.append((target, nprocs, join))
+        assert len(cases) == 2
+        for index, (current, _output) in enumerate(cases):
+            for rank in range(4):
+                layers = {}
+                for layer in range(48):
+                    names = (
+                        ("key", "value")
+                        if (layer + 1) % 4 == 0
+                        else ("convolution", "recurrent_state")
+                    )
+                    layers[layer] = {
+                        "output": torch.zeros((1, 16, 2048), dtype=torch.bfloat16),
+                        "subnodes": {
+                            name: torch.zeros(
+                                (1, 16, subnode_layout(name)[0]), dtype=subnode_layout(name)[1]
+                            )
+                            for name in SUBNODES
+                        },
+                        "states": {
+                            name: torch.zeros(
+                                (),
+                                dtype=(
+                                    torch.float32 if name == "recurrent_state" else torch.bfloat16
+                                ),
+                            ).expand(
+                                {
+                                    "convolution": (1, 2048, 3),
+                                    "recurrent_state": (1, 8, 128, 128),
+                                    "key": (1, 1, 23, 256),
+                                    "value": (1, 1, 23, 256),
+                                }[name]
+                            )
+                            for name in names
+                        },
+                    }
+                layers["model"] = {"embedding": torch.zeros((1, 16, 2048), dtype=torch.bfloat16)}
+                trace = {
+                    "rank": rank,
+                    "request_sha256": current["request_sha256"],
+                    "role": "recompute",
+                    "execution": current["execution"],
+                    "provider_binary": {
+                        "module": "rl_engine._C",
+                        "path": "/cpu-test-only/_C.so",
+                        "sha256": "c" * 64,
+                    },
+                    "layers": layers,
+                    "local_logits": torch.zeros((), dtype=torch.bfloat16).expand(1, 16, 37984),
+                    "logprobs": torch.zeros((1, 16)),
+                    "responses": torch.tensor(current["response_tokens"], dtype=torch.int64),
+                }
+                if rank == 0:
+                    trace["logits"] = torch.zeros((), dtype=torch.bfloat16).expand(1, 16, 151936)
+                save_tensor_artifact(Path(staging) / f"case-{index}-rank-{rank}.pt", trace)
+
+    monkeypatch.setattr(recompute.mp, "spawn", spawn)
+    outputs = [(request, tmp_path / "first.pt"), (second, tmp_path / "second.pt")]
+    recompute.run_recompute_cases(iter(outputs))
+    assert calls == [(recompute._rank_main, 4, True)]
+    for current, path in outputs:
+        artifact = load_tensor_artifact(path)
+        assert len(artifact["worker_traces"]) == 4
+        validate_native_worker_traces(artifact, current, artifact_root=tmp_path)
+    artifact = load_tensor_artifact(outputs[0][1])
+    for record in artifact["worker_traces"]:
+        assert Path(record["path"]).is_file()
+
+    missing_binary = copy.deepcopy(artifact)
+    missing_binary.pop("provider_binary")
+    with pytest.raises(ValueError, match="imported CUDA provider binary"):
+        validate_native_worker_traces(missing_binary, request, artifact_root=tmp_path)
+
+    # Merely retaining files is insufficient: even after rehashing the record,
+    # original hidden/state/local-logit bytes must reproduce the declared result.
+    record = artifact["worker_traces"][1]
+    original = load_tensor_artifact(record["path"])
+    for field, message in (
+        ("state", "state bytes"),
+        ("hidden", "layer output"),
+        ("local_logits", "local_logits bytes"),
+        ("binary", "provenance mismatch"),
+        ("convolution_truncated", "complete convolution state shape"),
+        ("convolution_transposed", "complete convolution state shape"),
+        ("recurrent_truncated", "complete recurrent_state state shape"),
+        ("key_short", "complete key state shape"),
+        ("key_long", "complete key state shape"),
+        ("value_truncated", "complete value state shape"),
+    ):
+        trace = copy.deepcopy(original)
+        if field == "state":
+            value = trace["layers"][0]["states"]["recurrent_state"].clone()
+            value[0, 0, 0, 0] = -0.0
+            trace["layers"][0]["states"]["recurrent_state"] = value
+        elif field == "convolution_truncated":
+            trace["layers"][0]["states"]["convolution"] = torch.zeros((1, 1), dtype=torch.bfloat16)
+        elif field == "convolution_transposed":
+            value = trace["layers"][0]["states"]["convolution"]
+            trace["layers"][0]["states"]["convolution"] = value.transpose(1, 2)
+        elif field == "recurrent_truncated":
+            value = trace["layers"][0]["states"]["recurrent_state"]
+            trace["layers"][0]["states"]["recurrent_state"] = value[:, :7]
+        elif field in ("key_short", "key_long"):
+            length = 22 if field == "key_short" else 24
+            trace["layers"][3]["states"]["key"] = torch.zeros(
+                (1, 1, length, 256), dtype=torch.bfloat16
+            )
+        elif field == "value_truncated":
+            value = trace["layers"][3]["states"]["value"]
+            trace["layers"][3]["states"]["value"] = value[..., :255]
+        elif field == "hidden":
+            trace["layers"][0]["output"][0, 0, 0] = -0.0
+        elif field == "binary":
+            trace["provider_binary"]["sha256"] = "d" * 64
+        else:
+            trace["local_logits"] = trace["local_logits"].clone()
+            trace["local_logits"][0, 0, 0] = -0.0
+        save_tensor_artifact(record["path"], trace)
+        with open(record["path"], "rb") as stream:
+            record["sha256"] = hashlib.file_digest(stream, "sha256").hexdigest()
+        with pytest.raises(ValueError, match=message):
+            validate_native_worker_traces(artifact, request, artifact_root=tmp_path)
+        if "complete " in message:
+            ranks = [load_tensor_artifact(item["path"]) for item in artifact["worker_traces"]]
+            with pytest.raises(ValueError, match=message):
+                recompute._assemble(request, ranks, artifact["worker_traces"])
+
+
+@pytest.mark.parametrize("batch", [1, 4])
+@pytest.mark.parametrize("prompt_length", [8, 64, 256, 1024])
+def test_complete_native_state_shapes_bind_every_consumed_token(batch, prompt_length):
+    from rl_engine.testing.qwen3_next_l2 import native_state_shapes
+
+    request = {
+        "prompt_tokens": [[1] * prompt_length for _ in range(batch)],
+        "consumed_response_tokens": 15,
+        "state_position": prompt_length + 14,
+    }
+    assert native_state_shapes(request) == {
+        "convolution": (batch, 2048, 3),
+        "recurrent_state": (batch, 8, 128, 128),
+        "key": (batch, 1, prompt_length + 15, 256),
+        "value": (batch, 1, prompt_length + 15, 256),
+    }
+    with pytest.raises(ValueError, match="exactly 15 responses"):
+        native_state_shapes({**request, "state_position": prompt_length + 15})
+    with pytest.raises(ValueError, match="exactly 15 responses"):
+        native_state_shapes({**request, "consumed_response_tokens": 16})
diff --git a/tests/test_qwen3_next_operator_gates.py b/tests/test_qwen3_next_operator_gates.py
new file mode 100644
index 000000000..d991a7c43
--- /dev/null
+++ b/tests/test_qwen3_next_operator_gates.py
@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Runner failures cannot reuse stale evidence or promote diagnostics."""
+
+import json
+from types import SimpleNamespace
+
+import pytest
+
+from rl_engine.testing.qwen3_next_cases import execution_verdict
+from scripts import qwen3_next_operator_gates as gates
+
+CASE = "tests/example.py::test_required"
+
+
+@pytest.mark.parametrize("mode", ["passed", "missing", "forged", "exit_failed", "diagnostics"])
+def test_run_suite_requires_current_complete_evidence(tmp_path, monkeypatch, mode):
+    suite = "diagnostics" if mode == "diagnostics" else "core"
+    manifest = tmp_path / "manifest.json"
+    manifest.write_text(json.dumps({"schema_version": 1, "suites": {suite: [CASE]}}))
+    monkeypatch.setattr(gates, "MANIFEST", manifest)
+    monkeypatch.setattr(gates.importlib.metadata, "version", lambda _: "test-version")
+    monkeypatch.setattr(gates.subprocess, "check_output", lambda *args, **kwargs: "")
+    out = tmp_path / "output"
+    out.mkdir()
+    complete = execution_verdict(
+        [CASE], [CASE], {CASE: {"setup": "passed", "call": "passed", "teardown": "passed"}}
+    )
+    # This must be removed before invoking pytest, even if pytest writes nothing.
+    (out / "case-results.json").write_text(json.dumps(complete))
+
+    def run(*args, **kwargs):
+        if mode != "missing":
+            report = dict(complete)
+            if mode == "forged":
+                report["outcomes"] = {}
+            (out / "case-results.json").write_text(json.dumps(report))
+        return SimpleNamespace(returncode=1 if mode == "exit_failed" else 0)
+
+    monkeypatch.setattr(gates.subprocess, "run", run)
+    result = gates.run_suite(out, suite)
+    if mode == "diagnostics":
+        assert result["status"] == "diagnostic_completed"
+        assert result["diagnostic_only"] is True
+        assert result["mandatory_case_ids"] == []
+    else:
+        assert result["status"] == ("passed" if mode == "passed" else "failed")
+        assert result["mandatory_case_ids"] == [CASE]
+    assert result["full_model_l2"] == "not_evaluated"
diff --git a/tests/test_qwen3_next_profile.py b/tests/test_qwen3_next_profile.py
new file mode 100644
index 000000000..670c8e0b2
--- /dev/null
+++ b/tests/test_qwen3_next_profile.py
@@ -0,0 +1,149 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""A label or environment flag cannot certify the CUSTOM provider."""
+
+import copy
+import sys
+from types import SimpleNamespace
+
+import pytest
+
+from rl_engine.integrations import qwen3_next_profile as profile
+
+
+def test_qualification_never_advertises_batch_invariance(monkeypatch):
+    monkeypatch.setenv("VLLM_BATCH_INVARIANT", "0")
+    assert profile.activate_for_identity({}) == "qualification"
+    assert not profile.profile_is_certified()
+
+
+@pytest.mark.parametrize("mode", ["1", "", "true"])
+def test_strict_flag_without_certificate_is_rejected(monkeypatch, mode):
+    monkeypatch.setenv("VLLM_BATCH_INVARIANT", mode)
+    with pytest.raises(ValueError, match="Uncertified"):
+        profile.activate_for_identity({})
+    assert not profile.profile_is_certified()
+
+
+def test_passed_label_without_matching_certificate_bytes_is_rejected(tmp_path, monkeypatch):
+    monkeypatch.setenv("VLLM_BATCH_INVARIANT", "1")
+    result = tmp_path / "result.json"
+    result.write_text('{"status": "passed"}')
+    identity = {"scheduler_profile_certificate": {"path": str(result), "sha256": "0" * 64}}
+    with pytest.raises(ValueError, match="content identity"):
+        profile.activate_for_identity(identity)
+    assert not profile.profile_is_certified()
+
+
+@pytest.fixture
+def updated_identity():
+    certified = {
+        "checkpoint": "/official/revision",
+        "checkpoint_identity": {"revision": "official", "metadata": {"tokenizer": "pinned"}},
+        "source_commit": "commit",
+        "source_tree": "tree",
+        "environment_sha256": "environment",
+        "provider_config": {"profile": "qwen3_next_tp4_shared_eager_v1"},
+        "topology": {"tp": 4, "pp": 1, "cp": 1, "ep": 1},
+        "weight_version": "baseline",
+        "architecture_layers": 48,
+        "weights_dtype": "bfloat16",
+        "recurrent_state_dtype": "float32",
+    }
+    record = {
+        "schema_version": 1,
+        "kind": "qwen3_next_native_hf_export",
+        "status": "complete",
+        "weight_version": "update1",
+        "step": 1,
+        "export_id": "unit-test-only",
+        "base_checkpoint": certified["checkpoint"],
+        "base_checkpoint_identity": certified["checkpoint_identity"],
+        "training_identity": copy.deepcopy(certified),
+        "architecture_layers": 48,
+        "weights_dtype": "bfloat16",
+        "weights_content_sha256": "new-weight-content",
+    }
+    identity = {
+        **copy.deepcopy(certified),
+        "weight_version": "update1",
+        "checkpoint": "/run/current-export",
+        "checkpoint_identity": record,
+        "base_checkpoint": certified["checkpoint"],
+        "base_checkpoint_identity": certified["checkpoint_identity"],
+    }
+    return identity, certified
+
+
+def test_certificate_rehashes_actual_updated_export_not_old_base(updated_identity, monkeypatch):
+    identity, certified = updated_identity
+    calls = []
+
+    def check(path, expected):
+        calls.append((path, expected))
+        return expected
+
+    monkeypatch.setitem(
+        sys.modules, "qwen3_next_training_export", SimpleNamespace(validate_training_export=check)
+    )
+    profile.validate_certified_weights(identity, certified)
+    assert calls == [(identity["checkpoint"], identity["checkpoint_identity"])]
+    assert calls[0][1] != certified["checkpoint_identity"]
+    assert not profile.profile_is_certified()  # A JSON-boundary unit check cannot activate BI.
+
+
+@pytest.mark.parametrize(
+    "field", ["base_checkpoint", "base_checkpoint_identity", "weight_version", "source_tree"]
+)
+def test_updated_identity_cannot_rebind_base_version_or_source(updated_identity, field):
+    identity, certified = updated_identity
+    identity[field] = "changed"
+    with pytest.raises(ValueError):
+        profile.validate_certified_weights(identity, certified)
+
+
+def test_changed_actual_export_bytes_are_rejected(updated_identity, monkeypatch):
+    identity, certified = updated_identity
+    monkeypatch.setitem(
+        sys.modules,
+        "qwen3_next_training_export",
+        SimpleNamespace(
+            validate_training_export=lambda path, expected: {
+                **expected,
+                "weights_content_sha256": "different",
+            },
+        ),
+    )
+    with pytest.raises(ValueError, match="Actual updated weight files"):
+        profile.validate_certified_weights(identity, certified)
+
+
+def test_updated_export_cannot_be_labeled_as_baseline(updated_identity):
+    identity, certified = updated_identity
+    identity["weight_version"] = "baseline"
+    with pytest.raises(ValueError, match="masquerade"):
+        profile.validate_certified_weights(identity, certified)
+
+
+def test_historical_export_identity_checks_do_not_open_deleted_shards(updated_identity):
+    from rl_engine.testing.qwen3_next_l2 import validate_weight_identity
+
+    identity, _ = updated_identity
+    validate_weight_identity(identity)
+    # No export files exist in this unit test; this verifies structural history
+    # only. Actual profile activation always invokes the deep file validator.
+
+
+def test_updated_model_identity_carries_actual_and_base_checkpoint(updated_identity):
+    from qwen3_next_l2 import model_identity
+
+    identity, _ = updated_identity
+    model = model_identity(identity)
+    assert model["checkpoint"] == "/run/current-export"
+    assert model["checkpoint_identity"]["weights_content_sha256"] == "new-weight-content"
+    assert model["base_checkpoint"] == "/official/revision"
+    assert model["base_checkpoint_identity"] == {
+        "revision": "official",
+        "metadata": {"tokenizer": "pinned"},
+    }
diff --git a/tests/test_qwen3_next_rl_fixture.py b/tests/test_qwen3_next_rl_fixture.py
new file mode 100644
index 000000000..c33578f1f
--- /dev/null
+++ b/tests/test_qwen3_next_rl_fixture.py
@@ -0,0 +1,58 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from argparse import Namespace
+
+import pytest
+import torch
+
+from rl_engine.testing.qwen3_next_rl_fixture import (
+    OPTIMIZER_SETTINGS,
+    POLICY_SETTINGS,
+    PolicyFixture,
+    require_policy_configuration,
+)
+
+
+def fixture():
+    return PolicyFixture.freeze(
+        torch.arange(320).reshape(4, 80),
+        torch.full((4, 16), -3.0),
+        baseline_identity="test-baseline",
+    )
+
+
+def test_policy_fixture_preserves_old_policy_and_native_loss_fields():
+    value = fixture()
+    batch = value.batch([2], device="cpu")
+    assert batch["partition"] == [2]
+    assert batch["total_lengths"] == [80]
+    assert batch["response_lengths"] == [16]
+    assert torch.equal(batch["advantages"][0], torch.full((16,), 0.75))
+    value.verify_initial_recompute(torch.full((4, 16), -3.0))
+    with pytest.raises(AssertionError):
+        value.verify_initial_recompute(torch.full((4, 16), -3.01))
+
+
+def test_policy_fixture_rejects_mutation_and_unfrozen_baseline():
+    value = fixture()
+    value.old_logprobs[0, 0] = -4
+    with pytest.raises(ValueError, match="modified"):
+        value.batch([0], device="cpu")
+    with pytest.raises(ValueError, match="frozen"):
+        PolicyFixture.freeze(
+            torch.arange(320).reshape(4, 80),
+            torch.zeros(4, 16, requires_grad=True),
+            baseline_identity="run",
+        )
+
+
+@pytest.mark.parametrize(
+    "changed", ["loss_type", "use_rollout_logprobs", "use_stateless_adam", "optimizer_cpu_offload"]
+)
+def test_policy_configuration_cannot_fall_back_or_substitute_optimizer(changed):
+    settings = {**POLICY_SETTINGS, **OPTIMIZER_SETTINGS}
+    require_policy_configuration(Namespace(**settings))
+    settings[changed] = "unexpected"
+    with pytest.raises(ValueError, match=changed):
+        require_policy_configuration(Namespace(**settings))
diff --git a/tests/test_qwen3_next_rotary_dtype.py b/tests/test_qwen3_next_rotary_dtype.py
new file mode 100644
index 000000000..4a9848ed4
--- /dev/null
+++ b/tests/test_qwen3_next_rotary_dtype.py
@@ -0,0 +1,58 @@
+# SPDX-License-Identifier: Apache-2.0
+"""The rotary tables must not depend on how the owning module was cast."""
+
+from types import SimpleNamespace
+
+import torch
+
+from rl_engine.integrations.qwen3_next_tp_blocks import TP4FullAttention
+
+
+def _reference_rotate(value, positions, rope_theta=10000000.0):
+    inv_freq = 1.0 / (rope_theta ** (torch.arange(0, 64, 2, dtype=torch.float32) / 64))
+    angles = positions.float()[:, None] * inv_freq[None, :]
+    angles = torch.cat((angles, angles), dim=-1)
+    cos, sin = angles.cos().to(value.dtype)[:, None], angles.sin().to(value.dtype)[:, None]
+    rotary, tail = value[..., :64], value[..., 64:]
+    rotated = torch.cat((-rotary[..., 32:], rotary[..., :32]), dim=-1)
+    return torch.cat((rotary * cos + rotated * sin, tail), dim=-1)
+
+
+def test_inverse_frequencies_are_fp32_and_exact_at_dim_zero():
+    inv_freq = TP4FullAttention.inverse_frequencies(10000000.0, torch.device("cpu"))
+    assert inv_freq.dtype == torch.float32 and inv_freq.shape == (32,)
+    assert inv_freq[0].item() == 1.0
+    assert torch.equal(
+        inv_freq, 1.0 / (10000000.0 ** (torch.arange(0, 64, 2, dtype=torch.float32) / 64))
+    )
+
+
+def test_rotate_matches_fp32_reference_for_any_module_dtype():
+    generator = torch.Generator().manual_seed(7)
+    value = (torch.randn((23, 1, 256), generator=generator) * 1.5).to(torch.bfloat16)
+    positions = torch.arange(23)
+    module = SimpleNamespace(rope_theta=10000000.0)
+    actual = TP4FullAttention._rotate(module, value, positions)
+    assert torch.equal(actual, _reference_rotate(value, positions))
+
+
+def test_bf16_rounded_frequencies_would_change_rotated_keys():
+    """A BF16-cast rotary table flips rotated dims 1..~47 at positions >= 1; keep it FP32."""
+    generator = torch.Generator().manual_seed(8)
+    value = (torch.randn((23, 1, 256), generator=generator) * 1.5).to(torch.bfloat16)
+    positions = torch.arange(23)
+    exact = _reference_rotate(value, positions)
+    rounded_theta = SimpleNamespace(rope_theta=10000000.0)
+    inv_freq = TP4FullAttention.inverse_frequencies(rounded_theta.rope_theta, value.device)
+    rounded = inv_freq.to(torch.bfloat16).float()
+    angles = positions.float()[:, None] * rounded[None, :]
+    angles = torch.cat((angles, angles), dim=-1)
+    cos, sin = angles.cos().to(value.dtype)[:, None], angles.sin().to(value.dtype)[:, None]
+    rotary, tail = value[..., :64], value[..., 64:]
+    rotated = torch.cat((-rotary[..., 32:], rotary[..., :32]), dim=-1)
+    with_rounded = torch.cat((rotary * cos + rotated * sin, tail), dim=-1)
+    differing = (with_rounded != exact)[:, 0]
+    assert differing.any()
+    assert not differing[0].any()  # position 0 has angle 0
+    assert not differing[:, 64:].any()  # the unrotated tail is untouched
+    assert not differing[:, 0].any() and not differing[:, 32].any()  # dim 0 is exact in BF16
diff --git a/tests/test_qwen3_next_run.py b/tests/test_qwen3_next_run.py
new file mode 100644
index 000000000..6a3fd3e82
--- /dev/null
+++ b/tests/test_qwen3_next_run.py
@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""The scheduler-independent Qwen3-Next chain launcher."""
+
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+
+import qwen3_next_run as run  # noqa: E402
+
+
+def manifest(tmp_path, stage="layers", script=None):
+    source = tmp_path / "source"
+    (source / "scripts").mkdir(parents=True, exist_ok=True)
+    if script is not None:
+        (source / run.STAGES[stage][3]).write_text(script)
+    return {
+        "run_id": "r",
+        "execution_id": "e" * 32,
+        "stage": stage,
+        "source": str(source),
+        "source_tree": "tree",
+        "python": sys.executable,
+        "cuda_home": str(tmp_path),
+        "cuda_identity": {"nvcc_sha256": "nvcc"},
+        "memory": "512G",
+        "environment": {"identity": {"base_prefix": sys.prefix, "build_tools": {"ninja": "1"}}},
+        "provider_config": {"runtime_env": {"TRITON_F32_DEFAULT": "ieee"}},
+        "scheduler_profile_certificate": {"path": "/certificate"},
+    }
+
+
+@pytest.fixture
+def frozen(monkeypatch):
+    monkeypatch.setattr(run, "verify_environment", lambda *args, **kwargs: None)
+    monkeypatch.setattr(run, "file_digest", lambda path: "nvcc")
+    monkeypatch.setattr(run, "_git", lambda *args, cwd=None: "tree" if "rev-parse" in args else "")
+
+
+def test_prepare_refuses_a_dirty_tree(monkeypatch, tmp_path):
+    monkeypatch.setattr(run, "_git", lambda *args, cwd=None: " M scripts/qwen3_next_run.py")
+    with pytest.raises(SystemExit, match="Commit the source"):
+        run.prepare("preflight", None, None)
+
+
+@pytest.mark.parametrize(
+    "stage,bi", [("preflight", "0"), ("layers", "0"), ("l2", "1"), ("train", "1")]
+)
+def test_stage_environment_pins_batch_invariance_and_the_declared_memory(tmp_path, stage, bi):
+    env = run.stage_environment(manifest(tmp_path, stage), tmp_path, "0")
+    assert env["VLLM_BATCH_INVARIANT"] == bi
+    assert env["QWEN3_NEXT_ALLOCATED_MEMORY"] == "512G"
+    assert env["QWEN3_NEXT_ATTEMPT"] == "0"
+    assert env["TRITON_F32_DEFAULT"] == "ieee"
+    assert env["PYTHONPATH"].endswith("source")
+    assert ("QWEN_SHARED_PROFILE_RESULT" in env) == (bi == "1")
+
+
+def test_execute_validates_and_publishes_a_passed_result(tmp_path, frozen, monkeypatch):
+    script = (
+        "import json, sys\n"
+        "out = sys.argv[sys.argv.index('--output') + 1]\n"
+        "json.dump({'status': 'passed', 'attempt': '0'}, open(out + '/result.json', 'w'))\n"
+    )
+    current = manifest(tmp_path, script=script)
+    seen = []
+    monkeypatch.setattr(run, "validate_result", lambda *args, **kwargs: seen.append(args))
+    status = run.execute(tmp_path, current)
+    assert status["status"] == "passed" and len(seen) == 1
+    assert json.loads((tmp_path / "result.json").read_text())["status"] == "passed"
+    assert not (tmp_path / "RUNNING").exists()
+
+
+def test_execute_keeps_the_stage_failure_kind(tmp_path, frozen):
+    script = (
+        "import json, sys\n"
+        "out = sys.argv[sys.argv.index('--output') + 1]\n"
+        "json.dump({'failure_kind': 'insufficient_disk', 'error': 'full'},"
+        " open(out + '/result.json', 'w'))\n"
+        "sys.exit(3)\n"
+    )
+    status = run.execute(tmp_path, manifest(tmp_path, script=script))
+    assert status["status"] == "failed" and status["exit_code"] == 3
+    assert status["failure_kind"] == "insufficient_disk"
+    assert not (tmp_path / "result.json").exists()
+    assert not (tmp_path / "RUNNING").exists()
+
+
+def test_execute_refuses_a_changed_source(tmp_path, monkeypatch, frozen):
+    monkeypatch.setattr(run, "_git", lambda *args, cwd=None: "other-tree")
+    with pytest.raises(RuntimeError, match="source_mismatch"):
+        run.execute(tmp_path, manifest(tmp_path, script="raise SystemExit(0)\n"))
+
+
+@pytest.mark.parametrize(
+    "arguments,message",
+    [
+        (["--stage", "all", "--from", "layers"], "needs --previous"),
+        (["--stage", "l2"], "needs --previous"),
+        (["--stage", "preflight", "--previous", "."], "needs --previous"),
+        (["--stage", "all", "--from", "train", "--until", "l2", "--previous", "."], "--from"),
+    ],
+)
+def test_stage_selection_requires_a_predecessor_exactly_after_preflight(
+    monkeypatch, tmp_path, arguments, message
+):
+    monkeypatch.setattr(
+        sys,
+        "argv",
+        ["run", "--runs-root", str(tmp_path), "--checkpoint", ".", "--cuda-home", ".", *arguments],
+    )
+    with pytest.raises(SystemExit, match=message):
+        run.main()
diff --git a/tests/test_qwen3_next_storage.py b/tests/test_qwen3_next_storage.py
new file mode 100644
index 000000000..024453a43
--- /dev/null
+++ b/tests/test_qwen3_next_storage.py
@@ -0,0 +1,334 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Faults at persistent filesystem boundaries, including an actual SIGKILL."""
+
+import json
+import os
+import signal
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+import qwen3_next_runtime as storage  # noqa: E402
+from qwen3_next_checkpoint import (  # noqa: E402
+    newest_complete_checkpoint,
+    publish_checkpoint,
+    validate_checkpoint,
+    write_rank_manifest,
+)
+
+IDENTITY = {"run_id": "storage-fault-test"}
+
+
+class Preempted(BaseException):
+    pass
+
+
+def fail_at(monkeypatch, boundary):
+    def crash(name):
+        if name == boundary:
+            raise Preempted(name)
+
+    monkeypatch.setattr(storage, "_boundary", crash)
+
+
+def metadata():
+    return {"identity": IDENTITY, "status": "staging", "step": 1}
+
+
+def complete(root, name="step-2", step=2):
+    staging, destination = storage.create_staging(root, name, metadata())
+    for rank in range(4):
+        files = {}
+        for role in ("model", "optimizer", "rng", "progress"):
+            relative = f"rank-{rank}-{role}.bin"
+            (staging / relative).write_bytes(f"{rank}:{role}".encode())
+            files[role] = [relative]
+        write_rank_manifest(
+            staging,
+            rank=rank,
+            world_size=4,
+            identity=IDENTITY,
+            step=step,
+            sample_position=step * 4,
+            files=files,
+        )
+    marker = publish_checkpoint(staging, destination, world_size=4, identity=IDENTITY)
+    return destination, marker
+
+
+@pytest.mark.parametrize("boundary", ["root_owner_persisted", "root_created"])
+def test_root_creation_recovers_without_internal_owner(monkeypatch, tmp_path, boundary):
+    root = tmp_path / "checkpoints"
+    fail_at(monkeypatch, boundary)
+    with pytest.raises(Preempted):
+        storage.owned_root(root, IDENTITY)
+    monkeypatch.setattr(storage, "_boundary", lambda name: None)
+    storage.owned_root(root, IDENTITY)
+    assert json.loads((root / "RUN_OWNER.json").read_text()) == IDENTITY
+    storage.recover_storage(root, IDENTITY)
+
+
+@pytest.mark.parametrize(
+    "boundary", ["staging_intent_persisted", "staging_created", "staging_marker_persisted"]
+)
+def test_staging_creation_recovers_every_window_without_loading_partial(
+    monkeypatch, tmp_path, boundary
+):
+    root = storage.owned_root(tmp_path / "checkpoints", IDENTITY)
+    retained, marker = complete(root)
+    fail_at(monkeypatch, boundary)
+    with pytest.raises(Preempted):
+        storage.create_staging(root, "next-step", metadata())
+    monkeypatch.setattr(storage, "_boundary", lambda name: None)
+    with pytest.raises((ValueError, FileNotFoundError)):
+        validate_checkpoint(root / "next-step.partial", IDENTITY)
+    storage.recover_storage(root, IDENTITY)
+    assert not (root / "next-step.partial").exists()
+    assert validate_checkpoint(retained, IDENTITY) == marker
+    assert newest_complete_checkpoint(root, IDENTITY)["checkpoint"] == str(retained)
+    assert storage.recover_storage(root, IDENTITY) == []
+
+
+@pytest.mark.parametrize(
+    "boundary",
+    [
+        "retirement_intent_persisted",
+        "retirement_renamed",
+        "retirement_removed",
+        "retirement_committed",
+    ],
+)
+def test_retirement_resumes_each_window_and_preserves_successor(monkeypatch, tmp_path, boundary):
+    root = storage.owned_root(tmp_path / "checkpoints", IDENTITY)
+    prior, old_marker = complete(root, "step-1", 1)
+    retained, marker = complete(root)
+    proof = {"complete": old_marker, "successor": str(retained), "successor_complete": marker}
+    fail_at(monkeypatch, boundary)
+    with pytest.raises(Preempted):
+        storage.retire_directory(prior, root, IDENTITY, proof=proof)
+    monkeypatch.setattr(storage, "_boundary", lambda name: None)
+    with pytest.raises(ValueError, match="Unpublished"):
+        validate_checkpoint(root / ".step-1.deleting", IDENTITY)
+    storage.recover_storage(root, IDENTITY)
+    assert not prior.exists() and not (root / ".step-1.deleting").exists()
+    assert validate_checkpoint(retained, IDENTITY) == marker
+    assert storage.recover_storage(root, IDENTITY) == []
+
+
+def test_preemption_after_internal_marker_deleted_does_not_strand_cleanup(monkeypatch, tmp_path):
+    root = storage.owned_root(tmp_path / "exports", IDENTITY)
+    stage, _ = storage.create_staging(root, "update-1", metadata())
+    (stage / "unfinished-weights").write_bytes(b"never load this")
+    remove = storage.shutil.rmtree
+
+    def interrupted(path):
+        (path / "STAGING.json").unlink()
+        raise Preempted("during rmtree")
+
+    monkeypatch.setattr(storage.shutil, "rmtree", interrupted)
+    with pytest.raises(Preempted):
+        storage.recover_storage(root, IDENTITY)
+    assert not stage.exists()
+    assert (root / ".update-1.partial.deleting" / "unfinished-weights").is_file()
+    monkeypatch.setattr(storage.shutil, "rmtree", remove)
+    storage.recover_storage(root, IDENTITY)
+    assert not (root / ".update-1.partial.deleting").exists()
+
+
+def test_pending_retirement_rechecks_successor_before_deleting_last_good_state(
+    monkeypatch, tmp_path
+):
+    root = storage.owned_root(tmp_path / "checkpoints", IDENTITY)
+    prior, marker = complete(root, "step-1", 1)
+    successor, next_marker = complete(root)
+    fail_at(monkeypatch, "retirement_intent_persisted")
+    with pytest.raises(Preempted):
+        storage.retire_directory(
+            prior,
+            root,
+            IDENTITY,
+            proof={
+                "complete": marker,
+                "successor": str(successor),
+                "successor_complete": next_marker,
+            },
+        )
+    monkeypatch.setattr(storage, "_boundary", lambda name: None)
+    (successor / "rank-0-model.bin").write_bytes(b"corrupt")
+    with pytest.raises(ValueError, match="checksum|size mismatch"):
+        storage.recover_storage(root, IDENTITY)
+    assert validate_checkpoint(prior, IDENTITY) == marker
+
+
+@pytest.mark.parametrize("foreign", ["missing_owner", "wrong_owner", "symlink"])
+def test_unrelated_partial_directories_are_never_deleted(tmp_path, foreign):
+    root = storage.owned_root(tmp_path / "exports", IDENTITY)
+    target = root / "unrelated.partial"
+    if foreign == "symlink":
+        outside = tmp_path / "outside"
+        outside.mkdir()
+        (outside / "STAGING.json").write_text(json.dumps(metadata()))
+        target.symlink_to(outside, target_is_directory=True)
+    else:
+        target.mkdir()
+        if foreign == "wrong_owner":
+            (target / "STAGING.json").write_text(
+                json.dumps({"status": "staging", "identity": {"run_id": "other"}})
+            )
+    with pytest.raises(ValueError):
+        storage.recover_storage(root, IDENTITY)
+    assert target.exists()
+
+
+def test_real_sigkill_between_mkdir_and_internal_marker_is_recoverable(tmp_path):
+    root = tmp_path / "exports"
+    script = """
+import os, signal, sys
+import qwen3_next_runtime as storage
+identity = {'run_id': 'storage-fault-test'}
+root = storage.owned_root(sys.argv[1], identity)
+def stop(name):
+    if name == 'staging_created':
+        os.kill(os.getpid(), signal.SIGKILL)
+storage._boundary = stop
+storage.create_staging(
+    root, 'crashed-export', {'identity': identity, 'status': 'staging', 'step': 1}
+)
+"""
+    environment = {**os.environ, "PYTHONPATH": str(Path(storage.__file__).parent)}
+    child = subprocess.run([sys.executable, "-c", script, str(root)], env=environment, timeout=30)
+    assert child.returncode == -signal.SIGKILL
+    assert (root / "crashed-export.partial").is_dir()
+    assert not (root / "crashed-export.partial" / "STAGING.json").exists()
+    storage.owned_root(root, IDENTITY)
+    storage.recover_storage(root, IDENTITY)
+    assert not (root / "crashed-export.partial").exists()
+
+
+@pytest.mark.parametrize("changed", ["identity", "target", "filename", "published", "status"])
+def test_malformed_parent_staging_intent_never_deletes_its_target(tmp_path, changed):
+    root = storage.owned_root(tmp_path / "exports", IDENTITY)
+    staging, _ = storage.create_staging(root, "unfinished", metadata())
+    intent = root / ".unfinished.staging.json"
+    record = json.loads(intent.read_text())
+    if changed == "identity":
+        record["identity"] = {"run_id": "another"}
+    elif changed == "target":
+        record["staging"] = "../outside.partial"
+    elif changed == "filename":
+        intent.rename(root / ".wrong-name.staging.json")
+        intent = root / ".wrong-name.staging.json"
+    elif changed == "published":
+        record["published"] = ".unfinished"
+        record["staging"] = ".unfinished.partial"
+    else:
+        record["metadata"]["status"] = "complete"
+    intent.write_text(json.dumps(record))
+    with pytest.raises(ValueError):
+        storage.recover_storage(root, IDENTITY)
+    assert staging.is_dir()
+
+
+def test_replaced_target_marker_stops_pending_retirement(monkeypatch, tmp_path):
+    root = storage.owned_root(tmp_path / "checkpoints", IDENTITY)
+    prior, previous = complete(root, "step-1", 1)
+    successor, current = complete(root)
+    fail_at(monkeypatch, "retirement_intent_persisted")
+    with pytest.raises(Preempted):
+        storage.retire_directory(
+            prior,
+            root,
+            IDENTITY,
+            proof={
+                "complete": previous,
+                "successor": str(successor),
+                "successor_complete": current,
+            },
+        )
+    monkeypatch.setattr(storage, "_boundary", lambda name: None)
+    (prior / "COMPLETE.json").write_text(json.dumps({**previous, "step": 99}))
+    with pytest.raises(ValueError, match="differs from its durable ownership"):
+        storage.recover_storage(root, IDENTITY)
+    assert prior.is_dir()
+
+
+@pytest.mark.parametrize("proof", [{}, {"status": "staging", "identity": IDENTITY}])
+def test_unsupported_proof_cannot_retire_unrelated_published_directory(tmp_path, proof):
+    root = storage.owned_root(tmp_path / "checkpoints", IDENTITY)
+    unrelated = root / "unrelated"
+    unrelated.mkdir()
+    with pytest.raises(ValueError):
+        storage.retire_directory(unrelated, root, IDENTITY, proof=proof)
+    assert unrelated.is_dir()
+    assert not (root / ".unrelated.delete.json").exists()
+
+
+@pytest.mark.parametrize("record_name", ["parent_owner", "root_owner", "intent", "marker"])
+def test_interrupted_atomic_creation_record_does_not_strand_recovery(
+    monkeypatch, tmp_path, record_name
+):
+    root = tmp_path / "exports"
+    atomic = storage.atomic_json
+    targets = {
+        "parent_owner": root.parent / ".exports.owner.json",
+        "root_owner": root / "RUN_OWNER.json",
+        "intent": root / ".unfinished.staging.json",
+        "marker": root / "unfinished.partial" / "STAGING.json",
+    }
+
+    def interrupted(path, value):
+        if path == targets[record_name]:
+            path.with_suffix(path.suffix + ".partial").write_text('{"interrupted":')
+            raise Preempted("during atomic record write")
+        atomic(path, value)
+
+    monkeypatch.setattr(storage, "atomic_json", interrupted)
+    with pytest.raises(Preempted):
+        storage.owned_root(root, IDENTITY)
+        storage.create_staging(root, "unfinished", metadata())
+    monkeypatch.setattr(storage, "atomic_json", atomic)
+    storage.owned_root(root, IDENTITY)
+    storage.recover_storage(root, IDENTITY)
+    assert not (root / "unfinished.partial").exists()
+    assert storage.recover_storage(root, IDENTITY) == []
+
+
+@pytest.mark.parametrize("status", ["retiring", "retired"])
+def test_interrupted_atomic_retirement_record_is_recoverable(monkeypatch, tmp_path, status):
+    root = storage.owned_root(tmp_path / "exports", IDENTITY)
+    staging, _ = storage.create_staging(root, "unfinished", metadata())
+    atomic = storage.atomic_json
+
+    def interrupted(path, value):
+        if path.name.endswith(".delete.json") and value["status"] == status:
+            path.with_suffix(path.suffix + ".partial").write_text('{"interrupted":')
+            raise Preempted("during atomic retirement record")
+        atomic(path, value)
+
+    monkeypatch.setattr(storage, "atomic_json", interrupted)
+    with pytest.raises(Preempted):
+        storage.recover_storage(root, IDENTITY)
+    monkeypatch.setattr(storage, "atomic_json", atomic)
+    storage.recover_storage(root, IDENTITY)
+    assert not staging.exists()
+    assert not (root / ".unfinished.partial.deleting").exists()
+    assert storage.recover_storage(root, IDENTITY) == []
+
+
+@pytest.mark.parametrize("name", ["foo.a", "foo.b", "foo.partial", ".hidden"])
+def test_published_names_cannot_alias_one_staging_directory(tmp_path, name):
+    root = storage.owned_root(tmp_path / "exports", IDENTITY)
+    with pytest.raises(ValueError, match="visible and have no suffix"):
+        storage.create_staging(root, name, metadata())
+    assert list(root.iterdir()) == [root / "RUN_OWNER.json"]
+
+
+def test_training_storage_root_is_the_declared_root_or_inside_the_run(tmp_path):
+    assert storage.training_storage_root({}, tmp_path) == tmp_path / "training-state"
+    shared = tmp_path / "shared" / "run-1"
+    assert storage.training_storage_root({"training_storage": str(shared)}, tmp_path) == shared
diff --git a/tests/test_qwen3_next_subnode.py b/tests/test_qwen3_next_subnode.py
new file mode 100644
index 000000000..672d18717
--- /dev/null
+++ b/tests/test_qwen3_next_subnode.py
@@ -0,0 +1,52 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""The sub-node recorder used for first-drift localisation inside a decoder layer."""
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from rl_engine.integrations import qwen3_next_subnode as subnode
+
+
+def test_buffer_requires_each_sub_node_once_with_its_original_storage():
+    buffer = subnode.SubnodeBuffer(batch=2)
+    row = torch.arange(2048, dtype=torch.float32).to(torch.bfloat16)
+    buffer.put(3, "mixer", 1, 15, row)
+    assert torch.equal(buffer.values["mixer"][3, 1, 15], row)
+    with pytest.raises(ValueError, match="twice"):
+        buffer.put(3, "mixer", 1, 15, row)
+    with pytest.raises(ValueError, match="int64"):
+        buffer.put(3, "router", 1, 15, torch.zeros(10, dtype=torch.int32))
+    with pytest.raises(ValueError, match="bfloat16"):
+        buffer.put(4, "mlp", 0, 0, row.float())
+    with pytest.raises(ValueError, match="not all observed"):
+        buffer.layer(3)
+    with pytest.raises(ValueError, match="Unknown"):
+        subnode.subnode_layout("gate")
+
+
+def test_complete_layer_and_embedding_are_returned_in_order():
+    buffer = subnode.SubnodeBuffer(batch=1)
+    for name in subnode.SUBNODES:
+        width, dtype = subnode.subnode_layout(name)
+        buffer.put_rows(0, name, torch.zeros(1, 16, width, dtype=dtype))
+    assert tuple(buffer.layer(0)) == subnode.SUBNODES
+    with pytest.raises(ValueError, match="every response position"):
+        buffer.embedding_rows()
+    for response in range(16):
+        buffer.put_embedding(0, response, torch.zeros(2048, dtype=torch.bfloat16))
+    assert buffer.embedding_rows().shape == (1, 16, 2048)
+
+
+def test_install_binds_the_layer_index_and_remove_clears_every_hook():
+    layers = [SimpleNamespace(mlp=SimpleNamespace()) for _ in range(3)]
+    seen = []
+    subnode.install(layers, subnode.vllm_modules, lambda *args: seen.append(args))
+    layers[2].subnode_hook("mixer", "rows")
+    layers[1].mlp.subnode_hook("router", "ids")
+    assert seen == [(2, "mixer", "rows"), (1, "router", "ids")]
+    subnode.remove(layers, subnode.vllm_modules)
+    assert all(layer.subnode_hook is None and layer.mlp.subnode_hook is None for layer in layers)
diff --git a/tests/test_qwen3_next_training_audit.py b/tests/test_qwen3_next_training_audit.py
new file mode 100644
index 000000000..b08508fa6
--- /dev/null
+++ b/tests/test_qwen3_next_training_audit.py
@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""CPU regression of observations; these are not native model training acceptance."""
+
+import random
+
+import numpy as np
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_training import (
+    capture_rng_state,
+    gradient_evidence,
+    observe_native_optimizer_step,
+    restore_rng_state,
+    state_digest,
+    tensor_digest,
+)
+
+
+def test_state_identity_preserves_bits_dtype_structure_and_key_order():
+    assert tensor_digest(torch.tensor(0.0)) != tensor_digest(torch.tensor(-0.0))
+    assert tensor_digest(torch.ones(2)) != tensor_digest(torch.ones(2, dtype=torch.bfloat16))
+    assert state_digest({"a": [1], "b": 2}) == state_digest({"b": 2, "a": [1]})
+    assert state_digest([1, 2]) != state_digest((1, 2))
+    assert state_digest({1: "a"}) != state_digest({"1": "a"})
+    value = torch.arange(12).reshape(3, 4).T
+    assert tensor_digest(value, chunk_bytes=3) == tensor_digest(value.contiguous())
+
+
+@pytest.mark.parametrize("value", [torch.tensor(float("nan")), float("inf"), object()])
+def test_state_identity_rejects_unknown_or_nonfinite_state(value):
+    with pytest.raises((ValueError, TypeError)):
+        state_digest(value)
+
+
+def test_rng_restores_python_numpy_torch_exactly():
+    state = capture_rng_state()
+    first = (random.random(), np.random.random(), torch.rand(4))
+    restore_rng_state(state)
+    second = (random.random(), np.random.random(), torch.rand(4))
+    assert first[:2] == second[:2]
+    assert torch.equal(first[2], second[2])
+
+
+def test_gradients_observe_main_grad_and_allow_inactive_expert():
+    active, inactive = torch.nn.Parameter(torch.ones(2)), torch.nn.Parameter(torch.ones(2))
+    active.main_grad = torch.ones(2)
+    inactive.grad = torch.zeros(2)
+    evidence = gradient_evidence(
+        {"layer.router": active, "layer.expert42": inactive},
+        required_groups={"routing": ["router"]},
+    )
+    assert evidence["active_groups"] == {"routing": ["layer.router"]}
+    assert not evidence["parameters"]["layer.expert42"]["nonzero"]
+    with pytest.raises(ValueError, match="Active paths"):
+        gradient_evidence({"expert": inactive}, required_groups={"experts": ["expert"]})
+    inactive.grad[0] = float("nan")
+    with pytest.raises(ValueError, match="nonfinite"):
+        gradient_evidence({"expert": inactive}, required_groups={"experts": ["expert"]})
+
+
+class _Optimizer:
+    """Test double only: exercise observer timing and cleanup, never acceptance."""
+
+    def __init__(self, parameter, change=True):
+        self.parameter, self.change = parameter, change
+
+    def step(self):
+        if self.change:
+            with torch.no_grad():
+                self.parameter.add_(1)
+        self.parameter.grad = None
+        return True, 1.0, 0
+
+
+def test_observer_captures_before_native_clear_and_restores_method():
+    model = torch.nn.Linear(2, 1, bias=False)
+    model.weight.grad = torch.ones_like(model.weight)
+    optimizer, evidence = _Optimizer(model.weight), {}
+    with observe_native_optimizer_step(
+        optimizer, [model], required_groups={"output": ["weight"]}, evidence=evidence
+    ):
+        optimizer.step()
+    assert model.weight.grad is None
+    assert "step" not in vars(optimizer)
+    assert evidence["changed_parameters"] == ["chunk0.weight"]
+    assert evidence["gradients"]["parameters"]["chunk0.weight"]["nonzero"]
+
+
+@pytest.mark.parametrize("mode", ["no_call", "no_change", "double_call"])
+def test_observer_rejects_incomplete_or_fake_update(mode):
+    model = torch.nn.Linear(2, 1, bias=False)
+    model.weight.grad = torch.ones_like(model.weight)
+    optimizer = _Optimizer(model.weight, change=mode != "no_change")
+    with pytest.raises(ValueError):
+        with observe_native_optimizer_step(
+            optimizer, [model], required_groups={"output": ["weight"]}, evidence={}
+        ):
+            if mode != "no_call":
+                optimizer.step()
+            if mode == "double_call":
+                optimizer.step()
+    assert "step" not in vars(optimizer)
+
+
+def test_gradient_nonzero_is_judged_across_tensor_parallel_ranks():
+    """A vocab-parallel shard with no tokens is all-zero locally yet trains as a whole."""
+    embedding, router = torch.nn.Parameter(torch.ones(4, 2)), torch.nn.Parameter(torch.ones(2))
+    embedding.main_grad = torch.zeros(4, 2)
+    router.main_grad = torch.ones(2)
+    groups = {"embedding": ["embedding"], "router": ["router"]}
+
+    def other_rank_saw_tokens(flags):
+        return [True for _ in flags]
+
+    evidence = gradient_evidence(
+        {"chunk0.embedding.word_embeddings.weight": embedding, "layer.router": router},
+        required_groups=groups,
+        reduce_flags=other_rank_saw_tokens,
+    )
+    record = evidence["parameters"]["chunk0.embedding.word_embeddings.weight"]
+    assert record["nonzero"] is True and record["nonzero_local"] is False
+    assert evidence["active_groups"]["embedding"] == ["chunk0.embedding.word_embeddings.weight"]
+    with pytest.raises(ValueError, match="Active paths.*embedding"):
+        gradient_evidence(
+            {"chunk0.embedding.word_embeddings.weight": embedding, "layer.router": router},
+            required_groups=groups,
+            reduce_flags=lambda flags: list(flags),
+        )
+    with pytest.raises(ValueError, match="one per parameter"):
+        gradient_evidence(
+            {"layer.router": router},
+            required_groups={"router": ["router"]},
+            reduce_flags=lambda flags: [],
+        )
diff --git a/tests/test_qwen3_next_training_checkpoint.py b/tests/test_qwen3_next_training_checkpoint.py
new file mode 100644
index 000000000..884b4e42a
--- /dev/null
+++ b/tests/test_qwen3_next_training_checkpoint.py
@@ -0,0 +1,539 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""CPU serialization/protocol regressions; these do not certify native GPU training."""
+
+import json
+import random
+import sys
+from datetime import timedelta
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+import qwen3_next_optimizer_checkpoint as optimizer_checkpoint  # noqa: E402
+import qwen3_next_training_checkpoint as checkpoint  # noqa: E402
+from qwen3_next_checkpoint import publish_checkpoint  # noqa: E402
+
+from rl_engine.integrations.qwen3_next_training import capture_rng_state, state_digest  # noqa: E402
+from rl_engine.testing.qwen3_next_rl_fixture import PolicyFixture  # noqa: E402
+
+IDENTITY = {"source": "pinned", "provider": "strict", "checkpoint": "official", "tp": 4}
+
+
+class ProtocolModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.arange(8, dtype=torch.bfloat16).reshape(2, 4))
+        self.register_buffer("counter", torch.tensor(12, dtype=torch.int64))
+
+    def state_dict_for_save_checkpoint(self):
+        return self.state_dict()
+
+
+class ProtocolOptimizer:
+    """Tiny CPU stand-in only for exercising serialization and corruption paths."""
+
+    def __init__(self, model):
+        self.model = model
+        self.calls = []
+        self.model_param_group_index_map = {model.weight: (0, 0)}
+        self.gbuf_ranges = [
+            {
+                (torch.bfloat16, torch.float32): [
+                    {
+                        "param_map": {
+                            model.weight: {
+                                "param": SimpleNamespace(start=0, end=8),
+                                "gbuf_local": SimpleNamespace(start=0, end=8),
+                            }
+                        }
+                    }
+                ]
+            }
+        ]
+        self.buffers = [SimpleNamespace(buckets=[SimpleNamespace(grad_data=torch.empty(8))])]
+        self.optimizer = SimpleNamespace(
+            cpu_optimizer_cls=torch.optim.AdamW, gpu_optimizer_cls=None
+        )
+        self.states = {
+            "param": model.weight.detach().float().clone(),
+            "exp_avg": torch.full_like(model.weight, 0.125, dtype=torch.float32),
+            "exp_avg_sq": torch.full_like(model.weight, 0.0625, dtype=torch.float32),
+            "step": torch.tensor(2.0),
+        }
+        self.step = 2
+        self.corrupt_load = False
+
+    def _get_main_param_and_optimizer_states(self, parameter):
+        assert parameter is self.model.weight
+        return self.states
+
+    def state_dict(self):
+        return {"step": self.step, "cpu_optimizer_cls": torch.optim.AdamW}
+
+    def load_state_dict(self, header):
+        self.calls.append("header")
+        assert header["cpu_optimizer_cls"] is torch.optim.AdamW
+        self.step = header["step"]
+        # Emulate native HybridDeviceOptimizer's RNG-consuming dummy initialization.
+        with torch.no_grad():
+            self.model.weight.copy_(torch.randn_like(self.model.weight))
+
+    def save_parameter_state(self, path):
+        raise AssertionError("Whole-bucket packing must never be called")
+
+    def load_parameter_state(self, path):
+        raise AssertionError("Whole-bucket unpacking must never be called")
+
+    def _set_main_param_and_optimizer_states(self, parameter, states):
+        assert parameter is self.model.weight
+        self.calls.append("native_set")
+        assert states is self.states
+        if self.corrupt_load:
+            self.states["exp_avg"][0, 0] = -0.125
+
+
+class ProtocolScheduler:
+    def __init__(self):
+        self.num_steps = 8
+
+    def state_dict(self):
+        return {"num_steps": self.num_steps, "lr": 1e-6}
+
+    def load_state_dict(self, state):
+        self.num_steps = state["num_steps"]
+
+
+@pytest.fixture
+def runtime(monkeypatch):
+    monkeypatch.setattr(torch.cuda, "synchronize", lambda: None)
+    monkeypatch.setattr(torch.cuda, "get_rng_state_all", lambda: [])
+    monkeypatch.setattr(torch.cuda, "set_rng_state_all", lambda values: None)
+    monkeypatch.setattr(torch.cuda, "device_count", lambda: 0)
+    monkeypatch.setattr(checkpoint.dist, "get_rank", lambda: 0)
+    model = ProtocolModel()
+    optimizer = ProtocolOptimizer(model)
+    scheduler = ProtocolScheduler()
+    fixture = PolicyFixture.freeze(
+        torch.arange(320, dtype=torch.int64).reshape(4, 80),
+        torch.full((4, 16), -2.0, dtype=torch.float32),
+        baseline_identity="real-worker-baseline-required-in-production",
+    )
+    return [model], optimizer, scheduler, fixture
+
+
+def save_rank(tmp_path, runtime):
+    model, optimizer, scheduler, fixture = runtime
+    staging = tmp_path / "step-2.partial"
+    staging.mkdir()
+    record = checkpoint._save_rank(
+        model,
+        optimizer,
+        scheduler,
+        fixture,
+        staging,
+        leaves=[optimizer],
+        identity=IDENTITY,
+        step=2,
+        sample_position=8,
+        tracker=None,
+        native_args=SimpleNamespace(consumed_train_samples=8),
+        update_evidence={"native_step": 2, "gradient_norm": 1.0},
+    )
+    return staging, record
+
+
+def load_rank(staging, runtime):
+    model, optimizer, scheduler, fixture = runtime
+    return checkpoint._load_rank(
+        model,
+        optimizer,
+        scheduler,
+        fixture,
+        staging,
+        leaves=[optimizer],
+        identity=IDENTITY,
+        complete={"step": 2, "sample_position": 8},
+        tracker=None,
+        native_args=SimpleNamespace(consumed_train_samples=0),
+    )
+
+
+def test_bounded_load_calls_hdo_sync(tmp_path, runtime, monkeypatch):
+    pytest.importorskip("megatron")
+    staging, _ = save_rank(tmp_path, runtime)
+    model, optimizer, _, _ = runtime
+    calls = []
+
+    class FakeHDO:
+        def _sync_hdo_state_to_sub_optimizers(self):
+            calls.append("sync")
+
+    optimizer.optimizer = FakeHDO()
+    monkeypatch.setattr(
+        "megatron.core.optimizer.cpu_offloading.hybrid_optimizer.HybridDeviceOptimizer",
+        FakeHDO,
+    )
+    optimizer_checkpoint.load_bounded_parameter_state(model, [optimizer], staging / "rank-0")
+    assert calls == ["sync"]
+
+
+def test_load_rank_fails_when_hdo_sync_skipped(tmp_path, runtime, monkeypatch):
+    staging, _ = save_rank(tmp_path, runtime)
+    model, optimizer, scheduler, fixture = runtime
+
+    def load_without_sync(model, leaves, rank_dir):
+        rank_dir = Path(rank_dir)
+        touched = []
+        index = json.loads((rank_dir / "optimizer-index.json").read_text())
+        current = optimizer_checkpoint.native_parameter_layout(model, leaves)
+        extras = torch.load(rank_dir / "optimizer-extra.pt", map_location="cpu", weights_only=True)
+        with torch.no_grad():
+            for name, record in current.items():
+                entry = index["parameters"][name]
+                states = optimizer_checkpoint.native_parameter_states(record)
+                for block in entry["blocks"]:
+                    start, end = block["start"], block["end"]
+                    payload = torch.load(
+                        rank_dir / block["path"], map_location="cpu", weights_only=True
+                    )
+                    for key in optimizer_checkpoint.PARAMETER_KEYS:
+                        states[key].reshape(-1)[start:end].copy_(payload[key])
+                for key, value in extras[name].items():
+                    if isinstance(states[key], torch.Tensor):
+                        states[key].copy_(value)
+                    else:
+                        states[key] = value
+                record["leaf"]._set_main_param_and_optimizer_states(record["parameter"], states)
+                if record["leaf"] not in touched:
+                    touched.append(record["leaf"])
+
+    monkeypatch.setattr(checkpoint, "load_bounded_parameter_state", load_without_sync)
+    monkeypatch.setattr(
+        checkpoint,
+        "verify_hdo_optimizer_master_aliases",
+        lambda model, leaves: (_ for _ in ()).throw(
+            ValueError("HDO FP32 master copy diverged from checkpoint-visible master weights")
+        ),
+    )
+    with pytest.raises(ValueError, match="FP32 master copy diverged"):
+        checkpoint._load_rank(
+            model,
+            optimizer,
+            scheduler,
+            fixture,
+            staging,
+            leaves=[optimizer],
+            identity=IDENTITY,
+            complete={"step": 2, "sample_position": 8},
+            tracker=None,
+            native_args=SimpleNamespace(consumed_train_samples=0),
+        )
+
+
+def test_rank_roundtrip_uses_parameter_state_and_restores_rng_last(tmp_path, runtime):
+    model, optimizer, scheduler, _ = runtime
+    original_rng = state_digest(capture_rng_state())
+    original_model = state_digest(model[0].state_dict())
+    staging, record = save_rank(tmp_path, runtime)
+    with torch.no_grad():
+        model[0].weight.fill_(-3)
+        model[0].counter.fill_(42)
+    optimizer.states["param"].fill_(5)
+    optimizer.states["exp_avg"].fill_(0)
+    optimizer.step = 100
+    scheduler.num_steps = 400
+    random.random()
+    np.random.rand()
+    torch.rand(3)
+    assert load_rank(staging, runtime) == record
+    assert optimizer.calls == ["header", "native_set"]
+    assert optimizer.step == 2 and scheduler.num_steps == 8
+    assert state_digest(model[0].state_dict()) == original_model
+    assert state_digest(capture_rng_state()) == original_rng
+
+
+def test_all_small_state_files_are_weights_only_safe(tmp_path, runtime):
+    staging, _ = save_rank(tmp_path, runtime)
+    for path in (staging / "rank-0").glob("*.pt"):
+        assert torch.load(path, map_location="cpu", weights_only=True) is not None
+
+
+def test_native_parameter_load_corruption_is_rejected(tmp_path, runtime):
+    staging, _ = save_rank(tmp_path, runtime)
+    runtime[1].corrupt_load = True
+    with pytest.raises(ValueError, match="master weights or Adam moments"):
+        load_rank(staging, runtime)
+
+
+def test_header_only_optimizer_is_not_a_complete_checkpoint(runtime):
+    del runtime[1].states["exp_avg_sq"]
+    with pytest.raises(ValueError, match="Adam moments are missing"):
+        checkpoint._optimizer_parameter_records(runtime[0], [runtime[1]])
+
+
+def test_optimizer_must_cover_each_model_parameter_exactly_once(runtime):
+    model, optimizer, _, _ = runtime
+    with pytest.raises(ValueError, match="partition model parameters"):
+        checkpoint._optimizer_parameter_records(model, [optimizer, optimizer])
+    optimizer.model_param_group_index_map = {}
+    with pytest.raises(ValueError, match="absent from optimizer groups"):
+        checkpoint._optimizer_parameter_records(model, [optimizer])
+
+
+def test_optimizer_master_dtype_is_never_silently_cast(runtime):
+    runtime[1].states["param"] = runtime[1].states["param"].bfloat16()
+    with pytest.raises(ValueError, match="Invalid fully offloaded native param"):
+        checkpoint._optimizer_parameter_records(runtime[0], [runtime[1]])
+
+
+def test_native_class_header_uses_only_running_optimizer_allowlist(runtime):
+    allowed = checkpoint._native_class_allowlist([runtime[1]])
+    header = {"group": {"cls": torch.optim.AdamW, "options": (1, True)}}
+    encoded = checkpoint._encode_header(header, allowed)
+    assert checkpoint._decode_header(encoded, allowed) == header
+    with pytest.raises(TypeError, match="Unpinned class"):
+        checkpoint._encode_header({"cls": ProtocolOptimizer}, allowed)
+    with pytest.raises(ValueError, match="absent from the running"):
+        checkpoint._decode_header({checkpoint.CLASS_TAG: "untrusted.arbitrary.Class"}, allowed)
+
+
+def test_saved_fixture_identity_cannot_change(tmp_path, runtime):
+    staging, _ = save_rank(tmp_path, runtime)
+    runtime[3].tokens[0, 0] += 1
+    with pytest.raises(ValueError, match="Frozen policy fixture"):
+        checkpoint.load_training_checkpoint(
+            None, *runtime, staging, identity=IDENTITY, tracker=None
+        )
+
+
+def test_model_schema_rejects_dtype_change(tmp_path, runtime):
+    staging, record = save_rank(tmp_path, runtime)
+    path = staging / "rank-0/model-0.pt"
+    value = torch.load(path, weights_only=True)
+    value["weight"] = value["weight"].float()
+    torch.save(value, path)
+    record["model_sha256"][0] = state_digest(value)
+    (staging / "rank-0/progress.json").write_text(json.dumps(record))
+    with pytest.raises(ValueError, match="dtypes, shapes or state keys"):
+        load_rank(staging, runtime)
+
+
+def test_numpy_rng_roundtrip_preserves_all_bits(runtime):
+    state = capture_rng_state()
+    assert state_digest(checkpoint._decode_rng(checkpoint._encode_rng(state))) == state_digest(
+        state
+    )
+
+
+def test_shared_storage_disk_estimate_counts_padding_once():
+    buffer = torch.arange(1024, dtype=torch.bfloat16)
+    assert checkpoint._tensor_bytes({"one": buffer[:8], "two": buffer[8:16]}) == 2048
+
+
+def test_bounded_blocks_do_not_serialize_underlying_bucket(tmp_path, runtime, monkeypatch):
+    import qwen3_next_optimizer_checkpoint as bounded
+
+    monkeypatch.setattr(bounded, "BLOCK_BYTES", 48)
+    optimizer = runtime[1]
+    for key in bounded.PARAMETER_KEYS:
+        backing = torch.arange(1024 * 1024, dtype=torch.float32)
+        optimizer.states[key] = backing[100:108].view(2, 4)
+    staging, _ = save_rank(tmp_path, runtime)
+    blocks = sorted((staging / "rank-0/optimizer-state").glob("*.pt"))
+    assert len(blocks) == 2
+    for path in blocks:
+        assert path.stat().st_size < 4096
+        payload = torch.load(path, weights_only=True)
+        assert sum(value.numel() * value.element_size() for value in payload.values()) == 48
+    load_rank(staging, runtime)
+
+
+@pytest.mark.parametrize("change", ["gap", "overlap", "truncated"])
+def test_bounded_block_ranges_must_cover_parameter_once(tmp_path, runtime, monkeypatch, change):
+    import qwen3_next_optimizer_checkpoint as bounded
+
+    monkeypatch.setattr(bounded, "BLOCK_BYTES", 48)
+    staging, _ = save_rank(tmp_path, runtime)
+    path = staging / "rank-0/optimizer-index.json"
+    index = json.loads(path.read_text())
+    blocks = index["parameters"]["chunk0.weight"]["blocks"]
+    if change == "gap":
+        blocks[1]["start"] += 1
+    elif change == "overlap":
+        blocks[1]["start"] -= 1
+    else:
+        blocks.pop()
+    path.write_text(json.dumps(index))
+    with pytest.raises(ValueError, match="cover each parameter exactly once|complete parameter"):
+        load_rank(staging, runtime)
+
+
+def test_native_dp_range_cannot_describe_partial_parameter(runtime):
+    optimizer = runtime[1]
+    ranges = next(iter(optimizer.gbuf_ranges[0].values()))[0]["param_map"][runtime[0][0].weight]
+    ranges["param"].start = 1
+    with pytest.raises(ValueError, match="cover each complete parameter"):
+        checkpoint._optimizer_parameter_records(runtime[0], [optimizer])
+
+
+def test_state_identity_includes_native_consumed_samples_and_rng(runtime, monkeypatch):
+    monkeypatch.setattr(checkpoint, "_native_leaves", lambda *args: [runtime[1]])
+    args = SimpleNamespace(consumed_train_samples=8)
+    first = checkpoint.training_state_identity(
+        args, *runtime, step=2, sample_position=8, identity=IDENTITY, tracker=None
+    )
+    args.consumed_train_samples = 12
+    second = checkpoint.training_state_identity(
+        args, *runtime, step=2, sample_position=8, identity=IDENTITY, tracker=None
+    )
+    assert first["state_sha256"] != second["state_sha256"]
+    args.consumed_train_samples = 8
+    torch.rand(1)
+    third = checkpoint.training_state_identity(
+        args, *runtime, step=2, sample_position=8, identity=IDENTITY, tracker=None
+    )
+    assert first["rng_sha256"] != third["rng_sha256"]
+
+
+def test_native_required_files_are_bound_to_checked_manifest(tmp_path, runtime, monkeypatch):
+    model, optimizer, scheduler, fixture = runtime
+    staging = tmp_path / "all.partial"
+    staging.mkdir()
+    for rank in range(4):
+        monkeypatch.setattr(checkpoint.dist, "get_rank", lambda rank=rank: rank)
+        checkpoint._save_rank(
+            model,
+            optimizer,
+            scheduler,
+            fixture,
+            staging,
+            leaves=[optimizer],
+            identity=IDENTITY,
+            step=2,
+            sample_position=8,
+            tracker=None,
+            native_args=SimpleNamespace(consumed_train_samples=8),
+            update_evidence={"native_step": 2, "gradient_norm": 1.0},
+        )
+    complete = tmp_path / "all"
+    publish_checkpoint(staging, complete, world_size=4, identity=IDENTITY)
+    assert checkpoint._validate_native_checkpoint(complete, IDENTITY, 1)["step"] == 2
+    with pytest.raises(ValueError, match="omits or substitutes"):
+        checkpoint._validate_native_checkpoint(complete, IDENTITY, 2)
+    (complete / "rank-3/optimizer-state/p00000-b00000.pt").write_bytes(b"corrupt")
+    monkeypatch.setattr(checkpoint.dist, "get_rank", lambda: 0)
+    monkeypatch.setattr(checkpoint, "_all_rank_call", lambda action, label: action())
+    monkeypatch.setattr(checkpoint, "_rank_zero_call", lambda action, label: action())
+    monkeypatch.setattr(checkpoint, "_native_leaves", lambda *args: [optimizer])
+    optimizer.calls.clear()
+    with pytest.raises(ValueError, match="checksum|size mismatch"):
+        checkpoint.load_training_checkpoint(
+            None, *runtime, complete, identity=IDENTITY, tracker=None
+        )
+    assert not optimizer.calls
+
+
+def _serial_worker(rank, root, fail_owner):
+    dist.init_process_group(
+        "gloo",
+        rank=rank,
+        world_size=4,
+        init_method=f"file://{root}/init-{fail_owner}",
+        timeout=timedelta(seconds=30),
+    )
+    try:
+
+        def action():
+            lock = Path(root) / f"lock-{fail_owner}"
+            with lock.open("x"):
+                path = Path(root) / f"visited-{fail_owner}"
+                with path.open("a") as stream:
+                    stream.write(f"{rank}\n")
+                if rank == fail_owner:
+                    raise ValueError("simulated native serialization failure")
+            lock.unlink()
+            return rank
+
+        try:
+            result = checkpoint._serial_rank_call(action, "protocol test")
+        except RuntimeError as exc:
+            result = str(exc)
+        (Path(root) / f"result-{fail_owner}-{rank}.json").write_text(json.dumps(result))
+    finally:
+        dist.destroy_process_group()
+
+
+@pytest.mark.parametrize("fail_owner", [-1, 2])
+def test_real_four_process_serial_protocol_propagates_failures(tmp_path, fail_owner):
+    mp.spawn(_serial_worker, args=(str(tmp_path), fail_owner), nprocs=4, join=True)
+    visited = (tmp_path / f"visited-{fail_owner}").read_text().splitlines()
+    assert visited == (["0", "1", "2", "3"] if fail_owner == -1 else ["0", "1", "2"])
+    for rank in range(4):
+        result = json.loads((tmp_path / f"result-{fail_owner}-{rank}.json").read_text())
+        if fail_owner == -1:
+            assert result == rank
+        else:
+            assert "simulated native serialization failure" in result
+
+
+def _wire_sharded_hdo(optimizer, monkeypatch, *, break_alias=False, diverge=False):
+    """Emulate Megatron: the HDO is keyed by the DP shard, the getter returns the shard's master."""
+    pytest.importorskip("megatron")
+    model_weight = optimizer.model.weight
+    shard = torch.nn.Parameter(model_weight.detach().clone().reshape(-1))
+    master = model_weight.detach().float().clone().reshape(-1)
+    fp32_inner = master.clone()
+    if diverge:
+        fp32_inner[0] += 1.0
+    hdo_state = {"master_param": master, "exp_avg": torch.zeros(8), "exp_avg_sq": torch.zeros(8)}
+
+    class FakeHDO:
+        pass
+
+    hdo = FakeHDO()
+    hdo.param_groups = [{"params": [shard]}]
+    hdo.state = {shard: hdo_state}
+    hdo.param_to_fp32_param = {shard: fp32_inner}
+    hdo.param_to_inner_param = {shard: fp32_inner}
+    cpu_state = {fp32_inner: {} if break_alias else hdo_state}
+    hdo.cpu_optimizers = [SimpleNamespace(param_groups=[{"params": [fp32_inner]}], state=cpu_state)]
+    monkeypatch.setattr(
+        "megatron.core.optimizer.cpu_offloading.hybrid_optimizer.HybridDeviceOptimizer", FakeHDO
+    )
+    monkeypatch.setattr(
+        optimizer_checkpoint,
+        "_hdo_param_generator",
+        lambda cpu_optimizer: [p for g in cpu_optimizer.param_groups for p in g["params"]],
+    )
+    optimizer.optimizer = hdo
+    optimizer.states["param"] = master  # the real getter returns state["master_param"]
+    return hdo
+
+
+def test_hdo_alias_check_uses_the_sharded_parameter_not_the_model_parameter(runtime, monkeypatch):
+    model, optimizer, _, _ = runtime
+    hdo = _wire_sharded_hdo(optimizer, monkeypatch)
+    assert model[0].weight not in hdo.param_to_fp32_param  # keyed by the shard, as in Megatron
+    optimizer_checkpoint.verify_hdo_optimizer_master_aliases(model, [optimizer])
+
+
+@pytest.mark.parametrize(
+    "kwargs, message",
+    [
+        ({"break_alias": True}, "not aliased"),
+        ({"diverge": True}, "diverged"),
+    ],
+)
+def test_hdo_alias_check_rejects_broken_wiring(runtime, monkeypatch, kwargs, message):
+    model, optimizer, _, _ = runtime
+    _wire_sharded_hdo(optimizer, monkeypatch, **kwargs)
+    with pytest.raises(ValueError, match=message):
+        optimizer_checkpoint.verify_hdo_optimizer_master_aliases(model, [optimizer])
diff --git a/tests/test_qwen3_next_training_closeout.py b/tests/test_qwen3_next_training_closeout.py
new file mode 100644
index 000000000..fbd59b087
--- /dev/null
+++ b/tests/test_qwen3_next_training_closeout.py
@@ -0,0 +1,430 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""CPU recovery/audit regressions; these never substitute for native GPU updates."""
+
+import copy
+import json
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_subnode import SUBNODES
+
+
+def test_requeue_repeats_final_reload_without_reusing_stale_attempt(monkeypatch):
+    import qwen3_next_training as runner
+
+    journal = {
+        "schema_version": 1,
+        "identity": {},
+        "events": list(runner.EVENTS),
+        "recovery": [],
+        "workers": {phase: {"path": phase} for phase in runner.PHASES},
+        "l2_reports": {
+            phase: {"path": phase} for phase in ("baseline", "update1", "update2", "reload")
+        },
+    }
+    monkeypatch.setattr(runner, "read_record", lambda record: {"attempt": "0"})
+    assert runner.repeat_reload_in_current_attempt(journal, "1")
+    runner.validate_journal(journal, {}, verify_files=False)
+    assert set(journal["workers"]) == {"update1", "update2"}
+    assert set(journal["l2_reports"]) == {"baseline", "update1", "update2"}
+    retired = journal["recovery"][0]
+    assert retired["from_attempt"] == "0" and retired["to_attempt"] == "1"
+    assert set(retired["workers"]) == {"reload", "continuous", "resumed"}
+    assert not runner.repeat_reload_in_current_attempt(journal, "1")
+
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+import qwen3_next_training as runner  # noqa: E402
+
+from rl_engine.integrations import qwen3_next_native_training as native  # noqa: E402
+from rl_engine.integrations import qwen3_next_training as observer  # noqa: E402
+
+
+def parameter_names():
+    return [
+        f"chunk0.decoder.layers.{layer}.{component}.weight"
+        for layer in range(48)
+        for component in ("self_attention", "mlp", "pre_mlp_layernorm")
+    ] + [
+        f"chunk0.{component}.weight"
+        for component in ("embedding", "output_layer", "final_layernorm")
+    ]
+
+
+def audit(step=1):
+    names = parameter_names()
+    before = {name: "1" * 64 for name in names}
+    after = {name: "2" * 64 for name in names}
+    return {
+        "step": step,
+        "sample_position": step * 4,
+        "fixture": "a" * 64,
+        "policy_loss_calls": 4,
+        "grad_norm": 0.25,
+        "metrics": {"loss": -0.25},
+        "parameter_hashes_before": before,
+        "parameter_hashes_after": after,
+        "changed_parameters": names,
+        "gradients": {
+            "parameters": {
+                name: {"present": True, "nonzero": True, "dtype": "torch.float32"} for name in names
+            },
+            "active_groups": {name: [name] for name in names},
+        },
+        "progress": {
+            "consumed_before": (step - 1) * 4,
+            "consumed_after": step * 4,
+            "scheduler_before": (step - 1) * 4,
+            "scheduler_after": step * 4,
+            "sample_counter_owner": "qwen3_next_native_training.policy_step",
+        },
+    }
+
+
+def state():
+    value = {
+        "schema_version": 1,
+        "scope": "native_vime_training_state",
+        "rank": 0,
+        "identity": {"fixture_sha256": "a" * 64},
+        "step": 1,
+        "sample_position": 4,
+        "consumed_train_samples": 4,
+        "fixture_sha256": "a" * 64,
+        "model_sha256": ["b" * 64],
+        "optimizer_parameter_sha256": {
+            name: {kind: "c" * 64 for kind in ("param", "exp_avg", "exp_avg_sq")}
+            for name in parameter_names()
+        },
+        "optimizer_header_sha256": "d" * 64,
+        "scheduler_sha256": "e" * 64,
+        "rng_sha256": "f" * 64,
+    }
+    return seal_state(value)
+
+
+def seal_state(value):
+    value["state_sha256"] = observer.state_digest(
+        {key: item for key, item in value.items() if key != "state_sha256"}
+    )
+    return value
+
+
+def validate_state(value):
+    runner.validate_state(
+        value, rank=0, step=1, identity={"fixture_sha256": "a" * 64}, parameters=parameter_names()
+    )
+
+
+def test_full_actor_update_and_recovery_state_contract():
+    runner.validate_update(audit(), step=1, fixture="a" * 64)
+    validate_state(state())
+
+
+@pytest.mark.parametrize(
+    "damage", ["no_change", "zero_gradient", "missing_layer", "nan_loss", "bad_progress"]
+)
+def test_update_rejects_fake_or_incomplete_actor_evidence(damage):
+    value = audit()
+    name = parameter_names()[0]
+    if damage == "no_change":
+        value["parameter_hashes_after"] = dict(value["parameter_hashes_before"])
+        value["changed_parameters"] = []
+    elif damage == "zero_gradient":
+        value["gradients"]["parameters"][name]["nonzero"] = False
+    elif damage == "missing_layer":
+        for key in ("parameter_hashes_before", "parameter_hashes_after"):
+            value[key] = {
+                name: digest for name, digest in value[key].items() if ".layers.47." not in name
+            }
+    elif damage == "nan_loss":
+        value["metrics"]["loss"] = float("nan")
+    else:
+        value["progress"]["consumed_after"] = 0
+    with pytest.raises(ValueError):
+        runner.validate_update(value, step=1, fixture="a" * 64)
+
+
+@pytest.mark.parametrize("field", ["rng_sha256", "scheduler_sha256", "optimizer_header_sha256"])
+def test_state_rejects_tampering_even_when_comparison_peer_was_copied(field):
+    value = state()
+    value[field] = "0" * 64
+    copied_peer = copy.deepcopy(value)
+    assert value == copied_peer
+    with pytest.raises(ValueError, match="digest"):
+        validate_state(value)
+
+
+@pytest.mark.parametrize("damage", ["consumed", "missing_master", "missing_parameter"])
+def test_resigned_state_still_requires_complete_progress_and_optimizer(damage):
+    value = state()
+    if damage == "consumed":
+        value["consumed_train_samples"] = 0
+    elif damage == "missing_master":
+        del value["optimizer_parameter_sha256"][parameter_names()[0]]["param"]
+    else:
+        del value["optimizer_parameter_sha256"][parameter_names()[0]]
+    with pytest.raises(ValueError):
+        validate_state(seal_state(value))
+
+
+@pytest.mark.parametrize("behavior", ["success", "native_failure", "missing_scheduler_step"])
+def test_controller_advances_samples_only_after_successful_native_scheduler_step(
+    monkeypatch, behavior
+):
+    args, scheduler = SimpleNamespace(consumed_train_samples=4), SimpleNamespace(num_steps=4)
+
+    def update(*unused, **kwargs):
+        if behavior == "native_failure":
+            raise RuntimeError("injected native optimizer failure")
+        if behavior == "success":
+            scheduler.num_steps += 4
+        return audit(step=2)
+
+    monkeypatch.setattr(observer, "native_policy_update", update)
+
+    def invoke():
+        return native.policy_step(args, [], None, scheduler, None, step=2, required_groups={})
+
+    if behavior == "success":
+        result = invoke()
+        assert args.consumed_train_samples == scheduler.num_steps == 8
+        assert result["progress"]["consumed_before"] == 4
+    else:
+        with pytest.raises((RuntimeError, ValueError)):
+            invoke()
+        assert args.consumed_train_samples == 4
+
+
+def test_journal_requires_l2_between_updates_and_before_reload_controls():
+    journal = {
+        "schema_version": 1,
+        "identity": {},
+        "events": list(runner.EVENTS[:3]),
+        "workers": {"update1": {}},
+        "l2_reports": {"baseline": {}, "update1": {}},
+    }
+    runner.validate_journal(journal, {}, verify_files=False)
+    journal["events"] = ["l2:baseline", "worker:update1", "worker:update2"]
+    with pytest.raises(ValueError, match="order"):
+        runner.validate_journal(journal, {}, verify_files=False)
+
+
+@pytest.mark.parametrize("missing", ["update2", "reload", "continuous", "resumed"])
+def test_final_acceptance_rejects_missing_phases_before_reading_evidence(missing):
+    report = {
+        "schema_version": 1,
+        "status": "passed",
+        "scope": "qwen3_next_training_and_reload",
+        "required_sections": list(runner.SECTIONS),
+        "events": list(runner.EVENTS),
+        "l2_reports": dict.fromkeys(("baseline", "update1", "update2", "reload"), {}),
+        "workers": dict.fromkeys(runner.PHASES, {}),
+    }
+    del report["workers"][missing]
+    with pytest.raises(ValueError, match="omitted"):
+        runner.validate_training_report(report)
+
+
+def test_cleanup_rejects_symlink_to_another_directory_in_same_run(tmp_path):
+    retained = tmp_path / "retained"
+    retained.mkdir()
+    (retained / "state").write_text("must survive")
+    alias = tmp_path / "alias"
+    alias.symlink_to(retained, target_is_directory=True)
+    with pytest.raises(ValueError, match="owned"):
+        runner._owned_remove(alias, tmp_path)
+    assert (retained / "state").read_text() == "must survive"
+
+
+def test_interrupted_cleanup_requires_matching_run_identity(tmp_path):
+    root = tmp_path / "checkpoints"
+    identity = {"run_id": "this-run"}
+    runner.owned_root(root, identity)
+    own = root / "step-1.partial"
+    own.mkdir()
+    (own / "STAGING.json").write_text(json.dumps({"identity": identity, "status": "staging"}))
+    assert len(runner.remove_interrupted(root, identity)) == 1
+    assert not own.exists()
+    foreign = root / "foreign.partial"
+    foreign.mkdir()
+    (foreign / "STAGING.json").write_text(
+        json.dumps({"identity": {"run_id": "other"}, "status": "staging"})
+    )
+    with pytest.raises(ValueError, match="another run"):
+        runner.remove_interrupted(root, identity)
+    assert foreign.exists()
+
+
+def test_actual_fault_injection_kills_writer_and_rejects_partial_or_corrupt_protocol(tmp_path):
+    result = runner.checkpoint_faults(tmp_path / "faults", {"run_id": "cpu-protocol-only"})
+    evidence = runner.read_record(result)
+    assert set(evidence["cases"]) == set(runner.FAULTS)
+    assert all(case["passed"] for case in evidence["cases"].values())
+    assert evidence["cases"]["interrupted_writer"]["exit_code"] < 0
+    # A passed label cannot survive repair/substitution of the rejected bytes.
+    corrupt = Path(evidence["cases"]["hash_error"]["checkpoint"]) / "rank-2-optimizer.bin"
+    corrupt.write_bytes(b"2:optimizer")
+    with pytest.raises(ValueError, match="no longer reproduces"):
+        runner.validate_fault_evidence(evidence, {"run_id": "cpu-protocol-only"})
+
+
+def test_checkpoint_retention_keeps_successor_and_only_removes_older_owned_state(tmp_path):
+    identity = {"run_id": "retention-unit-contract"}
+    runner.owned_root(tmp_path, identity)
+    checkpoints = []
+    for step in (1, 2):
+        path = tmp_path / f"step-{step}"
+        path.mkdir()
+        (path / "COMPLETE.json").write_text(
+            json.dumps({"identity": identity, "status": "complete", "step": step})
+        )
+        checkpoints.append(path)
+    retired = native.retire_prior_checkpoints(checkpoints[1], tmp_path, identity)
+    assert retired[0]["complete"]["step"] == 1
+    assert not checkpoints[0].exists()
+    assert checkpoints[1].is_dir()
+
+
+def test_checkpoint_retention_cannot_delete_a_foreign_run(tmp_path):
+    identity = {"run_id": "this-run"}
+    runner.owned_root(tmp_path, identity)
+    successor, foreign = tmp_path / "step-2", tmp_path / "other-run"
+    for path, owner, step in ((successor, identity, 2), (foreign, {"run_id": "other"}, 1)):
+        path.mkdir()
+        (path / "COMPLETE.json").write_text(
+            json.dumps({"identity": owner, "status": "complete", "step": step})
+        )
+    with pytest.raises(ValueError, match="unrelated"):
+        native.retire_prior_checkpoints(successor, tmp_path, identity)
+    assert successor.exists() and foreign.exists()
+
+
+@pytest.mark.parametrize("difference", [None, "signed_zero", "state"])
+def test_reload_comparison_reopens_original_tensors_and_rejects_cross_phase_drift(
+    tmp_path, difference
+):
+    # Small tensors test the raw comparison plumbing. Full matrix shape and
+    # provenance remain the independent validate_matrix_report prerequisite.
+    payload = {
+        "response_tokens": torch.ones((1, 16), dtype=torch.int64),
+        "logits": torch.zeros((1, 16, 2), dtype=torch.bfloat16),
+        "logprobs": torch.zeros((1, 16), dtype=torch.float32),
+        "embedding_digest": {"sample_sha256": ["d" * 64], "sha256": "e" * 64},
+        "layer_digests": [
+            {
+                "layer": index,
+                "sample_output_sha256": ["a" * 64],
+                "sample_state_sha256": ["b" * 64],
+                "sample_subnode_sha256": {name: ["f" * 64] for name in SUBNODES},
+            }
+            for index in range(48)
+        ],
+    }
+    left, right = tmp_path / "update.pt", tmp_path / "reload.pt"
+    torch.save(payload, left)
+    if difference == "signed_zero":
+        payload["logits"][0, 0, 0] = -0.0
+    elif difference == "state":
+        payload["layer_digests"][3]["sample_state_sha256"] = ["c" * 64]
+    torch.save(payload, right)
+    ids = [case.case_id for case in runner.case_matrix()]
+
+    def matrix(path):
+        return {
+            "case_ids": ids,
+            "cases": {
+                name: {
+                    "artifacts": {role: runner.record(path) for role in ("rollout", "recompute")}
+                }
+                for name in ids
+            },
+        }
+
+    if difference is None:
+        result = runner.compare_reload_matrices(matrix(left), matrix(right))
+        assert result["status"] == "passed" and len(result["cases"]) == 16
+    else:
+        with pytest.raises(ValueError, match="original.*tensor bits/state"):
+            runner.compare_reload_matrices(matrix(left), matrix(right))
+
+
+def test_stage_phase_ranges_and_continuation_identity():
+    assert runner.stage_phases("train1") == ("update1",)
+    assert runner.stage_phases("train2") == ("update1", "update2")
+    assert runner.stage_phases("train") == runner.PHASES
+    model = {"source_tree": "t", "environment_sha256": "e"}
+    journal_identity = {**model, "run_id": "r1", "execution_id": "1", "fixture_sha256": "f"}
+    assert runner.continuation_identity_matches(journal_identity, model, "f")
+    assert not runner.continuation_identity_matches(journal_identity, model, "g")
+    assert not runner.continuation_identity_matches(
+        journal_identity, {**model, "environment_sha256": "x"}, "f"
+    )
+
+
+def test_partial_training_evidence_uses_the_phase_export_identity(tmp_path, monkeypatch):
+    import qwen3_next_contract as contract
+    import qwen3_next_l2
+
+    from rl_engine.testing import qwen3_next_l2 as l2_testing
+
+    manifest = {"stage": "train1", "checkpoint": "/official"}
+    export = {"checkpoint": "/export", "weight_version": "update-1", "base_checkpoint": "/official"}
+    monkeypatch.setattr(qwen3_next_l2, "model_identity", lambda m: {"ckpt": m["checkpoint"]})
+    monkeypatch.setattr(runner, "read_record", lambda value: {"export": export})
+    seen = []
+    monkeypatch.setattr(
+        l2_testing,
+        "validate_matrix_report",
+        lambda report, expected_identity, artifact_root: seen.append(expected_identity),
+    )
+    matrix = tmp_path / "l2-matrix.json"
+    matrix.write_text("{}")
+    progress = {
+        "status": "passed",
+        "scope": contract.SCOPES["train1"],
+        "identity": {"ckpt": "/official"},
+        "phases": ["update1"],
+        "workers": {"update1": {"path": "w"}},
+        "l2_reports": {"update1": {"sha256": "abc"}},
+    }
+    path = tmp_path / "progress.json"
+    path.write_text(json.dumps(progress))
+    evidence = {"updates": {"path": str(path)}, "l2_matrix": {"path": str(matrix), "sha256": "abc"}}
+    contract.validate_partial_training_evidence(manifest, tmp_path, evidence, attempt_root=tmp_path)
+    assert seen == [{"ckpt": "/export"}]  # not the base model identity
+    with pytest.raises(ValueError, match="does not reference"):
+        contract.validate_partial_training_evidence(
+            manifest,
+            tmp_path,
+            {**evidence, "l2_matrix": {**evidence["l2_matrix"], "sha256": "x"}},
+            attempt_root=tmp_path,
+        )
+    export["base_checkpoint"] = "/elsewhere"
+    with pytest.raises(ValueError, match="pinned base"):
+        contract.validate_partial_training_evidence(
+            manifest, tmp_path, evidence, attempt_root=tmp_path
+        )
+
+
+def test_continued_storage_follows_the_chain_back_to_train1(tmp_path):
+    def run(name, stage, previous=None):
+        directory = tmp_path / name
+        directory.mkdir()
+        manifest = {"stage": stage, "previous": str(previous) if previous else None}
+        (directory / "run.json").write_text(json.dumps(manifest))
+        return directory
+
+    l2 = run("l2", "l2")
+    train1 = run("train1", "train1", l2)
+    train2 = run("train2", "train2", train1)
+    assert runner.continued_training_storage(train1) == train1 / "training-state"
+    assert runner.continued_training_storage(train2) == train1 / "training-state"
+    with pytest.raises(ValueError, match="back to a train1 run"):
+        runner.continued_training_storage(l2)
diff --git a/tests/test_qwen3_next_training_export.py b/tests/test_qwen3_next_training_export.py
new file mode 100644
index 000000000..18a6bad55
--- /dev/null
+++ b/tests/test_qwen3_next_training_export.py
@@ -0,0 +1,288 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Tiny CPU export-publication regressions, not full-model numerical acceptance."""
+
+import copy
+import json
+import shutil
+import sys
+from pathlib import Path
+
+import pytest
+import torch
+from safetensors.torch import save_file
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
+import qwen3_next_training_export as export  # noqa: E402
+from qwen3_next_runtime import file_digest  # noqa: E402
+
+
+def write_weights(root, values, filename):
+    save_file(values, root / filename)
+    index = {
+        "metadata": {
+            "total_size": sum(value.numel() * value.element_size() for value in values.values())
+        },
+        "weight_map": {name: filename for name in values},
+    }
+    (root / export.INDEX_FILE).write_text(json.dumps(index))
+
+
+@pytest.fixture
+def staged(tmp_path, monkeypatch):
+    base = tmp_path / "unit-official"
+    base.mkdir()
+    (base / "config.json").write_text(
+        json.dumps({"model_type": "qwen3_next", "num_hidden_layers": 48})
+    )
+    (base / "tokenizer.json").write_text(json.dumps({"unit_fixture": "not a real tokenizer"}))
+    values = {
+        f"model.layers.{layer}.input_layernorm.weight": torch.ones(2, dtype=torch.bfloat16)
+        for layer in range(48)
+    }
+    values.update(
+        {
+            "model.embed_tokens.weight": torch.ones((4, 2), dtype=torch.bfloat16),
+            "model.norm.weight": torch.ones(2, dtype=torch.bfloat16),
+            "lm_head.weight": torch.ones((4, 2), dtype=torch.bfloat16),
+        }
+    )
+    write_weights(base, values, "base.safetensors")
+    base_identity = {
+        "revision": base.name,
+        "metadata": {
+            path.name: file_digest(path) for path in base.iterdir() if path.suffix != ".safetensors"
+        },
+        "shards": {"base.safetensors": {"size": (base / "base.safetensors").stat().st_size}},
+    }
+    monkeypatch.setattr(export, "_reference", lambda: {"revision": base.name})
+    schema_reference = {
+        "tensor_count": len(values),
+        "weight_schema_sha256": export._json_digest(
+            export._weight_schema(base, check_finite=False)
+        ),
+    }
+    monkeypatch.setattr(export, "_schema_reference", lambda: schema_reference)
+
+    def identity(path):
+        assert Path(path) == base
+        return copy.deepcopy(base_identity)
+
+    monkeypatch.setattr(export, "checkpoint_identity", identity)
+    staging, destination = tmp_path / "export.partial", tmp_path / "export-complete"
+    staging.mkdir()
+    for name in ("config.json", "tokenizer.json"):
+        shutil.copy2(base / name, staging / name)
+    write_weights(
+        staging, {name: value + 1 for name, value in values.items()}, "updated.safetensors"
+    )
+    (staging / "STAGING.json").write_text(json.dumps({"scope": export.KIND, "status": "staging"}))
+    training = {
+        "source_commit": "unit-source",
+        "source_tree": "unit-tree",
+        "environment_sha256": "unit-env",
+        "provider_config": {"provider": "strict"},
+        "topology": {"tp": 4, "pp": 1, "cp": 1, "ep": 1},
+        "checkpoint": str(base),
+        "checkpoint_identity": base_identity,
+    }
+    return {
+        "staging": staging,
+        "destination": destination,
+        "step": 1,
+        "weight_version": "update-1",
+        "identity": training,
+        "base_checkpoint": base,
+        "base_checkpoint_identity": base_identity,
+        "model_hashes": [str(rank) * 64 for rank in range(4)],
+    }
+
+
+def publish(staged):
+    return export._publish_export(**staged)
+
+
+def test_atomic_native_export_identity_and_all_file_verification(staged):
+    record = publish(staged)
+    assert not staged["staging"].exists()
+    assert (
+        record["metadata"][export.INDEX_FILE]
+        != staged["base_checkpoint_identity"]["metadata"][export.INDEX_FILE]
+    )
+    assert export.validate_training_export(staged["destination"], record) == record
+    assert record["tensor_count"] == 51
+    assert record["model_identity_method"] == "native_parameter_hashes_after"
+
+
+def test_history_record_survives_weight_retirement_but_cannot_load(staged):
+    record = publish(staged)
+    (staged["destination"] / "updated.safetensors").unlink()
+    assert export.validate_training_export_record(record, record) == record
+    with pytest.raises(ValueError, match="file inventory changed"):
+        export.validate_training_export(staged["destination"], record)
+
+
+def test_same_size_weight_corruption_is_rejected(staged):
+    record = publish(staged)
+    path = staged["destination"] / "updated.safetensors"
+    payload = bytearray(path.read_bytes())
+    payload[-1] ^= 1
+    path.write_bytes(payload)
+    with pytest.raises(ValueError, match="checksum"):
+        export.validate_training_export(staged["destination"], record)
+
+
+def test_export_requires_completion_publication(staged):
+    with pytest.raises(ValueError, match="unpublished"):
+        export.validate_training_export(staged["staging"], {})
+
+
+@pytest.mark.parametrize("missing", ["model.layers.47.input_layernorm.weight", "lm_head.weight"])
+def test_reduced_model_or_missing_head_cannot_publish(staged, missing):
+    from safetensors.torch import load_file
+
+    values = load_file(staged["staging"] / "updated.safetensors")
+    del values[missing]
+    write_weights(staged["staging"], values, "updated.safetensors")
+    with pytest.raises(ValueError, match="omitted trained official tensors"):
+        publish(staged)
+    assert not (staged["staging"] / export.COMPLETE_FILE).exists()
+
+
+@pytest.mark.parametrize("mutation", ["dtype", "nonfinite", "shape"])
+def test_export_preserves_finite_original_weight_schema(staged, mutation):
+    from safetensors.torch import load_file
+
+    values = load_file(staged["staging"] / "updated.safetensors")
+    key = "lm_head.weight"
+    if mutation == "dtype":
+        values[key] = values[key].float()
+    elif mutation == "nonfinite":
+        values[key][0, 0] = float("nan")
+    else:
+        values[key] = values[key][:2].contiguous()
+    write_weights(staged["staging"], values, "updated.safetensors")
+    with pytest.raises(ValueError, match="BF16|Nonfinite|complete official tensor schema"):
+        publish(staged)
+
+
+def test_modified_tokenizer_cannot_publish(staged):
+    (staged["staging"] / "tokenizer.json").write_text("changed")
+    with pytest.raises(ValueError, match="tokenizer/configuration assets"):
+        publish(staged)
+
+
+def test_different_requested_export_identity_is_rejected(staged):
+    record = publish(staged)
+    expected = {**record, "weight_version": "reload-2", "step": 2}
+    with pytest.raises(ValueError, match="requested weights"):
+        export.validate_training_export(staged["destination"], expected)
+
+
+@pytest.mark.parametrize("field", ["weights_content_sha256", "metadata", "model_identity_method"])
+def test_self_consistent_expected_record_cannot_bypass_required_proofs(staged, field):
+    record = publish(staged)
+    record[field] = {} if field == "metadata" else "wrong"
+    with pytest.raises(ValueError, match="proof|parameter audit"):
+        export.validate_training_export_record(record, record)
+
+
+def test_export_base_and_native_training_provenance_must_match(staged):
+    record = publish(staged)
+    record["training_identity"]["checkpoint"] = "/different-base"
+    with pytest.raises(ValueError, match="baseline/source/environment provenance"):
+        export.validate_training_export_record(record, record)
+
+
+def test_no_unmanifested_file_or_symlink_is_accepted(staged):
+    record = publish(staged)
+    destination = staged["destination"]
+    (destination / "extra").write_text("unmanifested")
+    with pytest.raises(ValueError, match="file inventory changed"):
+        export.validate_training_export(destination, record)
+    (destination / "extra").unlink()
+    (destination / "tokenizer.json").unlink()
+    (destination / "tokenizer.json").symlink_to(staged["base_checkpoint"] / "tokenizer.json")
+    with pytest.raises(ValueError, match="external HF export"):
+        export.validate_training_export(destination, record)
+
+
+def _add_mtp_head_to_base(staged, monkeypatch, n_tensors=3):
+    """Give the official base an untrained MTP head the native saver never writes."""
+    from safetensors.torch import load_file
+
+    base = staged["base_checkpoint"]
+    values = load_file(base / "base.safetensors")
+    mtp = {
+        f"mtp.layers.0.mlp.experts.{i}.down_proj.weight": torch.full(
+            (2, 2), float(i + 1), dtype=torch.bfloat16
+        )
+        for i in range(n_tensors)
+    }
+    values.update(mtp)
+    write_weights(base, values, "base.safetensors")
+    staged["base_checkpoint_identity"]["metadata"][export.INDEX_FILE] = file_digest(
+        base / export.INDEX_FILE
+    )
+    staged["identity"]["checkpoint_identity"] = staged["base_checkpoint_identity"]
+    schema_reference = {
+        "tensor_count": len(values),
+        "weight_schema_sha256": export._json_digest(
+            export._weight_schema(base, check_finite=False)
+        ),
+    }
+    monkeypatch.setattr(export, "_schema_reference", lambda: schema_reference)
+    return mtp
+
+
+def test_untrained_mtp_head_is_carried_from_the_base(staged, monkeypatch):
+    from safetensors.torch import load_file
+
+    mtp = _add_mtp_head_to_base(staged, monkeypatch)
+    record = publish(staged)
+    passthrough = record["base_passthrough"]
+    assert passthrough["tensor_count"] == len(mtp) and passthrough["prefixes"] == ["mtp."]
+    (shard,) = passthrough["shards"]
+    assert passthrough["shards"][shard] == {"source": "base.safetensors", "tensor_count": len(mtp)}
+    index = json.loads((staged["destination"] / export.INDEX_FILE).read_text())
+    assert {name: index["weight_map"][name] for name in mtp} == {name: shard for name in mtp}
+    carried = load_file(staged["destination"] / shard)
+    assert all(torch.equal(carried[name], value) for name, value in mtp.items())
+    assert record["tensor_count"] == 51 + len(mtp)
+    assert export.validate_training_export(staged["destination"], record) == record
+
+
+def test_passthrough_is_limited_to_the_mtp_head(staged, monkeypatch):
+    from safetensors.torch import load_file
+
+    _add_mtp_head_to_base(staged, monkeypatch)
+    values = load_file(staged["staging"] / "updated.safetensors")
+    del values["model.norm.weight"]
+    write_weights(staged["staging"], values, "updated.safetensors")
+    with pytest.raises(
+        ValueError, match="omitted trained official tensors: \\['model.norm.weight'\\]"
+    ):
+        publish(staged)
+    assert not (staged["staging"] / export.COMPLETE_FILE).exists()
+
+
+def test_saver_must_not_write_part_of_the_passthrough_head(staged, monkeypatch):
+    from safetensors.torch import load_file
+
+    mtp = _add_mtp_head_to_base(staged, monkeypatch)
+    values = load_file(staged["staging"] / "updated.safetensors")
+    name = next(iter(mtp))
+    values[name] = mtp[name]
+    write_weights(staged["staging"], values, "updated.safetensors")
+    with pytest.raises(ValueError, match="part of the base passthrough head"):
+        publish(staged)
+
+
+def test_record_must_declare_the_base_passthrough(staged):
+    record = publish(staged)
+    assert record["base_passthrough"] == {"prefixes": ["mtp."], "tensor_count": 0, "shards": {}}
+    broken = copy.deepcopy(record)
+    del broken["base_passthrough"]
+    with pytest.raises(ValueError, match="came from the base"):
+        export.validate_training_export_record(broken, broken)
diff --git a/tests/test_qwen3_next_vllm_attention_metadata.py b/tests/test_qwen3_next_vllm_attention_metadata.py
new file mode 100644
index 000000000..02150f052
--- /dev/null
+++ b/tests/test_qwen3_next_vllm_attention_metadata.py
@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from rl_engine.integrations.qwen3_next_vllm_attention import (
+    SharedAttentionBackend,
+    SharedAttentionBuilder,
+    SharedAttentionImpl,
+    validate_boundaries,
+)
+
+
+def _config():
+    return SimpleNamespace(
+        parallel_config=SimpleNamespace(
+            tensor_parallel_size=4,
+            pipeline_parallel_size=1,
+            decode_context_parallel_size=1,
+            prefill_context_parallel_size=1,
+        ),
+        model_config=SimpleNamespace(enforce_eager=True),
+        cache_config=SimpleNamespace(enable_prefix_caching=False),
+        speculative_config=None,
+    )
+
+
+def test_real_builder_preserves_mixed_absolute_prefixes_and_ignores_optimistic_bounds():
+    builder = SharedAttentionBuilder(None, ["layer3"], _config(), torch.device("cpu"))
+    metadata = builder.build(
+        0,
+        SimpleNamespace(
+            num_reqs=3,
+            num_actual_tokens=5,
+            query_start_loc=torch.tensor([0, 1, 1, 5]),
+            seq_lens=torch.tensor([65, 0, 4]),
+            seq_lens_cpu_upper_bound=torch.tensor([100, 0, 999]),
+            block_table_tensor=torch.tensor([[3, 4], [0, 0], [8, 0]]),
+            slot_mapping=torch.tensor([128, 256, 257, 258, 259, -1]),
+            causal=True,
+        ),
+    )
+    assert metadata.seq_lens == (65, 0, 4)
+    assert metadata.query_start_loc == (0, 1, 1, 5)
+    assert metadata.slot_mapping.numel() == 5
+    assert not SharedAttentionBackend.supports_batch_invariance()
+
+
+@pytest.mark.parametrize(
+    "starts,lengths,n",
+    [
+        ((0, 2), (1,), 2),
+        ((0, 1), (1,), 2),
+        ((1, 2), (2,), 2),
+        ((0, 1), (1041,), 1),
+        ((0, 2, 1), (2, 1), 1),
+    ],
+)
+def test_invalid_scheduler_boundaries_fail(starts, lengths, n):
+    with pytest.raises(ValueError):
+        validate_boundaries(starts, lengths, n)
+
+
+def test_empty_batch_and_profile_guards():
+    validate_boundaries((0,), (), 0)
+    config = _config()
+    config.cache_config.enable_prefix_caching = True
+    with pytest.raises(ValueError, match="Prefix reuse"):
+        SharedAttentionBuilder(None, [], config, torch.device("cpu"))
+    with pytest.raises(ValueError, match="four Q heads"):
+        SharedAttentionImpl(8, 256, 1 / 16, 1)
+    SharedAttentionImpl(4, 256, 1 / 16, 1)

From 5ca3022371a833cb4b338f9c225647648695b347 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 03:11:51 +0000
Subject: [PATCH 51/70] feat(qwen3-next): batch-invariance matrix for the full
 model

RFC #428 batch_invariance_ci (CUDA).

- Stage bi (after a passed l2, same certified profile): 97 pinned cells in
  rl_engine/testing/qwen3_next_bi_cases.py / qwen3_next_bi_manifest.json.
  concurrency (1-64 requests, target first or last), interference
  (companion content, lengths, arrival during prefill/decode, generation
  lengths), chunk boundaries (prompts 63-1024 around 64/128/528/1024, chunk
  64/128, first chunk 1/63/65, compared with the replay recompute) and
  replay THD packing (1-7 neighbours, target first or last). A quick CI
  subset runs one cell per family plus 64 concurrent requests.
- Native rollout: version 2 requests run one traced target next to
  untraced companions; the exact-prefill scheduler admits each request at
  its arrival step, pins the target's planned chunk at every step and
  fails on any preemption; the trace records only the target.
- Replay: packed_layout() packs variable-length neighbours with the traced
  target in one THD batch.
- bi-matrix.json names each failing cell's first divergent layer, sub-node
  and layer family; the chain report gains a batch_invariance section, and
  the evidence publisher takes the bi run with --also.
- gtest: bitwise judgments compare raw bits (signed zero, NaN/Inf and
  dtype now count), not allclose with zero tolerance.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .github/workflows/ci.yml                      |    3 +-
 .github/workflows/qwen3-next-provider-gpu.yml |    3 +-
 docs/usage/qwen3-next-batch-invariance.md     |   55 +
 .../qwen3_next_native_recompute.py            |   53 +-
 .../integrations/qwen3_next_native_rollout.py |  305 +-
 rl_engine/kernels/gtest/forward_invariance.py |    6 +-
 .../kernels/gtest/qwen3_next_chain_report.py  |   25 +
 rl_engine/testing/qwen3_next_bi_cases.py      |  370 ++
 rl_engine/testing/qwen3_next_bi_manifest.json | 3857 +++++++++++++++++
 scripts/qwen3_next_batch_invariance.py        |  337 ++
 scripts/qwen3_next_chain_report.py            |   12 +
 scripts/qwen3_next_contract.py                |   17 +
 scripts/qwen3_next_publish_evidence.py        |   14 +
 scripts/qwen3_next_run.py                     |   15 +-
 scripts/qwen3_next_runtime.py                 |    2 +
 tests/check_qwen3_next_bi_requests.py         |   44 +
 tests/test_qwen3_next_bi_cases.py             |   94 +
 tests/test_qwen3_next_chain_report.py         |   38 +
 tests/test_tensor_identity.py                 |   37 +
 19 files changed, 5251 insertions(+), 36 deletions(-)
 create mode 100644 docs/usage/qwen3-next-batch-invariance.md
 create mode 100644 rl_engine/testing/qwen3_next_bi_cases.py
 create mode 100644 rl_engine/testing/qwen3_next_bi_manifest.json
 create mode 100644 scripts/qwen3_next_batch_invariance.py
 create mode 100644 tests/check_qwen3_next_bi_requests.py
 create mode 100644 tests/test_qwen3_next_bi_cases.py

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index f50c73cef..e6e1f2aa7 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -92,7 +92,8 @@ jobs:
             tests/test_qwen3_next_subnode.py \
             tests/test_qwen3_next_chain_report.py \
             tests/test_qwen3_next_run.py \
-            tests/test_qwen3_next_evidence_redaction.py
+            tests/test_qwen3_next_evidence_redaction.py \
+            tests/test_qwen3_next_bi_cases.py
 
       - name: Run Cross-Configuration Contract Tests (CPU-safe)
         run: |
diff --git a/.github/workflows/qwen3-next-provider-gpu.yml b/.github/workflows/qwen3-next-provider-gpu.yml
index ca5bf6f4d..9b19dcf89 100644
--- a/.github/workflows/qwen3-next-provider-gpu.yml
+++ b/.github/workflows/qwen3-next-provider-gpu.yml
@@ -90,4 +90,5 @@ jobs:
             tests/check_qwen3_next_gdn_bridge.py tests/check_qwen3_next_gdn_sequence.py \
             tests/check_qwen3_next_conv_bridge.py tests/check_qwen3_next_core_matrix.py \
             tests/check_qwen3_next_shared_core.py \
-            tests/check_qwen3_next_paged_attention.py tests/check_qwen3_next_batch_invariance.py
+            tests/check_qwen3_next_paged_attention.py tests/check_qwen3_next_batch_invariance.py \
+            tests/check_qwen3_next_bi_requests.py
diff --git a/docs/usage/qwen3-next-batch-invariance.md b/docs/usage/qwen3-next-batch-invariance.md
new file mode 100644
index 000000000..603deccce
--- /dev/null
+++ b/docs/usage/qwen3-next-batch-invariance.md
@@ -0,0 +1,55 @@
+# Qwen3-Next batch invariance (RFC #428 `batch_invariance_ci`)
+
+The `bi` stage checks that a Qwen3-Next-80B-A3B-Instruct sequence gets the
+same bits whatever else the engines are doing: other requests in the same
+vLLM step, how many there are, when they arrive, how its prompt is chunked,
+and how the replay engine packs it with other sequences. It runs after a
+passed `l2` stage of the [full-model chain](qwen3-next-chain.md), under the
+same certified provider profile.
+
+```bash
+python scripts/qwen3_next_run.py --stage bi --previous <runs>/<...-l2-...> \
+    --runs-root <runs> --checkpoint <checkpoint> --cuda-home <cuda-13.0> \
+    --python <venv>/bin/python [--bi-suite quick]
+python scripts/qwen3_next_chain_report.py --last <...-train-...> --bi <...-bi-...> --out chain-report.json
+```
+
+## Cells
+
+Every cell has one *target* (16 generated tokens) and compares it bitwise -
+response tokens, BF16 logits, FP32 logprobs, the embedding and every layer,
+sub-node and recurrent/KV-state digest - with a reference. The cells are
+generated by `rl_engine/testing/qwen3_next_bi_cases.py` and pinned by
+`qwen3_next_bi_manifest.json`.
+
+| Family | Cells | The target runs | Compared with |
+| --- | --- | --- | --- |
+| `concurrency` | 13 | with 0, 1, 3, 7, 15, 31 or 63 companions (prompts 37-1000 tokens), first or last in the FCFS order | the target alone, same chunk plan |
+| `interference` | 10 | with companions of the same text, very short or long prompts, mixed lengths, arriving during its prefill, during its decode or at its last decode step, generating 1 or 24 tokens; or itself arriving after them | the target alone, same chunk plan |
+| `chunk` | 66 | alone, prompt 63/64/65, 127/128/129, 527/528/529, 1023/1024 tokens, chunk 64 or 128, first chunk 1, 63 or 65 | the replay engine's recompute of the same tokens (the train/rollout boundary) |
+| `packing` | 8 | in the replay engine, packed into one THD batch with 1, 3 or 7 neighbours (16-1015 tokens), first or last | the replay of the target alone |
+
+`--bi-suite quick` runs five cells (one per family plus 64 concurrent
+requests) for CI. Prompt 1025 is outside the pinned rollout profile
+(`max_model_len` 1040 = 1024 + 16).
+
+## How a cell runs
+
+All requests of a cell are submitted together; the exact-prefill scheduler
+admits a request to the waiting queue only at its `arrival_step`, counted in
+scheduled steps, so arrivals are reproducible. Every prefilling request
+shares one `long_prefill_token_threshold`: the target's next planned chunk
+while the target prefills, the cell's chunk size otherwise. The scheduler
+fails the cell if the target does not get exactly its planned tokens at
+every step until its last input, or if any request is preempted. Only the
+target is traced; companions generate greedily and are never recorded.
+The rollout caps (64 sequences, 16384 tokens per step, 4 GiB of KV cache per
+GPU) are part of the certified profile the `layers` stage qualifies.
+
+## Results
+
+`bi-matrix.json` lists every cell with its comparisons, the first divergent
+layer and sub-node, and its layer family (`embedding`, `norm`, `gdn`,
+`full_attention`, `moe` or `lm_head_logprob`). The chain report's
+`batch_invariance` section summarises it per family; a failing cell fails
+the chain report.
diff --git a/rl_engine/integrations/qwen3_next_native_recompute.py b/rl_engine/integrations/qwen3_next_native_recompute.py
index a59e89dc7..0624ffeff 100644
--- a/rl_engine/integrations/qwen3_next_native_recompute.py
+++ b/rl_engine/integrations/qwen3_next_native_recompute.py
@@ -27,19 +27,44 @@
 from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal as exact
 
 
+def packed_layout(request):
+    """``(sequences, traced)``: every packed sequence's tokens and which ones are traced.
+
+    An L2 request packs its equal-length samples and traces all of them. A
+    batch-invariance packing request (``request["packing"]``) packs its target
+    with untraced companions of any length; only the target is traced.
+    """
+    traced = request["consumed_input_tokens"]
+    packing = request.get("packing")
+    if packing is None:
+        return traced, list(range(len(traced)))
+    sequences, index = packing["sequences"], packing["target_index"]
+    if (
+        len(traced) != 1
+        or index not in range(len(sequences))
+        or sequences[index] != traced[0]
+        or any(not row for row in sequences)
+    ):
+        raise ValueError("Packing request must contain its traced target exactly once")
+    return sequences, [index]
+
+
 def response_rows(value, request):
-    """Keep original BF16 values at P-1..P+14 for every packed sample."""
-    batch = len(request["prompt_tokens"])
+    """Keep original values at P-1..P+14 of every traced packed sample."""
+    sequences, traced = packed_layout(request)
+    starts = [0]
+    for row in sequences:
+        starts.append(starts[-1] + len(row))
     length = len(request["consumed_input_tokens"][0])
     if value.dtype not in (torch.bfloat16, torch.int64) or value.numel() == 0:
         raise ValueError("Response traces must preserve nonempty original BF16/int64 storage")
     value = value.reshape(-1, value.shape[-1])
-    if value.shape[0] < batch * length:
+    if value.shape[0] < starts[-1]:
         raise ValueError("Packed model output does not cover every frozen sample")
     positions = torch.tensor(request["prediction_positions"], device=value.device)
     if positions.shape != (16,) or positions[-1] != length - 1:
         raise ValueError("Response positions must end before consuming the last target token")
-    return torch.stack([value[index * length + positions] for index in range(batch)])
+    return torch.stack([value[starts[index] + positions] for index in traced])
 
 
 def _rank_main(rank, cases, staging):
@@ -89,18 +114,22 @@ def _rank_main(rank, cases, staging):
                         "request": str(Path(_output).with_suffix(".request.json")),
                     },
                 )
-            consumed = torch.tensor(
-                request["consumed_input_tokens"], device="cuda", dtype=torch.long
-            )
-            batch, length = consumed.shape
+            sequences, traced_rows = packed_layout(request)
+            batch, length = len(traced_rows), len(request["consumed_input_tokens"][0])
             if length != request["state_position"] + 1:
                 raise ValueError("Recompute recurrent-state position is inconsistent")
-            cu = torch.arange(batch + 1, device="cuda", dtype=torch.int32) * length
+            consumed = torch.tensor(
+                [token for row in sequences for token in row], device="cuda", dtype=torch.long
+            )
+            lengths = [len(row) for row in sequences]
+            cu = torch.tensor(
+                [0, *torch.tensor(lengths).cumsum(0).tolist()], device="cuda", dtype=torch.int32
+            )
             packed = PackedSeqParams(
                 cu_seqlens_q=cu,
                 cu_seqlens_kv=cu,
-                max_seqlen_q=length,
-                max_seqlen_kv=length,
+                max_seqlen_q=max(lengths),
+                max_seqlen_kv=max(lengths),
                 qkv_format="thd",
             )
             traces, handles = {}, []
@@ -117,7 +146,7 @@ def hook(_module, _inputs, output):
 
             def capture_state(index):
                 def hook(state, indices, _cu):
-                    slots = indices[:batch].long()
+                    slots = indices[traced_rows].long()
                     if (index + 1) % 4:
                         values = {
                             "convolution": state.convolution[slots],
diff --git a/rl_engine/integrations/qwen3_next_native_rollout.py b/rl_engine/integrations/qwen3_next_native_rollout.py
index 912354597..64012765b 100644
--- a/rl_engine/integrations/qwen3_next_native_rollout.py
+++ b/rl_engine/integrations/qwen3_next_native_rollout.py
@@ -85,22 +85,25 @@ def runtime_profile(config):
 
 
 def validate_request(request):
-    if request.get("role") != "rollout" or request.get("schema_version") != 1:
-        raise ValueError("A version1 rollout request is required")
+    if request.get("role") != "rollout" or request.get("schema_version") not in (1, 2):
+        raise ValueError("A version 1 (L2) or 2 (batch-invariance) rollout request is required")
+    # Version 2 is one traced target next to untraced companions.
+    bi = request["schema_version"] == 2
     digest_input = {key: value for key, value in request.items() if key != "request_sha256"}
     if json_digest(digest_input) != request.get("request_sha256"):
         raise ValueError("Rollout request identity mismatch")
     prompts, samples = request["prompt_tokens"], request["sample_ids"]
     batch = len(prompts)
     if (
-        batch not in (1, 4)
+        batch not in ((1,) if bi else (1, 4))
         or len(samples) != batch
         or any(type(sample) is not int for sample in samples)
         or len(set(samples)) != batch
     ):
         raise ValueError("Rollout requires one or four uniquely identified samples")
     length = len(prompts[0])
-    if length not in (8, 64, 256, 1024) or any(len(row) != length for row in prompts):
+    lengths = range(1, 1025) if bi else (8, 64, 256, 1024)
+    if length not in lengths or any(len(row) != length for row in prompts):
         raise ValueError("Rollout prompts must use one official matrix length")
     responses = request["response_tokens"]
     for rows in (prompts, responses or []):
@@ -137,7 +140,9 @@ def validate_request(request):
     ):
         raise ValueError("Rollout execution or response offsets differ from the pinned contract")
     if responses is None:
-        if batch != 4 or chunks != [length] or request["consumed_input_tokens"] is not None:
+        if request["consumed_input_tokens"] is not None or (
+            not bi and (batch != 4 or chunks != [length])
+        ):
             raise ValueError("Only baseline B4 full prefill may define frozen responses")
     elif request["consumed_input_tokens"] != [
         prompt + response[:-1] for prompt, response in zip(prompts, responses, strict=True)
@@ -145,6 +150,10 @@ def validate_request(request):
         raise ValueError("Consumed input must end before response token sixteen")
     if any(type(layer) is not int or layer not in range(48) for layer in request["trace_layers"]):
         raise ValueError("Debug layer indices must be in [0,48)")
+    if bi:
+        validate_companions(request, length)
+    elif "companions" in request:
+        raise ValueError("Only a version 2 request may run companions")
     identity = request["identity"]
     if identity["architecture_layers"] != 48 or identity["topology"] != {
         "tp": 4,
@@ -163,6 +172,37 @@ def validate_request(request):
     return batch, length
 
 
+def validate_companions(request, length):
+    companions = request["companions"]
+    tokens, arrivals = request["companion_max_tokens"], request["arrival_steps"]
+    max_len = NATIVE_ROLLOUT_PROFILE["max_model_len"]
+    if (
+        not isinstance(companions, list)
+        or len(companions) > NATIVE_ROLLOUT_PROFILE["max_num_seqs"] - 1
+        or len(tokens) != len(companions)
+        or len(arrivals.get("companions", [])) != len(companions)
+        or type(arrivals.get("target")) is not int
+        or arrivals["target"] < 0
+        or any(type(step) is not int or step < 0 for step in arrivals["companions"])
+        or type(request["chunk_size"]) is not int
+        or request["chunk_size"] < 1
+        or length + RESPONSE_LENGTH > max_len
+    ):
+        raise ValueError("Batch-invariance companions, arrivals or chunk size are malformed")
+    for prompt, count in zip(companions, tokens, strict=True):
+        if (
+            not prompt
+            or type(count) is not int
+            or count < 1
+            or len(prompt) + count > max_len
+            or any(type(token) is not int or not 0 <= token < 151936 for token in prompt)
+        ):
+            raise ValueError("A companion does not fit the pinned rollout profile")
+
+
+RESPONSE_LENGTH = 16
+
+
 def next_scheduled_tokens(computed, prompt_length, chunks):
     boundary = 0
     for size in chunks:
@@ -181,6 +221,12 @@ def schedule(self, throttle_prefills=False):
         active = [request for request in self.requests.values() if not request.is_finished()]
         if not active:
             return super().schedule(throttle_prefills)
+        if any(
+            (request.sampling_params.extra_args or {}).get("shared_bi_companion") is not None
+            or ((request.sampling_params.extra_args or {}).get("shared_l2") or {}).get("bi")
+            for request in active
+        ):
+            return self._schedule_batch_invariance(active, throttle_prefills)
         descriptors = [
             (request.sampling_params.extra_args or {}).get("shared_l2") for request in active
         ]
@@ -235,6 +281,82 @@ def schedule(self, throttle_prefills=False):
             )
         return result
 
+    def _schedule_batch_invariance(self, active, throttle_prefills):
+        """One traced target keeps its exact chunk plan; companions fill the step.
+
+        All requests of a cell are admitted before the first step (as for L2).
+        A request joins the waiting queue only at its ``arrival_step``, counted
+        in scheduled steps from the first one, so arrivals are reproducible
+        whatever the engine-core timing. Every prefilling request shares one
+        ``long_prefill_token_threshold``: the target's next planned chunk while
+        it prefills, the cell's chunk size otherwise. The target must get
+        exactly its planned token count at every step until it has consumed its
+        last input, and no request may be preempted.
+        """
+        descriptors = {}
+        for request in self.requests.values():
+            extra = request.sampling_params.extra_args or {}
+            descriptor = extra.get("shared_l2") or extra.get("shared_bi_companion")
+            if descriptor is None:
+                raise ValueError("Exact Qwen scheduler accepts only identified requests")
+            descriptors[request.request_id] = (request, descriptor, "shared_l2" in extra)
+        cells = {item[1]["request_sha256"] for item in descriptors.values()}
+        targets = [item for item in descriptors.values() if item[2]]
+        if len(cells) != 1 or len(targets) > 1:
+            raise ValueError("One batch-invariance cell runs exactly one traced target")
+        # Once the target has finished, the scheduler frees it; companions go on.
+        target, contract, _ = (
+            targets[0] if targets else (None, *next(iter(descriptors.values()))[1:])
+        )
+        cell = contract["request_sha256"]
+        if getattr(self, "_bi_cell", None) != cell:
+            self._bi_cell, self._bi_step, self._bi_started = cell, 0, False
+        if not self._bi_started and len(descriptors) < contract["total_requests"]:
+            previous = self._pause_state
+            self._pause_state = PauseState.PAUSED_ALL
+            try:
+                result = super().schedule(throttle_prefills)
+            finally:
+                self._pause_state = previous
+            if result.total_num_scheduled_tokens:
+                raise RuntimeError("The native scheduler violated the cell admission barrier")
+            return result
+        self._bi_started = True
+        held = [
+            r for r in self.waiting if descriptors[r.request_id][1]["arrival_step"] > self._bi_step
+        ]
+        if len(held) == len(self.waiting) and not self.running and held:
+            # Nothing can run before the next arrival: that arrival is now.
+            self._bi_step = min(descriptors[r.request_id][1]["arrival_step"] for r in held)
+            held = [r for r in held if descriptors[r.request_id][1]["arrival_step"] > self._bi_step]
+        self.waiting.remove_requests(held)
+        target_active = target is not None and not target.is_finished() and target not in held
+        count = None
+        if target_active:
+            count = next_scheduled_tokens(
+                target.num_computed_tokens, contract["prompt_length"], contract["prefill_chunks"]
+            )
+        prefilling = target_active and target.num_computed_tokens < contract["prompt_length"]
+        self.scheduler_config.long_prefill_token_threshold = (
+            count if prefilling else contract["chunk_size"]
+        )
+        try:
+            result = super().schedule(throttle_prefills)
+        finally:
+            for request in held:
+                self.waiting.add_request(request)
+        if result.total_num_scheduled_tokens:
+            self._bi_step += 1
+        if result.preempted_req_ids or (
+            count is not None and result.num_scheduled_tokens.get(target.request_id) != count
+        ):
+            raise RuntimeError(
+                "exact_scheduler_mismatch: target expected "
+                f"{count}, actual {result.num_scheduled_tokens.get(target.request_id)}, "
+                f"preempted {sorted(result.preempted_req_ids)}"
+            )
+        return result
+
 
 class ForceFrozenTokens(AdapterLogitsProcessor):
     """Sampling-only teacher forcing; raw model logits are observed beforehand."""
@@ -243,6 +365,8 @@ def is_argmax_invariant(self):
         return False
 
     def new_req_logits_processor(self, params):
+        if (params.extra_args or {}).get("shared_bi_companion") is not None:
+            return None  # companions generate greedily and are never traced
         descriptor = (params.extra_args or {}).get("shared_l2")
         if descriptor is None:
             raise ValueError("L2 sampling requires explicit request metadata")
@@ -301,7 +425,11 @@ def __init__(self, worker, request, output_directory):
             )
         self.row_by_sample = {sample: row for row, sample in enumerate(request["sample_ids"])}
         self.request_rows, self.progress = {}, {}
-        self.current, self.schedule = [], []
+        # Batch-invariance requests run untraced companions next to the traced
+        # target; their tokens share every step but are never recorded.
+        self.companions = request.get("companions")
+        self.untraced = set()
+        self.current, self.current_index, self.packed, self.schedule = [], [], [], []
         self.logits = torch.empty(self.batch, 16, 151936, dtype=torch.bfloat16)
         self.local_logits = torch.empty(self.batch, 16, 37984, dtype=torch.bfloat16)
         self.logprobs = torch.empty(self.batch, 16, dtype=torch.float32)
@@ -339,7 +467,21 @@ def subnode(self, layer_index, name, packed):
 
     def before_execute(self, scheduled):
         for new in scheduled.scheduled_new_reqs:
-            descriptor = (new.sampling_params.extra_args or {}).get("shared_l2", {})
+            extra = new.sampling_params.extra_args or {}
+            companion = extra.get("shared_bi_companion")
+            if companion is not None:
+                if (
+                    self.companions is None
+                    or companion.get("request_sha256") != self.request["request_sha256"]
+                    or new.prompt_token_ids != self.companions[companion["index"]]
+                    or new.num_computed_tokens != 0
+                    or new.req_id in self.untraced
+                ):
+                    raise ValueError("Untraced companion does not belong to this request")
+                self.untraced.add(new.req_id)
+                self.progress[new.req_id] = 0
+                continue
+            descriptor = extra.get("shared_l2", {})
             if descriptor.get("request_sha256") != self.request["request_sha256"]:
                 raise ValueError("Scheduler request belongs to a different L2 case")
             row = self.row_by_sample[descriptor["sample_id"]]
@@ -354,11 +496,29 @@ def before_execute(self, scheduled):
         self.scheduled = scheduled
         if not scheduled.total_num_scheduled_tokens:
             return
-        if len(scheduled.num_scheduled_tokens) != self.batch or scheduled.preempted_req_ids:
+        scheduled_ids = set(scheduled.num_scheduled_tokens)
+        traced = scheduled_ids & set(self.request_rows)
+        unfinished = {
+            req_id
+            for req_id in self.request_rows
+            if self.progress[req_id] < self.prompt_length + 15
+        }
+        # A batch-invariance target may wait for its arrival step; once it has
+        # started it must take part in every step until its last input.
+        started = {req_id for req_id in unfinished if self.progress[req_id] > 0}
+        if (
+            scheduled.preempted_req_ids
+            or scheduled_ids - traced - self.untraced
+            or (self.companions is None and len(traced) != self.batch)
+            or (self.companions is not None and not started <= traced <= unfinished)
+        ):
             raise RuntimeError("The actual native step did not preserve its required batch")
         step = {}
         for req_id, count in scheduled.num_scheduled_tokens.items():
             computed = self.progress[req_id]
+            if req_id in self.untraced:
+                step[req_id] = (None, computed, count)
+                continue
             expected = next_scheduled_tokens(
                 computed, self.prompt_length, self.request["prefill_chunks"]
             )
@@ -373,23 +533,31 @@ def begin_forward(self, input_ids, positions):
         ids = list(self.worker.model_runner.input_batch.req_ids)
         if set(ids) != set(self.step):
             raise ValueError("Actual model input batch differs from SchedulerOutput")
-        self.current = []
+        self.current, self.current_index, self.packed = [], [], []
         begin = 0
-        for req_id in ids:
+        for index, req_id in enumerate(ids):
             row, computed, count = self.step[req_id]
             end = begin + count
             if positions[begin:end].cpu().tolist() != list(range(computed, computed + count)):
                 raise ValueError("Native absolute positions differ from the scheduled offsets")
+            self.packed.append((begin, end, computed))
+            if row is None:
+                self.progress[req_id] = computed + count
+                begin = end
+                continue
             known = self.request["prompt_tokens"][row] + self.responses[row].tolist()
             if input_ids[begin:end].cpu().tolist() != known[computed : computed + count]:
                 raise ValueError(
                     "Native engine consumed tokens other than the prompt/frozen response prefix"
                 )
             self.current.append((row, begin, end, computed))
+            self.current_index.append(index)
             self.progress[req_id] = computed + count
             begin = end
         if begin != self.scheduled.total_num_scheduled_tokens:
             raise ValueError("Packed model input does not cover actual scheduled tokens")
+        if not self.current:
+            return
         self.schedule.append(
             {
                 "samples": [self.request["sample_ids"][row] for row, _, _, _ in self.current],
@@ -431,14 +599,16 @@ def layer(self, layer_index, output, layer):
             indices, cu, _reset = metadata_sequences(
                 meta, self.scheduled.total_num_scheduled_tokens
             )
-            if cu.tolist() != [0] + [end for _, _, end, _ in self.current]:
+            if cu.tolist() != [0] + [end for _, end, _ in self.packed]:
                 raise ValueError("GDN metadata reordered sequences without matching model inputs")
             conv, recurrent = module.kv_cache
             if not is_conv_state_dim_first():
                 conv = conv.transpose(-1, -2)
-            for (row, begin, end, computed), slot in zip(
-                self.current, indices.tolist(), strict=True
-            ):
+            slots = indices.tolist()
+            if len(slots) != len(self.packed):
+                raise ValueError("GDN metadata does not cover every packed sequence")
+            for (row, begin, end, computed), index in zip(self.current, self.current_index):
+                slot = slots[index]
                 if computed + end - begin == self.prompt_length + 15:
                     self.states[layer_index]["convolution"][row] = conv[slot].detach().cpu().clone()
                     self.states[layer_index]["recurrent_state"][row] = (
@@ -449,10 +619,10 @@ def layer(self, layer_index, output, layer):
             meta, cache = metadata[attention.layer_name], attention.kv_cache
             if not isinstance(meta, SharedAttentionMetadata):
                 raise ValueError("Full attention did not use shared native paged-KV metadata")
-            if list(meta.query_start_loc) != [0] + [end for _, _, end, _ in self.current]:
+            if list(meta.query_start_loc) != [0] + [end for _, end, _ in self.packed]:
                 raise ValueError("Full attention boundaries differ from actual packed input")
             block_size = cache.shape[2]
-            for index, (row, begin, end, computed) in enumerate(self.current):
+            for index, (row, begin, end, computed) in zip(self.current_index, self.current):
                 length = computed + end - begin
                 if length != self.prompt_length + 15:
                     continue
@@ -472,13 +642,13 @@ def record_logits(self, hidden, local, full):
         from rl_engine.integrations.qwen3_next_logprob import qwen_logprob_contract
         from rl_engine.kernels.logprob_contract import LogprobRole
 
-        if full.shape != (len(self.current), 151936) or local.shape != (len(self.current), 37984):
+        if full.shape != (len(self.packed), 151936) or local.shape != (len(self.packed), 37984):
             raise ValueError(
                 "Native sampling rows do not correspond one-to-one with active requests"
             )
         eligible = [
             (index, row, computed + end - begin - self.prompt_length)
-            for index, (row, begin, end, computed) in enumerate(self.current)
+            for index, (row, begin, end, computed) in zip(self.current_index, self.current)
             if computed + end - begin >= self.prompt_length
         ]
         if not eligible:
@@ -775,3 +945,102 @@ def run_rollout_cases(cases):
 
 def run_rollout_case(request, output):
     run_rollout_cases([(request, output)])
+
+
+def batch_invariance_descriptors(request):
+    """Scheduler descriptors for one version 2 request, in FCFS submission order."""
+    common = {
+        "request_sha256": request["request_sha256"],
+        "total_requests": 1 + len(request["companions"]),
+        "chunk_size": request["chunk_size"],
+    }
+    responses = request["response_tokens"]
+    target = {
+        **common,
+        "bi": True,
+        "sample_id": request["sample_ids"][0],
+        "batch_size": 1,
+        "prompt_length": len(request["prompt_tokens"][0]),
+        "prefill_chunks": request["prefill_chunks"],
+        "response_tokens": None if responses is None else responses[0],
+        "arrival_step": request["arrival_steps"]["target"],
+    }
+    companions = [
+        (
+            prompt,
+            count,
+            {
+                **common,
+                "index": index,
+                "arrival_step": request["arrival_steps"]["companions"][index],
+            },
+        )
+        for index, (prompt, count) in enumerate(
+            zip(request["companions"], request["companion_max_tokens"], strict=True)
+        )
+    ]
+    entries = [(request["prompt_tokens"][0], RESPONSE_LENGTH, target, "shared_l2")] + [
+        (prompt, count, descriptor, "shared_bi_companion")
+        for prompt, count, descriptor in companions
+    ]
+    if request.get("target_last"):
+        entries = entries[1:] + entries[:1]
+    return entries
+
+
+def run_batch_invariance_cases(cases):
+    """Run version 2 cells in one engine; each artifact describes the traced target only."""
+    from vllm import SamplingParams
+
+    engine, identity = None, None
+    try:
+        for request, output in cases:
+            validate_request(request)
+            if request["schema_version"] != 2:
+                raise ValueError("Batch-invariance cells use version 2 requests")
+            if engine is None:
+                identity = request["identity"]
+                engine = _new_engine(identity)
+            elif request["identity"] != identity:
+                raise ValueError("One native rollout process cannot change its identity")
+            output = Path(output).resolve()
+            records = engine.collective_rpc(
+                "configure_l2_trace", args=(request, str(output.with_suffix(".worker-traces")))
+            )
+            if len(records) != 4 or any(
+                record["request_sha256"] != request["request_sha256"] for record in records
+            ):
+                raise RuntimeError("All TP workers must acknowledge the exact trace request")
+            entries = batch_invariance_descriptors(request)
+            sampling = [
+                SamplingParams(
+                    temperature=0,
+                    top_p=1,
+                    top_k=-1,
+                    seed=1234,
+                    ignore_eos=True,
+                    max_tokens=count,
+                    detokenize=False,
+                    extra_args={key: descriptor},
+                )
+                for _, count, descriptor, key in entries
+            ]
+            generated = engine.generate(
+                [{"prompt_token_ids": prompt} for prompt, _, _, _ in entries],
+                sampling,
+                use_tqdm=False,
+            )
+            responses = None
+            for (prompt, count, _, key), item in zip(entries, generated, strict=True):
+                tokens = list(item.outputs[0].token_ids)
+                if item.prompt_token_ids != prompt or len(tokens) != count:
+                    raise RuntimeError("Native engine did not complete every request of the cell")
+                if key == "shared_l2":
+                    responses = [tokens]
+            records = engine.collective_rpc("collect_l2_trace")
+            save_tensor_artifact(output, assemble_trace(request, records, responses))
+    finally:
+        if engine is not None:
+            engine.llm_engine.engine_core.shutdown()
+            del engine
+            gc.collect()
diff --git a/rl_engine/kernels/gtest/forward_invariance.py b/rl_engine/kernels/gtest/forward_invariance.py
index 04d6fed21..0937ce7ea 100644
--- a/rl_engine/kernels/gtest/forward_invariance.py
+++ b/rl_engine/kernels/gtest/forward_invariance.py
@@ -32,6 +32,7 @@
     resolve_tolerance,
     validate_backend_provenance,
 )
+from rl_engine.testing.tensor_identity import tensor_bitwise_equal
 from rl_engine.testing.ws1_workload import (
     LogicalBatch,
     PaddedBatch,
@@ -349,7 +350,10 @@ def _compare_logical_tensors(
         rel_error = abs_error / canonical_fp32.abs().clamp_min(1e-12)
         max_rel = float(rel_error.max().item())
 
-    passed = bool(torch.allclose(transformed_fp32, canonical_fp32, atol=atol, rtol=rtol))
+    if spec.mode == "bitwise":
+        passed = tensor_bitwise_equal(transformed, canonical)
+    else:
+        passed = bool(torch.allclose(transformed_fp32, canonical_fp32, atol=atol, rtol=rtol))
 
     return TensorComparisonDetail(
         tensor_name=tensor_name,
diff --git a/rl_engine/kernels/gtest/qwen3_next_chain_report.py b/rl_engine/kernels/gtest/qwen3_next_chain_report.py
index 3326dd0af..20fdbd621 100644
--- a/rl_engine/kernels/gtest/qwen3_next_chain_report.py
+++ b/rl_engine/kernels/gtest/qwen3_next_chain_report.py
@@ -117,11 +117,14 @@ def build_report(
     device: str,
     gpu_name: str | None,
     compute_capability: str | None,
+    bi_matrix: Mapping[str, Any] | None = None,
 ) -> dict[str, Any]:
     """Build the chain report from L2 matrices and their loaded case artifacts.
 
     ``matrices[version]`` is an ``l2-matrix.json`` report; ``artifacts[version]
     [case_id][role]`` is the loaded rollout/recompute artifact of that case.
+    ``bi_matrix`` (a ``bi-matrix.json`` from the batch-invariance stage) adds a
+    ``batch_invariance`` section and must pass for the report to pass.
     """
     if not matrices or set(matrices) - set(WEIGHT_VERSIONS):
         raise ValueError(f"Weight versions must be a subset of {WEIGHT_VERSIONS}")
@@ -204,6 +207,7 @@ def build_report(
         first is None
         and all(matrix["status"] == "passed" for matrix in matrices.values())
         and all(item.passed for item in invariance)
+        and (bi_matrix is None or bi_matrix["status"] == "passed")
     )
     report = ChainGateReport(
         backend_profile=PROFILE,
@@ -264,5 +268,26 @@ def build_report(
         first_drift_cell=None if first is None else first[3],
         first_drift_layer=None if first is None else first[1],
         first_drift_family=None if first is None else first[2],
+        batch_invariance=None if bi_matrix is None else batch_invariance_section(bi_matrix),
     )
     return payload
+
+
+def batch_invariance_section(matrix: Mapping[str, Any]) -> dict[str, Any]:
+    """Per-family pass counts and the first failing cell of a ``bi-matrix.json``."""
+    families: dict[str, dict[str, int]] = {}
+    for cell_id in matrix["cell_ids"]:
+        cell = matrix["cells"][cell_id]
+        counts = families.setdefault(cell["family"], {"cells": 0, "passed": 0})
+        counts["cells"] += 1
+        counts["passed"] += int(cell["passed"])
+    first = matrix.get("first_failure")
+    return {
+        "suite": matrix["suite"],
+        "passed": matrix["status"] == "passed",
+        "families": families,
+        "first_failure": first,
+        "first_failure_family": (
+            None if first is None else matrix["cells"][first]["first_drift_family"]
+        ),
+    }
diff --git a/rl_engine/testing/qwen3_next_bi_cases.py b/rl_engine/testing/qwen3_next_bi_cases.py
new file mode 100644
index 000000000..60004877b
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_bi_cases.py
@@ -0,0 +1,370 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Batch-invariance cells for the Qwen3-Next full model (RFC #428 ``batch_invariance_ci``).
+
+Every cell runs one *target* sequence through the native rollout, optionally
+next to *companion* sequences, and compares the target bitwise - response
+tokens, BF16 logits, FP32 logprobs, the embedding and every layer, sub-node
+and recurrent/KV-state digest - with a *reference*: the same target, alone,
+with the same prefill chunk plan. A cell passes only if nothing about the
+target changed. Families:
+
+* ``concurrency`` - N = 1, 2, 4, 8, 16, 32, 64 requests in one engine, the
+  target first or last, all arriving together;
+* ``interference`` - companion content, lengths and arrival (during the
+  target's prefill, during its decode, after it) varied around one target;
+* ``chunk`` - the target alone across prefill chunk boundaries (prompt
+  lengths at and around 64, 128, 528 and 1024 tokens, chunk 64 or 128, first
+  chunk 1, 63 or 65), compared with the replay engine's full-sequence
+  recompute of the same tokens: the train/rollout boundary itself;
+* ``packing`` - the replay engine packs the target with companions into one
+  THD sequence batch (offsets and padding varied) and the target must equal
+  its recompute alone.
+
+Prompts repeat the four pinned L2 texts to any length, so a cell is fully
+determined by its parameters. :func:`cells` is pinned by
+``qwen3_next_bi_manifest.json``.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from dataclasses import asdict, dataclass
+from pathlib import Path
+
+from rl_engine.testing.qwen3_next_l2 import json_digest
+
+MANIFEST = Path(__file__).with_name("qwen3_next_bi_manifest.json")
+MAX_MODEL_LEN = 1040
+RESPONSE_LENGTH = 16
+CONCURRENCY = (1, 2, 4, 8, 16, 32, 64)
+CHUNK_PROMPTS = (63, 64, 65, 127, 128, 129, 527, 528, 529, 1023, 1024)
+CHUNK_SIZES = (64, 128)
+FIRST_CHUNKS = (1, 63, 65)
+
+
+@dataclass(frozen=True)
+class Sequence:
+    """One request: which pinned text it repeats, how long its prompt is, when it arrives."""
+
+    text: int
+    prompt_length: int
+    max_tokens: int = RESPONSE_LENGTH
+    arrival_step: int = 0
+    offset: int = 0
+
+
+@dataclass(frozen=True)
+class Cell:
+    cell_id: str
+    family: str
+    target: Sequence
+    first_chunk: int
+    chunk_size: int
+    companions: tuple[Sequence, ...] = ()
+    target_last: bool = False
+    engine: str = "rollout"
+
+    def chunk_plan(self):
+        """The target's exact prefill chunks: first chunk, then fixed chunks, then the rest."""
+        remaining, plan = self.target.prompt_length, []
+        size = min(self.first_chunk, remaining)
+        while remaining:
+            plan.append(size)
+            remaining -= size
+            size = min(self.chunk_size, remaining)
+        return plan
+
+    def reference(self):
+        """The same target, alone, with the same chunk plan."""
+        return Cell(
+            cell_id=f"ref-{self.target.text}-{self.target.prompt_length}"
+            f"-{self.target.offset}-{self.first_chunk}-{self.chunk_size}",
+            family="reference",
+            target=self.target,
+            first_chunk=self.first_chunk,
+            chunk_size=self.chunk_size,
+            engine=self.engine,
+        )
+
+    def to_dict(self):
+        return asdict(self)
+
+
+def _companions(count, *, seed, lengths=(37, 256, 512, 1000), arrival=0, tokens=RESPONSE_LENGTH):
+    return tuple(
+        Sequence(
+            text=(seed + index) % 4,
+            prompt_length=lengths[(seed + index) % len(lengths)],
+            max_tokens=tokens,
+            arrival_step=arrival,
+            offset=7 * (index + 1),
+        )
+        for index in range(count)
+    )
+
+
+def cells():
+    out = []
+    target = Sequence(text=0, prompt_length=256)
+    for n in CONCURRENCY:
+        for last in (False, True) if n > 1 else (False,):
+            out.append(
+                Cell(
+                    cell_id=f"concurrency-n{n:02d}-{'last' if last else 'first'}",
+                    family="concurrency",
+                    target=target,
+                    first_chunk=64,
+                    chunk_size=64,
+                    companions=_companions(n - 1, seed=n),
+                    target_last=last,
+                )
+            )
+    interference = {
+        "same-text": _companions(3, seed=0, lengths=(256,)),
+        "short-companions": _companions(7, seed=1, lengths=(1, 2, 3, 5)),
+        "long-companions": _companions(3, seed=2, lengths=(1000, 1024)),
+        "mixed-lengths": _companions(15, seed=3, lengths=(8, 63, 129, 777, 1024)),
+        "arrive-in-prefill": _companions(4, seed=4, arrival=2),
+        "arrive-in-decode": _companions(4, seed=5, arrival=6),
+        # The 256-token target prefills in four steps and decodes in fifteen: step 18
+        # is its last decode.
+        "arrive-at-last-decode": _companions(4, seed=6, arrival=18),
+        "long-generations": _companions(4, seed=7, tokens=24),
+        "single-token-generations": _companions(8, seed=8, tokens=1),
+    }
+    for name, companions in interference.items():
+        out.append(
+            Cell(
+                cell_id=f"interference-{name}",
+                family="interference",
+                target=target,
+                first_chunk=64,
+                chunk_size=64,
+                companions=companions,
+            )
+        )
+    out.append(
+        Cell(
+            cell_id="interference-target-last-arrival",
+            family="interference",
+            target=Sequence(text=0, prompt_length=256, arrival_step=3),
+            first_chunk=64,
+            chunk_size=64,
+            companions=_companions(4, seed=9),
+        )
+    )
+    for prompt in CHUNK_PROMPTS:
+        for chunk in CHUNK_SIZES:
+            for first in FIRST_CHUNKS:
+                out.append(
+                    Cell(
+                        cell_id=f"chunk-p{prompt:04d}-c{chunk}-f{first:02d}",
+                        family="chunk",
+                        target=Sequence(text=prompt % 4, prompt_length=prompt),
+                        first_chunk=first,
+                        chunk_size=chunk,
+                    )
+                )
+    packing = {
+        "two-sequences": _companions(1, seed=10),
+        "four-sequences": _companions(3, seed=11),
+        "eight-mixed": _companions(7, seed=12, lengths=(8, 63, 129, 777)),
+        "one-token-neighbours": _companions(3, seed=13, lengths=(1,)),
+    }
+    for name, companions in packing.items():
+        for last in (False, True):
+            out.append(
+                Cell(
+                    cell_id=f"packing-{name}-{'last' if last else 'first'}",
+                    family="packing",
+                    target=target,
+                    first_chunk=target.prompt_length,
+                    chunk_size=target.prompt_length,
+                    companions=companions,
+                    target_last=last,
+                    engine="replay",
+                )
+            )
+    for cell in out:
+        validate(cell)
+    if len({cell.cell_id for cell in out}) != len(out):
+        raise ValueError("Batch-invariance cell IDs must be unique")
+    return out
+
+
+def quick_cells():
+    """A CI-sized subset: one cell per family, plus the largest concurrency."""
+    keep = {
+        "concurrency-n04-last",
+        "concurrency-n64-first",
+        "interference-arrive-in-decode",
+        "chunk-p0129-c64-f63",
+        "packing-four-sequences-last",
+    }
+    return [cell for cell in cells() if cell.cell_id in keep]
+
+
+def validate(cell):
+    sequences = (cell.target, *cell.companions)
+    if any(
+        s.prompt_length < 1
+        or s.max_tokens < 1
+        or s.prompt_length + s.max_tokens > MAX_MODEL_LEN
+        or s.text not in range(4)
+        or s.arrival_step < 0
+        for s in sequences
+    ):
+        raise ValueError(f"{cell.cell_id}: a sequence does not fit the pinned rollout profile")
+    if cell.target.max_tokens != RESPONSE_LENGTH:
+        raise ValueError(f"{cell.cell_id}: the target always generates sixteen tokens")
+    if len(sequences) > 64:
+        raise ValueError(f"{cell.cell_id}: at most 64 concurrent requests are certified")
+    if cell.first_chunk < 1 or cell.chunk_size < 1:
+        raise ValueError(f"{cell.cell_id}: chunks must be positive")
+    if cell.engine not in ("rollout", "replay"):
+        raise ValueError(f"{cell.cell_id}: unknown engine {cell.engine}")
+
+
+def prompt_tokens(encoded_texts, sequence):
+    """The pinned text ``sequence.text`` repeated, rotated by ``offset``, cut to length."""
+    row = encoded_texts[sequence.text]
+    start = sequence.offset % len(row)
+    rotated = row[start:] + row[:start]
+    return (rotated * ((sequence.prompt_length + len(rotated) - 1) // len(rotated)))[
+        : sequence.prompt_length
+    ]
+
+
+def manifest_payload():
+    entries = [cell.to_dict() for cell in cells()]
+    return {
+        "schema_version": 1,
+        "description": "Qwen3-Next batch-invariance cells; regenerate with "
+        "python -m rl_engine.testing.qwen3_next_bi_cases --write",
+        "cells": entries,
+        "cells_sha256": hashlib.sha256(
+            json.dumps(entries, sort_keys=True, separators=(",", ":")).encode()
+        ).hexdigest(),
+    }
+
+
+def load_manifest():
+    payload = json.loads(MANIFEST.read_text())
+    if payload != json.loads(json.dumps(manifest_payload())):
+        raise ValueError("qwen3_next_bi_manifest.json differs from the generated cells")
+    return payload
+
+
+if __name__ == "__main__":
+    import sys
+
+    text = json.dumps(manifest_payload(), indent=2) + "\n"
+    if "--write" in sys.argv:
+        MANIFEST.write_text(text)
+    elif not MANIFEST.is_file() or MANIFEST.read_text() != text:
+        raise SystemExit("qwen3_next_bi_manifest.json is stale; rerun with --write")
+
+
+# --------------------------------------------------------------------------- #
+# Worker requests
+# --------------------------------------------------------------------------- #
+
+
+def _common(case_id, role, identity, prompt, responses, trace_layers):
+    length = len(prompt)
+    return {
+        "case_id": case_id,
+        "role": role,
+        "identity": identity,
+        "sample_ids": [0],
+        "prompt_tokens": [prompt],
+        "response_tokens": None if responses is None else [responses],
+        "response_length": RESPONSE_LENGTH,
+        "prediction_positions": list(range(length - 1, length + RESPONSE_LENGTH - 1)),
+        "consumed_response_tokens": RESPONSE_LENGTH - 1,
+        "consumed_input_tokens": None if responses is None else [prompt + responses[:-1]],
+        "state_position": length + RESPONSE_LENGTH - 2,
+        "logits_dtype": "bfloat16",
+        "logprobs_dtype": "float32",
+        "vocab_size": 151936,
+        "trace_layers": list(trace_layers or []),
+        "required_layer_digests": list(range(48)),
+        "generation": {
+            "temperature": 0.0,
+            "top_p": 1.0,
+            "top_k": -1,
+            "seed": 1234,
+            "ignore_eos": True,
+        },
+    }
+
+
+def rollout_request(cell, encoded_texts, identity, *, trace_layers=None):
+    """Version 2 rollout request: the generating target plus untraced companions."""
+    if cell.engine != "rollout":
+        raise ValueError(f"{cell.cell_id} is not a rollout cell")
+    plan = cell.chunk_plan()
+    request = {
+        "schema_version": 2,
+        **_common(
+            cell.cell_id,
+            "rollout",
+            identity,
+            prompt_tokens(encoded_texts, cell.target),
+            None,
+            trace_layers,
+        ),
+        "rollout_prefill_chunks": plan,
+        "prefill_chunks": plan,
+        "execution": {
+            "rollout_mode": "autoregressive",
+            "prefill_chunks": plan,
+            "response_mode": "generate",
+        },
+        "companions": [prompt_tokens(encoded_texts, s) for s in cell.companions],
+        "companion_max_tokens": [s.max_tokens for s in cell.companions],
+        "arrival_steps": {
+            "target": cell.target.arrival_step,
+            "companions": [s.arrival_step for s in cell.companions],
+        },
+        "chunk_size": cell.chunk_size,
+        "target_last": cell.target_last,
+    }
+    request["request_sha256"] = json_digest(request)
+    return request
+
+
+def replay_request(case_id, prompt, responses, identity, *, packing=None, trace_layers=None):
+    """Version 1 recompute request for one target; ``packing`` adds untraced neighbours."""
+    if len(responses) != RESPONSE_LENGTH:
+        raise ValueError("Replay needs the target's sixteen response tokens")
+    request = {
+        "schema_version": 1,
+        **_common(case_id, "recompute", identity, prompt, responses, trace_layers),
+        "rollout_prefill_chunks": [len(prompt)],
+        "prefill_chunks": None,
+        "execution": {
+            "recompute_mode": "full_from_tokens",
+            "input_sequence_length": len(prompt) + RESPONSE_LENGTH - 1,
+        },
+    }
+    if packing is not None:
+        request["packing"] = packing
+    request["request_sha256"] = json_digest(request)
+    return request
+
+
+def packing_layout(cell, encoded_texts, target_tokens):
+    """The replay packing of one cell: neighbours are prompt plus continuation tokens."""
+    neighbours = [
+        prompt_tokens(
+            encoded_texts,
+            Sequence(s.text, s.prompt_length + s.max_tokens - 1, offset=s.offset),
+        )
+        for s in cell.companions
+    ]
+    sequences = (neighbours + [target_tokens]) if cell.target_last else [target_tokens, *neighbours]
+    return {"sequences": sequences, "target_index": len(neighbours) if cell.target_last else 0}
diff --git a/rl_engine/testing/qwen3_next_bi_manifest.json b/rl_engine/testing/qwen3_next_bi_manifest.json
new file mode 100644
index 000000000..182f4d196
--- /dev/null
+++ b/rl_engine/testing/qwen3_next_bi_manifest.json
@@ -0,0 +1,3857 @@
+{
+  "schema_version": 1,
+  "description": "Qwen3-Next batch-invariance cells; regenerate with python -m rl_engine.testing.qwen3_next_bi_cases --write",
+  "cells": [
+    {
+      "cell_id": "concurrency-n01-first",
+      "family": "concurrency",
+      "target": {
+        "text": 0,
+        "prompt_length": 256,
+        "max_tokens": 16,
+        "arrival_step": 0,
+        "offset": 0
+      },
+      "first_chunk": 64,
+      "chunk_size": 64,
+      "companions": [],
+      "target_last": false,
+      "engine": "rollout"
+    },
+    {
+      "cell_id": "concurrency-n02-first",
+      "family": "concurrency",
+      "target": {
+        "text": 0,
+        "prompt_length": 256,
+        "max_tokens": 16,
+        "arrival_step": 0,
+        "offset": 0
+      },
+      "first_chunk": 64,
+      "chunk_size": 64,
+      "companions": [
+        {
+          "text": 2,
+          "prompt_length": 512,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 7
+        }
+      ],
+      "target_last": false,
+      "engine": "rollout"
+    },
+    {
+      "cell_id": "concurrency-n02-last",
+      "family": "concurrency",
+      "target": {
+        "text": 0,
+        "prompt_length": 256,
+        "max_tokens": 16,
+        "arrival_step": 0,
+        "offset": 0
+      },
+      "first_chunk": 64,
+      "chunk_size": 64,
+      "companions": [
+        {
+          "text": 2,
+          "prompt_length": 512,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 7
+        }
+      ],
+      "target_last": true,
+      "engine": "rollout"
+    },
+    {
+      "cell_id": "concurrency-n04-first",
+      "family": "concurrency",
+      "target": {
+        "text": 0,
+        "prompt_length": 256,
+        "max_tokens": 16,
+        "arrival_step": 0,
+        "offset": 0
+      },
+      "first_chunk": 64,
+      "chunk_size": 64,
+      "companions": [
+        {
+          "text": 0,
+          "prompt_length": 37,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 7
+        },
+        {
+          "text": 1,
+          "prompt_length": 256,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 14
+        },
+        {
+          "text": 2,
+          "prompt_length": 512,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 21
+        }
+      ],
+      "target_last": false,
+      "engine": "rollout"
+    },
+    {
+      "cell_id": "concurrency-n04-last",
+      "family": "concurrency",
+      "target": {
+        "text": 0,
+        "prompt_length": 256,
+        "max_tokens": 16,
+        "arrival_step": 0,
+        "offset": 0
+      },
+      "first_chunk": 64,
+      "chunk_size": 64,
+      "companions": [
+        {
+          "text": 0,
+          "prompt_length": 37,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 7
+        },
+        {
+          "text": 1,
+          "prompt_length": 256,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 14
+        },
+        {
+          "text": 2,
+          "prompt_length": 512,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 21
+        }
+      ],
+      "target_last": true,
+      "engine": "rollout"
+    },
+    {
+      "cell_id": "concurrency-n08-first",
+      "family": "concurrency",
+      "target": {
+        "text": 0,
+        "prompt_length": 256,
+        "max_tokens": 16,
+        "arrival_step": 0,
+        "offset": 0
+      },
+      "first_chunk": 64,
+      "chunk_size": 64,
+      "companions": [
+        {
+          "text": 0,
+          "prompt_length": 37,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 7
+        },
+        {
+          "text": 1,
+          "prompt_length": 256,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 14
+        },
+        {
+          "text": 2,
+          "prompt_length": 512,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 21
+        },
+        {
+          "text": 3,
+          "prompt_length": 1000,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 28
+        },
+        {
+          "text": 0,
+          "prompt_length": 37,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 35
+        },
+        {
+          "text": 1,
+          "prompt_length": 256,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 42
+        },
+        {
+          "text": 2,
+          "prompt_length": 512,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 49
+        }
+      ],
+      "target_last": false,
+      "engine": "rollout"
+    },
+    {
+      "cell_id": "concurrency-n08-last",
+      "family": "concurrency",
+      "target": {
+        "text": 0,
+        "prompt_length": 256,
+        "max_tokens": 16,
+        "arrival_step": 0,
+        "offset": 0
+      },
+      "first_chunk": 64,
+      "chunk_size": 64,
+      "companions": [
+        {
+          "text": 0,
+          "prompt_length": 37,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 7
+        },
+        {
+          "text": 1,
+          "prompt_length": 256,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 14
+        },
+        {
+          "text": 2,
+          "prompt_length": 512,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 21
+        },
+        {
+          "text": 3,
+          "prompt_length": 1000,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 28
+        },
+        {
+          "text": 0,
+          "prompt_length": 37,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 35
+        },
+        {
+          "text": 1,
+          "prompt_length": 256,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 42
+        },
+        {
+          "text": 2,
+          "prompt_length": 512,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 49
+        }
+      ],
+      "target_last": true,
+      "engine": "rollout"
+    },
+    {
+      "cell_id": "concurrency-n16-first",
+      "family": "concurrency",
+      "target": {
+        "text": 0,
+        "prompt_length": 256,
+        "max_tokens": 16,
+        "arrival_step": 0,
+        "offset": 0
+      },
+      "first_chunk": 64,
+      "chunk_size": 64,
+      "companions": [
+        {
+          "text": 0,
+          "prompt_length": 37,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 7
+        },
+        {
+          "text": 1,
+          "prompt_length": 256,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 14
+        },
+        {
+          "text": 2,
+          "prompt_length": 512,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 21
+        },
+        {
+          "text": 3,
+          "prompt_length": 1000,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 28
+        },
+        {
+          "text": 0,
+          "prompt_length": 37,
+          "max_tokens": 16,
+          "arrival_step": 0,
+          "offset": 35
+        },
+        {
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+          "prompt_length": 256,
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diff --git a/scripts/qwen3_next_batch_invariance.py b/scripts/qwen3_next_batch_invariance.py
new file mode 100644
index 000000000..df9381dac
--- /dev/null
+++ b/scripts/qwen3_next_batch_invariance.py
@@ -0,0 +1,337 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Stage ``bi``: the Qwen3-Next batch-invariance matrix (RFC #428 ``batch_invariance_ci``).
+
+Runs after a passed ``l2`` stage, under the same certified provider profile
+(``VLLM_BATCH_INVARIANT=1``). The cells are ``rl_engine/testing/qwen3_next_bi_cases.py``
+(pinned by ``qwen3_next_bi_manifest.json``); ``bi_suite: quick`` in the manifest
+runs the CI subset. Two independent worker processes, as in ``l2``:
+
+1. rollout: every rollout cell and its reference (the target alone, same chunk
+   plan) in one native vLLM engine; the target generates its sixteen tokens;
+2. replay: for every ``chunk`` cell, the VIME recompute of the target's tokens;
+   for every ``packing`` cell, the target packed with its neighbours and alone.
+
+Then, bitwise on response tokens, logits, logprobs, the embedding and every
+layer, sub-node and state digest: concurrency and interference cells against
+their reference, chunk cells against their replay, packing cells against the
+replay alone. ``bi-matrix.json`` records every cell, the first divergence and
+its layer family.
+"""
+
+import argparse
+import json
+import os
+import shutil
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+sys.path.insert(0, str(ROOT / "scripts"))
+
+from qwen3_next_contract import evidence_record, result_envelope, validate_result  # noqa: E402
+from qwen3_next_l2 import (  # noqa: E402
+    check_ready,
+    model_identity,
+    run_process,
+    verify_before_model_load,
+    wait_gpu_release,
+)
+from qwen3_next_runtime import (  # noqa: E402
+    atomic_json,
+    failure_fields,
+    file_digest,
+    memory_limits,
+    parse_memory_request,
+    require_predecessor,
+    verify_environment,
+)
+
+from rl_engine.kernels.gtest.qwen3_next_chain_report import family_of  # noqa: E402
+from rl_engine.testing import qwen3_next_bi_cases as bi  # noqa: E402
+from rl_engine.testing.qwen3_next_binary import (  # noqa: E402
+    archive_provider_binary,
+    validate_binary_record,
+    verify_imported_provider,
+)
+from rl_engine.testing.qwen3_next_l2 import (  # noqa: E402
+    FROZEN_TEXTS,
+    compare_artifacts,
+    compare_batch_prefill,
+    first_divergent_node,
+    load_tensor_artifact,
+    validate_model_artifact,
+)
+
+
+def suite_cells(manifest):
+    suite = manifest.get("bi_suite", "full")
+    if suite not in ("full", "quick"):
+        raise ValueError(f"Unknown batch-invariance suite {suite!r}")
+    bi.load_manifest()
+    return bi.quick_cells() if suite == "quick" else bi.cells()
+
+
+def encoded_texts(manifest):
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        manifest["checkpoint"], local_files_only=True, trust_remote_code=False
+    )
+    return [tokenizer.encode(text, add_special_tokens=False) for text in FROZEN_TEXTS]
+
+
+def packing_source(cell):
+    """The rollout that defines a packing cell's target tokens: the target alone, 64/64."""
+    return bi.Cell(
+        cell_id=cell.cell_id,
+        family=cell.family,
+        target=cell.target,
+        first_chunk=64,
+        chunk_size=64,
+    ).reference()
+
+
+def rollout_requests(manifest, cells):
+    """References first, each once; then every rollout cell."""
+    texts, identity = encoded_texts(manifest), model_identity(manifest)
+    requests, seen = [], set()
+    for cell in cells:
+        reference = packing_source(cell) if cell.engine == "replay" else cell.reference()
+        for item in (reference, cell):
+            if item.engine == "rollout" and item.cell_id not in seen:
+                seen.add(item.cell_id)
+                requests.append(bi.rollout_request(item, texts, identity))
+    return requests
+
+
+def replay_requests(manifest, cells, rollout_dir):
+    """Chunk-cell replays and packing cells (each with its unpacked replay)."""
+    texts, identity = encoded_texts(manifest), model_identity(manifest)
+    requests, seen = [], set()
+    for cell in cells:
+        if cell.family not in ("chunk", "packing"):
+            continue
+        source = cell.cell_id if cell.family == "chunk" else packing_source(cell).cell_id
+        artifact = load_tensor_artifact(rollout_dir / f"{source}.pt")
+        prompt = artifact["prompt_tokens"][0].tolist()
+        responses = artifact["response_tokens"][0].tolist()
+        alone = f"replay-{source}"
+        if alone not in seen:
+            seen.add(alone)
+            requests.append(bi.replay_request(alone, prompt, responses, identity))
+        if cell.family == "packing":
+            layout = bi.packing_layout(cell, texts, prompt + responses[:-1])
+            requests.append(
+                bi.replay_request(cell.cell_id, prompt, responses, identity, packing=layout)
+            )
+    return requests
+
+
+def worker(role, manifest_path, output, provider_binary):
+    """One independent process: run every request of ``role`` into ``output``."""
+    manifest = json.loads(Path(manifest_path).read_text())
+    module, _capabilities = check_ready()
+    verify_environment(manifest["environment"], require_venv=True)
+    output.mkdir(parents=True, exist_ok=True)
+    verify_before_model_load(manifest, output)
+    actual_binary = verify_imported_provider(provider_binary, source_tree=manifest["source_tree"])
+    cells = suite_cells(manifest)
+    if role == "rollout":
+        from rl_engine.integrations.qwen3_next_native_rollout import run_batch_invariance_cases
+
+        requests = rollout_requests(manifest, cells)
+        run = run_batch_invariance_cases
+    else:
+        requests = replay_requests(manifest, cells, output.parent / "rollout")
+        run = module.run_recompute_cases
+    pending = []
+    for request in requests:
+        path = output / f"{request['case_id']}.pt"
+        atomic_json(path.with_suffix(".request.json"), request)
+        pending.append((request, path))
+    run(pending)
+    records = {}
+    for request, path in pending:
+        artifact = load_tensor_artifact(path)
+        validate_model_artifact(artifact, request)
+        if artifact.get("provider_binary") != actual_binary:
+            raise ValueError("A batch-invariance case used a different CUDA provider")
+        records[request["case_id"]] = {"path": str(path), "sha256": file_digest(path)}
+    atomic_json(
+        output / "worker-result.json",
+        {"status": "passed", "role": role, "pid": os.getpid(), "cases": records},
+    )
+
+
+def judge(cells, output):
+    rollout, replay = output / "rollout", output / "replay"
+    load = {}
+
+    def artifact(directory, case_id):
+        key = (directory, case_id)
+        if key not in load:
+            load[key] = load_tensor_artifact(directory / f"{case_id}.pt")
+        return load[key]
+
+    results = {}
+    for cell in cells:
+        if cell.family == "chunk":
+            lhs, rhs = artifact(rollout, cell.cell_id), artifact(replay, f"replay-{cell.cell_id}")
+            paired = compare_artifacts(lhs, rhs)
+            comparisons, against = paired["comparisons"], "replay"
+        elif cell.family == "packing":
+            source = packing_source(cell).cell_id
+            lhs, rhs = artifact(replay, f"replay-{source}"), artifact(replay, cell.cell_id)
+            comparisons, against = compare_batch_prefill(lhs, rhs), "replay alone"
+        else:
+            lhs = artifact(rollout, cell.reference().cell_id)
+            rhs = artifact(rollout, cell.cell_id)
+            comparisons, against = compare_batch_prefill(lhs, rhs), "reference alone"
+        node = first_divergent_node(lhs, rhs)
+        passed = all(comparisons.values())
+        if node is None and not passed:
+            node = {"layer": None, "node": next(k for k, v in comparisons.items() if not v)}
+        results[cell.cell_id] = {
+            "family": cell.family,
+            "compared_with": against,
+            "passed": passed,
+            "comparisons": comparisons,
+            "first_divergent_node": None if passed else node,
+            "first_drift_family": (None if passed else family_of(node["layer"], node["node"])),
+        }
+    return results
+
+
+def execute(manifest_path, output):
+    manifest = json.loads(Path(manifest_path).read_text())
+    output.mkdir(parents=True, exist_ok=True)
+    previous = Path(manifest["previous"])
+    require_predecessor("bi", previous, Path(manifest["checkpoint"]), manifest)
+    check_ready()
+    verify_environment(manifest["environment"], require_venv=True)
+    memory, requested = memory_limits(), parse_memory_request(manifest["memory"])
+    if any(
+        value is None or value < requested
+        for value in (memory["effective_bytes"], memory["available_bytes"])
+    ):
+        raise RuntimeError("insufficient_memory: batch-invariance stage budget unavailable")
+    run_process(
+        [manifest["python"], str(ROOT / "setup.py"), "build_ext", "--inplace"],
+        log_path=output / "build.log",
+        timeout=1800,
+    )
+    provider_binary = archive_provider_binary(ROOT, output, source_tree=manifest["source_tree"])
+    validate_binary_record(provider_binary, source_tree=manifest["source_tree"])
+    probe = output / "gpu-probe.json"
+    run_process(
+        [
+            manifest["python"],
+            str(ROOT / "scripts/qwen3_next_l2.py"),
+            "--gpu-probe",
+            "--output",
+            str(probe),
+        ],
+        log_path=output / "gpu-probe.log",
+        timeout=180,
+    )
+    uuids = json.loads(probe.read_text())["uuids"]
+    wait_gpu_release(uuids)
+    atomic_json(
+        output / "environment.json",
+        {"environment": manifest["environment"], "memory": memory, "gpu_uuids": uuids},
+    )
+    for role in ("rollout", "replay"):
+        run_process(
+            [
+                manifest["python"],
+                str(Path(__file__)),
+                "--worker",
+                role,
+                "--manifest",
+                str(manifest_path),
+                "--output",
+                str(output / role),
+                "--provider-binary",
+                json.dumps(provider_binary),
+            ],
+            log_path=output / f"{role}.log",
+            timeout=8 * 3600,
+        )
+        wait_gpu_release(uuids)
+    cells = suite_cells(manifest)
+    results = judge(cells, output)
+    failed = [cell_id for cell_id, item in results.items() if not item["passed"]]
+    matrix = {
+        "schema_version": 1,
+        "scope": "qwen3_next_batch_invariance",
+        "status": "passed" if not failed else "failed",
+        "suite": manifest.get("bi_suite", "full"),
+        "identity": model_identity(manifest),
+        "cells_sha256": bi.manifest_payload()["cells_sha256"],
+        "cell_ids": [cell.cell_id for cell in cells],
+        "cells": results,
+        "failed": failed,
+        "first_failure": failed[0] if failed else None,
+        "provider_binary": provider_binary,
+    }
+    atomic_json(output / "bi-matrix.json", matrix)
+    prior = json.loads((previous / "result.json").read_text())
+    shutil.copyfile(previous / prior["evidence"]["operators"]["path"], output / "operators.json")
+    evidence = {
+        name: evidence_record(output / filename, Path(manifest_path).parent)
+        for name, filename in {
+            "operators": "operators.json",
+            "environment": "environment.json",
+            "provider_binary": "provider-binary.json",
+            "bi_matrix": "bi-matrix.json",
+        }.items()
+    }
+    if failed:
+        raise RuntimeError(f"Batch invariance failed in {len(failed)} cells; first: {failed[0]}")
+    result = result_envelope(manifest, os.environ["QWEN3_NEXT_ATTEMPT"], evidence=evidence)
+    validate_result(
+        manifest,
+        result,
+        run_dir=Path(manifest_path).parent,
+        attempt=os.environ["QWEN3_NEXT_ATTEMPT"],
+    )
+    atomic_json(output / "result.json", result)
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--manifest", type=Path)
+    parser.add_argument("--output", type=Path)
+    parser.add_argument("--worker", choices=("rollout", "replay"))
+    parser.add_argument("--provider-binary")
+    parser.add_argument("--check-ready", action="store_true")
+    args = parser.parse_args()
+    if args.check_ready:
+        _, capabilities = check_ready()
+        print(json.dumps(capabilities))
+        return
+    if args.worker:
+        worker(args.worker, args.manifest, args.output, json.loads(args.provider_binary))
+        return
+    try:
+        execute(args.manifest, args.output)
+    except Exception as exc:
+        args.output.mkdir(parents=True, exist_ok=True)
+        atomic_json(
+            args.output / "result.json",
+            {
+                "status": "failed",
+                "attempt": os.environ.get("QWEN3_NEXT_ATTEMPT"),
+                "phase": "batch_invariance",
+                **failure_fields(exc),
+            },
+        )
+        raise
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/qwen3_next_chain_report.py b/scripts/qwen3_next_chain_report.py
index df01bee54..6cf75b18f 100644
--- a/scripts/qwen3_next_chain_report.py
+++ b/scripts/qwen3_next_chain_report.py
@@ -52,6 +52,7 @@ def main():
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--last", type=Path, required=True, help="last passed stage run")
     parser.add_argument("--out", type=Path, required=True)
+    parser.add_argument("--bi", type=Path, help="passed batch-invariance (bi) stage run")
     args = parser.parse_args()
     matrices, artifacts = {}, {}
     gpu_name = None
@@ -74,6 +75,16 @@ def main():
         if gpu_name is None and environment.is_file():
             gpu_name = json.loads(environment.read_text()).get("gpu_name")
     identity = manifest["checkpoint_identity"]
+    bi_matrix = None
+    if args.bi is not None:
+        bi_manifest = json.loads((args.bi / "run.json").read_text())
+        bi_result = json.loads((args.bi / "result.json").read_text())
+        if (
+            bi_manifest["stage"] != "bi"
+            or bi_manifest["source_commit"] != manifest["source_commit"]
+        ):
+            raise SystemExit("--bi must name a bi run on the same commit as the chain")
+        bi_matrix = json.loads((args.bi / bi_result["evidence"]["bi_matrix"]["path"]).read_text())
     report = build_report(
         matrices,
         artifacts,
@@ -84,6 +95,7 @@ def main():
         device="cuda",
         gpu_name=gpu_name,
         compute_capability=None,
+        bi_matrix=bi_matrix,
     )
     args.out.parent.mkdir(parents=True, exist_ok=True)
     args.out.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n")
diff --git a/scripts/qwen3_next_contract.py b/scripts/qwen3_next_contract.py
index 88d55dd6c..e0642b399 100644
--- a/scripts/qwen3_next_contract.py
+++ b/scripts/qwen3_next_contract.py
@@ -20,6 +20,7 @@
     "train1": "qwen3_next_training_update1",
     "train2": "qwen3_next_training_update2",
     "train": "qwen3_next_training_and_reload",
+    "bi": "qwen3_next_batch_invariance",
 }
 # Stages that evaluate the full model against updated weights (an L2 matrix each).
 TRAIN_STAGES = ("train1", "train2", "train")
@@ -232,6 +233,8 @@ def validate_result(manifest, result, *, run_dir=None, attempt=None):
         required.add("provider_binary")
     if manifest["stage"] == "layers":
         required |= {"layers", "checkpoint", "provider_binary"}
+    if manifest["stage"] == "bi":
+        required |= {"bi_matrix", "provider_binary"}
     if manifest["stage"] == "train":
         required |= {"updates", "reload"}
     if manifest["stage"] in ("train1", "train2"):
@@ -283,6 +286,20 @@ def validate_result(manifest, result, *, run_dir=None, attempt=None):
             counts.get(key, 0) for key in ("skipped", "errors", "failures")
         ):
             raise ValueError("Required operator tests must pass without skips")
+        if manifest["stage"] == "bi":
+            from qwen3_next_l2 import model_identity
+
+            from rl_engine.testing import qwen3_next_bi_cases as bi
+
+            matrix = json.loads((root / evidence["bi_matrix"]["path"]).read_text())
+            suite = bi.quick_cells() if manifest.get("bi_suite") == "quick" else bi.cells()
+            if (
+                matrix.get("status") != "passed"
+                or matrix.get("identity") != model_identity(manifest)
+                or matrix.get("cell_ids") != [cell.cell_id for cell in suite]
+                or any(not matrix["cells"][cell.cell_id]["passed"] for cell in suite)
+            ):
+                raise ValueError("Batch-invariance matrix is incomplete or failed")
         if manifest["stage"] in ("layers", "l2", *TRAIN_STAGES):
             from qwen3_next_l2 import model_identity
 
diff --git a/scripts/qwen3_next_publish_evidence.py b/scripts/qwen3_next_publish_evidence.py
index 1cb6cb2e7..5ea7ec348 100644
--- a/scripts/qwen3_next_publish_evidence.py
+++ b/scripts/qwen3_next_publish_evidence.py
@@ -129,9 +129,23 @@ def main():
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--last", type=Path, required=True)
     parser.add_argument("--chain-report", type=Path)
+    parser.add_argument(
+        "--also",
+        type=Path,
+        action="append",
+        default=[],
+        help="another finished run of the same commit, e.g. the bi stage (repeatable)",
+    )
     parser.add_argument("--out", type=Path, required=True)
     args = parser.parse_args()
     runs = chain_runs(args.last)
+    for extra in args.also:
+        manifest = json.loads((extra / "run.json").read_text())
+        if manifest["source_commit"] != runs[0][0]["source_commit"]:
+            raise SystemExit(f"{extra} ran another commit")
+        if manifest["stage"] in {item[0]["stage"] for item in runs}:
+            raise SystemExit(f"{extra} repeats the stage {manifest['stage']}")
+        runs.append((manifest, extra.resolve()))
     if any(not (run / "result.json").is_file() for _, run in runs):
         raise SystemExit("Every stage on the chain must have finished")
     publish(runs, args.chain_report, args.out)
diff --git a/scripts/qwen3_next_run.py b/scripts/qwen3_next_run.py
index db8133d94..75adefce6 100644
--- a/scripts/qwen3_next_run.py
+++ b/scripts/qwen3_next_run.py
@@ -14,7 +14,8 @@
 scheduler gates, which certify the shared provider), ``l2`` (full-model
 train/prefill/decode raw-bit matrix), then ``train1``, ``train2`` and ``train``
 (two optimizer updates, export, L2 on the updated weights, reload and
-resumption). A single stage takes ``--previous <run dir>`` of its predecessor.
+resumption). A single stage takes ``--previous <run dir>`` of its predecessor;
+``bi`` (the batch-invariance matrix) runs after a passed ``l2``.
 
 Every run directory holds ``run.json`` (the immutable manifest, identified by a
 fresh ``execution_id``), ``attempts/<n>/`` (the stage's output) and, once the
@@ -139,6 +140,8 @@ def prepare(stage, previous, args):
         "status": "prepared",
         "full_model_l2": "not_evaluated",
     }
+    if stage == "bi":
+        manifest["bi_suite"] = args.bi_suite
     if args.training_storage_root and stage == "train1":
         manifest["training_storage"] = str(args.training_storage_root.resolve(strict=True) / run_id)
     environment = probe_environment(python)
@@ -160,7 +163,7 @@ def prepare(stage, previous, args):
             "path": str(certificate),
             "sha256": file_digest(certificate),
         }
-    elif stage in ("train1", "train2", "train"):
+    elif stage in ("train1", "train2", "train", "bi"):
         manifest["scheduler_profile_certificate"] = json.loads((previous / "run.json").read_text())[
             "scheduler_profile_certificate"
         ]
@@ -288,7 +291,7 @@ def main():
     parser = argparse.ArgumentParser(
         description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
     )
-    parser.add_argument("--stage", choices=(*ORDER, "all"), required=True)
+    parser.add_argument("--stage", choices=(*ORDER, "bi", "all"), required=True)
     parser.add_argument("--runs-root", type=Path, required=True)
     parser.add_argument("--checkpoint", type=Path, required=True)
     parser.add_argument("--cuda-home", type=Path, required=True)
@@ -308,6 +311,12 @@ def main():
         "--memory",
         help="host memory this allocation grants (e.g. 1280G); default: the stage's budget",
     )
+    parser.add_argument(
+        "--bi-suite",
+        choices=("full", "quick"),
+        default="full",
+        help="with --stage bi: the whole batch-invariance matrix or the CI subset",
+    )
     parser.add_argument(
         "--training-storage-root",
         type=Path,
diff --git a/scripts/qwen3_next_runtime.py b/scripts/qwen3_next_runtime.py
index 321597ac5..e4985239b 100644
--- a/scripts/qwen3_next_runtime.py
+++ b/scripts/qwen3_next_runtime.py
@@ -443,6 +443,8 @@ def wait_owned_child(child, interrupted, *, grace_seconds=10):
     "train1": ("1280G", "06:00:00", "l2", "scripts/qwen3_next_training.py"),
     "train2": ("1280G", "07:00:00", "train1", "scripts/qwen3_next_training.py"),
     "train": ("1280G", "09:00:00", "train2", "scripts/qwen3_next_training.py"),
+    # Batch invariance branches off a passed l2, under the same certified profile.
+    "bi": ("512G", "06:00:00", "l2", "scripts/qwen3_next_batch_invariance.py"),
 }
 
 
diff --git a/tests/check_qwen3_next_bi_requests.py b/tests/check_qwen3_next_bi_requests.py
new file mode 100644
index 000000000..4eb2ccaee
--- /dev/null
+++ b/tests/check_qwen3_next_bi_requests.py
@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Batch-invariance rollout requests against the native rollout contract (imports vLLM)."""
+
+import pytest
+
+from rl_engine.integrations import qwen3_next_native_rollout as rollout
+from rl_engine.testing import qwen3_next_bi_cases as bi
+
+TEXTS = [list(range(1000 + 50 * i, 1000 + 50 * i + 17 + i)) for i in range(4)]
+IDENTITY = {
+    "architecture_layers": 48,
+    "topology": {"tp": 4, "pp": 1, "cp": 1, "ep": 1},
+    "checkpoint": "/checkpoint",
+    "provider_config": {"native_rollout_kv_cache_memory_bytes": 4 << 30},
+}
+
+
+def test_every_rollout_cell_builds_a_valid_rollout_request():
+    for cell in bi.cells():
+        if cell.engine != "rollout":
+            continue
+        request = bi.rollout_request(cell, TEXTS, IDENTITY)
+        assert rollout.validate_request(request) == (1, cell.target.prompt_length)
+        entries = rollout.batch_invariance_descriptors(request)
+        assert len(entries) == 1 + len(cell.companions)
+        target = [entry for entry in entries if entry[3] == "shared_l2"]
+        assert len(target) == 1 and target[0][2]["prefill_chunks"] == cell.chunk_plan()
+        assert (entries[-1] if cell.target_last else entries[0])[3] == "shared_l2"
+
+
+def test_requests_reject_companions_that_do_not_fit_the_profile():
+    request = bi.rollout_request(
+        bi.Cell("y", "interference", bi.Sequence(0, 256), 64, 64, (bi.Sequence(1, 8),)),
+        TEXTS,
+        IDENTITY,
+    )
+    request["companion_max_tokens"] = [2000]
+    request["request_sha256"] = bi.json_digest(
+        {k: v for k, v in request.items() if k != "request_sha256"}
+    )
+    with pytest.raises(ValueError, match="does not fit"):
+        rollout.validate_request(request)
diff --git a/tests/test_qwen3_next_bi_cases.py b/tests/test_qwen3_next_bi_cases.py
new file mode 100644
index 000000000..df478f60d
--- /dev/null
+++ b/tests/test_qwen3_next_bi_cases.py
@@ -0,0 +1,94 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Batch-invariance cells, their pinned manifest and the worker requests built from them."""
+
+import collections
+
+import pytest
+
+from rl_engine.integrations.qwen3_next_native_recompute import packed_layout
+from rl_engine.testing import qwen3_next_bi_cases as bi
+
+TEXTS = [list(range(1000 + 50 * i, 1000 + 50 * i + 17 + i)) for i in range(4)]
+IDENTITY = {
+    "architecture_layers": 48,
+    "topology": {"tp": 4, "pp": 1, "cp": 1, "ep": 1},
+    "checkpoint": "/checkpoint",
+    "provider_config": {"native_rollout_kv_cache_memory_bytes": 4 << 30},
+}
+
+
+def test_manifest_pins_the_generated_cells():
+    payload = bi.load_manifest()
+    assert len(payload["cells"]) == len(bi.cells()) == 97
+    families = collections.Counter(cell.family for cell in bi.cells())
+    assert families == {"chunk": 66, "concurrency": 13, "interference": 10, "packing": 8}
+
+
+def test_concurrency_covers_one_to_sixty_four_requests():
+    sizes = {1 + len(cell.companions) for cell in bi.cells() if cell.family == "concurrency"}
+    assert sizes == set(bi.CONCURRENCY)
+
+
+@pytest.mark.parametrize(
+    "prompt,first,chunk,plan",
+    [
+        (64, 1, 64, [1, 63]),
+        (65, 63, 64, [63, 2]),
+        (129, 65, 64, [65, 64]),
+        (1024, 1, 128, [1] + [128] * 7 + [127]),
+        (63, 65, 128, [63]),
+    ],
+)
+def test_chunk_plan_is_first_chunk_then_fixed_chunks(prompt, first, chunk, plan):
+    cell = bi.Cell("c", "chunk", bi.Sequence(0, prompt), first, chunk)
+    assert cell.chunk_plan() == plan and sum(plan) == prompt
+
+
+def test_quick_subset_has_one_cell_per_family_and_the_largest_batch():
+    quick = bi.quick_cells()
+    assert {cell.family for cell in quick} == {"concurrency", "interference", "chunk", "packing"}
+    assert max(1 + len(cell.companions) for cell in quick) == 64
+
+
+def test_reference_is_the_target_alone_with_the_same_plan():
+    cell = next(c for c in bi.cells() if c.cell_id == "concurrency-n16-last")
+    reference = cell.reference()
+    assert reference.companions == () and reference.chunk_plan() == cell.chunk_plan()
+    assert reference.target == cell.target
+
+
+def test_cells_reject_companions_that_do_not_fit_the_profile():
+    cell = bi.Cell(
+        "x", "interference", bi.Sequence(0, 256), 64, 64, (bi.Sequence(1, 1030, max_tokens=16),)
+    )
+    with pytest.raises(ValueError, match="does not fit"):
+        bi.validate(cell)
+
+
+def test_prompts_repeat_and_rotate_the_pinned_text():
+    tokens = bi.prompt_tokens(TEXTS, bi.Sequence(1, 40, offset=3))
+    row = TEXTS[1]
+    assert len(tokens) == 40 and tokens[:5] == row[3:8]
+    assert tokens[len(row) - 3] == row[0]
+
+
+@pytest.mark.parametrize("last", [False, True])
+def test_packing_traces_only_the_target(last):
+    cell = next(
+        c
+        for c in bi.cells()
+        if c.cell_id == f"packing-four-sequences-{'last' if last else 'first'}"
+    )
+    target = list(range(271))
+    layout = bi.packing_layout(cell, TEXTS, target)
+    request = bi.replay_request(
+        cell.cell_id, target[:256], target[256:] + [7], IDENTITY, packing=layout
+    )
+    sequences, traced = packed_layout(request)
+    assert len(sequences) == 4 and traced == [3 if last else 0]
+    assert sequences[traced[0]] == target
+    layout["sequences"][traced[0]] = target[:-1]
+    with pytest.raises(ValueError, match="exactly once"):
+        packed_layout({**request, "packing": layout})
diff --git a/tests/test_qwen3_next_chain_report.py b/tests/test_qwen3_next_chain_report.py
index 71b0eacde..e863091d8 100644
--- a/tests/test_qwen3_next_chain_report.py
+++ b/tests/test_qwen3_next_chain_report.py
@@ -147,3 +147,41 @@ def test_unknown_nodes_and_versions_are_rejected():
     matrices["update3"] = matrices["update1"]
     with pytest.raises(ValueError, match="subset"):
         build(matrices, artifacts)
+
+
+def test_batch_invariance_section_counts_families_and_gates_the_report():
+    matrix = {
+        "suite": "quick",
+        "status": "failed",
+        "cell_ids": ["concurrency-n04-last", "chunk-p0129-c64-f63"],
+        "first_failure": "chunk-p0129-c64-f63",
+        "cells": {
+            "concurrency-n04-last": {"family": "concurrency", "passed": True},
+            "chunk-p0129-c64-f63": {
+                "family": "chunk",
+                "passed": False,
+                "first_drift_family": "gdn",
+            },
+        },
+    }
+    matrices, artifacts = inputs()
+    report = chain.build_report(
+        matrices,
+        artifacts,
+        contract=load_contract(),
+        measured_commit="c" * 40,
+        weight_source="w",
+        weight_hash="h",
+        device="cuda",
+        gpu_name=None,
+        compute_capability=None,
+        bi_matrix=matrix,
+    )
+    section = report["batch_invariance"]
+    assert not report["passed"] and not section["passed"]
+    assert section["families"] == {
+        "concurrency": {"cells": 1, "passed": 1},
+        "chunk": {"cells": 1, "passed": 0},
+    }
+    assert section["first_failure_family"] == "gdn"
+    assert build(matrices, artifacts)["batch_invariance"] is None
diff --git a/tests/test_tensor_identity.py b/tests/test_tensor_identity.py
index f85c0c4dd..c25d3d6d5 100644
--- a/tests/test_tensor_identity.py
+++ b/tests/test_tensor_identity.py
@@ -6,6 +6,8 @@
 import pytest
 import torch
 
+from rl_engine.kernels.gtest.forward_invariance import _compare_logical_tensors
+from rl_engine.kernels.gtest.tolerance import load_contract
 from rl_engine.testing.tensor_identity import assert_tensor_bitwise_equal, tensor_bitwise_equal
 
 
@@ -39,3 +41,38 @@ def test_adjacent_bfloat16_values_are_rejected():
 )
 def test_logical_identity_does_not_require_matching_strides(value):
     assert_tensor_bitwise_equal(value, value.contiguous().clone())
+
+
+@pytest.mark.parametrize("judgment", ["forward_invariance", "gradient_invariance"])
+@pytest.mark.parametrize("fault", ["signed_zero", "dtype", "nan"])
+def test_gtest_invariance_uses_original_bits(judgment, fault):
+    canonical = torch.zeros(1, dtype=torch.bfloat16)
+    changed = torch.tensor([-0.0], dtype=torch.bfloat16)
+    if fault == "dtype":
+        changed = canonical.float()
+    elif fault == "nan":
+        canonical = torch.tensor([float("nan")], dtype=torch.bfloat16)
+        changed = canonical.clone()
+    result = _compare_logical_tensors(
+        canonical,
+        changed,
+        judgment=judgment,
+        contract=load_contract(),
+        op_class="reduction",
+        dtype=torch.bfloat16,
+        backend_profile="cuda_bf16",
+    )
+    assert result.passed is False
+
+
+def test_accuracy_keeps_its_tolerance_semantics():
+    result = _compare_logical_tensors(
+        torch.ones(1),
+        torch.tensor([1.0078125], dtype=torch.bfloat16),
+        judgment="forward_accuracy",
+        contract=load_contract(),
+        op_class="reduction",
+        dtype=torch.bfloat16,
+        backend_profile="cuda_bf16",
+    )
+    assert result.passed is True

From 759755e08f8a801d617a273fe23b55831f8de194 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 05:13:53 +0000
Subject: [PATCH 52/70] fix(qwen3-next): native state shapes for any prompt
 that fits the rollout profile

Batch-invariance chunk cells use prompts such as 129 tokens; the state-shape check only admitted the four L2 lengths, which their own validators already pin.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 rl_engine/testing/qwen3_next_l2.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/rl_engine/testing/qwen3_next_l2.py b/rl_engine/testing/qwen3_next_l2.py
index 7f8778072..15ab886f6 100644
--- a/rl_engine/testing/qwen3_next_l2.py
+++ b/rl_engine/testing/qwen3_next_l2.py
@@ -654,8 +654,10 @@ def native_state_shapes(request):
         raise ValueError("Native state evidence requires the fixed equal-length batch1/4 matrix")
     prompt_length = len(prompts[0])
     consumed = prompt_length + RESPONSE_LENGTH - 1
+    # L2 requests are pinned to PROMPT_LENGTHS by their own validators; batch-invariance
+    # cells use any prompt that fits the rollout profile (1024 + 16 = max_model_len).
     if (
-        prompt_length not in PROMPT_LENGTHS
+        not 1 <= prompt_length <= 1024
         or request.get("consumed_response_tokens") != RESPONSE_LENGTH - 1
         or request.get("state_position") != consumed - 1
     ):

From 3025497cff70396feeb7a346cd16d1606663df95 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 07:35:22 +0000
Subject: [PATCH 53/70] fix(qwen3-next): evidence publisher redacts every
 recorded site path

Editable sources, base interpreters and build directories in package metadata now keep only their last path component; cgroup paths keep only their file name. The leftover check matches absolute paths only, so package paths such as vllm/.../cpu/data/ are not flagged, and any mention of the batch scheduler blocks publication.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 scripts/qwen3_next_publish_evidence.py      | 22 +++++++++++++-----
 tests/test_qwen3_next_evidence_redaction.py | 25 ++++++++++++++++-----
 2 files changed, 35 insertions(+), 12 deletions(-)

diff --git a/scripts/qwen3_next_publish_evidence.py b/scripts/qwen3_next_publish_evidence.py
index 1cb6cb2e7..41312eb53 100644
--- a/scripts/qwen3_next_publish_evidence.py
+++ b/scripts/qwen3_next_publish_evidence.py
@@ -27,13 +27,15 @@
 import socket
 from pathlib import Path
 
+# Absolute site paths only: "cpu/data/" inside a package path is not a site path.
+_ABSOLUTE = r"(?<![\w.>-])"
 FORBIDDEN = (
-    r"/data/",
-    r"/home/",
-    r"/raid/",
-    r"/scratch",
+    _ABSOLUTE + r"/data/",
+    _ABSOLUTE + r"/home/",
+    _ABSOLUTE + r"/raid/",
+    _ABSOLUTE + r"/scratch",
     r"\bdgx-",
-    r"SLURM_",
+    r"(?i)slurm",
 )
 TEXT_SUFFIXES = (".json", ".xml", ".txt", ".log")
 
@@ -74,14 +76,22 @@ def redact(text, pairs, counts):
             counts[placeholder] = counts.get(placeholder, 0) + text.count(value)
             text = text.replace(value, placeholder)
     for pattern, placeholder in (
+        (r"/sys/fs/cgroup/[^\s\"']*/([^/\s\"']+)", r"<CGROUP>/\1"),
         (r"/[^\s\"']*/site-packages", "<SITE>"),
+        # Anything else under a site root (editable sources, base interpreters,
+        # build directories in package metadata) keeps only its last component.
+        (
+            _ABSOLUTE + r"/(?:data|home|raid|scratch)/[^\s\"'\\]*?([^/\s\"'\\]+)(?=[\s\"'\\]|$)",
+            r"<PATH>/\1",
+        ),
         (r"\bdgx-[A-Za-z0-9-]+", "<HOST>"),
         (rf"\b{re.escape(socket.gethostname())}\b", "<HOST>"),
         (rf"\b{re.escape(getpass.getuser())}\b", "<USER>"),
     ):
         text, count = re.subn(pattern, placeholder, text)
         if count:
-            counts[placeholder] = counts.get(placeholder, 0) + count
+            label = placeholder.split("/", 1)[0]
+            counts[label] = counts.get(label, 0) + count
     return text
 
 
diff --git a/tests/test_qwen3_next_evidence_redaction.py b/tests/test_qwen3_next_evidence_redaction.py
index 5f045e8ab..eb2a1ff56 100644
--- a/tests/test_qwen3_next_evidence_redaction.py
+++ b/tests/test_qwen3_next_evidence_redaction.py
@@ -16,12 +16,12 @@
 import qwen3_next_publish_evidence as publisher  # noqa: E402
 
 PATTERNS = (
-    r"/data/",
-    r"/home/",
-    r"/raid/",
-    r"/scratch",
+    r"(?<![\w.>-])/data/",
+    r"(?<![\w.>-])/home/",
+    r"(?<![\w.>-])/raid/",
+    r"(?<![\w.>-])/scratch",
     r"\bdgx-",
-    r"SLURM_",
+    r"(?i)slurm",
     r"\bsbatch\b",
     r"\bsrun\b",
     r"\bwrd\b",
@@ -99,7 +99,20 @@ def test_publisher_replaces_every_recorded_root(tmp_path):
 def test_publisher_refuses_text_that_survives_redaction(tmp_path):
     last = chain(tmp_path)
     leak = last / "attempts" / "0" / "environment.json"
-    leak.write_text(json.dumps({"note": "copied from /scratch/elsewhere"}))
+    leak.write_text(json.dumps({"note": "SLURM_JOB_ID=123"}))
     with pytest.raises(SystemExit, match="survived redaction"):
         publisher.publish(publisher.chain_runs(last), None, tmp_path / "published")
     assert not (tmp_path / "published").exists()
+
+
+def test_relative_package_paths_are_not_site_paths(tmp_path):
+    counts = {}
+    text = publisher.redact(
+        '"vllm/ec_connector/cpu/data/base.py" "file:///data/x/editable/vime" "/home/b/env/bin/py"'
+        ' "/sys/fs/cgroup/memory/system.slice/slurmd.service/memory.limit_in_bytes"',
+        [],
+        counts,
+    )
+    assert "cpu/data/base.py" in text and "<PATH>/vime" in text and "<PATH>/py" in text
+    assert "<CGROUP>/memory.limit_in_bytes" in text
+    assert not publisher.leftovers(text)

From bd9ba13759b1d97940e17d67ddc19b5befe21f1b Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 07:36:02 +0000
Subject: [PATCH 54/70] docs(qwen3-next): batch-invariance evidence, 97/97
 cells bitwise (B200, 759755e)

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../README.md                                 |   44 +
 .../SHA256SUMS                                |   31 +
 .../bi/bi_matrix.json                         | 1698 ++++++
 .../bi/environment.json                       | 4510 ++++++++++++++
 .../bi/operators.json                         |  304 +
 .../bi/provider_binary.json                   |   13 +
 .../bi/result.json                            |  771 +++
 .../bi/run.json                               | 5253 +++++++++++++++++
 .../chain-report.json                         | 2152 +++++++
 .../l2/environment.json                       | 4511 ++++++++++++++
 .../l2/l2_matrix.json                         | 1186 ++++
 .../l2/operators.json                         |  304 +
 .../l2/predecessor.json                       |    5 +
 .../l2/provider_binary.json                   |   13 +
 .../l2/result.json                            |  775 +++
 .../l2/run.json                               | 5252 ++++++++++++++++
 .../layers/checkpoint.json                    |   14 +
 .../layers/environment.json                   | 4511 ++++++++++++++
 .../layers/layers.json                        |  457 ++
 .../layers/operators.json                     |  304 +
 .../layers/provider_binary.json               |   13 +
 .../layers/result.json                        |  775 +++
 .../layers/run.json                           | 5248 ++++++++++++++++
 .../measured-commit.txt                       |    1 +
 .../preflight/checkpoint.json                 |   14 +
 .../preflight/device_guards.xml               |    1 +
 .../preflight/environment.json                |   40 +
 .../preflight/operators.json                  |  304 +
 .../preflight/result.json                     |  797 +++
 .../preflight/rollout_smoke.json              |   75 +
 .../preflight/run.json                        | 5248 ++++++++++++++++
 .../preflight/tp4.json                        |   49 +
 .../redaction.json                            |   14 +
 33 files changed, 44687 insertions(+)
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/README.md
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/SHA256SUMS
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/bi_matrix.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/provider_binary.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/chain-report.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/l2_matrix.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/predecessor.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/provider_binary.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/checkpoint.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/layers.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/provider_binary.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/measured-commit.txt
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/checkpoint.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/device_guards.xml
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/rollout_smoke.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/tp4.json
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/redaction.json

diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/README.md b/docs/usage/evidence/qwen3-next-batch-invariance-b200/README.md
new file mode 100644
index 000000000..6f6b93093
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/README.md
@@ -0,0 +1,44 @@
+# Qwen3-Next batch invariance (B200)
+
+Measured at commit `759755e` (`measured-commit.txt`) from clean clones, one
+stage after another on one node with four B200 GPUs, in one environment
+(`environment_sha256` `118384371a88…` in every `run.json`):
+
+```bash
+python scripts/qwen3_next_run.py --stage all --until l2 \
+    --runs-root <runs> --checkpoint <checkpoint> --cuda-home <cuda-13.0> --python <venv>/bin/python
+python scripts/qwen3_next_run.py --stage bi --bi-suite full --previous <runs>/<...-l2-...> \
+    --runs-root <runs> --checkpoint <checkpoint> --cuda-home <cuda-13.0> --python <venv>/bin/python
+python scripts/qwen3_next_chain_report.py --last <...-l2-...> --bi <...-bi-...> --out chain-report.json
+python scripts/qwen3_next_publish_evidence.py --last <...-l2-...> --also <...-bi-...> \
+    --chain-report chain-report.json --out docs/usage/evidence/qwen3-next-batch-invariance-b200
+```
+
+The stages ran the code of `759755e`; the copy in this directory was made by
+the publisher of `3025497`, which only adds redaction rules (`redaction.json`
+counts the placeholders) and changes no measured byte. `SHA256SUMS` covers
+every file.
+
+| Stage | Result |
+| --- | --- |
+| `preflight` | passed: environment, mandatory operator cases and norm gates, device guards, TP4 probe, checkpoint hashes, rollout smoke |
+| `layers` | passed: GDN, attention, MoE, GDN-adapter and embedding/head TP4 gates on real weights; native scheduler on 16 cases |
+| `l2` | passed: 16/16 cases bitwise rollout vs replay, every layer, sub-node and state digest equal |
+| `bi` (full) | passed: **97/97 cells bitwise** |
+
+| `bi` family | Cells | Passed | Compared with |
+| --- | --- | --- | --- |
+| concurrency (1-64 requests, target first or last) | 13 | 13 | the target alone, same chunk plan |
+| interference (content, lengths, arrival, generation length) | 10 | 10 | the target alone, same chunk plan |
+| chunk (prompts 63-1024, chunk 64/128, first chunk 1/63/65) | 66 | 66 | the replay engine's recompute |
+| packing (replay THD batch with 1-7 neighbours) | 8 | 8 | the replay of the target alone |
+
+Each comparison is raw bits of the target's 16 response tokens, its BF16
+logits and FP32 logprobs over the full vocabulary, the embedding rows and, for
+all 48 layers, the five sub-node outputs, the layer output and the
+convolution/recurrent or KV state. `bi/bi_matrix.json` lists every cell;
+`chain-report.json` is the chain-gate report with its `batch_invariance`
+section.
+
+Site paths, host names and the scheduler are replaced by placeholders
+(`<CHECKPOINT>`, `<RUN>`, `<PYTHON>`, `<SITE>`, `<PATH>/name`, `<CGROUP>/file`).
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/SHA256SUMS b/docs/usage/evidence/qwen3-next-batch-invariance-b200/SHA256SUMS
new file mode 100644
index 000000000..d9791572a
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/SHA256SUMS
@@ -0,0 +1,31 @@
+10ff2c602da1b7e195e3ff4878248519734fbc6856e23e5bc8044cf8aeff993b  bi/bi_matrix.json
+092bb1bb11c47d604c1bfb69292ddaf0e3720dce5ce31742b795be4b0a36c316  bi/environment.json
+bb96af8122ba0c79d94ba00fc8f67c18d1d5cb1d3806a4c372ec0f0f19ab52c5  bi/operators.json
+bb49a465b63ddc24b4bd650deb9e3502f0e63b7d27bb9b881160ccc20d926390  bi/provider_binary.json
+dae27bfed4e1beac3bebbc43927ff3dc1fdca757591db516d1546b99d43f9e5a  bi/result.json
+35a969fedf51687ba6738be2109ed7d3efd4ff2570c6896b027b906de61135e6  bi/run.json
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/bi_matrix.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/bi_matrix.json
new file mode 100644
index 000000000..9845ae241
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/bi_matrix.json
@@ -0,0 +1,1698 @@
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/environment.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/environment.json
new file mode 100644
index 000000000..5c34e39a1
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/environment.json
@@ -0,0 +1,4510 @@
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/operators.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/operators.json
new file mode 100644
index 000000000..a6e914e66
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/operators.json
@@ -0,0 +1,304 @@
+{
+  "scope": "qwen3_next_operators_only",
+  "status": "passed",
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+  "diagnostic_only": false,
+  "full_model_l2": "not_evaluated",
+  "git_commit": "759755e08f8a801d617a273fe23b55831f8de194",
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+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[256]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[64]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[8]",
+    "tests/check_qwen3_next_attention.py::test_d256_input_device_guard_and_nondefault_stream",
+    "tests/check_qwen3_next_attention.py::test_native_backward_rejects_malformed_probability_and_gradient_buffers",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype1]",
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+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[2]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype0]",
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/provider_binary.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/provider_binary.json
new file mode 100644
index 000000000..9e5553eb3
--- /dev/null
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/result.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/result.json
new file mode 100644
index 000000000..bdb5ec534
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/run.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/run.json
new file mode 100644
index 000000000..33f2fff78
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/bi/run.json
@@ -0,0 +1,5253 @@
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/chain-report.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/chain-report.json
new file mode 100644
index 000000000..c677bf792
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/chain-report.json
@@ -0,0 +1,2152 @@
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/environment.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/environment.json
new file mode 100644
index 000000000..bf967b038
--- /dev/null
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/l2_matrix.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/l2_matrix.json
new file mode 100644
index 000000000..89c29d48b
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/l2_matrix.json
@@ -0,0 +1,1186 @@
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+}
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/operators.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/operators.json
new file mode 100644
index 000000000..a6e914e66
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/operators.json
@@ -0,0 +1,304 @@
+{
+  "scope": "qwen3_next_operators_only",
+  "status": "passed",
+  "suite": "all",
+  "diagnostic_only": false,
+  "full_model_l2": "not_evaluated",
+  "git_commit": "759755e08f8a801d617a273fe23b55831f8de194",
+  "git_dirty": false,
+  "versions": {
+    "torch": "2.13.0",
+    "vllm": "0.30.0",
+    "transformers": "5.17.0",
+    "triton": "3.7.1"
+  },
+  "mandatory_case_ids": [
+    "tests/check_qwen3_next_attention.py::test_d256_backward_with_cached_prefix_matches_float64_formula",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[1024]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[256]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[64]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[8]",
+    "tests/check_qwen3_next_attention.py::test_d256_input_device_guard_and_nondefault_stream",
+    "tests/check_qwen3_next_attention.py::test_native_backward_rejects_malformed_probability_and_gradient_buffers",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[2]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_target_inputs_do_not_depend_on_companions",
+    "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_is_batch_invariant",
+    "tests/check_qwen3_next_conv_bridge.py::test_chunked_and_reordered_sequences_match",
+    "tests/check_qwen3_next_conv_bridge.py::test_empty_sequence_preserves_cache_and_gradients",
+    "tests/check_qwen3_next_conv_bridge.py::test_response_gradient_reaches_prompt_and_convolution_weight",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths0]",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths1]",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths2]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[1024-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[1024-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[256-1]",
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/predecessor.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/predecessor.json
new file mode 100644
index 000000000..8b3df6c6a
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/predecessor.json
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new file mode 100644
index 000000000..f7dfae1e7
--- /dev/null
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new file mode 100644
index 000000000..7184cfae9
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+      "tests/test_qwen3_next_norm.py::test_cuda_gated_weight_offset_is_applied_in_fp32",
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+      "tests/test_qwen3_next_norm.py::test_deterministic_repeat",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype2]",
+      "tests/test_qwen3_next_norm.py::test_eps_inside_sqrt",
+      "tests/test_qwen3_next_norm.py::test_gated_batch_invariance_slice",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedHFOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_conventions_diverge_in_low_precision",
+      "tests/test_qwen3_next_norm.py::test_gated_cuda_op_rejects_bad_activation_at_construction",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_handles_empty_batch_and_nondefault_stream",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[dy_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[gate_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[rstd_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_shape]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_matches_hf_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_gated_shape_mismatch_raises",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_matches_vllm_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[128]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[2048]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_offset_applied_in_fp32_not_folded_into_bf16",
+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
+      "tests/test_qwen3_next_norm.py::test_weight_offset_is_one",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
+    ],
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+      "qwen3_next_rms_norm-gradient.json",
+      "rms_norm_gated-forward.json",
+      "rms_norm_gated-gradient.json"
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+            "routes-replicated-256",
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+            "routes-replicated-1024",
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+      "sha256": "abad81c1a17ddcdb833a37f8390831eb83c20525b8031e67277b660e3df422d6"
+    }
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/run.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/run.json
new file mode 100644
index 000000000..a42569b69
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/l2/run.json
@@ -0,0 +1,5252 @@
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+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[rstd_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_shape]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_matches_hf_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_gated_shape_mismatch_raises",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_matches_vllm_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[128]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[2048]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_offset_applied_in_fp32_not_folded_into_bf16",
+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
+      "tests/test_qwen3_next_norm.py::test_weight_offset_is_one",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/checkpoint.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/checkpoint.json
new file mode 100644
index 000000000..4488f19f1
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/checkpoint.json
@@ -0,0 +1,14 @@
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/environment.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/environment.json
new file mode 100644
index 000000000..f43503b9c
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/environment.json
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/layers.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/layers.json
new file mode 100644
index 000000000..d992b1b31
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/layers.json
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/operators.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/operators.json
new file mode 100644
index 000000000..a6e914e66
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/operators.json
@@ -0,0 +1,304 @@
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/provider_binary.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/provider_binary.json
new file mode 100644
index 000000000..34ba0d006
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/provider_binary.json
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/result.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/result.json
new file mode 100644
index 000000000..4d6ab93a3
--- /dev/null
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+      "tests/test_qwen3_next_norm.py::test_gated_batch_invariance_slice",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedHFOp]",
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+      "tests/test_qwen3_next_norm.py::test_gated_conventions_diverge_in_low_precision",
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+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[rstd_dtype]",
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+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_shape]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype0]",
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+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype0]",
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+      "tests/test_qwen3_next_norm.py::test_gated_strict_matches_vllm_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[128]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[2048]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_offset_applied_in_fp32_not_folded_into_bf16",
+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
+      "tests/test_qwen3_next_norm.py::test_weight_offset_is_one",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
+    ],
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+      "qwen3_next_rms_norm-gradient.json",
+      "rms_norm_gated-forward.json",
+      "rms_norm_gated-gradient.json"
+    ]
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+        "output_option": "--output",
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+            "full-chunk-reorder-256",
+            "routes-replicated-1024",
+            "full-chunk-reorder-1024",
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+      "heads/cases/entropy-interface",
+      "heads/cases/hf-embedding-roundtrip",
+      "heads/cases/hf-lm-head-roundtrip",
+      "heads/cases/logprob-original-and-promoted-logits",
+      "heads/cases/nonzero-finite-embedding-head-gradient",
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+      "sha256": "7ceb9dab2ce8c0a2551a970819f9f0adbb538f04c4124982458a42f997d7c934"
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+      "sha256": "69675741f6c0d9acecfa93cd842d68d7105a577773124e1f03c85a2006cd2339"
+    }
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/run.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/run.json
new file mode 100644
index 000000000..ae5126282
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/layers/run.json
@@ -0,0 +1,5248 @@
+{
+  "run_id": "20261009T052932Z-layers-759755e0-cb5e9870",
+  "execution_id": "cb5e9870d45442a3b93cdf8cee8ab4db",
+  "contract_version": 4,
+  "suite": "all",
+  "execution_gpus": 4,
+  "source_tree": "d8da35c889c3ffe57d8569fd9d2c51b063de16ce",
+  "stage": "layers",
+  "source_commit": "759755e08f8a801d617a273fe23b55831f8de194",
+  "source": "<RUN>/source",
+  "python": "<PYTHON>",
+  "cuda_home": "<CUDA_HOME>",
+  "checkpoint": "<CHECKPOINT>",
+  "memory": "512G",
+  "previous": "<RUN>",
+  "status": "prepared",
+  "full_model_l2": "not_evaluated",
+  "environment": {
+    "identity": {
+      "base_prefix": "<HOME>/miniconda3/envs/rlkernel",
+      "binary": "<PATH>/python3.12",
+      "binary_sha256": "6a1eb3ffc29f3a6b0ece607b016b4cfa5ca3ac57b34f17ed3266b404f78d2729",
+      "build_tools": {
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+          "sha256": "08639e194fffa7f08b259fc4abfa4803aff66b64de52549cee42ec527d55cea6"
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+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[16]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[1]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[2]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[32]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[48]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[512]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[64]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[8]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_within_tolerance_of_golden",
+      "tests/test_qwen3_next_norm.py::test_deterministic_repeat",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype2]",
+      "tests/test_qwen3_next_norm.py::test_eps_inside_sqrt",
+      "tests/test_qwen3_next_norm.py::test_gated_batch_invariance_slice",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedHFOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_conventions_diverge_in_low_precision",
+      "tests/test_qwen3_next_norm.py::test_gated_cuda_op_rejects_bad_activation_at_construction",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_handles_empty_batch_and_nondefault_stream",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[dy_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[gate_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[rstd_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_shape]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_matches_hf_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_gated_shape_mismatch_raises",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_matches_vllm_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[128]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[2048]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_offset_applied_in_fp32_not_folded_into_bf16",
+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
+      "tests/test_qwen3_next_norm.py::test_weight_offset_is_one",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
+    ],
+    "case_ids_sha256": "694c931b65f9cfa5db963f2eca6d11bd309546c824a1df52f9da2ab128e83e0f",
+    "norm_gates": [
+      "qwen3_next_rms_norm-forward.json",
+      "qwen3_next_rms_norm-gradient.json",
+      "rms_norm_gated-forward.json",
+      "rms_norm_gated-gradient.json"
+    ]
+  },
+  "layer_requirements": {
+    "schema_version": 1,
+    "gates": {
+      "gdn": {
+        "script": "scripts/qwen3_next_tp_gdn_check.py",
+        "output_option": "--output",
+        "scope": "tp4_gdn_layer0",
+        "groups": {
+          "cases": [
+            "full-chunk-8",
+            "full-chunk-64",
+            "full-chunk-256",
+            "full-chunk-1024",
+            "prompt-gradient",
+            "replicated-norm-gradient",
+            "hf-load-export"
+          ]
+        },
+        "script_sha256": "cc4338893f90af004c51c20fc67d1a9a3f4bf8d26775277f72671c02983e4df5"
+      },
+      "attention": {
+        "script": "scripts/qwen3_next_tp_attention_check.py",
+        "output_option": "--output",
+        "scope": "tp4_full_attention_layer3",
+        "groups": {
+          "attention.cases": [
+            "hf-load-export",
+            "full-chunk-8",
+            "full-chunk-64",
+            "full-chunk-256",
+            "full-chunk-1024",
+            "batch4-varlen-reorder",
+            "sixteen-token-decode",
+            "prompt-gradient",
+            "kv-pair-gradient",
+            "qk-norm-gradient"
+          ]
+        },
+        "script_sha256": "d27d43efb05415a8ef5892c3c2238a2b9744a4821f5466faf46c6510e6e01bec"
+      },
+      "moe": {
+        "script": "scripts/qwen3_next_tp_moe_check.py",
+        "output_option": "--output",
+        "scope": "tp4_moe_block_layer0",
+        "groups": {
+          "moe.cases": [
+            "hf-load-export-all512experts",
+            "routes-replicated-8",
+            "full-chunk-reorder-8",
+            "routes-replicated-64",
+            "full-chunk-reorder-64",
+            "routes-replicated-256",
+            "full-chunk-reorder-256",
+            "routes-replicated-1024",
+            "full-chunk-reorder-1024",
+            "training-forward",
+            "all-parameter-gradient",
+            "replicated-router-gradient"
+          ]
+        },
+        "script_sha256": "d5c1bbbb82e408b65871b0cb1638d7a6cf8cfe07f0a3315a657b7dc232d544be"
+      },
+      "adapters": {
+        "script": "scripts/qwen3_next_adapter_check.py",
+        "output_option": "--out",
+        "scope": "gdn_engine_adapter_component",
+        "groups": {
+          "cases": [
+            "batch1-length8",
+            "batch1-length64",
+            "batch1-length256",
+            "batch1-length1024",
+            "batch4-length8",
+            "batch4-length64",
+            "batch4-length256",
+            "batch4-length1024",
+            "mixed-prefill-decode16-reorder"
+          ]
+        },
+        "fields": {
+          "prompt_gradient": "passed",
+          "hf_conv_roundtrip": "passed",
+          "batch_invariance_capability_registered": false
+        },
+        "script_sha256": "3ce45bb317237c1a06533116d65fb77c2a23b6f503ae46406ede543036eb0f20"
+      },
+      "heads": {
+        "script": "scripts/qwen3_next_tp_head_check.py",
+        "output_option": "--output",
+        "scope": "tp4_embedding_lm_head_only",
+        "groups": {
+          "cases": [
+            "hf-embedding-roundtrip",
+            "hf-lm-head-roundtrip",
+            "replicated-norm-gradient",
+            "nonzero-finite-embedding-head-gradient",
+            "embedding-repeat-100",
+            "batch-invariant-prompt8",
+            "batch-invariant-prompt64",
+            "batch-invariant-prompt256",
+            "batch-invariant-prompt1024",
+            "logprob-original-and-promoted-logits",
+            "entropy-interface"
+          ]
+        },
+        "script_sha256": "0504185d21a75405c8e994bc57b016cf4ec357d8b16e7430772c11934f2af876"
+      }
+    },
+    "scheduler_case_ids": [
+      "p0008-b1-full",
+      "p0008-b1-chunked",
+      "p0008-b4-full",
+      "p0008-b4-chunked",
+      "p0064-b1-full",
+      "p0064-b1-chunked",
+      "p0064-b4-full",
+      "p0064-b4-chunked",
+      "p0256-b1-full",
+      "p0256-b1-chunked",
+      "p0256-b4-full",
+      "p0256-b4-chunked",
+      "p1024-b1-full",
+      "p1024-b1-chunked",
+      "p1024-b4-full",
+      "p1024-b4-chunked"
+    ],
+    "registry_sha256": "e3949a750cb27c1897a000f3b023f912629906612ae8949c1575d0acc36df5ec",
+    "case_ids_per_rank": [
+      "adapters/cases/batch1-length1024",
+      "adapters/cases/batch1-length256",
+      "adapters/cases/batch1-length64",
+      "adapters/cases/batch1-length8",
+      "adapters/cases/batch4-length1024",
+      "adapters/cases/batch4-length256",
+      "adapters/cases/batch4-length64",
+      "adapters/cases/batch4-length8",
+      "adapters/cases/mixed-prefill-decode16-reorder",
+      "attention/attention.cases/batch4-varlen-reorder",
+      "attention/attention.cases/full-chunk-1024",
+      "attention/attention.cases/full-chunk-256",
+      "attention/attention.cases/full-chunk-64",
+      "attention/attention.cases/full-chunk-8",
+      "attention/attention.cases/hf-load-export",
+      "attention/attention.cases/kv-pair-gradient",
+      "attention/attention.cases/prompt-gradient",
+      "attention/attention.cases/qk-norm-gradient",
+      "attention/attention.cases/sixteen-token-decode",
+      "gdn/cases/full-chunk-1024",
+      "gdn/cases/full-chunk-256",
+      "gdn/cases/full-chunk-64",
+      "gdn/cases/full-chunk-8",
+      "gdn/cases/hf-load-export",
+      "gdn/cases/prompt-gradient",
+      "gdn/cases/replicated-norm-gradient",
+      "heads/cases/batch-invariant-prompt1024",
+      "heads/cases/batch-invariant-prompt256",
+      "heads/cases/batch-invariant-prompt64",
+      "heads/cases/batch-invariant-prompt8",
+      "heads/cases/embedding-repeat-100",
+      "heads/cases/entropy-interface",
+      "heads/cases/hf-embedding-roundtrip",
+      "heads/cases/hf-lm-head-roundtrip",
+      "heads/cases/logprob-original-and-promoted-logits",
+      "heads/cases/nonzero-finite-embedding-head-gradient",
+      "heads/cases/replicated-norm-gradient",
+      "moe/moe.cases/all-parameter-gradient",
+      "moe/moe.cases/full-chunk-reorder-1024",
+      "moe/moe.cases/full-chunk-reorder-256",
+      "moe/moe.cases/full-chunk-reorder-64",
+      "moe/moe.cases/full-chunk-reorder-8",
+      "moe/moe.cases/hf-load-export-all512experts",
+      "moe/moe.cases/replicated-router-gradient",
+      "moe/moe.cases/routes-replicated-1024",
+      "moe/moe.cases/routes-replicated-256",
+      "moe/moe.cases/routes-replicated-64",
+      "moe/moe.cases/routes-replicated-8",
+      "moe/moe.cases/training-forward"
+    ],
+    "case_ids_sha256": "be74417dfbd6031ba27978d06d04bb15daaab2aeb57d132c7e0d41176b28b46e"
+  },
+  "topology": {
+    "tp": 4,
+    "pp": 1,
+    "cp": 1,
+    "ep": 1
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/measured-commit.txt b/docs/usage/evidence/qwen3-next-batch-invariance-b200/measured-commit.txt
new file mode 100644
index 000000000..047e12672
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/measured-commit.txt
@@ -0,0 +1 @@
+759755e08f8a801d617a273fe23b55831f8de194
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/checkpoint.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/checkpoint.json
new file mode 100644
index 000000000..4488f19f1
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/checkpoint.json
@@ -0,0 +1,14 @@
+{
+  "model_id": "Qwen/Qwen3-Next-80B-A3B-Instruct",
+  "revision": "9c7f2fbe84465e40164a94cc16cd30b6999b0cc7",
+  "content_hash": "00a6a3b5573ccbac2e01fadb673c2cd7bca172d40ddd73f95ed7afa5cbdb9dcd",
+  "shards_verified": 41,
+  "metadata_sha256": {
+    "config.json": "2d483c7cabad7c8704478ed4038fa7e7b2eff840bc00a118eccbe38e2b488303",
+    "generation_config.json": "d7115cb81df546589f9937943302901a1b75b08909293d1fc96782750b1c3217",
+    "model.safetensors.index.json": "0e5274f538d750abdd7fd55a36bad64100f9e881d59c65f6a59f364d2f8b6dd7",
+    "tokenizer.json": "aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4",
+    "tokenizer_config.json": "a62ff0a2472a0fa1b8eaabcb57c59b58afa42a22831dc141400b6e0cf2b65ce3",
+    "vocab.json": "ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910"
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/device_guards.xml b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/device_guards.xml
new file mode 100644
index 000000000..3be79ed74
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/device_guards.xml
@@ -0,0 +1 @@
+<?xml version="1.0" encoding="utf-8"?><testsuites name="pytest tests"><testsuite name="pytest" errors="0" failures="0" skipped="0" tests="6" time="4.991" timestamp="2026-10-09T05:22:07.449222+00:00" hostname="<HOST>"><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[False-1]" time="1.249" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[False-2]" time="0.018" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[False-3]" time="0.017" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[True-1]" time="0.001" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[True-2]" time="0.001" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[True-3]" time="0.001" /></testsuite></testsuites>
\ No newline at end of file
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/environment.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/environment.json
new file mode 100644
index 000000000..ab7f3e469
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/environment.json
@@ -0,0 +1,40 @@
+{
+  "source_commit": "759755e08f8a801d617a273fe23b55831f8de194",
+  "environment_sha256": "118384371a883b84289a48b5322a96b6563aec0584f74cee4a38777cfe7ff7ca",
+  "visible_gpu_ids": "0,1,2,3",
+  "execution_gpus": 4,
+  "python": "3.12.14 | packaged by Anaconda, Inc. | (main, Aug 27 2026, 14:46:43) [GCC 14.3.0]",
+  "executable": "<PYTHON>",
+  "platform": "Linux-6.8.0-111-generic-x86_64-with-glibc2.39",
+  "versions": {
+    "torch": "2.13.0",
+    "vllm": "0.30.0",
+    "triton": "3.7.1",
+    "transformers": "5.17.0",
+    "flashinfer-python": "0.6.18.post1",
+    "safetensors": "0.8.0"
+  },
+  "torch_cuda": "13.0",
+  "nvcc": "nvcc: NVIDIA (R) Cuda compiler driver\nCopyright (c) 2005-2025 NVIDIA Corporation\nBuilt on Wed_Aug_20_01:58:59_PM_PDT_2025\nCuda compilation tools, release 13.0, V13.0.88\nBuild cuda_13.0.r13.0/compiler.36424714_0\n",
+  "compiler": "g++ (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0\nCopyright (C) 2023 Free Software Foundation, Inc.\nThis is free software; see the source for copying conditions.  There is NO\nwarranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n\n",
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+  ],
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+    },
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+  },
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+}
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/operators.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/operators.json
new file mode 100644
index 000000000..a6e914e66
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/operators.json
@@ -0,0 +1,304 @@
+{
+  "scope": "qwen3_next_operators_only",
+  "status": "passed",
+  "suite": "all",
+  "diagnostic_only": false,
+  "full_model_l2": "not_evaluated",
+  "git_commit": "759755e08f8a801d617a273fe23b55831f8de194",
+  "git_dirty": false,
+  "versions": {
+    "torch": "2.13.0",
+    "vllm": "0.30.0",
+    "transformers": "5.17.0",
+    "triton": "3.7.1"
+  },
+  "mandatory_case_ids": [
+    "tests/check_qwen3_next_attention.py::test_d256_backward_with_cached_prefix_matches_float64_formula",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[1024]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[256]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[64]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[8]",
+    "tests/check_qwen3_next_attention.py::test_d256_input_device_guard_and_nondefault_stream",
+    "tests/check_qwen3_next_attention.py::test_native_backward_rejects_malformed_probability_and_gradient_buffers",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[2]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_target_inputs_do_not_depend_on_companions",
+    "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_is_batch_invariant",
+    "tests/check_qwen3_next_conv_bridge.py::test_chunked_and_reordered_sequences_match",
+    "tests/check_qwen3_next_conv_bridge.py::test_empty_sequence_preserves_cache_and_gradients",
+    "tests/check_qwen3_next_conv_bridge.py::test_response_gradient_reaches_prompt_and_convolution_weight",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths0]",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths1]",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths2]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[1024-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[1024-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[256-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[256-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[64-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[64-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_shared_core_one_hundred_repetitions[1]",
+    "tests/check_qwen3_next_core_matrix.py::test_shared_core_one_hundred_repetitions[4]",
+    "tests/check_qwen3_next_core_matrix.py::test_variable_lengths_empty_and_mixed_step_match_independent_sequences",
+    "tests/check_qwen3_next_forward.py::test_combine_has_fixed_route_order_and_differentiable_weights",
+    "tests/check_qwen3_next_forward.py::test_full_vocabulary_logprob_entropy_modes_and_gradient",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[1-128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[1-512]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[13-128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[13-512]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[300-128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[300-512]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[7-128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[7-512]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_fail_closed_on_the_tensor_descriptor_path",
+    "tests/check_qwen3_next_forward.py::test_grouped_projection_is_bitwise_the_per_expert_gemm[128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_projection_is_bitwise_the_per_expert_gemm[512]",
+    "tests/check_qwen3_next_forward.py::test_logprob_nan_rejected_before_comparison",
+    "tests/check_qwen3_next_forward.py::test_moe_vjp_matches_independent_double_formula",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[1024-2048]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[128-2048]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-16]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-256]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-3072]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-37984]",
+    "tests/check_qwen3_next_forward.py::test_qwen_moe_actual_tp4_shapes_and_independent_training_forward",
+    "tests/check_qwen3_next_forward.py::test_route_weights_are_batch_invariant",
+    "tests/check_qwen3_next_forward.py::test_shared_linear_vjp_and_empty_tokens",
+    "tests/check_qwen3_next_forward.py::test_stable_route_tie_boundary_and_router_gradient",
+    "tests/check_qwen3_next_gdn_bridge.py::test_bridge_backward_matches_recomputed_recurrence_vjp",
+    "tests/check_qwen3_next_gdn_bridge.py::test_bridge_forward_is_provider_exact_and_does_not_mutate_inputs",
+    "tests/check_qwen3_next_gdn_bridge.py::test_bridge_keeps_prompt_state_gradient_through_response",
+    "tests/check_qwen3_next_gdn_bridge.py::test_inactive_state_is_identity_in_backward[False]",
+    "tests/check_qwen3_next_gdn_bridge.py::test_inactive_state_is_identity_in_backward[True]",
+    "tests/check_qwen3_next_gdn_bridge.py::test_initial_state_vjp_matches_provider_directional_difference",
+    "tests/check_qwen3_next_gdn_sequence.py::test_1024_token_chunk_continuation_is_bitwise_exact",
+    "tests/check_qwen3_next_gdn_sequence.py::test_bad_packing_is_rejected[bad0]",
+    "tests/check_qwen3_next_gdn_sequence.py::test_bad_packing_is_rejected[bad1]",
+    "tests/check_qwen3_next_gdn_sequence.py::test_bad_packing_is_rejected[bad2]",
+    "tests/check_qwen3_next_gdn_sequence.py::test_chunk_boundary_retains_prompt_and_initial_state_gradients",
+    "tests/check_qwen3_next_gdn_sequence.py::test_packed_matches_independent_sequences_and_reordering[lengths0]",
+    "tests/check_qwen3_next_gdn_sequence.py::test_packed_matches_independent_sequences_and_reordering[lengths1]",
+    "tests/check_qwen3_next_gdn_sequence.py::test_packed_matches_independent_sequences_and_reordering[lengths2]",
+    "tests/check_qwen3_next_gdn_sequence.py::test_reserved_and_duplicate_slots_are_rejected",
+    "tests/check_qwen3_next_norm_providers.py::test_custom_op_has_distinct_native_and_cuda_paths",
+    "tests/check_qwen3_next_norm_providers.py::test_gdn_decode_provider_env_defaults_are_recorded",
+    "tests/check_qwen3_next_norm_providers.py::test_our_gated_op_is_batch_invariant",
+    "tests/check_qwen3_next_norm_providers.py::test_vllm_gated_norm_is_batch_invariant[forward_cuda]",
+    "tests/check_qwen3_next_norm_providers.py::test_vllm_gated_norm_is_batch_invariant[forward_native]",
+    "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[1024-1]",
+    "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[1024-4]",
+    "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[65-1]",
+    "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[65-4]",
+    "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[8-1]",
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/result.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/result.json
new file mode 100644
index 000000000..26660581e
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/result.json
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/rollout_smoke.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/rollout_smoke.json
new file mode 100644
index 000000000..495b8e7b2
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/rollout_smoke.json
@@ -0,0 +1,75 @@
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+      "text": " Paris. The capital of Germany is Berlin"
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diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/run.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/run.json
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index 000000000..2d0a94b44
--- /dev/null
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+            "routes-replicated-256",
+            "full-chunk-reorder-256",
+            "routes-replicated-1024",
+            "full-chunk-reorder-1024",
+            "training-forward",
+            "all-parameter-gradient",
+            "replicated-router-gradient"
+          ]
+        },
+        "script_sha256": "d5c1bbbb82e408b65871b0cb1638d7a6cf8cfe07f0a3315a657b7dc232d544be"
+      },
+      "adapters": {
+        "script": "scripts/qwen3_next_adapter_check.py",
+        "output_option": "--out",
+        "scope": "gdn_engine_adapter_component",
+        "groups": {
+          "cases": [
+            "batch1-length8",
+            "batch1-length64",
+            "batch1-length256",
+            "batch1-length1024",
+            "batch4-length8",
+            "batch4-length64",
+            "batch4-length256",
+            "batch4-length1024",
+            "mixed-prefill-decode16-reorder"
+          ]
+        },
+        "fields": {
+          "prompt_gradient": "passed",
+          "hf_conv_roundtrip": "passed",
+          "batch_invariance_capability_registered": false
+        },
+        "script_sha256": "3ce45bb317237c1a06533116d65fb77c2a23b6f503ae46406ede543036eb0f20"
+      },
+      "heads": {
+        "script": "scripts/qwen3_next_tp_head_check.py",
+        "output_option": "--output",
+        "scope": "tp4_embedding_lm_head_only",
+        "groups": {
+          "cases": [
+            "hf-embedding-roundtrip",
+            "hf-lm-head-roundtrip",
+            "replicated-norm-gradient",
+            "nonzero-finite-embedding-head-gradient",
+            "embedding-repeat-100",
+            "batch-invariant-prompt8",
+            "batch-invariant-prompt64",
+            "batch-invariant-prompt256",
+            "batch-invariant-prompt1024",
+            "logprob-original-and-promoted-logits",
+            "entropy-interface"
+          ]
+        },
+        "script_sha256": "0504185d21a75405c8e994bc57b016cf4ec357d8b16e7430772c11934f2af876"
+      }
+    },
+    "scheduler_case_ids": [
+      "p0008-b1-full",
+      "p0008-b1-chunked",
+      "p0008-b4-full",
+      "p0008-b4-chunked",
+      "p0064-b1-full",
+      "p0064-b1-chunked",
+      "p0064-b4-full",
+      "p0064-b4-chunked",
+      "p0256-b1-full",
+      "p0256-b1-chunked",
+      "p0256-b4-full",
+      "p0256-b4-chunked",
+      "p1024-b1-full",
+      "p1024-b1-chunked",
+      "p1024-b4-full",
+      "p1024-b4-chunked"
+    ],
+    "registry_sha256": "e3949a750cb27c1897a000f3b023f912629906612ae8949c1575d0acc36df5ec",
+    "case_ids_per_rank": [
+      "adapters/cases/batch1-length1024",
+      "adapters/cases/batch1-length256",
+      "adapters/cases/batch1-length64",
+      "adapters/cases/batch1-length8",
+      "adapters/cases/batch4-length1024",
+      "adapters/cases/batch4-length256",
+      "adapters/cases/batch4-length64",
+      "adapters/cases/batch4-length8",
+      "adapters/cases/mixed-prefill-decode16-reorder",
+      "attention/attention.cases/batch4-varlen-reorder",
+      "attention/attention.cases/full-chunk-1024",
+      "attention/attention.cases/full-chunk-256",
+      "attention/attention.cases/full-chunk-64",
+      "attention/attention.cases/full-chunk-8",
+      "attention/attention.cases/hf-load-export",
+      "attention/attention.cases/kv-pair-gradient",
+      "attention/attention.cases/prompt-gradient",
+      "attention/attention.cases/qk-norm-gradient",
+      "attention/attention.cases/sixteen-token-decode",
+      "gdn/cases/full-chunk-1024",
+      "gdn/cases/full-chunk-256",
+      "gdn/cases/full-chunk-64",
+      "gdn/cases/full-chunk-8",
+      "gdn/cases/hf-load-export",
+      "gdn/cases/prompt-gradient",
+      "gdn/cases/replicated-norm-gradient",
+      "heads/cases/batch-invariant-prompt1024",
+      "heads/cases/batch-invariant-prompt256",
+      "heads/cases/batch-invariant-prompt64",
+      "heads/cases/batch-invariant-prompt8",
+      "heads/cases/embedding-repeat-100",
+      "heads/cases/entropy-interface",
+      "heads/cases/hf-embedding-roundtrip",
+      "heads/cases/hf-lm-head-roundtrip",
+      "heads/cases/logprob-original-and-promoted-logits",
+      "heads/cases/nonzero-finite-embedding-head-gradient",
+      "heads/cases/replicated-norm-gradient",
+      "moe/moe.cases/all-parameter-gradient",
+      "moe/moe.cases/full-chunk-reorder-1024",
+      "moe/moe.cases/full-chunk-reorder-256",
+      "moe/moe.cases/full-chunk-reorder-64",
+      "moe/moe.cases/full-chunk-reorder-8",
+      "moe/moe.cases/hf-load-export-all512experts",
+      "moe/moe.cases/replicated-router-gradient",
+      "moe/moe.cases/routes-replicated-1024",
+      "moe/moe.cases/routes-replicated-256",
+      "moe/moe.cases/routes-replicated-64",
+      "moe/moe.cases/routes-replicated-8",
+      "moe/moe.cases/training-forward"
+    ],
+    "case_ids_sha256": "be74417dfbd6031ba27978d06d04bb15daaab2aeb57d132c7e0d41176b28b46e"
+  },
+  "topology": {
+    "tp": 4,
+    "pp": 1,
+    "cp": 1,
+    "ep": 1
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/tp4.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/tp4.json
new file mode 100644
index 000000000..38447a87b
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/preflight/tp4.json
@@ -0,0 +1,49 @@
+{
+  "status": "passed",
+  "ranks": [
+    {
+      "status": "passed",
+      "rank": 0,
+      "world_size": 4,
+      "device": "NVIDIA B200",
+      "nccl": [
+        2,
+        29,
+        7
+      ]
+    },
+    {
+      "status": "passed",
+      "rank": 1,
+      "world_size": 4,
+      "device": "NVIDIA B200",
+      "nccl": [
+        2,
+        29,
+        7
+      ]
+    },
+    {
+      "status": "passed",
+      "rank": 2,
+      "world_size": 4,
+      "device": "NVIDIA B200",
+      "nccl": [
+        2,
+        29,
+        7
+      ]
+    },
+    {
+      "status": "passed",
+      "rank": 3,
+      "world_size": 4,
+      "device": "NVIDIA B200",
+      "nccl": [
+        2,
+        29,
+        7
+      ]
+    }
+  ]
+}
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/redaction.json b/docs/usage/evidence/qwen3-next-batch-invariance-b200/redaction.json
new file mode 100644
index 000000000..b2e471edd
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/redaction.json
@@ -0,0 +1,14 @@
+{
+  "placeholders": {
+    "<CGROUP>": 12,
+    "<CHECKPOINT>": 8,
+    "<CUDA_HOME>": 12,
+    "<HOME>": 4529,
+    "<HOST>": 1,
+    "<PATH>": 63,
+    "<PYTHON>": 12,
+    "<PYTHON_ENV>": 56,
+    "<RUN>": 84,
+    "<SITE>": 4557
+  }
+}

From c58816bf54940195b543beda14e0de65a71182c6 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 19:49:38 +0000
Subject: [PATCH 55/70] test(qwen3-next): measure Megatron-core/TE and SGLang
 in the MoE prior-art runner

- Megatron-core 0.16 MoELayer with Transformer Engine grouped GEMM and fused
  permute, configured as VIME runs Qwen3-Next (FP32 softmax router, top-10,
  all-to-all dispatcher), including its backward.
- SGLang 0.5.21 Triton fused_moe in default and deterministic-inference mode.
  sgl_kernel cannot load next to torch 2.13, so its two kernels on this path
  are replaced by SGLang's own Triton moe_sum_reduce and JIT
  moe_align_block_size; any other sgl_kernel call fails loudly.
- --only and --merge combine reports from different Python environments.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 rl_engine/testing/qwen3_next_moe_prior_art.py | 264 +++++++++++++++++-
 scripts/plot_qwen3_next_moe_prior_art.py      |   5 +-
 scripts/qwen3_next_moe_prior_art.py           |  31 +-
 3 files changed, 290 insertions(+), 10 deletions(-)

diff --git a/rl_engine/testing/qwen3_next_moe_prior_art.py b/rl_engine/testing/qwen3_next_moe_prior_art.py
index d8ad92234..90ed6dea3 100644
--- a/rl_engine/testing/qwen3_next_moe_prior_art.py
+++ b/rl_engine/testing/qwen3_next_moe_prior_art.py
@@ -298,6 +298,224 @@ def fwd(x):
     }
 
 
+def _megatron_layer(w):
+    """Megatron-core MoELayer configured as VIME runs Qwen3-Next.
+
+    VIME's ``scripts/models/qwen3-next-80B-A3B.sh``:
+
+    Softmax router computed in FP32, top-10 of 512, all-to-all dispatcher, TE
+    grouped GEMM and TE fused permute, no auxiliary loss. Single process, TP1/EP1.
+    """
+    import os
+
+    import torch.distributed as dist
+    from megatron.core import parallel_state
+    from megatron.core.models.gpt.moe_module_specs import get_moe_module_spec
+    from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+    from megatron.core.transformer.spec_utils import build_module
+    from megatron.core.transformer.transformer_config import TransformerConfig
+
+    if not dist.is_initialized():
+        os.environ.setdefault("MASTER_ADDR", "127.0.0.1")
+        os.environ.setdefault("MASTER_PORT", "29531")
+        device = torch.device("cuda", torch.cuda.current_device())
+        dist.init_process_group("nccl", rank=0, world_size=1, device_id=device)
+    if not parallel_state.model_parallel_is_initialized():
+        parallel_state.initialize_model_parallel(1, 1)
+        model_parallel_cuda_manual_seed(0)
+    config = TransformerConfig(
+        num_layers=1,
+        hidden_size=HIDDEN,
+        num_attention_heads=16,
+        ffn_hidden_size=WIDTH,
+        moe_ffn_hidden_size=WIDTH,
+        num_moe_experts=EXPERTS,
+        moe_router_topk=TOPK,
+        moe_router_score_function="softmax",
+        moe_router_dtype="fp32",
+        moe_token_dispatcher_type="alltoall",
+        moe_grouped_gemm=True,
+        moe_permute_fusion=True,
+        moe_aux_loss_coeff=0.0,
+        moe_router_load_balancing_type="none",
+        add_bias_linear=False,
+        gated_linear_unit=True,
+        activation_func=F.silu,
+        bf16=True,
+        params_dtype=torch.bfloat16,
+    )
+    spec = get_moe_module_spec(use_te=True, num_experts=EXPERTS, moe_grouped_gemm=True)
+    layer = build_module(spec, config=config).cuda()
+    with torch.no_grad():
+        layer.router.weight.copy_(w["router"])
+        for expert in range(EXPERTS):
+            getattr(layer.experts.linear_fc1, f"weight{expert}").copy_(w["gate_up"][expert])
+            getattr(layer.experts.linear_fc2, f"weight{expert}").copy_(w["down"][expert])
+    return layer
+
+
+def _megatron(w):
+    import megatron.core
+    import transformer_engine
+
+    layer = _megatron_layer(w)
+    gate_up = [getattr(layer.experts.linear_fc1, f"weight{e}") for e in range(EXPERTS)]
+    down = [getattr(layer.experts.linear_fc2, f"weight{e}") for e in range(EXPERTS)]
+
+    def route(x):
+        with torch.no_grad():
+            probs, routing_map = layer.router(x)
+        weights, ids = probs.topk(TOPK, dim=-1)
+        if not bool(routing_map.gather(1, ids).all()):
+            raise RuntimeError("Megatron routing map disagrees with its routed probabilities")
+        return ids, weights
+
+    def call(x):
+        return layer(x.unsqueeze(1))[0].squeeze(1)
+
+    def fwd(x):
+        with torch.no_grad():
+            return call(x)
+
+    def grads(x, dy):
+        leaf = x.detach().clone().requires_grad_(True)
+        dx, *dw = torch.autograd.grad(call(leaf), [leaf, *gate_up, *down], dy)
+        return dx, torch.stack(dw[:EXPERTS]), torch.stack(dw[EXPERTS:])
+
+    return {
+        "name": "Megatron-core MoELayer + TE grouped GEMM (VIME's Qwen3-Next config)",
+        "source": f"megatron-core {megatron.core.__version__}, "
+        f"transformer-engine {transformer_engine.__version__}",
+        "route": route,
+        "fwd": fwd,
+        "grads": grads,
+    }
+
+
+def _sglang_shims():
+    """Make SGLang's Triton MoE path importable next to torch 2.13.
+
+    ``sgl_kernel`` 0.3.21 is built against another libtorch ABI and cannot load
+    here. On CUDA, SGLang's Triton MoE calls two of its kernels; both are
+    replaced by SGLang's own implementations of the same operation: the Triton
+    ``moe_sum_reduce_triton`` and the JIT ``moe_align_block_size`` that SGLang
+    registers with the same signature. Every other ``sgl_kernel`` symbol only
+    has to import; calling one raises.
+    """
+    import importlib.abc
+    import importlib.machinery
+    import sys
+    import types
+
+    class Missing:
+        def __init__(self, name):
+            self.name = name
+
+        def __getattr__(self, name):
+            if name.startswith("__"):
+                raise AttributeError(name)
+            return Missing(f"{self.name}.{name}")
+
+        def __call__(self, *args, **kwargs):
+            raise RuntimeError(f"{self.name} called, but sgl_kernel cannot load next to torch 2.13")
+
+    class Stub(types.ModuleType):
+        def __getattr__(self, name):
+            if name.startswith("__"):
+                raise AttributeError(name)
+            return Missing(f"{self.__name__}.{name}")
+
+    class Finder(importlib.abc.MetaPathFinder, importlib.abc.Loader):
+        def find_spec(self, name, path=None, target=None):
+            if name in ("sgl_kernel", "gguf") or name.startswith("sgl_kernel."):
+                return importlib.machinery.ModuleSpec(name, self, is_package=True)
+
+        def create_module(self, spec):
+            module = Stub(spec.name)
+            module.__path__ = []
+            return module
+
+        def exec_module(self, module):
+            pass
+
+    if not any(type(f).__name__ == "Finder" for f in sys.meta_path):
+        sys.meta_path.insert(0, Finder())
+    import sgl_kernel
+    import sglang.kernels.ops.moe as moe_ops
+    from sglang.kernels.ops.moe.fused_moe_triton_kernels import moe_sum_reduce_triton
+    from sglang.kernels.spec import KernelBackend
+
+    sgl_kernel.moe_sum_reduce = moe_sum_reduce_triton
+    lookup = moe_ops.get_kernel
+    moe_ops.get_kernel = lambda op, backend: lookup(
+        op, KernelBackend.JIT if op == "moe.moe_align_block_size" else backend
+    )
+
+
+def _sglang(w, deterministic: bool):
+    import sglang
+
+    _sglang_shims()
+    from sglang.srt.layers.moe.moe_runner.base import MoeRunnerConfig
+    from sglang.srt.layers.moe.moe_runner.triton_utils import fused_moe as fm
+    from sglang.srt.layers.moe.moe_runner.triton_utils import fused_moe_triton_config as fc
+    from sglang.srt.layers.moe.topk import StandardTopKOutput, fused_topk_torch_native
+
+    class Bag:
+        def __init__(self, **values):
+            self.__dict__.update(values)
+
+        def __getattr__(self, name):
+            return False
+
+    # SGLang reads these switches from its published server config; publishing
+    # needs a full server (and sgl_kernel). Only the deterministic switch matters.
+    settings = Bag(deterministic=Bag(enable_deterministic_inference=deterministic), moe=Bag())
+    from sglang.srt.batch_invariant_ops import batch_invariant_ops as bio
+
+    def linear(x, weight):
+        # Deterministic mode: what enable_batch_invariant_mode() installs for aten::mm,
+        # called directly so the override does not leak into other candidates.
+        return bio.mm_batch_invariant(x, weight.t()) if deterministic else F.linear(x, weight)
+
+    config = MoeRunnerConfig(
+        num_experts=EXPERTS,
+        num_local_experts=EXPERTS,
+        hidden_size=HIDDEN,
+        intermediate_size_per_partition=WIDTH,
+        top_k=TOPK,
+        params_dtype=torch.bfloat16,
+        inplace=False,
+    )
+
+    def set_mode():
+        fm.get_exec = fc.get_exec = lambda: settings
+        fm.is_batch_invariant_mode_enabled = lambda: deterministic
+        # One process, no TP group: symmetric memory is disabled, the group unused.
+        fm.get_parallel = lambda: Bag(tp_group=None)
+        fm.is_allocation_symmetric = lambda: False
+
+    def route(x):
+        set_mode()
+        logits = linear(x, w["router"])
+        weights, ids = fused_topk_torch_native(x, logits, TOPK, renormalize=True)[:2]
+        return ids.long(), weights
+
+    def fwd(x):
+        with torch.no_grad():
+            ids, weights = route(x)
+            topk = StandardTopKOutput(weights, ids.int(), None)
+            return fm.fused_experts(x, w["gate_up"], w["down"], topk, config)
+
+    mode = "deterministic inference" if deterministic else "default"
+    return {
+        "name": f"SGLang fused_moe (Triton), {mode}",
+        "source": f"sglang {sglang.__version__} moe_runner/triton_utils",
+        "route": route,
+        "fwd": fwd,
+    }
+
+
 def candidates(w) -> list[dict[str, Any]]:
     return [
         _guard("rl_kernel_cuda", lambda: _rl_kernel(w)),
@@ -305,6 +523,9 @@ def candidates(w) -> list[dict[str, Any]]:
         _guard("vllm_bi0", lambda: _vllm(w, False)),
         _guard("vllm_bi1", lambda: _vllm(w, True)),
         _guard("flashinfer_cutlass", lambda: _flashinfer(w)),
+        _guard("megatron_te", lambda: _megatron(w)),
+        _guard("sglang_triton", lambda: _sglang(w, False)),
+        _guard("sglang_deterministic", lambda: _sglang(w, True)),
     ]
 
 
@@ -338,14 +559,25 @@ def _rows_bi(fn, x) -> dict[str, bool]:
     return result
 
 
+def has_backward(cand) -> bool:
+    return "graph" in cand or "grads" in cand
+
+
+def cand_grads(cand, rows, dy_rows):
+    """``(dx, d gate_up, d down)`` of one backward."""
+    if "grads" in cand:
+        return cand["grads"](rows, dy_rows)
+    y, leaves = cand["graph"](rows)
+    return torch.autograd.grad(y, leaves, dy_rows)
+
+
 def _grad_bi(cand, x) -> dict[str, Any]:
     """dx of the probe rows, and dW with zero-gradient rows appended."""
     g = torch.Generator(device="cuda").manual_seed(7)
     dy = torch.randn(x.shape, device="cuda", generator=g).to(torch.bfloat16)
 
     def grads(rows, dy_rows):
-        y, leaves = cand["graph"](rows)
-        return torch.autograd.grad(y, leaves, dy_rows)
+        return cand_grads(cand, rows, dy_rows)
 
     alone = grads(x[:PROBE], dy[:PROBE])
     dx, dw = {}, {}
@@ -380,7 +612,7 @@ def _one(cand, x, w) -> dict[str, Any]:
         out["route_rows_bitwise"] = _rows_bi(cand["route"], x)
         out["output_rows_bitwise"] = _rows_bi(cand["fwd"], x)
         out["repeatable"] = bool(_bitwise(cand["fwd"](x[:256]), cand["fwd"](x[:256])))
-        if "graph" in cand:
+        if has_backward(cand):
             out.update(_grad_bi(cand, x))
         out["accuracy"] = _accuracy(cand, x[:256], w)
     except Exception as exc:  # noqa: BLE001 - a crash is a result, not a harness failure
@@ -414,23 +646,39 @@ def _latency(cands, tokens) -> dict[str, Any]:
         dy = torch.randn_like(x)
         calls = {}
         for cand in ready:
-            if "graph" not in cand:
+            if not has_backward(cand):
                 continue
 
             def step(c=cand):
-                y, leaves = c["graph"](x)
-                torch.autograd.grad(y, leaves, dy)
+                cand_grads(c, x, dy)
 
             calls[cand["key"]] = step
         backward[str(count)] = time_interleaved(calls, warmup=2, iters=10)
     return {"forward_us": forward, "forward_plus_backward_us": backward}
 
 
-def moe_report() -> dict[str, Any]:
+def moe_report(only=None) -> dict[str, Any]:
+    """The report for every candidate, or only for the keys in ``only``.
+
+    Some candidates need another Python environment (SGLang's pinned torch ABI,
+    Megatron's training stack); run each environment with ``only`` and merge.
+    """
     torch.manual_seed(0)
     w = make_weights()
     x = make_tokens(max(BI_SIZES), seed=1)
-    cands = candidates(w)
+    keys = [
+        "rl_kernel_cuda",
+        "hf_transformers",
+        "vllm_bi0",
+        "vllm_bi1",
+        "flashinfer_cutlass",
+        "megatron_te",
+        "sglang_triton",
+        "sglang_deterministic",
+    ]
+    if only is not None and set(only) - set(keys):
+        raise ValueError(f"Unknown candidates: {sorted(set(only) - set(keys))}")
+    cands = [c for c in candidates(w) if only is None or c["key"] in only]
     return {
         "op": "qwen3_next_routed_moe",
         "shape": {"hidden": HIDDEN, "experts": EXPERTS, "top_k": TOPK, "expert_width": WIDTH},
diff --git a/scripts/plot_qwen3_next_moe_prior_art.py b/scripts/plot_qwen3_next_moe_prior_art.py
index 17a966b61..f7e358b63 100644
--- a/scripts/plot_qwen3_next_moe_prior_art.py
+++ b/scripts/plot_qwen3_next_moe_prior_art.py
@@ -34,7 +34,7 @@
 PASS = "#1baf7a"
 FAIL = "#d64545"
 NA = "#e4e3dd"
-EXISTING = ("#eb6834", "#b8501f", "#f29a62", "#8a3a15")
+EXISTING = ("#eb6834", "#b8501f", "#f29a62", "#8a3a15", "#f6bd94", "#d9773f", "#6b2a0e")
 
 LABELS = {
     "rl_kernel_cuda": "RL-Kernel shared_moe",
@@ -42,6 +42,9 @@
     "vllm_bi0": "vLLM fused_moe (BI=0)",
     "vllm_bi1": "vLLM fused_moe (BI=1)",
     "flashinfer_cutlass": "FlashInfer cutlass_fused_moe",
+    "megatron_te": "Megatron-core + TE (VIME config)",
+    "sglang_triton": "SGLang fused_moe (default)",
+    "sglang_deterministic": "SGLang fused_moe (deterministic)",
 }
 CHECKS = (
     ("route_rows_bitwise", "routes"),
diff --git a/scripts/qwen3_next_moe_prior_art.py b/scripts/qwen3_next_moe_prior_art.py
index 398245eee..ae3a028b4 100644
--- a/scripts/qwen3_next_moe_prior_art.py
+++ b/scripts/qwen3_next_moe_prior_art.py
@@ -35,6 +35,8 @@
     "transformers",
     "triton",
     "sglang",
+    "sgl-kernel",
+    "megatron_core",
     "megatron-core",
     "transformer_engine",
     "vime",
@@ -54,9 +56,34 @@ def _version(name: str) -> str | None:
         return None
 
 
+def merge(base: dict, extra: dict) -> dict:
+    """``base`` with ``extra``'s candidates and latencies added (same commit and GPU)."""
+    for field in ("rl_kernel_commit", "tracked_tree_dirty", "shape", "op"):
+        if base[field] != extra[field]:
+            raise SystemExit(f"Reports differ in {field}; they cannot be merged")
+    if base["environment"]["gpu"] != extra["environment"]["gpu"]:
+        raise SystemExit("Reports were measured on different GPUs")
+    keys = {c["key"] for c in base["candidates"]}
+    added = [c for c in extra["candidates"] if c["key"] not in keys]
+    merged = dict(base, candidates=base["candidates"] + added)
+    merged["latency"] = {
+        section: {
+            size: {**values, **extra["latency"][section].get(size, {})}
+            for size, values in sizes.items()
+        }
+        for section, sizes in base["latency"].items()
+    }
+    merged["environments"] = {"main": base["environment"], "extra": extra["environment"]}
+    return merged
+
+
 def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--out", type=Path, required=True)
+    parser.add_argument("--only", help="comma-separated candidate keys (default: all)")
+    parser.add_argument(
+        "--merge", type=Path, help="a report from another environment to merge into this one"
+    )
     args = parser.parse_args()
     if not torch.cuda.is_available():
         raise SystemExit("needs a CUDA device")
@@ -73,8 +100,10 @@ def main() -> None:
             "cuda": torch.version.cuda,
             "libraries": {name: _version(name) for name in LIBRARIES},
         },
-        **moe_report(),
+        **moe_report(args.only.split(",") if args.only else None),
     }
+    if args.merge is not None:
+        report = merge(json.loads(args.merge.read_text()), report)
     args.out.parent.mkdir(parents=True, exist_ok=True)
     args.out.write_text(json.dumps(report, indent=2) + "\n")
     print(

From 27b73393a10d960559d916446a2f5ea3060c4b43 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Fri, 9 Oct 2026 23:49:57 +0000
Subject: [PATCH 56/70] docs(qwen3-next): MoE prior art with Megatron-core/TE
 and SGLang measured (B200, c58816b)

Every library in the reuse table is now measured: Megatron-core 0.16 + TE 2.16 as VIME configures Qwen3-Next, and SGLang 0.5.21 in default and deterministic-inference mode. The TP4 gate evidence is unchanged (2e950f3, identical MoE code).

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../operators/qwen3-next-moe-route-combine.md |  37 +--
 .../qwen3-next-moe-route-b200/README.md       |  23 +-
 .../qwen3-next-moe-route-b200/figure.png      | Bin 232121 -> 312811 bytes
 .../qwen3-next-moe-route-b200/report.json     | 221 +++++++++++++++---
 4 files changed, 224 insertions(+), 57 deletions(-)

diff --git a/docs/operators/qwen3-next-moe-route-combine.md b/docs/operators/qwen3-next-moe-route-combine.md
index ed11301a0..20ed1d6ba 100644
--- a/docs/operators/qwen3-next-moe-route-combine.md
+++ b/docs/operators/qwen3-next-moe-route-combine.md
@@ -69,7 +69,9 @@ the selected probabilities; the indices are discrete.
 | vLLM, `VLLM_BATCH_INVARIANT=1` | yes | yes | none | no VJP for training; BF16 router logits (Qwen3-Next and VIME route in FP32); routed weight applied in the expert GEMM epilogue on the BF16 output, then routes summed by `moe_sum` |
 | HF `Qwen3NextExperts` (eager) | yes | yes | autograd | `dx`/`dW` change with batch size (cuBLAS shape heuristics); BF16 router; `index_add_` combine in BF16 |
 | FlashInfer `cutlass_fused_moe` | (torch routing) | no | none | output changes with batch size; no VJP |
-| SGLang, Megatron-core, Transformer Engine | not measured | not measured | - | not installable next to the pinned vLLM 0.30.0 / torch 2.13 runtime |
+| Megatron-core 0.16 `MoELayer` + TE 2.16 grouped GEMM, as VIME configures Qwen3-Next | no (256+) | no (1024) | autograd, not BI | FP32 router routes like FP64, but the routing, output, dx and dW change with batch size |
+| SGLang 0.5.21 Triton `fused_moe`, default | no (256+) | no (256+) | none | tuned configs and the BF16 router GEMM depend on the token count |
+| SGLang, deterministic inference | yes | yes | none | inference only; BF16 router logits; its deterministic tile (64/64/32) is the one vLLM uses in batch-invariant mode |
 
 What RL-Kernel reuses: vLLM's batch-invariant GEMM for the router and the
 backward, and vLLM's `fused_moe` Triton kernel for the forward expert GEMMs,
@@ -91,25 +93,32 @@ commands are in
 
 TP1 shape (H=2048, 512 experts, top-10, width 512), random weights, BF16:
 
-| | RL-Kernel `shared_moe` | vLLM BI=1 | vLLM BI=0 | FlashInfer CUTLASS | HF eager |
-| --- | --- | --- | --- | --- | --- |
-| routes / output BI (16-1024 tokens) | **yes / yes** | yes / yes | no / no (256+) | no / no (256+) | yes / yes |
-| dx / dW BI | **yes / yes** | no backward | no backward | no backward | no / no (1024) |
-| rel. L2 error vs FP64 | **3.9e-3** | 7.0e-2 | 7.0e-2 | 7.0e-2 | 7.0e-2 |
-| tokens routed unlike FP64 (of 256) | **0** | 11 | 11 | 11 | 11 |
-| forward, 1 / 64 / 1024 / 4096 tokens (ms) | 1.38 / 1.71 / 2.05 / 3.05 | 0.37 / 0.66 / 0.88 / 1.06 | 0.36 / 0.65 / 0.87 / 1.06 | 0.34 / 0.67 / 0.85 / 1.08 | 2.0 / 61 / 89 / 89 |
-| forward + backward, 64 / 1024 tokens (ms) | 112 / 166 | - | - | - | 878 / 1283 |
-
-* The accuracy gap is the router: every other candidate computes BF16 router
-  logits, and 11 of 256 tokens then select a different expert set than FP64
-  routing does. RL-Kernel routes in FP32 and selects the same experts.
+| | routes / output BI (16-1024 tokens) | dx / dW BI | rel. L2 vs FP64 | tokens routed unlike FP64 (of 256) | forward, 1 / 64 / 1024 / 4096 tokens (ms) | fwd + bwd, 64 / 1024 (ms) |
+| --- | --- | --- | --- | --- | --- | --- |
+| **RL-Kernel `shared_moe`** | **yes / yes** | **yes / yes** | **3.9e-3** | **0** | 1.39 / 1.69 / 2.04 / 3.05 | 114 / 169 |
+| Megatron-core + TE (VIME config) | no / no | no / no (1024) | 4.6e-3 | 0 | 3.8 / 9.7 / 11.7 / 12.1 | 45 / 51 |
+| vLLM BI=1 | yes / yes | no backward | 7.0e-2 | 11 | 0.39 / 0.66 / 0.87 / 1.05 | - |
+| SGLang deterministic | yes / yes | no backward | 7.0e-2 | 11 | 0.35 / 0.66 / 0.84 / 1.37 | - |
+| vLLM BI=0 | no / no | no backward | 7.0e-2 | 11 | 0.38 / 0.66 / 0.87 / 1.06 | - |
+| SGLang default | no / no | no backward | 7.0e-2 | 11 | 0.34 / 0.61 / 0.83 / 1.35 | - |
+| FlashInfer CUTLASS | no / no | no backward | 7.0e-2 | 11 | 0.37 / 0.72 / 0.90 / 1.12 | - |
+| HF eager | yes / yes | no / no (1024) | 7.0e-2 | 11 | 2.1 / 62 / 89 / 87 | 877 / 1284 |
+
+* The accuracy gap is the router: every candidate with BF16 router logits sends
+  11 of 256 tokens to a different expert set than FP64 routing does. RL-Kernel
+  and Megatron-core (VIME's training path) route in FP32 and select the same
+  experts as FP64.
+* The only batch-invariant candidates are RL-Kernel and the inference-only
+  modes of vLLM and SGLang; of these, only RL-Kernel has a backward, so only it
+  can run the same forward on the training and the rollout side.
 * RL-Kernel's forward is 2-4x slower than vLLM's. About 0.5 ms of it, at any
   token count, is the FP32 IEEE router GEMM, which is part of the contract.
   The expert GEMMs themselves run in vLLM's kernel. The per-expert GEMM loop
   they replace (same bits) took 47 / 66 / 71 ms at 64 / 1024 / 4096 tokens in
   a local B200 run of the same runner; that run is not part of this evidence.
 * The backward is still a per-expert loop of pinned GEMMs (512 experts x 4
-  GEMMs) and dominates a training step's MoE time.
+  GEMMs): 169 ms at 1024 tokens, against 51 ms for Megatron-core + TE, which is
+  not batch-invariant. It dominates a training step's MoE time.
 
 TP4 gate on the real layer-0 weights (`tp4-moe/rank-*.json`): all twelve cases
 pass on all four ranks, including the round trip of all 512 experts, identical
diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/README.md b/docs/usage/evidence/qwen3-next-moe-route-b200/README.md
index 52f528486..863f3f5fd 100644
--- a/docs/usage/evidence/qwen3-next-moe-route-b200/README.md
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/README.md
@@ -1,13 +1,26 @@
 # Qwen3-Next routed MoE: prior art and TP4 gate (B200)
 
-Measured at commit `2e950f3` from a clean clone (`tracked_tree_dirty: false` in
-every file), with that clone's own `rl_engine._C`, on one node with four B200
-GPUs (driver 580.126.20), torch 2.13.0+cu130, vLLM 0.30.0, Triton 3.7.1,
-transformers 5.17.0, FlashInfer 0.6.18.post1, NCCL 2.29.7.
+`report.json` and `figure.png` were measured at commit `c58816b` from a clean
+clone (`tracked_tree_dirty: false`) on one B200 (driver 580.126.20, torch
+2.13.0+cu130, Triton 3.7.1), in two Python environments because no single one
+can import every candidate:
+
+* the training environment (VIME 0.3.2 with Megatron-core 0.16.0rc0 and
+  Transformer Engine 2.16.1, vLLM 0.30.0, FlashInfer 0.6.18.post1,
+  transformers 5.17.0): RL-Kernel, HF, vLLM, FlashInfer, Megatron-core + TE;
+* a second environment with SGLang 0.5.21: SGLang in default and
+  deterministic-inference mode. Its compiled `sgl_kernel` 0.3.21 is built for
+  another libtorch ABI and cannot load next to torch 2.13, so SGLang's two
+  kernels on this path are replaced by SGLang's own implementations of the same
+  operations (Triton `moe_sum_reduce`, JIT `moe_align_block_size`); calling any
+  other `sgl_kernel` symbol fails.
+
+`environments` in `report.json` records both. The TP4 gate files were measured
+at `2e950f3`, whose MoE code is identical (`c58816b` only adds runner candidates).
 
 | File | Produced by |
 | --- | --- |
-| `report.json` | `TRITON_F32_DEFAULT=ieee python scripts/qwen3_next_moe_prior_art.py --out report.json` (one GPU) |
+| `report.json` | `TRITON_F32_DEFAULT=ieee python scripts/qwen3_next_moe_prior_art.py --only rl_kernel_cuda,hf_transformers,vllm_bi0,vllm_bi1,flashinfer_cutlass,megatron_te --out main.json` (training environment), then `... --only sglang_triton,sglang_deterministic --merge main.json --out report.json` (SGLang environment); one GPU |
 | `figure.png` | `python scripts/plot_qwen3_next_moe_prior_art.py report.json` |
 | `tp4-moe/rank-{0..3}.json` | `TRITON_F32_DEFAULT=ieee torchrun --nproc-per-node 4 scripts/qwen3_next_tp_moe_check.py --checkpoint <Qwen3-Next-80B-A3B-Instruct> --output tp4-moe` |
 
diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/figure.png b/docs/usage/evidence/qwen3-next-moe-route-b200/figure.png
index f4515650768420573d93ab951431906f2081e07a..e09ec93ac599e935b27015d81c5d71df6decbf17 100644
GIT binary patch
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diff --git a/docs/usage/evidence/qwen3-next-moe-route-b200/report.json b/docs/usage/evidence/qwen3-next-moe-route-b200/report.json
index 271e69c8a..5a9fa5056 100644
--- a/docs/usage/evidence/qwen3-next-moe-route-b200/report.json
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/report.json
@@ -1,7 +1,7 @@
 {
   "kind": "qwen3_next_prior_art_report",
   "rfc": 428,
-  "rl_kernel_commit": "2e950f386bd3c4c2c07f41472f48878be94ea3b8",
+  "rl_kernel_commit": "c58816bf54940195b543beda14e0de65a71182c6",
   "tracked_tree_dirty": false,
   "environment": {
     "gpu": "NVIDIA B200",
@@ -13,9 +13,11 @@
       "transformers": "5.17.0",
       "triton": "3.7.1",
       "sglang": null,
-      "megatron-core": null,
-      "transformer_engine": null,
-      "vime": null
+      "sgl-kernel": null,
+      "megatron_core": "0.16.0rc0",
+      "megatron-core": "0.16.0rc0",
+      "transformer_engine": "2.16.1+c9877beb",
+      "vime": "0.3.2"
     }
   },
   "op": "qwen3_next_routed_moe",
@@ -175,61 +177,204 @@
         "tokens": 256
       },
       "batch_invariant": false
+    },
+    {
+      "key": "megatron_te",
+      "name": "Megatron-core MoELayer + TE grouped GEMM (VIME's Qwen3-Next config)",
+      "source": "megatron-core 0.16.0rc0, transformer-engine 2.16.1+c9877beb",
+      "route_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": false,
+        "1024": false
+      },
+      "output_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": false
+      },
+      "repeatable": true,
+      "dx_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": false
+      },
+      "dweight_zero_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": false
+      },
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.00456482750054121,
+        "max_abs_vs_fp64": 0.0022296928321409726,
+        "tokens_with_different_expert_set": 0,
+        "tokens": 256
+      },
+      "batch_invariant": false
+    },
+    {
+      "key": "sglang_triton",
+      "name": "SGLang fused_moe (Triton), default",
+      "source": "sglang 0.5.21 moe_runner/triton_utils",
+      "route_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": false,
+        "1024": false
+      },
+      "output_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": false,
+        "1024": false
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.06984328047814348,
+        "max_abs_vs_fp64": 0.10739971750663532,
+        "tokens_with_different_expert_set": 11,
+        "tokens": 256
+      },
+      "batch_invariant": false
+    },
+    {
+      "key": "sglang_deterministic",
+      "name": "SGLang fused_moe (Triton), deterministic inference",
+      "source": "sglang 0.5.21 moe_runner/triton_utils",
+      "route_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "output_rows_bitwise": {
+        "16": true,
+        "64": true,
+        "256": true,
+        "1024": true
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.06984328047814348,
+        "max_abs_vs_fp64": 0.10739971750663532,
+        "tokens_with_different_expert_set": 11,
+        "tokens": 256
+      },
+      "batch_invariant": true
     }
   ],
   "latency": {
     "forward_us": {
       "1": {
-        "rl_kernel_cuda": 1376.3200044631958,
-        "hf_transformers": 2020.9439992904663,
-        "vllm_bi0": 356.7200005054474,
-        "vllm_bi1": 367.8240031003952,
-        "flashinfer_cutlass": 341.74399077892303
+        "rl_kernel_cuda": 1386.0480189323425,
+        "hf_transformers": 2066.7200088500977,
+        "vllm_bi0": 374.9440014362335,
+        "vllm_bi1": 387.91999220848083,
+        "flashinfer_cutlass": 368.51200461387634,
+        "megatron_te": 3787.440061569214,
+        "sglang_triton": 339.1679972410202,
+        "sglang_deterministic": 349.7439920902252
       },
       "8": {
-        "rl_kernel_cuda": 1379.8879981040955,
-        "hf_transformers": 12903.615951538086,
-        "vllm_bi0": 364.6879941225052,
-        "vllm_bi1": 366.5599972009659,
-        "flashinfer_cutlass": 385.2960020303726
+        "rl_kernel_cuda": 1395.039975643158,
+        "hf_transformers": 13072.400093078613,
+        "vllm_bi0": 381.9680064916611,
+        "vllm_bi1": 385.328009724617,
+        "flashinfer_cutlass": 432.2880059480667,
+        "megatron_te": 5061.583995819092,
+        "sglang_triton": 332.8000009059906,
+        "sglang_deterministic": 353.5839915275574
       },
       "64": {
-        "rl_kernel_cuda": 1706.6399455070496,
-        "hf_transformers": 60610.20851135254,
-        "vllm_bi0": 653.984010219574,
-        "vllm_bi1": 660.3359878063202,
-        "flashinfer_cutlass": 672.1920073032379
+        "rl_kernel_cuda": 1687.5839829444885,
+        "hf_transformers": 62156.3835144043,
+        "vllm_bi0": 660.0319743156433,
+        "vllm_bi1": 658.3200097084045,
+        "flashinfer_cutlass": 723.4559953212738,
+        "megatron_te": 9674.047946929932,
+        "sglang_triton": 606.8799793720245,
+        "sglang_deterministic": 660.75199842453
       },
       "256": {
-        "rl_kernel_cuda": 1892.9439783096313,
-        "hf_transformers": 85160.57586669922,
-        "vllm_bi0": 778.6239981651306,
-        "vllm_bi1": 785.8560085296631,
-        "flashinfer_cutlass": 831.5519988536835
+        "rl_kernel_cuda": 1846.1920022964478,
+        "hf_transformers": 85269.61517333984,
+        "vllm_bi0": 779.8559963703156,
+        "vllm_bi1": 790.6079888343811,
+        "flashinfer_cutlass": 858.7839901447296,
+        "megatron_te": 11665.247917175293,
+        "sglang_triton": 759.0239942073822,
+        "sglang_deterministic": 819.1200196743011
       },
       "1024": {
-        "rl_kernel_cuda": 2048.975944519043,
-        "hf_transformers": 88626.1100769043,
-        "vllm_bi0": 872.2079992294312,
-        "vllm_bi1": 881.5999925136566,
-        "flashinfer_cutlass": 850.0800132751465
+        "rl_kernel_cuda": 2043.7918901443481,
+        "hf_transformers": 89267.74215698242,
+        "vllm_bi0": 874.2719888687134,
+        "vllm_bi1": 868.9280152320862,
+        "flashinfer_cutlass": 902.5439918041229,
+        "megatron_te": 11738.048076629639,
+        "sglang_triton": 833.1039845943451,
+        "sglang_deterministic": 841.6959941387177
       },
       "4096": {
-        "rl_kernel_cuda": 3047.216057777405,
-        "hf_transformers": 88805.53817749023,
-        "vllm_bi0": 1060.5120062828064,
-        "vllm_bi1": 1061.1519813537598,
-        "flashinfer_cutlass": 1079.200029373169
+        "rl_kernel_cuda": 3053.2480478286743,
+        "hf_transformers": 87444.65637207031,
+        "vllm_bi0": 1059.056043624878,
+        "vllm_bi1": 1051.2160062789917,
+        "flashinfer_cutlass": 1124.2560148239136,
+        "megatron_te": 12122.560024261475,
+        "sglang_triton": 1346.0160493850708,
+        "sglang_deterministic": 1367.3120141029358
       }
     },
     "forward_plus_backward_us": {
       "64": {
-        "rl_kernel_cuda": 111661.79275512695,
-        "hf_transformers": 877700.2563476562
+        "rl_kernel_cuda": 113590.37017822266,
+        "hf_transformers": 876752.3193359375,
+        "megatron_te": 44856.64176940918
       },
       "1024": {
-        "rl_kernel_cuda": 166025.42114257812,
-        "hf_transformers": 1282811.6455078125
+        "rl_kernel_cuda": 169058.31909179688,
+        "hf_transformers": 1284274.658203125,
+        "megatron_te": 50968.2559967041
+      }
+    }
+  },
+  "environments": {
+    "main": {
+      "gpu": "NVIDIA B200",
+      "torch": "2.13.0+cu130",
+      "cuda": "13.0",
+      "libraries": {
+        "vllm": "0.30.0",
+        "flashinfer-python": "0.6.18.post1",
+        "transformers": "5.17.0",
+        "triton": "3.7.1",
+        "sglang": null,
+        "sgl-kernel": null,
+        "megatron_core": "0.16.0rc0",
+        "megatron-core": "0.16.0rc0",
+        "transformer_engine": "2.16.1+c9877beb",
+        "vime": "0.3.2"
+      }
+    },
+    "extra": {
+      "gpu": "NVIDIA B200",
+      "torch": "2.13.0+cu130",
+      "cuda": "13.0",
+      "libraries": {
+        "vllm": "0.30.0",
+        "flashinfer-python": "0.6.18.post1",
+        "transformers": "5.17.0",
+        "triton": "3.7.1",
+        "sglang": "0.5.21",
+        "sgl-kernel": "0.3.21",
+        "megatron_core": null,
+        "megatron-core": null,
+        "transformer_engine": "2.20.2",
+        "vime": null
       }
     }
   }

From 4ef7dcbf3f7dced9717a2f522128c4acfbcce9e7 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sat, 10 Oct 2026 00:51:36 +0000
Subject: [PATCH 57/70] test(qwen3-next): measure FlashAttention-4, Transformer
 Engine and Megatron-core attention

Add three engines to the attention prior-art runner:
- FlashAttention-4 (CuTe DSL) varlen as vendored by vLLM, forward and backward.
- Transformer Engine DotProductAttention on THD-packed sequences, in
  inference mode (cuDNN fused) and in training mode (on SM100 TE has no
  fused head-dim-256 backward and falls back to its unfused path).
- Megatron-core's local DotProductAttention, one launch per sequence.

The backward check now compares the target's dq/dk/dv alone against the
same sequence packed first and last, so engines with a packed backward are
checked for batch invariance rather than repeatability. A prefill/decode
crash is recorded per candidate without skipping its other checks.
--only and --merge combine reports from different Python environments.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../testing/qwen3_next_attention_prior_art.py | 231 ++++++++++++++++--
 .../plot_qwen3_next_attention_prior_art.py    |  22 +-
 scripts/qwen3_next_attention_prior_art.py     |  30 ++-
 3 files changed, 261 insertions(+), 22 deletions(-)

diff --git a/rl_engine/testing/qwen3_next_attention_prior_art.py b/rl_engine/testing/qwen3_next_attention_prior_art.py
index 8a089b84a..68d98eab2 100644
--- a/rl_engine/testing/qwen3_next_attention_prior_art.py
+++ b/rl_engine/testing/qwen3_next_attention_prior_art.py
@@ -9,7 +9,8 @@
 
 * **batch invariance** - a target sequence's output computed alone, and inside
   a batch with three other sequences (placed first and last), must be bitwise
-  equal; for candidates with a backward, the same for its dq/dk/dv;
+  equal; for candidates with a backward, the same for its dq/dk/dv, in one
+  packed launch where the engine has a packed backward;
 * **prefill/decode invariance** - the last 64 query rows, and the last single
   row, computed against the full KV must equal the same rows of the full
   prefill. This is the replay-vs-rollout boundary of RFC #428;
@@ -286,6 +287,171 @@ def run(seqs):
     }
 
 
+def _bottom_right_mask(lq, lk, device):
+    """True where query row i (aligned to the end of the KV sequence) may not attend."""
+    rows = torch.arange(lq, device=device)[:, None] + (lk - lq)
+    return torch.arange(lk, device=device)[None, :] > rows
+
+
+def _fa4():
+    """FlashAttention-4 (CuTe DSL), as vendored by vLLM 0.30.0: varlen forward and backward."""
+    import vllm
+    from vllm.vllm_flash_attn.cute import interface as fa4
+
+    def call(q, k, v, lq, lk):
+        out = fa4.flash_attn_varlen_func(
+            q,
+            k,
+            v,
+            cu_seqlens_q=_cu(lq),
+            cu_seqlens_k=_cu(lk),
+            max_seqlen_q=max(lq),
+            max_seqlen_k=max(lk),
+            softmax_scale=SCALE,
+            causal=True,
+        )
+        return out[0] if isinstance(out, tuple) else out
+
+    def run(seqs):
+        lq, lk = [s[0].shape[0] for s in seqs], [s[1].shape[0] for s in seqs]
+        q, k, v = (torch.cat([s[i] for s in seqs]) for i in range(3))
+        with torch.no_grad():
+            return _split(call(q, k, v, lq, lk), lq)
+
+    def grad_batch(seqs, dys):
+        lq, lk = [s[0].shape[0] for s in seqs], [s[1].shape[0] for s in seqs]
+        leaves = [torch.cat([s[i] for s in seqs]).detach().requires_grad_(True) for i in range(3)]
+        dq, dk, dv = torch.autograd.grad(call(*leaves, lq, lk), leaves, torch.cat(dys))
+        return list(zip(_split(dq, lq), _split(dk, lk), _split(dv, lk)))
+
+    return {
+        "name": "FlashAttention-4 (CuTe DSL) varlen, vendored by vLLM",
+        "source": f"vllm {vllm.__version__} vllm_flash_attn/cute "
+        "(its head-dim-256 SM100 backward has no deterministic mode)",
+        "run": run,
+        "grad_batch": grad_batch,
+    }
+
+
+def _te(training: bool):
+    """Transformer Engine DotProductAttention on THD-packed sequences.
+
+    With cuDNN 9.20 on SM100, TE has a fused (cuDNN) head-dim-256 backend only for
+    inference; in training mode it falls back to its unfused PyTorch path. Both are
+    measured, the fused one forward-only.
+    """
+    import transformer_engine
+    import transformer_engine.pytorch as te
+
+    attention = te.DotProductAttention(
+        HQ,
+        D,
+        num_gqa_groups=HKV,
+        attention_dropout=0.0,
+        qkv_format="thd",
+        attn_mask_type="padding_causal_bottom_right",
+        softmax_scale=SCALE,
+    )
+    attention.train(training)
+
+    def call(q, k, v, lq, lk):
+        out = attention(
+            q,
+            k,
+            v,
+            cu_seqlens_q=_cu(lq),
+            cu_seqlens_kv=_cu(lk),
+            max_seqlen_q=max(lq),
+            max_seqlen_kv=max(lk),
+        )
+        return out.view(q.shape[0], HQ, D)
+
+    def run(seqs):
+        lq, lk = [s[0].shape[0] for s in seqs], [s[1].shape[0] for s in seqs]
+        q, k, v = (torch.cat([s[i] for s in seqs]) for i in range(3))
+        with torch.no_grad():
+            return _split(call(q, k, v, lq, lk), lq)
+
+    def grad_batch(seqs, dys):
+        lq, lk = [s[0].shape[0] for s in seqs], [s[1].shape[0] for s in seqs]
+        leaves = [torch.cat([s[i] for s in seqs]).detach().requires_grad_(True) for i in range(3)]
+        dq, dk, dv = torch.autograd.grad(call(*leaves, lq, lk), leaves, torch.cat(dys))
+        return list(zip(_split(dq, lq), _split(dk, lk), _split(dv, lk)))
+
+    version = f"transformer-engine {transformer_engine.__version__}"
+    if not training:
+        return {
+            "name": "Transformer Engine DotProductAttention (THD), inference: cuDNN fused",
+            "source": f"{version} (no fused head-dim-256 backward on SM100)",
+            "run": run,
+        }
+    return {
+        "name": "Transformer Engine DotProductAttention (THD), training: unfused fallback",
+        "source": f"{version} (selected for head-dim-256 training on SM100)",
+        "run": run,
+        "grad_batch": grad_batch,
+    }
+
+
+def _megatron_local():
+    """Megatron-core's own (non-TE) DotProductAttention; it has no packed-sequence path."""
+    import os
+
+    import megatron.core
+    import torch.distributed as dist
+    from megatron.core import parallel_state
+    from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+    from megatron.core.transformer.dot_product_attention import DotProductAttention
+    from megatron.core.transformer.enums import AttnMaskType
+    from megatron.core.transformer.transformer_config import TransformerConfig
+
+    if not dist.is_initialized():
+        os.environ.setdefault("MASTER_ADDR", "127.0.0.1")
+        os.environ.setdefault("MASTER_PORT", "29532")
+        device = torch.device("cuda", torch.cuda.current_device())
+        dist.init_process_group("nccl", rank=0, world_size=1, device_id=device)
+    if not parallel_state.model_parallel_is_initialized():
+        parallel_state.initialize_model_parallel(1, 1)
+        model_parallel_cuda_manual_seed(0)
+    config = TransformerConfig(
+        num_layers=1,
+        hidden_size=HQ * D,
+        num_attention_heads=HQ,
+        num_query_groups=HKV,
+        kv_channels=D,
+        attention_dropout=0.0,
+        bf16=True,
+        params_dtype=torch.bfloat16,
+    )
+    attention = DotProductAttention(
+        config,
+        layer_number=1,
+        attn_mask_type=AttnMaskType.padding,
+        attention_type="self",
+        softmax_scale=SCALE,
+    )
+
+    def one(q, k, v):
+        mask = _bottom_right_mask(q.shape[0], k.shape[0], q.device)[None, None]
+        out = attention(q.unsqueeze(1), k.unsqueeze(1), v.unsqueeze(1), mask)
+        return out.view(q.shape[0], HQ, D)
+
+    def run(seqs):
+        with torch.no_grad():
+            return [one(*s) for s in seqs]
+
+    def grad(q, k, v, dy):
+        leaves = [t.detach().clone().requires_grad_(True) for t in (q, k, v)]
+        return torch.autograd.grad(one(*leaves), leaves, dy)
+
+    return {
+        "name": "Megatron-core DotProductAttention (local, one launch per sequence)",
+        "source": f"megatron-core {megatron.core.__version__}",
+        "run": run,
+        "grad": grad,
+    }
+
+
 def candidates() -> list[dict[str, Any]]:
     return [
         _guard("rl_kernel_cuda", _rl_kernel),
@@ -294,6 +460,10 @@ def candidates() -> list[dict[str, Any]]:
         _guard("vllm_fa2_split1", lambda: _vllm_fa(1)),
         _guard("vllm_triton_2d", _vllm_triton),
         _guard("flashinfer", _flashinfer),
+        _guard("fa4_cute", _fa4),
+        _guard("te_fused", lambda: _te(False)),
+        _guard("te_training", lambda: _te(True)),
+        _guard("megatron_local", _megatron_local),
     ]
 
 
@@ -319,17 +489,33 @@ def _prefill_decode(cand, target) -> dict[str, bool]:
     return result
 
 
+def has_backward(cand) -> bool:
+    return "grad" in cand or "grad_batch" in cand
+
+
+def grad_batch(cand, seqs, dys):
+    """Each sequence's (dq, dk, dv): one packed launch where the engine has one."""
+    if "grad_batch" in cand:
+        return cand["grad_batch"](seqs, dys)
+    return [cand["grad"](*s, dy) for s, dy in zip(seqs, dys)]
+
+
 def _grad_bi(cand, target, others) -> dict[str, bool]:
+    """The target's dq/dk/dv alone vs. packed first and last with the other sequences."""
     g = torch.Generator(device="cuda").manual_seed(5)
-    dy = torch.randn(target[0].shape, device="cuda", generator=g).to(torch.bfloat16)
-    alone = cand["grad"](*target, dy)
-    # A backward is per sequence in every engine here; what can change is the
-    # kernel the shape selects, so compare against a second target placed after
-    # other sequences have been differentiated.
-    for other in others:
-        cand["grad"](*other, torch.ones_like(other[0]))
-    again = cand["grad"](*target, dy)
-    return {name: _bitwise(a, b) for name, a, b in zip(("dq", "dk", "dv"), again, alone)}
+
+    def dy_for(q):
+        return torch.randn(q.shape, device="cuda", generator=g).to(torch.bfloat16)
+
+    dy = dy_for(target[0])
+    dys = [dy_for(o[0]) for o in others]
+    alone = grad_batch(cand, [target], [dy])[0]
+    first = grad_batch(cand, [target, *others], [dy, *dys])[0]
+    last = grad_batch(cand, [*others, target], [*dys, dy])[-1]
+    return {
+        name: _bitwise(f, a) and _bitwise(l_, a)
+        for name, f, l_, a in zip(("dq", "dk", "dv"), first, last, alone)
+    }
 
 
 def _one(cand, target, others) -> dict[str, Any]:
@@ -338,10 +524,14 @@ def _one(cand, target, others) -> dict[str, Any]:
     out = {"key": cand["key"], "name": cand["name"], "source": cand["source"]}
     try:
         out["batch_bitwise"] = _batch_bi(cand, target, others)
-        out["prefill_decode_bitwise"] = _prefill_decode(cand, target)
+        try:
+            out["prefill_decode_bitwise"] = _prefill_decode(cand, target)
+        except Exception as exc:  # noqa: BLE001 - recorded; the other checks still run
+            out["prefill_decode_failed"] = f"{type(exc).__name__}: {str(exc)[:300]}"
+            out["prefill_decode_bitwise"] = {"last_64": False, "last_1": False}
         out["repeatable"] = _bitwise(cand["run"]([target])[0], cand["run"]([target])[0])
-        if "grad" in cand:
-            out["backward_repeat_bitwise"] = _grad_bi(cand, target, others)
+        if has_backward(cand):
+            out["backward_batch_bitwise"] = _grad_bi(cand, target, others)
         ref = fp64_reference(*target)
         got = cand["run"]([target])[0].double()
         out["accuracy"] = {
@@ -354,7 +544,7 @@ def _one(cand, target, others) -> dict[str, Any]:
         "failed" not in out
         and all(out["batch_bitwise"].values())
         and all(out["prefill_decode_bitwise"].values())
-        and all(out.get("backward_repeat_bitwise", {"": True}).values())
+        and all(out.get("backward_batch_bitwise", {"": True}).values())
     )
     return out
 
@@ -379,7 +569,11 @@ def calls_for(seqs):
     q, k, v = make_sequence(BACKWARD_TOKENS, 402)
     dy = torch.randn_like(q)
     backward = time_interleaved(
-        {c["key"]: (lambda c=c: c["grad"](q, k, v, dy)) for c in ready if "grad" in c},
+        {
+            c["key"]: (lambda c=c: grad_batch(c, [(q, k, v)], [dy]))
+            for c in ready
+            if has_backward(c)
+        },
         warmup=3,
         iters=15,
     )
@@ -390,10 +584,13 @@ def calls_for(seqs):
     }
 
 
-def attention_report() -> dict[str, Any]:
+def attention_report(only=None) -> dict[str, Any]:
+    """Every candidate, or only the keys in ``only`` (run each Python environment separately)."""
     target = make_sequence(TARGET, 1)
     others = [make_sequence(n, 10 + i) for i, n in enumerate(COMPANIONS)]
-    cands = candidates()
+    cands = [c for c in candidates() if only is None or c["key"] in only]
+    if only is not None and set(only) - {c["key"] for c in cands}:
+        raise ValueError(f"Unknown candidates: {sorted(set(only) - {c['key'] for c in cands})}")
     return {
         "op": "qwen3_next_tp4_attention",
         "shape": {"q_heads": HQ, "kv_heads": HKV, "head_dim": D, "scale": SCALE},
diff --git a/scripts/plot_qwen3_next_attention_prior_art.py b/scripts/plot_qwen3_next_attention_prior_art.py
index 8343c5851..fbfa94d8a 100644
--- a/scripts/plot_qwen3_next_attention_prior_art.py
+++ b/scripts/plot_qwen3_next_attention_prior_art.py
@@ -31,7 +31,17 @@
 PASS = "#1baf7a"
 FAIL = "#d64545"
 NA = "#e4e3dd"
-EXISTING = ("#eb6834", "#b8501f", "#f29a62", "#8a3a15", "#f6bd94", "#d9773f")
+EXISTING = (
+    "#eb6834",
+    "#b8501f",
+    "#f29a62",
+    "#8a3a15",
+    "#f6bd94",
+    "#d9773f",
+    "#6b2a0e",
+    "#c96a2c",
+    "#f0a878",
+)
 
 LABELS = {
     "rl_kernel_cuda": "RL-Kernel deterministic",
@@ -40,15 +50,19 @@
     "vllm_fa2_split1": "vLLM FA2 (num_splits=1)",
     "vllm_triton_2d": "vLLM Triton unified (2D)",
     "flashinfer": "FlashInfer ragged prefill",
+    "fa4_cute": "FlashAttention-4 (CuTe)",
+    "te_fused": "TE, cuDNN fused (inference)",
+    "te_training": "TE, unfused (training)",
+    "megatron_local": "Megatron-core local",
 }
 COLUMNS = (
     ("batch_bitwise", "first", "batch: first"),
     ("batch_bitwise", "last", "batch: last"),
     ("prefill_decode_bitwise", "last_64", "chunk: last 64"),
     ("prefill_decode_bitwise", "last_1", "decode: last 1"),
-    ("backward_repeat_bitwise", "dq", "bwd dq"),
-    ("backward_repeat_bitwise", "dk", "bwd dk"),
-    ("backward_repeat_bitwise", "dv", "bwd dv"),
+    ("backward_batch_bitwise", "dq", "bwd dq (packed)"),
+    ("backward_batch_bitwise", "dk", "bwd dk (packed)"),
+    ("backward_batch_bitwise", "dv", "bwd dv (packed)"),
 )
 
 
diff --git a/scripts/qwen3_next_attention_prior_art.py b/scripts/qwen3_next_attention_prior_art.py
index 2ded9d0ca..c68219621 100644
--- a/scripts/qwen3_next_attention_prior_art.py
+++ b/scripts/qwen3_next_attention_prior_art.py
@@ -36,6 +36,7 @@
     "triton",
     "sglang",
     "megatron-core",
+    "megatron_core",
     "transformer_engine",
     "vime",
 )
@@ -54,9 +55,34 @@ def _version(name: str) -> str | None:
         return None
 
 
+def merge(base: dict, extra: dict) -> dict:
+    """``base`` with ``extra``'s candidates and latencies added (same commit and GPU)."""
+    for field in ("rl_kernel_commit", "tracked_tree_dirty", "shape", "op"):
+        if base[field] != extra[field]:
+            raise SystemExit(f"Reports differ in {field}; they cannot be merged")
+    if base["environment"]["gpu"] != extra["environment"]["gpu"]:
+        raise SystemExit("Reports were measured on different GPUs")
+    keys = {c["key"] for c in base["candidates"]}
+    added = [c for c in extra["candidates"] if c["key"] not in keys]
+    merged = dict(base, candidates=base["candidates"] + added)
+    merged["latency"] = {
+        section: {
+            size: {**values, **extra["latency"][section].get(size, {})}
+            for size, values in sizes.items()
+        }
+        for section, sizes in base["latency"].items()
+    }
+    merged["environments"] = {"main": base["environment"], "extra": extra["environment"]}
+    return merged
+
+
 def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--out", type=Path, required=True)
+    parser.add_argument("--only", help="comma-separated candidate keys (default: all)")
+    parser.add_argument(
+        "--merge", type=Path, help="a report from another process to merge into this one"
+    )
     args = parser.parse_args()
     if not torch.cuda.is_available():
         raise SystemExit("needs a CUDA device")
@@ -73,8 +99,10 @@ def main() -> None:
             "cuda": torch.version.cuda,
             "libraries": {name: _version(name) for name in LIBRARIES},
         },
-        **attention_report(),
+        **attention_report(args.only.split(",") if args.only else None),
     }
+    if args.merge is not None:
+        report = merge(json.loads(args.merge.read_text()), report)
     args.out.parent.mkdir(parents=True, exist_ok=True)
     args.out.write_text(json.dumps(report, indent=2) + "\n")
     print(

From 703c6dca23da2f42b288fefe70f2073ef6a97986 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sat, 10 Oct 2026 02:21:34 +0000
Subject: [PATCH 58/70] fix(qwen3-next): merge flat and per-size attention
 latency sections

The decode and forward+backward latency sections are keyed by candidate
directly, the prefill section by size first; merge them at any depth.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 scripts/qwen3_next_attention_prior_art.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/scripts/qwen3_next_attention_prior_art.py b/scripts/qwen3_next_attention_prior_art.py
index c68219621..289f778bd 100644
--- a/scripts/qwen3_next_attention_prior_art.py
+++ b/scripts/qwen3_next_attention_prior_art.py
@@ -55,6 +55,17 @@ def _version(name: str) -> str | None:
         return None
 
 
+def _merge_latency(base: dict, extra: dict) -> dict:
+    """Per-candidate latencies merged at any depth (prefill is keyed by size first)."""
+    merged = dict(base)
+    for key, value in extra.items():
+        if isinstance(value, dict) and isinstance(base.get(key), dict):
+            merged[key] = _merge_latency(base[key], value)
+        else:
+            merged[key] = value
+    return merged
+
+
 def merge(base: dict, extra: dict) -> dict:
     """``base`` with ``extra``'s candidates and latencies added (same commit and GPU)."""
     for field in ("rl_kernel_commit", "tracked_tree_dirty", "shape", "op"):
@@ -65,13 +76,7 @@ def merge(base: dict, extra: dict) -> dict:
     keys = {c["key"] for c in base["candidates"]}
     added = [c for c in extra["candidates"] if c["key"] not in keys]
     merged = dict(base, candidates=base["candidates"] + added)
-    merged["latency"] = {
-        section: {
-            size: {**values, **extra["latency"][section].get(size, {})}
-            for size, values in sizes.items()
-        }
-        for section, sizes in base["latency"].items()
-    }
+    merged["latency"] = _merge_latency(base["latency"], extra["latency"])
     merged["environments"] = {"main": base["environment"], "extra": extra["environment"]}
     return merged
 

From 414a53ed969ddf33ce71d65757889c9f93e20722 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sat, 10 Oct 2026 10:16:21 +0000
Subject: [PATCH 59/70] docs(qwen3-next): attention prior art with
 FlashAttention-4, TE and Megatron-core measured (B200, 703c6dc)

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../qwen3-next-tp4-mixers-b200/README.md      |  43 +++-
 .../attention/figure.png                      | Bin 204631 -> 273860 bytes
 .../attention/report.json                     | 210 +++++++++++++++---
 3 files changed, 217 insertions(+), 36 deletions(-)

diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/README.md b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/README.md
index 40d824d59..d7214c513 100644
--- a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/README.md
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/README.md
@@ -5,14 +5,14 @@
 | `tp4-gdn/` | `9e6c4f2` | `torchrun --nproc-per-node 4 scripts/qwen3_next_tp_gdn_check.py --checkpoint <ckpt> --output tp4-gdn` |
 | `tp4-attention/` | `9e6c4f2` | `torchrun --nproc-per-node 4 scripts/qwen3_next_tp_attention_check.py --checkpoint <ckpt> --output tp4-attention` |
 | `tp4-moe/` | `9e6c4f2` | `TRITON_F32_DEFAULT=ieee torchrun --nproc-per-node 4 scripts/qwen3_next_tp_moe_check.py --checkpoint <ckpt> --output tp4-moe` |
-| `attention/report.json`, `attention/figure.png` | `6517077` | `python scripts/qwen3_next_attention_prior_art.py --out attention/report.json`, then `scripts/plot_qwen3_next_attention_prior_art.py` |
+| `attention/report.json`, `attention/figure.png` | `703c6dc` | two processes, then `scripts/plot_qwen3_next_attention_prior_art.py attention/report.json` (see below) |
 
 Every file records its commit and `tracked_tree_dirty: false`; each run used a
 clean clone with its own `rl_engine._C` built in place, on B200 GPUs (driver
 580.126.20), torch 2.13.0+cu130, vLLM 0.30.0, NCCL 2.29.7. `<ckpt>` is the
 official `Qwen/Qwen3-Next-80B-A3B-Instruct` revision
 `9c7f2fbe84465e40164a94cc16cd30b6999b0cc7`: the GDN and MoE gates read layer 0,
-the attention gate layer 3. `6517077` adds only the attention prior-art runner
+the attention gate layer 3. `703c6dc` adds only the attention prior-art runner
 and plot on top of `9e6c4f2` (and the MoE evidence merge).
 
 In the job that ran the gates, the mixer GPU checks
@@ -31,6 +31,41 @@ scale 1/16, causal. A 1000-token target sequence is computed alone and in a
 batch with sequences of 777, 1500 and 64 tokens (first and last);
 `prefill_decode_bitwise` compares the last 64 rows and the last row computed
 against the full KV with the same rows of the full prefill;
-`backward_repeat_bitwise` repeats the target's backward after the companions'
-backwards ran. Accuracy is against FP64; latencies are CUDA-event medians with
+`backward_batch_bitwise` compares the target's dq/dk/dv computed alone with the
+same sequence packed first and last among the companions (one launch where the
+engine has a packed backward). Accuracy is against FP64; latencies are CUDA-event medians with
 the candidate order reversed every iteration.
+
+The report was produced in two processes of the same job and merged:
+
+```bash
+python scripts/qwen3_next_attention_prior_art.py \
+  --only rl_kernel_cuda,torch_sdpa,vllm_fa2_auto,vllm_fa2_split1,vllm_triton_2d,flashinfer,fa4_cute,te_training,megatron_local \
+  --out report-main.json
+python scripts/qwen3_next_attention_prior_art.py --only te_fused --merge report-main.json --out report.json
+```
+
+Both run in the training environment (vLLM 0.30.0, Megatron-core 0.16.0rc0,
+Transformer Engine 2.16.1, cuDNN 9.20) with `CUDNN_HOME` pointing at the
+`nvidia-cudnn-cu13` wheel. Notes on the engines added in this revision:
+
+- **FlashAttention-4** is the CuTe-DSL build vendored by vLLM
+  (`vllm.vllm_flash_attn.cute`), varlen forward and its default backward. Its
+  `deterministic=True` backward asserts for head dim 256 on SM100.
+- **Transformer Engine**: with cuDNN 9.20 on SM100, TE offers its fused cuDNN
+  backend for head dim 256 only in inference mode; in training mode it selects
+  the unfused PyTorch fallback (`NVTE_DEBUG=1` shows the selection). Both are
+  measured. The unfused fallback raises a shape error when the query is shorter
+  than the KV (`padding_causal_bottom_right`, THD), recorded as
+  `prefill_decode_failed`. `NVTE_ALLOW_NONDETERMINISTIC_ALGO` only affects the
+  fused backward, which does not exist here, so it is not a separate row.
+- The fused TE row runs in its own process because cudnn-frontend's runtime
+  loader refuses to start when both `libcudart.so.12` and `libcudart.so.13`
+  can be `dlopen`ed, and the compute nodes' system image provides a CUDA 12
+  runtime next to the CUDA 13 one this stack uses. That process had a
+  non-loadable `libcudart.so.12` stub first on `LD_LIBRARY_PATH`; only
+  `libcudart.so.13` was loaded in either process. Its latencies come from that
+  process, not interleaved with the other candidates.
+- **Megatron-core**'s own `DotProductAttention` (not the TE extension) has no
+  packed-sequence path, so it runs one launch per sequence with a bottom-right
+  causal mask; its batch invariance follows from that.
diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/figure.png b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/figure.png
index b6ce6874180e516e153c3885f6b12a9af5c788fa..cb0c65b02ab9a4be4fbcafeee66098b8d86c1eba 100644
GIT binary patch
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diff --git a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/report.json b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/report.json
index 4a95898a2..3d55ea2aa 100644
--- a/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/report.json
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/report.json
@@ -1,7 +1,7 @@
 {
   "kind": "qwen3_next_prior_art_report",
   "rfc": 428,
-  "rl_kernel_commit": "65170777576cb957fb5557ef13bb46ce695a9f1a",
+  "rl_kernel_commit": "703c6dca23da2f42b288fefe70f2073ef6a97986",
   "tracked_tree_dirty": false,
   "environment": {
     "gpu": "NVIDIA B200",
@@ -13,9 +13,10 @@
       "transformers": "5.17.0",
       "triton": "3.7.1",
       "sglang": null,
-      "megatron-core": null,
-      "transformer_engine": null,
-      "vime": null
+      "megatron-core": "0.16.0rc0",
+      "megatron_core": "0.16.0rc0",
+      "transformer_engine": "2.16.1+c9877beb",
+      "vime": "0.3.2"
     }
   },
   "op": "qwen3_next_tp4_attention",
@@ -45,7 +46,7 @@
         "last_1": true
       },
       "repeatable": true,
-      "backward_repeat_bitwise": {
+      "backward_batch_bitwise": {
         "dq": true,
         "dk": true,
         "dv": true
@@ -69,7 +70,7 @@
         "last_1": false
       },
       "repeatable": true,
-      "backward_repeat_bitwise": {
+      "backward_batch_bitwise": {
         "dq": false,
         "dk": true,
         "dv": true
@@ -155,46 +156,191 @@
         "max_abs_vs_fp64": 0.0066821561991154965
       },
       "batch_invariant": false
+    },
+    {
+      "key": "fa4_cute",
+      "name": "FlashAttention-4 (CuTe DSL) varlen, vendored by vLLM",
+      "source": "vllm 0.30.0 vllm_flash_attn/cute (its head-dim-256 SM100 backward has no deterministic mode)",
+      "batch_bitwise": {
+        "first": true,
+        "last": true
+      },
+      "prefill_decode_bitwise": {
+        "last_64": true,
+        "last_1": true
+      },
+      "repeatable": true,
+      "backward_batch_bitwise": {
+        "dq": true,
+        "dk": true,
+        "dv": true
+      },
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.0020224522459116262,
+        "max_abs_vs_fp64": 0.0066821561991154965
+      },
+      "batch_invariant": true
+    },
+    {
+      "key": "te_training",
+      "name": "Transformer Engine DotProductAttention (THD), training: unfused fallback",
+      "source": "transformer-engine 2.16.1+c9877beb (selected for head-dim-256 training on SM100)",
+      "batch_bitwise": {
+        "first": false,
+        "last": false
+      },
+      "prefill_decode_failed": "RuntimeError: The size of tensor a (1000) must match the size of tensor b (64) at non-singleton dimension 3",
+      "prefill_decode_bitwise": {
+        "last_64": false,
+        "last_1": false
+      },
+      "repeatable": true,
+      "backward_batch_bitwise": {
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+        "dk": false,
+        "dv": false
+      },
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.003577689514635195,
+        "max_abs_vs_fp64": 0.012889566535712937
+      },
+      "batch_invariant": false
+    },
+    {
+      "key": "megatron_local",
+      "name": "Megatron-core DotProductAttention (local, one launch per sequence)",
+      "source": "megatron-core 0.16.0rc0",
+      "batch_bitwise": {
+        "first": true,
+        "last": true
+      },
+      "prefill_decode_bitwise": {
+        "last_64": true,
+        "last_1": true
+      },
+      "repeatable": true,
+      "backward_batch_bitwise": {
+        "dq": true,
+        "dk": true,
+        "dv": true
+      },
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.003577688032086663,
+        "max_abs_vs_fp64": 0.012889566535712937
+      },
+      "batch_invariant": true
+    },
+    {
+      "key": "te_fused",
+      "name": "Transformer Engine DotProductAttention (THD), inference: cuDNN fused",
+      "source": "transformer-engine 2.16.1+c9877beb (no fused head-dim-256 backward on SM100)",
+      "batch_bitwise": {
+        "first": true,
+        "last": true
+      },
+      "prefill_decode_bitwise": {
+        "last_64": true,
+        "last_1": true
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.002051519013469327,
+        "max_abs_vs_fp64": 0.0066821561991154965
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+      "batch_invariant": true
     }
   ],
   "latency": {
     "prefill_us": {
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-        "vllm_triton_2d": 250.46399980783463,
-        "flashinfer": 220.7840010523796
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       "2048": {
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       "8192": {
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     "decode_us_kv8192": {
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+      "te_training": 4540.832042694092,
+      "megatron_local": 304.56000566482544,
+      "te_fused": 443.83999705314636
     },
     "forward_plus_backward_us_2048": {
-      "rl_kernel_cuda": 25106.52732849121,
-      "torch_sdpa": 785.3440046310425
+      "rl_kernel_cuda": 25026.527404785156,
+      "torch_sdpa": 741.6639924049377,
+      "fa4_cute": 912.8959774971008,
+      "te_training": 10321.47216796875,
+      "megatron_local": 1336.4800214767456
+    }
+  },
+  "environments": {
+    "main": {
+      "gpu": "NVIDIA B200",
+      "torch": "2.13.0+cu130",
+      "cuda": "13.0",
+      "libraries": {
+        "vllm": "0.30.0",
+        "flashinfer-python": "0.6.18.post1",
+        "transformers": "5.17.0",
+        "triton": "3.7.1",
+        "sglang": null,
+        "megatron-core": "0.16.0rc0",
+        "megatron_core": "0.16.0rc0",
+        "transformer_engine": "2.16.1+c9877beb",
+        "vime": "0.3.2"
+      }
+    },
+    "extra": {
+      "gpu": "NVIDIA B200",
+      "torch": "2.13.0+cu130",
+      "cuda": "13.0",
+      "libraries": {
+        "vllm": "0.30.0",
+        "flashinfer-python": "0.6.18.post1",
+        "transformers": "5.17.0",
+        "triton": "3.7.1",
+        "sglang": null,
+        "megatron-core": "0.16.0rc0",
+        "megatron_core": "0.16.0rc0",
+        "transformer_engine": "2.16.1+c9877beb",
+        "vime": "0.3.2"
+      }
     }
   }
 }

From 1513b7140241afb8c88849ae70efd29b74113496 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sat, 10 Oct 2026 20:00:16 +0000
Subject: [PATCH 60/70] style(tools): mark the Qwen3-Next TP gate imports after
 sys.path setup as E402-exempt

The gate scripts insert the repo root into sys.path before importing
rl_engine and tools, which flake8 reports as E402. Mark those imports
with noqa, as the other tools/ scripts do.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 tools/validation/models/qwen3_next_tp_moe_check.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/tools/validation/models/qwen3_next_tp_moe_check.py b/tools/validation/models/qwen3_next_tp_moe_check.py
index c9079b7df..775c9a84b 100644
--- a/tools/validation/models/qwen3_next_tp_moe_check.py
+++ b/tools/validation/models/qwen3_next_tp_moe_check.py
@@ -32,21 +32,23 @@
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
 
-import torch
-import torch.distributed as dist
-from safetensors import safe_open
+import torch  # noqa: E402
+import torch.distributed as dist  # noqa: E402
+from safetensors import safe_open  # noqa: E402
 
-from rl_engine.models.qwen3_next.qwen3_next_forward import (
+from rl_engine.models.qwen3_next.qwen3_next_forward import (  # noqa: E402
     FORWARD_PROVIDER_ID,
     shared_router,
     stable_top10_routes,
 )
-from rl_engine.models.qwen3_next.qwen3_next_tp_blocks import (
+from rl_engine.models.qwen3_next.qwen3_next_tp_blocks import (  # noqa: E402
     TP4MoE,
     assemble_moe_weight,
     moe_hf_shapes,
 )
-from rl_engine.validation.common.tensor_identity import assert_tensor_bitwise_equal as exact
+from rl_engine.validation.common.tensor_identity import (  # noqa: E402
+    assert_tensor_bitwise_equal as exact,
+)
 
 ROOT = Path(__file__).resolve().parents[3]
 TOKEN_COUNTS = (8, 64, 256, 1024)

From 69d015a222fb981dc3941fa2da95ec6e0f71a54b Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sat, 10 Oct 2026 20:00:17 +0000
Subject: [PATCH 61/70] style(tools): mark the Qwen3-Next TP gate imports after
 sys.path setup as E402-exempt

The gate scripts insert the repo root into sys.path before importing
rl_engine and tools, which flake8 reports as E402. Mark those imports
with noqa, as the other tools/ scripts do.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../models/qwen3_next_tp_attention_check.py    | 12 +++++++-----
 .../models/qwen3_next_tp_gdn_check.py          | 18 +++++++++++-------
 .../models/qwen3_next_tp_moe_check.py          | 17 +++++++++++------
 3 files changed, 29 insertions(+), 18 deletions(-)

diff --git a/tools/validation/models/qwen3_next_tp_attention_check.py b/tools/validation/models/qwen3_next_tp_attention_check.py
index 6a39585e2..a94487e35 100644
--- a/tools/validation/models/qwen3_next_tp_attention_check.py
+++ b/tools/validation/models/qwen3_next_tp_attention_check.py
@@ -20,16 +20,18 @@
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
 
-import torch
-import torch.distributed as dist
+import torch  # noqa: E402
+import torch.distributed as dist  # noqa: E402
 
-from rl_engine.models.qwen3_next.qwen3_next_tp_blocks import (
+from rl_engine.models.qwen3_next.qwen3_next_tp_blocks import (  # noqa: E402
     ATTENTION_HF_SHAPES,
     TP4FullAttention,
     assemble_attention_weights,
 )
-from rl_engine.validation.common.tensor_identity import assert_tensor_bitwise_equal as exact
-from tools.validation.models.qwen3_next_gate_common import (
+from rl_engine.validation.common.tensor_identity import (  # noqa: E402
+    assert_tensor_bitwise_equal as exact,
+)
+from tools.validation.models.qwen3_next_gate_common import (  # noqa: E402
     CheckpointWeights,
     check_gradients,
     gather,
diff --git a/tools/validation/models/qwen3_next_tp_gdn_check.py b/tools/validation/models/qwen3_next_tp_gdn_check.py
index 79a1fb29c..65fee3815 100644
--- a/tools/validation/models/qwen3_next_tp_gdn_check.py
+++ b/tools/validation/models/qwen3_next_tp_gdn_check.py
@@ -20,18 +20,22 @@
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
 
-import torch
-import torch.distributed as dist
-from safetensors import safe_open
+import torch  # noqa: E402
+import torch.distributed as dist  # noqa: E402
+from safetensors import safe_open  # noqa: E402
 
-from rl_engine.integrations.engines.train.vllm.qwen3_next_provider import GDNProviderConfig
-from rl_engine.models.qwen3_next.qwen3_next_tp_gdn import (
+from rl_engine.integrations.engines.train.vllm.qwen3_next_provider import (  # noqa: E402
+    GDNProviderConfig,
+)
+from rl_engine.models.qwen3_next.qwen3_next_tp_gdn import (  # noqa: E402
     _GLOBAL_SHAPES,
     TP4GDN,
     assemble_gdn_weights,
 )
-from rl_engine.validation.common.tensor_identity import assert_tensor_bitwise_equal as exact
-from tools.validation.models.qwen3_next_gate_common import check_gradients, run_gate
+from rl_engine.validation.common.tensor_identity import (  # noqa: E402
+    assert_tensor_bitwise_equal as exact,
+)
+from tools.validation.models.qwen3_next_gate_common import check_gradients, run_gate  # noqa: E402
 
 
 def load_first_gdn(checkpoint):
diff --git a/tools/validation/models/qwen3_next_tp_moe_check.py b/tools/validation/models/qwen3_next_tp_moe_check.py
index 80c88ec2f..c18b5b1fd 100644
--- a/tools/validation/models/qwen3_next_tp_moe_check.py
+++ b/tools/validation/models/qwen3_next_tp_moe_check.py
@@ -25,17 +25,22 @@
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
 
-import torch
-import torch.distributed as dist
+import torch  # noqa: E402
+import torch.distributed as dist  # noqa: E402
 
-from rl_engine.models.qwen3_next.qwen3_next_forward import shared_router, stable_top10_routes
-from rl_engine.models.qwen3_next.qwen3_next_tp_blocks import (
+from rl_engine.models.qwen3_next.qwen3_next_forward import (  # noqa: E402
+    shared_router,
+    stable_top10_routes,
+)
+from rl_engine.models.qwen3_next.qwen3_next_tp_blocks import (  # noqa: E402
     TP4MoE,
     assemble_moe_weight,
     moe_hf_shapes,
 )
-from rl_engine.validation.common.tensor_identity import assert_tensor_bitwise_equal as exact
-from tools.validation.models.qwen3_next_gate_common import (
+from rl_engine.validation.common.tensor_identity import (  # noqa: E402
+    assert_tensor_bitwise_equal as exact,
+)
+from tools.validation.models.qwen3_next_gate_common import (  # noqa: E402
     CheckpointWeights,
     check_gradients,
     gather,

From 37bc9532ad4a841f9083e97d8ec54e8e04967957 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 11 Oct 2026 00:34:36 +0000
Subject: [PATCH 62/70] fix(qwen3-next): type annotations for mypy in the MoE
 prior-art runner

Annotate the latency samples and accept the default-argument lambdas used as
timed calls. No behaviour change.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 rl_engine/validation/models/qwen3_next_moe_prior_art.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/rl_engine/validation/models/qwen3_next_moe_prior_art.py b/rl_engine/validation/models/qwen3_next_moe_prior_art.py
index c0582726f..7c0cc33c9 100644
--- a/rl_engine/validation/models/qwen3_next_moe_prior_art.py
+++ b/rl_engine/validation/models/qwen3_next_moe_prior_art.py
@@ -64,14 +64,14 @@ def _sample_us(fn: Callable[[], Any]) -> float:
     return start.elapsed_time(end) * 1000.0
 
 
-def time_interleaved(calls: dict[str, Callable[[], Any]], warmup: int = 5, iters: int = 30):
+def time_interleaved(calls: dict[str, Callable[..., Any]], warmup: int = 5, iters: int = 30):
     """Median latency per candidate; the order alternates so none always runs first."""
     keys = list(calls)
     orders = (keys, list(reversed(keys)))
     for i in range(warmup):
         for key in orders[i % 2]:
             calls[key]()
-    samples = {key: [] for key in keys}
+    samples: dict[str, list[float]] = {key: [] for key in keys}
     for i in range(iters):
         for key in orders[i % 2]:
             samples[key].append(_sample_us(calls[key]))

From fac8f92e7b07b4f9829e789ac6635f92e3565250 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 11 Oct 2026 00:37:02 +0000
Subject: [PATCH 63/70] fix(qwen3-next): type annotations for mypy in the
 attention prior-art runner

Annotate the latency samples and accept the default-argument lambdas used as
timed calls. No behaviour change.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 rl_engine/validation/models/qwen3_next_attention_prior_art.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/rl_engine/validation/models/qwen3_next_attention_prior_art.py b/rl_engine/validation/models/qwen3_next_attention_prior_art.py
index 785040e46..db705b56f 100644
--- a/rl_engine/validation/models/qwen3_next_attention_prior_art.py
+++ b/rl_engine/validation/models/qwen3_next_attention_prior_art.py
@@ -56,13 +56,13 @@ def _sample_us(fn: Callable[[], Any]) -> float:
     return start.elapsed_time(end) * 1000.0
 
 
-def time_interleaved(calls: dict[str, Callable[[], Any]], warmup: int = 5, iters: int = 30):
+def time_interleaved(calls: dict[str, Callable[..., Any]], warmup: int = 5, iters: int = 30):
     keys = list(calls)
     orders = (keys, list(reversed(keys)))
     for i in range(warmup):
         for key in orders[i % 2]:
             calls[key]()
-    samples = {key: [] for key in keys}
+    samples: dict[str, list[float]] = {key: [] for key in keys}
     for i in range(iters):
         for key in orders[i % 2]:
             samples[key].append(_sample_us(calls[key]))

From 41d5bb01826b453f227083655574324ab326144b Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 11 Oct 2026 01:32:35 +0000
Subject: [PATCH 64/70] docs(qwen3-next): full-model chain evidence, preflight
 to train, 64/64 cells bitwise (B200, c67ec14)

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../evidence/qwen3-next-chain-b200/README.md  |   41 +
 .../evidence/qwen3-next-chain-b200/SHA256SUMS |   44 +
 .../qwen3-next-chain-b200/chain-report.json   | 8083 +++++++++++++++++
 .../qwen3-next-chain-b200/l2/environment.json | 4511 +++++++++
 .../qwen3-next-chain-b200/l2/l2_matrix.json   | 1186 +++
 .../qwen3-next-chain-b200/l2/operators.json   |  304 +
 .../qwen3-next-chain-b200/l2/predecessor.json |    5 +
 .../l2/provider_binary.json                   |   13 +
 .../qwen3-next-chain-b200/l2/result.json      |  775 ++
 .../qwen3-next-chain-b200/l2/run.json         | 5252 +++++++++++
 .../layers/checkpoint.json                    |   14 +
 .../layers/environment.json                   | 4511 +++++++++
 .../qwen3-next-chain-b200/layers/layers.json  |  457 +
 .../layers/operators.json                     |  304 +
 .../layers/provider_binary.json               |   13 +
 .../qwen3-next-chain-b200/layers/result.json  |  775 ++
 .../qwen3-next-chain-b200/layers/run.json     | 5248 +++++++++++
 .../qwen3-next-chain-b200/measured-commit.txt |    1 +
 .../preflight/checkpoint.json                 |   14 +
 .../preflight/device_guards.xml               |    1 +
 .../preflight/environment.json                |   40 +
 .../preflight/operators.json                  |  304 +
 .../preflight/result.json                     |  797 ++
 .../preflight/rollout_smoke.json              |   75 +
 .../qwen3-next-chain-b200/preflight/run.json  | 5248 +++++++++++
 .../qwen3-next-chain-b200/preflight/tp4.json  |   49 +
 .../qwen3-next-chain-b200/redaction.json      |   15 +
 .../train/environment.json                    | 4500 +++++++++
 .../train/l2_matrix.json                      | 2236 +++++
 .../train/operators.json                      |  304 +
 .../qwen3-next-chain-b200/train/reload.json   | 2021 +++++
 .../qwen3-next-chain-b200/train/result.json   |  776 ++
 .../qwen3-next-chain-b200/train/run.json      | 5252 +++++++++++
 .../qwen3-next-chain-b200/train/updates.json  | 2021 +++++
 .../train1/environment.json                   | 4500 +++++++++
 .../train1/l2_matrix.json                     | 2236 +++++
 .../train1/operators.json                     |  304 +
 .../qwen3-next-chain-b200/train1/result.json  |  774 ++
 .../qwen3-next-chain-b200/train1/run.json     | 5252 +++++++++++
 .../qwen3-next-chain-b200/train1/updates.json |  685 ++
 .../train2/environment.json                   | 4500 +++++++++
 .../train2/l2_matrix.json                     | 2236 +++++
 .../train2/operators.json                     |  304 +
 .../qwen3-next-chain-b200/train2/result.json  |  775 ++
 .../qwen3-next-chain-b200/train2/run.json     | 5252 +++++++++++
 .../qwen3-next-chain-b200/train2/updates.json | 1695 ++++
 46 files changed, 83703 insertions(+)
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/README.md
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/SHA256SUMS
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/chain-report.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/l2/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/l2/l2_matrix.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/l2/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/l2/predecessor.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/l2/provider_binary.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/l2/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/l2/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/layers/checkpoint.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/layers/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/layers/layers.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/layers/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/layers/provider_binary.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/layers/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/layers/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/measured-commit.txt
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/preflight/checkpoint.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/preflight/device_guards.xml
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/preflight/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/preflight/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/preflight/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/preflight/rollout_smoke.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/preflight/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/preflight/tp4.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/redaction.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train/l2_matrix.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train/reload.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train/updates.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train1/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train1/l2_matrix.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train1/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train1/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train1/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train1/updates.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train2/environment.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train2/l2_matrix.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train2/operators.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train2/result.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train2/run.json
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/train2/updates.json

diff --git a/docs/usage/evidence/qwen3-next-chain-b200/README.md b/docs/usage/evidence/qwen3-next-chain-b200/README.md
new file mode 100644
index 000000000..177077593
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/README.md
@@ -0,0 +1,41 @@
+# Qwen3-Next full-model chain (B200)
+
+Measured at commit `c67ec14` (`measured-commit.txt`) from clean clones, one
+stage after another on nodes with four B200 GPUs, in one environment
+(`environment_sha256` `118384371a88…` in every `run.json`), on the official
+`Qwen/Qwen3-Next-80B-A3B-Instruct` revision
+`9c7f2fbe84465e40164a94cc16cd30b6999b0cc7`. Each stage was launched with the
+launcher of that commit, `python scripts/qwen3_next_run.py --stage <stage>
+--previous <run of the stage before> --runs-root <runs> --checkpoint <checkpoint>
+--cuda-home <cuda-13.0> --python <venv>/bin/python` (see
+`docs/usage/qwen3-next-chain.md`), and the reports were made with:
+
+```bash
+python scripts/qwen3_next_chain_report.py --last <runs>/<...-train-...> --out chain-report.json
+bash ci/run_qwen3_next_chain_gate.sh chain-report.json
+python scripts/qwen3_next_publish_evidence.py --last <runs>/<...-train-...> \
+    --chain-report chain-report.json --out docs/usage/evidence/qwen3-next-chain-b200
+```
+
+| Directory | Stage | Result |
+| --- | --- | --- |
+| `preflight/` | operator gates, device guards, TP4 NCCL probe, checkpoint, rollout smoke | passed |
+| `layers/` | per-layer TP4 contracts on real weights | passed |
+| `l2/` | vLLM rollout vs VIME recompute, 16 cases, raw bits | passed, 16/16 |
+| `train1/`, `train2/` | one RL update each, L2 on the updated weights | passed, 16/16 each |
+| `train/` | checkpoint reload, L2 on the reloaded weights, continuous and resumed controls | passed, 16/16; reload == update 2 |
+| `chain-report.json` | chain-gate report (`qwen3-next-chain-v1`, compatible with `ws1-c10-c11-v5`) | passed, 64 cells, `first_drift: null` |
+
+The stages ran the code of `c67ec14`; the copy in this directory was made by
+the publisher of `3025497`, which only adds redaction rules (`redaction.json`
+counts the placeholders) and changes no measured byte. `SHA256SUMS` covers
+every file except this README.
+
+`train` was preempted and restarted several times. The training journal lets a
+restarted run continue from its last durable step, and one restarted run reused
+the final reload of an interrupted run; the chain report rejected it
+(`Tensor artifact escaped its current attempt`). Before the run published here,
+the journal's reload, continuous and resumed entries were retired, so this run
+repeated all three itself; `train/reload.json` lists the retirements under
+`recovery` (`repeat_final_reload_and_controls_in_fresh_train_run`). Updates 1
+and 2 are the ones `train1/` and `train2/` recorded in the journal.
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/SHA256SUMS b/docs/usage/evidence/qwen3-next-chain-b200/SHA256SUMS
new file mode 100644
index 000000000..15115a6d3
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/SHA256SUMS
@@ -0,0 +1,44 @@
+7dc93415fddc7563d6765ab623d43a63c76dd367b62eaf36d42ea59cf9ca9134  chain-report.json
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new file mode 100644
index 000000000..729ecd2ac
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/chain-report.json
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/l2/environment.json b/docs/usage/evidence/qwen3-next-chain-b200/l2/environment.json
new file mode 100644
index 000000000..b4ae8f9a3
--- /dev/null
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/l2/l2_matrix.json b/docs/usage/evidence/qwen3-next-chain-b200/l2/l2_matrix.json
new file mode 100644
index 000000000..9fe45617f
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/l2/l2_matrix.json
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+}
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/l2/operators.json b/docs/usage/evidence/qwen3-next-chain-b200/l2/operators.json
new file mode 100644
index 000000000..500251cce
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/l2/operators.json
@@ -0,0 +1,304 @@
+{
+  "scope": "qwen3_next_operators_only",
+  "status": "passed",
+  "suite": "all",
+  "diagnostic_only": false,
+  "full_model_l2": "not_evaluated",
+  "git_commit": "c67ec14401b0847ac96441a24acfaac0b17b77ec",
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+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[256]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[64]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[8]",
+    "tests/check_qwen3_next_attention.py::test_d256_input_device_guard_and_nondefault_stream",
+    "tests/check_qwen3_next_attention.py::test_native_backward_rejects_malformed_probability_and_gradient_buffers",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[2]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_target_inputs_do_not_depend_on_companions",
+    "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_is_batch_invariant",
+    "tests/check_qwen3_next_conv_bridge.py::test_chunked_and_reordered_sequences_match",
+    "tests/check_qwen3_next_conv_bridge.py::test_empty_sequence_preserves_cache_and_gradients",
+    "tests/check_qwen3_next_conv_bridge.py::test_response_gradient_reaches_prompt_and_convolution_weight",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths0]",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths1]",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths2]",
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+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[1024-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[256-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[256-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[64-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[64-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_shared_core_one_hundred_repetitions[1]",
+    "tests/check_qwen3_next_core_matrix.py::test_shared_core_one_hundred_repetitions[4]",
+    "tests/check_qwen3_next_core_matrix.py::test_variable_lengths_empty_and_mixed_step_match_independent_sequences",
+    "tests/check_qwen3_next_forward.py::test_combine_has_fixed_route_order_and_differentiable_weights",
+    "tests/check_qwen3_next_forward.py::test_full_vocabulary_logprob_entropy_modes_and_gradient",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[1-128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[1-512]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[13-128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[13-512]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[300-128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[300-512]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[7-128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[7-512]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_fail_closed_on_the_tensor_descriptor_path",
+    "tests/check_qwen3_next_forward.py::test_grouped_projection_is_bitwise_the_per_expert_gemm[128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_projection_is_bitwise_the_per_expert_gemm[512]",
+    "tests/check_qwen3_next_forward.py::test_logprob_nan_rejected_before_comparison",
+    "tests/check_qwen3_next_forward.py::test_moe_vjp_matches_independent_double_formula",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[1024-2048]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[128-2048]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-16]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-256]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-3072]",
+    "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-37984]",
+    "tests/check_qwen3_next_forward.py::test_qwen_moe_actual_tp4_shapes_and_independent_training_forward",
+    "tests/check_qwen3_next_forward.py::test_route_weights_are_batch_invariant",
+    "tests/check_qwen3_next_forward.py::test_shared_linear_vjp_and_empty_tokens",
+    "tests/check_qwen3_next_forward.py::test_stable_route_tie_boundary_and_router_gradient",
+    "tests/check_qwen3_next_gdn_bridge.py::test_bridge_backward_matches_recomputed_recurrence_vjp",
+    "tests/check_qwen3_next_gdn_bridge.py::test_bridge_forward_is_provider_exact_and_does_not_mutate_inputs",
+    "tests/check_qwen3_next_gdn_bridge.py::test_bridge_keeps_prompt_state_gradient_through_response",
+    "tests/check_qwen3_next_gdn_bridge.py::test_inactive_state_is_identity_in_backward[False]",
+    "tests/check_qwen3_next_gdn_bridge.py::test_inactive_state_is_identity_in_backward[True]",
+    "tests/check_qwen3_next_gdn_bridge.py::test_initial_state_vjp_matches_provider_directional_difference",
+    "tests/check_qwen3_next_gdn_sequence.py::test_1024_token_chunk_continuation_is_bitwise_exact",
+    "tests/check_qwen3_next_gdn_sequence.py::test_bad_packing_is_rejected[bad0]",
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/l2/predecessor.json b/docs/usage/evidence/qwen3-next-chain-b200/l2/predecessor.json
new file mode 100644
index 000000000..28ce9bec5
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/l2/predecessor.json
@@ -0,0 +1,5 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/l2/provider_binary.json b/docs/usage/evidence/qwen3-next-chain-b200/l2/provider_binary.json
new file mode 100644
index 000000000..fbd6a2721
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/l2/provider_binary.json
@@ -0,0 +1,13 @@
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/l2/result.json b/docs/usage/evidence/qwen3-next-chain-b200/l2/result.json
new file mode 100644
index 000000000..03c539b31
--- /dev/null
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+      "tests/test_qwen3_next_norm.py::test_deterministic_repeat",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype2]",
+      "tests/test_qwen3_next_norm.py::test_eps_inside_sqrt",
+      "tests/test_qwen3_next_norm.py::test_gated_batch_invariance_slice",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedHFOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_conventions_diverge_in_low_precision",
+      "tests/test_qwen3_next_norm.py::test_gated_cuda_op_rejects_bad_activation_at_construction",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_handles_empty_batch_and_nondefault_stream",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[dy_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[gate_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[rstd_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_shape]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_matches_hf_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_gated_shape_mismatch_raises",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_matches_vllm_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[128]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[2048]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_offset_applied_in_fp32_not_folded_into_bf16",
+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
+      "tests/test_qwen3_next_norm.py::test_weight_offset_is_one",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
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+      "qwen3_next_rms_norm-gradient.json",
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/l2/run.json b/docs/usage/evidence/qwen3-next-chain-b200/l2/run.json
new file mode 100644
index 000000000..3b7414597
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/l2/run.json
@@ -0,0 +1,5252 @@
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index 000000000..4488f19f1
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index 000000000..35314d506
--- /dev/null
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/layers/layers.json b/docs/usage/evidence/qwen3-next-chain-b200/layers/layers.json
new file mode 100644
index 000000000..0eb4ff345
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/layers/layers.json
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/layers/operators.json b/docs/usage/evidence/qwen3-next-chain-b200/layers/operators.json
new file mode 100644
index 000000000..500251cce
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/layers/operators.json
@@ -0,0 +1,304 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/layers/provider_binary.json b/docs/usage/evidence/qwen3-next-chain-b200/layers/provider_binary.json
new file mode 100644
index 000000000..4f70ebb27
--- /dev/null
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/layers/result.json b/docs/usage/evidence/qwen3-next-chain-b200/layers/result.json
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index 000000000..bada855df
--- /dev/null
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+    "native_rollout_kv_cache_memory_bytes": 4294967296,
+    "runtime_env": {
+      "TRITON_F32_DEFAULT": "ieee",
+      "VLLM_USE_V2_MODEL_RUNNER": "0"
+    }
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+  "topology": {
+    "tp": 4,
+    "pp": 1,
+    "cp": 1,
+    "ep": 1
+  },
+  "operator_requirements": {
+    "suite": "all",
+    "case_ids": [
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+      "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[1024]",
+      "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[256]",
+      "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[64]",
+      "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[8]",
+      "tests/check_qwen3_next_attention.py::test_d256_input_device_guard_and_nondefault_stream",
+      "tests/check_qwen3_next_attention.py::test_native_backward_rejects_malformed_probability_and_gradient_buffers",
+      "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype0]",
+      "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype1]",
+      "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[0]",
+      "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[1]",
+      "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[2]",
+      "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype0]",
+      "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype1]",
+      "tests/check_qwen3_next_batch_invariance.py::test_target_inputs_do_not_depend_on_companions",
+      "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_is_batch_invariant",
+      "tests/check_qwen3_next_conv_bridge.py::test_chunked_and_reordered_sequences_match",
+      "tests/check_qwen3_next_conv_bridge.py::test_empty_sequence_preserves_cache_and_gradients",
+      "tests/check_qwen3_next_conv_bridge.py::test_response_gradient_reaches_prompt_and_convolution_weight",
+      "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths0]",
+      "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths1]",
+      "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths2]",
+      "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[1024-1]",
+      "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[1024-4]",
+      "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[256-1]",
+      "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[256-4]",
+      "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[64-1]",
+      "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[64-4]",
+      "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-1]",
+      "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-4]",
+      "tests/check_qwen3_next_core_matrix.py::test_shared_core_one_hundred_repetitions[1]",
+      "tests/check_qwen3_next_core_matrix.py::test_shared_core_one_hundred_repetitions[4]",
+      "tests/check_qwen3_next_core_matrix.py::test_variable_lengths_empty_and_mixed_step_match_independent_sequences",
+      "tests/check_qwen3_next_forward.py::test_combine_has_fixed_route_order_and_differentiable_weights",
+      "tests/check_qwen3_next_forward.py::test_full_vocabulary_logprob_entropy_modes_and_gradient",
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+      "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[1-512]",
+      "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[13-128]",
+      "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[13-512]",
+      "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[300-128]",
+      "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[300-512]",
+      "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[7-128]",
+      "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[7-512]",
+      "tests/check_qwen3_next_forward.py::test_grouped_experts_fail_closed_on_the_tensor_descriptor_path",
+      "tests/check_qwen3_next_forward.py::test_grouped_projection_is_bitwise_the_per_expert_gemm[128]",
+      "tests/check_qwen3_next_forward.py::test_grouped_projection_is_bitwise_the_per_expert_gemm[512]",
+      "tests/check_qwen3_next_forward.py::test_logprob_nan_rejected_before_comparison",
+      "tests/check_qwen3_next_forward.py::test_moe_vjp_matches_independent_double_formula",
+      "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[1024-2048]",
+      "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[128-2048]",
+      "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-16]",
+      "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-256]",
+      "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-3072]",
+      "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-37984]",
+      "tests/check_qwen3_next_forward.py::test_qwen_moe_actual_tp4_shapes_and_independent_training_forward",
+      "tests/check_qwen3_next_forward.py::test_route_weights_are_batch_invariant",
+      "tests/check_qwen3_next_forward.py::test_shared_linear_vjp_and_empty_tokens",
+      "tests/check_qwen3_next_forward.py::test_stable_route_tie_boundary_and_router_gradient",
+      "tests/check_qwen3_next_gdn_bridge.py::test_bridge_backward_matches_recomputed_recurrence_vjp",
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+      "tests/check_qwen3_next_gdn_bridge.py::test_inactive_state_is_identity_in_backward[False]",
+      "tests/check_qwen3_next_gdn_bridge.py::test_inactive_state_is_identity_in_backward[True]",
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+      "tests/check_qwen3_next_gdn_sequence.py::test_packed_matches_independent_sequences_and_reordering[lengths2]",
+      "tests/check_qwen3_next_gdn_sequence.py::test_reserved_and_duplicate_slots_are_rejected",
+      "tests/check_qwen3_next_norm_providers.py::test_custom_op_has_distinct_native_and_cuda_paths",
+      "tests/check_qwen3_next_norm_providers.py::test_gdn_decode_provider_env_defaults_are_recorded",
+      "tests/check_qwen3_next_norm_providers.py::test_our_gated_op_is_batch_invariant",
+      "tests/check_qwen3_next_norm_providers.py::test_vllm_gated_norm_is_batch_invariant[forward_cuda]",
+      "tests/check_qwen3_next_norm_providers.py::test_vllm_gated_norm_is_batch_invariant[forward_native]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[1024-1]",
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+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[65-1]",
+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[65-4]",
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+      "tests/check_qwen3_next_paged_attention.py::test_paged_attention_full_chunk_reorder_and_cache_bits[8-4]",
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+      "tests/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[64]",
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+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_within_tolerance_of_golden",
+      "tests/test_qwen3_next_norm.py::test_deterministic_repeat",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype2]",
+      "tests/test_qwen3_next_norm.py::test_eps_inside_sqrt",
+      "tests/test_qwen3_next_norm.py::test_gated_batch_invariance_slice",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedHFOp]",
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+      "tests/test_qwen3_next_norm.py::test_gated_extension_handles_empty_batch_and_nondefault_stream",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[dy_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[gate_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[rstd_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_shape]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_matches_hf_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_gated_shape_mismatch_raises",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_matches_vllm_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[128]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[2048]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_offset_applied_in_fp32_not_folded_into_bf16",
+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
+      "tests/test_qwen3_next_norm.py::test_weight_offset_is_one",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
+    ],
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+      "qwen3_next_rms_norm-gradient.json",
+      "rms_norm_gated-forward.json",
+      "rms_norm_gated-gradient.json"
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+            "full-chunk-reorder-64",
+            "routes-replicated-256",
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+            "routes-replicated-1024",
+            "full-chunk-reorder-1024",
+            "training-forward",
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+      "p0064-b4-full",
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/layers/run.json b/docs/usage/evidence/qwen3-next-chain-b200/layers/run.json
new file mode 100644
index 000000000..b6b556f19
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/layers/run.json
@@ -0,0 +1,5248 @@
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+        "scope": "tp4_moe_block_layer0",
+        "groups": {
+          "moe.cases": [
+            "hf-load-export-all512experts",
+            "routes-replicated-8",
+            "full-chunk-reorder-8",
+            "routes-replicated-64",
+            "full-chunk-reorder-64",
+            "routes-replicated-256",
+            "full-chunk-reorder-256",
+            "routes-replicated-1024",
+            "full-chunk-reorder-1024",
+            "training-forward",
+            "all-parameter-gradient",
+            "replicated-router-gradient"
+          ]
+        },
+        "script_sha256": "d5c1bbbb82e408b65871b0cb1638d7a6cf8cfe07f0a3315a657b7dc232d544be"
+      },
+      "adapters": {
+        "script": "scripts/qwen3_next_adapter_check.py",
+        "output_option": "--out",
+        "scope": "gdn_engine_adapter_component",
+        "groups": {
+          "cases": [
+            "batch1-length8",
+            "batch1-length64",
+            "batch1-length256",
+            "batch1-length1024",
+            "batch4-length8",
+            "batch4-length64",
+            "batch4-length256",
+            "batch4-length1024",
+            "mixed-prefill-decode16-reorder"
+          ]
+        },
+        "fields": {
+          "prompt_gradient": "passed",
+          "hf_conv_roundtrip": "passed",
+          "batch_invariance_capability_registered": false
+        },
+        "script_sha256": "3ce45bb317237c1a06533116d65fb77c2a23b6f503ae46406ede543036eb0f20"
+      },
+      "heads": {
+        "script": "scripts/qwen3_next_tp_head_check.py",
+        "output_option": "--output",
+        "scope": "tp4_embedding_lm_head_only",
+        "groups": {
+          "cases": [
+            "hf-embedding-roundtrip",
+            "hf-lm-head-roundtrip",
+            "replicated-norm-gradient",
+            "nonzero-finite-embedding-head-gradient",
+            "embedding-repeat-100",
+            "batch-invariant-prompt8",
+            "batch-invariant-prompt64",
+            "batch-invariant-prompt256",
+            "batch-invariant-prompt1024",
+            "logprob-original-and-promoted-logits",
+            "entropy-interface"
+          ]
+        },
+        "script_sha256": "0504185d21a75405c8e994bc57b016cf4ec357d8b16e7430772c11934f2af876"
+      }
+    },
+    "scheduler_case_ids": [
+      "p0008-b1-full",
+      "p0008-b1-chunked",
+      "p0008-b4-full",
+      "p0008-b4-chunked",
+      "p0064-b1-full",
+      "p0064-b1-chunked",
+      "p0064-b4-full",
+      "p0064-b4-chunked",
+      "p0256-b1-full",
+      "p0256-b1-chunked",
+      "p0256-b4-full",
+      "p0256-b4-chunked",
+      "p1024-b1-full",
+      "p1024-b1-chunked",
+      "p1024-b4-full",
+      "p1024-b4-chunked"
+    ],
+    "registry_sha256": "e3949a750cb27c1897a000f3b023f912629906612ae8949c1575d0acc36df5ec",
+    "case_ids_per_rank": [
+      "adapters/cases/batch1-length1024",
+      "adapters/cases/batch1-length256",
+      "adapters/cases/batch1-length64",
+      "adapters/cases/batch1-length8",
+      "adapters/cases/batch4-length1024",
+      "adapters/cases/batch4-length256",
+      "adapters/cases/batch4-length64",
+      "adapters/cases/batch4-length8",
+      "adapters/cases/mixed-prefill-decode16-reorder",
+      "attention/attention.cases/batch4-varlen-reorder",
+      "attention/attention.cases/full-chunk-1024",
+      "attention/attention.cases/full-chunk-256",
+      "attention/attention.cases/full-chunk-64",
+      "attention/attention.cases/full-chunk-8",
+      "attention/attention.cases/hf-load-export",
+      "attention/attention.cases/kv-pair-gradient",
+      "attention/attention.cases/prompt-gradient",
+      "attention/attention.cases/qk-norm-gradient",
+      "attention/attention.cases/sixteen-token-decode",
+      "gdn/cases/full-chunk-1024",
+      "gdn/cases/full-chunk-256",
+      "gdn/cases/full-chunk-64",
+      "gdn/cases/full-chunk-8",
+      "gdn/cases/hf-load-export",
+      "gdn/cases/prompt-gradient",
+      "gdn/cases/replicated-norm-gradient",
+      "heads/cases/batch-invariant-prompt1024",
+      "heads/cases/batch-invariant-prompt256",
+      "heads/cases/batch-invariant-prompt64",
+      "heads/cases/batch-invariant-prompt8",
+      "heads/cases/embedding-repeat-100",
+      "heads/cases/entropy-interface",
+      "heads/cases/hf-embedding-roundtrip",
+      "heads/cases/hf-lm-head-roundtrip",
+      "heads/cases/logprob-original-and-promoted-logits",
+      "heads/cases/nonzero-finite-embedding-head-gradient",
+      "heads/cases/replicated-norm-gradient",
+      "moe/moe.cases/all-parameter-gradient",
+      "moe/moe.cases/full-chunk-reorder-1024",
+      "moe/moe.cases/full-chunk-reorder-256",
+      "moe/moe.cases/full-chunk-reorder-64",
+      "moe/moe.cases/full-chunk-reorder-8",
+      "moe/moe.cases/hf-load-export-all512experts",
+      "moe/moe.cases/replicated-router-gradient",
+      "moe/moe.cases/routes-replicated-1024",
+      "moe/moe.cases/routes-replicated-256",
+      "moe/moe.cases/routes-replicated-64",
+      "moe/moe.cases/routes-replicated-8",
+      "moe/moe.cases/training-forward"
+    ],
+    "case_ids_sha256": "be74417dfbd6031ba27978d06d04bb15daaab2aeb57d132c7e0d41176b28b46e"
+  },
+  "topology": {
+    "tp": 4,
+    "pp": 1,
+    "cp": 1,
+    "ep": 1
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/measured-commit.txt b/docs/usage/evidence/qwen3-next-chain-b200/measured-commit.txt
new file mode 100644
index 000000000..40b91b81a
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/measured-commit.txt
@@ -0,0 +1 @@
+c67ec14401b0847ac96441a24acfaac0b17b77ec
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/preflight/checkpoint.json b/docs/usage/evidence/qwen3-next-chain-b200/preflight/checkpoint.json
new file mode 100644
index 000000000..4488f19f1
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/preflight/checkpoint.json
@@ -0,0 +1,14 @@
+{
+  "model_id": "Qwen/Qwen3-Next-80B-A3B-Instruct",
+  "revision": "9c7f2fbe84465e40164a94cc16cd30b6999b0cc7",
+  "content_hash": "00a6a3b5573ccbac2e01fadb673c2cd7bca172d40ddd73f95ed7afa5cbdb9dcd",
+  "shards_verified": 41,
+  "metadata_sha256": {
+    "config.json": "2d483c7cabad7c8704478ed4038fa7e7b2eff840bc00a118eccbe38e2b488303",
+    "generation_config.json": "d7115cb81df546589f9937943302901a1b75b08909293d1fc96782750b1c3217",
+    "model.safetensors.index.json": "0e5274f538d750abdd7fd55a36bad64100f9e881d59c65f6a59f364d2f8b6dd7",
+    "tokenizer.json": "aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4",
+    "tokenizer_config.json": "a62ff0a2472a0fa1b8eaabcb57c59b58afa42a22831dc141400b6e0cf2b65ce3",
+    "vocab.json": "ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910"
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/preflight/device_guards.xml b/docs/usage/evidence/qwen3-next-chain-b200/preflight/device_guards.xml
new file mode 100644
index 000000000..f4624846d
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/preflight/device_guards.xml
@@ -0,0 +1 @@
+<?xml version="1.0" encoding="utf-8"?><testsuites name="pytest tests"><testsuite name="pytest" errors="0" failures="0" skipped="0" tests="6" time="5.190" timestamp="2026-10-09T03:18:15.592504+00:00" hostname="<HOST>"><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[False-1]" time="1.133" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[False-2]" time="0.017" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[False-3]" time="0.017" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[True-1]" time="0.001" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[True-2]" time="0.001" /><testcase classname="tests.check_qwen3_next_multigpu" name="test_norm_guard_restores_current_device_and_uses_input_stream[True-3]" time="0.001" /></testsuite></testsuites>
\ No newline at end of file
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/preflight/environment.json b/docs/usage/evidence/qwen3-next-chain-b200/preflight/environment.json
new file mode 100644
index 000000000..e7ccbb35a
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/preflight/environment.json
@@ -0,0 +1,40 @@
+{
+  "source_commit": "c67ec14401b0847ac96441a24acfaac0b17b77ec",
+  "environment_sha256": "118384371a883b84289a48b5322a96b6563aec0584f74cee4a38777cfe7ff7ca",
+  "visible_gpu_ids": "0,1,2,3",
+  "execution_gpus": 4,
+  "python": "3.12.14 | packaged by Anaconda, Inc. | (main, Aug 27 2026, 14:46:43) [GCC 14.3.0]",
+  "executable": "<PYTHON>",
+  "platform": "Linux-6.8.0-111-generic-x86_64-with-glibc2.39",
+  "versions": {
+    "torch": "2.13.0",
+    "vllm": "0.30.0",
+    "triton": "3.7.1",
+    "transformers": "5.17.0",
+    "flashinfer-python": "0.6.18.post1",
+    "safetensors": "0.8.0"
+  },
+  "torch_cuda": "13.0",
+  "nvcc": "nvcc: NVIDIA (R) Cuda compiler driver\nCopyright (c) 2005-2025 NVIDIA Corporation\nBuilt on Wed_Aug_20_01:58:59_PM_PDT_2025\nCuda compilation tools, release 13.0, V13.0.88\nBuild cuda_13.0.r13.0/compiler.36424714_0\n",
+  "compiler": "g++ (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0\nCopyright (C) 2023 Free Software Foundation, Inc.\nThis is free software; see the source for copying conditions.  There is NO\nwarranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n\n",
+  "nvidia_smi": "Fri Oct  9 03:11:54 2026       \n+-----------------------------------------------------------------------------------------+\n| NVIDIA-SMI 580.126.20             Driver Version: 580.126.20     CUDA Version: 13.0     |\n+-----------------------------------------+------------------------+----------------------+\n| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n|                                         |                        |               MIG M. |\n|=========================================+========================+======================|\n|   0  NVIDIA B200                    On  |   00000000:1B:00.0 Off |                    0 |\n| N/A   35C    P0            253W / 1000W |       0MiB / 183359MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n|   1  NVIDIA B200                    On  |   00000000:43:00.0 Off |                    0 |\n| N/A   36C    P0            254W / 1000W |       0MiB / 183359MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n|   2  NVIDIA B200                    On  |   00000000:52:00.0 Off |                    0 |\n| N/A   41C    P0            256W / 1000W |       0MiB / 183359MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n|   3  NVIDIA B200                    On  |   00000000:61:00.0 Off |                    0 |\n| N/A   41C    P0            256W / 1000W |       0MiB / 183359MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n|   4  NVIDIA B200                    On  |   00000000:9D:00.0 Off |                    0 |\n| N/A   36C    P0            250W / 1000W |       0MiB / 183359MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n|   5  NVIDIA B200                    On  |   00000000:C3:00.0 Off |                    0 |\n| N/A   36C    P0            250W / 1000W |       0MiB / 183359MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n|   6  NVIDIA B200                    On  |   00000000:D1:00.0 Off |                    0 |\n| N/A   42C    P0            256W / 1000W |       0MiB / 183359MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n|   7  NVIDIA B200                    On  |   00000000:DF:00.0 Off |                    0 |\n| N/A   41C    P0            253W / 1000W |       0MiB / 183359MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n\n+-----------------------------------------------------------------------------------------+\n| Processes:                                                                              |\n|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n|        ID   ID                                                               Usage      |\n|=========================================================================================|\n|  No running processes found                                                             |\n+-----------------------------------------------------------------------------------------+\n",
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+  "visible_gpus": [
+    "_CudaDeviceProperties(name='NVIDIA B200', major=10, minor=0, total_memory=182631MB, multi_processor_count=148, uuid=f84b0ae3-604b-7194-e772-280f5d102d46, pci_bus_id=27, pci_device_id=0, pci_domain_id=0, L2_cache_size=126MB)",
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+    "_CudaDeviceProperties(name='NVIDIA B200', major=10, minor=0, total_memory=182631MB, multi_processor_count=148, uuid=79936bd2-1377-8dcf-bdb1-e6e772d0e0f3, pci_bus_id=97, pci_device_id=0, pci_domain_id=0, L2_cache_size=126MB)"
+  ],
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/preflight/operators.json b/docs/usage/evidence/qwen3-next-chain-b200/preflight/operators.json
new file mode 100644
index 000000000..500251cce
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/preflight/operators.json
@@ -0,0 +1,304 @@
+{
+  "scope": "qwen3_next_operators_only",
+  "status": "passed",
+  "suite": "all",
+  "diagnostic_only": false,
+  "full_model_l2": "not_evaluated",
+  "git_commit": "c67ec14401b0847ac96441a24acfaac0b17b77ec",
+  "git_dirty": false,
+  "versions": {
+    "torch": "2.13.0",
+    "vllm": "0.30.0",
+    "transformers": "5.17.0",
+    "triton": "3.7.1"
+  },
+  "mandatory_case_ids": [
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+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[256]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[64]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[8]",
+    "tests/check_qwen3_next_attention.py::test_d256_input_device_guard_and_nondefault_stream",
+    "tests/check_qwen3_next_attention.py::test_native_backward_rejects_malformed_probability_and_gradient_buffers",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[2]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_target_inputs_do_not_depend_on_companions",
+    "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_is_batch_invariant",
+    "tests/check_qwen3_next_conv_bridge.py::test_chunked_and_reordered_sequences_match",
+    "tests/check_qwen3_next_conv_bridge.py::test_empty_sequence_preserves_cache_and_gradients",
+    "tests/check_qwen3_next_conv_bridge.py::test_response_gradient_reaches_prompt_and_convolution_weight",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths0]",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths1]",
+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths2]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[1024-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[1024-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[256-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[256-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[64-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[64-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-4]",
+    "tests/check_qwen3_next_core_matrix.py::test_shared_core_one_hundred_repetitions[1]",
+    "tests/check_qwen3_next_core_matrix.py::test_shared_core_one_hundred_repetitions[4]",
+    "tests/check_qwen3_next_core_matrix.py::test_variable_lengths_empty_and_mixed_step_match_independent_sequences",
+    "tests/check_qwen3_next_forward.py::test_combine_has_fixed_route_order_and_differentiable_weights",
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+    "tests/check_qwen3_next_forward.py::test_grouped_experts_are_bitwise_the_per_expert_gemms[7-512]",
+    "tests/check_qwen3_next_forward.py::test_grouped_experts_fail_closed_on_the_tensor_descriptor_path",
+    "tests/check_qwen3_next_forward.py::test_grouped_projection_is_bitwise_the_per_expert_gemm[128]",
+    "tests/check_qwen3_next_forward.py::test_grouped_projection_is_bitwise_the_per_expert_gemm[512]",
+    "tests/check_qwen3_next_forward.py::test_logprob_nan_rejected_before_comparison",
+    "tests/check_qwen3_next_forward.py::test_moe_vjp_matches_independent_double_formula",
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+    "tests/check_qwen3_next_forward.py::test_qwen_moe_actual_tp4_shapes_and_independent_training_forward",
+    "tests/check_qwen3_next_forward.py::test_route_weights_are_batch_invariant",
+    "tests/check_qwen3_next_forward.py::test_shared_linear_vjp_and_empty_tokens",
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+    "tests/check_qwen3_next_gdn_bridge.py::test_bridge_backward_matches_recomputed_recurrence_vjp",
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/preflight/result.json b/docs/usage/evidence/qwen3-next-chain-b200/preflight/result.json
new file mode 100644
index 000000000..5458370f0
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/preflight/result.json
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index 000000000..4029711ca
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+      "tests/check_qwen3_next_forward.py::test_moe_vjp_matches_independent_double_formula",
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+      "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-37984]",
+      "tests/check_qwen3_next_forward.py::test_qwen_moe_actual_tp4_shapes_and_independent_training_forward",
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+      "tests/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp32-128-silu-0.0]",
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+      "tests/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp32-2048-sigmoid-1.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp32-2048-silu-0.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp32-2048-silu-1.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp32-5120-sigmoid-0.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp32-5120-sigmoid-1.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp32-5120-silu-0.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp32-5120-silu-1.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_sigmoid_activation",
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+      "tests/test_qwen3_next_norm.py::test_cuda_gated_signed_zero_weight_is_preserved[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_signed_zero_weight_is_preserved[dtype2]",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_swish_is_an_alias_for_silu",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_unit_weight_is_plain_norm_times_silu",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_weight_offset_is_applied_in_fp32",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_wrapper_rejects_equal_numel_wrong_shape",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_zero_gate_zeroes_output",
+      "tests/test_qwen3_next_norm.py::test_cuda_offset_equals_explicit_fp32_weight",
+      "tests/test_qwen3_next_norm.py::test_cuda_offset_is_not_folded_through_bfloat16",
+      "tests/test_qwen3_next_norm.py::test_cuda_parameter_contributions_reproduce_backward[dtype0-shape0-0.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_parameter_contributions_reproduce_backward[dtype0-shape0-1.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_parameter_contributions_reproduce_backward[dtype0-shape1-0.0]",
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+      "tests/test_qwen3_next_norm.py::test_cuda_parameter_contributions_reproduce_backward[dtype1-shape0-0.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_parameter_contributions_reproduce_backward[dtype1-shape0-1.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_parameter_contributions_reproduce_backward[dtype1-shape1-0.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_parameter_contributions_reproduce_backward[dtype1-shape1-1.0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_plain_signed_zero_is_preserved[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_cuda_plain_signed_zero_is_preserved[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_cuda_plain_signed_zero_is_preserved[dtype2]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_backward_is_offset_aware",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[16]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[1]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[2]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[32]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[48]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[512]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[64]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_batch_invariance[8]",
+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_within_tolerance_of_golden",
+      "tests/test_qwen3_next_norm.py::test_deterministic_repeat",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_dtype_paths[dtype2]",
+      "tests/test_qwen3_next_norm.py::test_eps_inside_sqrt",
+      "tests/test_qwen3_next_norm.py::test_gated_batch_invariance_slice",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedHFOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_conventions_diverge_in_low_precision",
+      "tests/test_qwen3_next_norm.py::test_gated_cuda_op_rejects_bad_activation_at_construction",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_handles_empty_batch_and_nondefault_stream",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[dy_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[gate_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[rstd_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_shape]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_matches_hf_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_gated_shape_mismatch_raises",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_matches_vllm_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[128]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[2048]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_offset_applied_in_fp32_not_folded_into_bf16",
+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
+      "tests/test_qwen3_next_norm.py::test_weight_offset_is_one",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
+    ],
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+      "qwen3_next_rms_norm-gradient.json",
+      "rms_norm_gated-forward.json",
+      "rms_norm_gated-gradient.json"
+    ]
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+        "output_option": "--output",
+        "scope": "tp4_moe_block_layer0",
+        "groups": {
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+            "routes-replicated-8",
+            "full-chunk-reorder-8",
+            "routes-replicated-64",
+            "full-chunk-reorder-64",
+            "routes-replicated-256",
+            "full-chunk-reorder-256",
+            "routes-replicated-1024",
+            "full-chunk-reorder-1024",
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+            "replicated-router-gradient"
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/preflight/tp4.json b/docs/usage/evidence/qwen3-next-chain-b200/preflight/tp4.json
new file mode 100644
index 000000000..38447a87b
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/preflight/tp4.json
@@ -0,0 +1,49 @@
+{
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+}
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/redaction.json b/docs/usage/evidence/qwen3-next-chain-b200/redaction.json
new file mode 100644
index 000000000..96d008b7e
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/redaction.json
@@ -0,0 +1,15 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train/environment.json b/docs/usage/evidence/qwen3-next-chain-b200/train/environment.json
new file mode 100644
index 000000000..074a21752
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train/environment.json
@@ -0,0 +1,4500 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train/l2_matrix.json b/docs/usage/evidence/qwen3-next-chain-b200/train/l2_matrix.json
new file mode 100644
index 000000000..879ebe919
--- /dev/null
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train/operators.json b/docs/usage/evidence/qwen3-next-chain-b200/train/operators.json
new file mode 100644
index 000000000..500251cce
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train/operators.json
@@ -0,0 +1,304 @@
+{
+  "scope": "qwen3_next_operators_only",
+  "status": "passed",
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+  "git_commit": "c67ec14401b0847ac96441a24acfaac0b17b77ec",
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+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[256]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[64]",
+    "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[8]",
+    "tests/check_qwen3_next_attention.py::test_d256_input_device_guard_and_nondefault_stream",
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+    "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype1]",
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+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[2]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype0]",
+    "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype1]",
+    "tests/check_qwen3_next_batch_invariance.py::test_target_inputs_do_not_depend_on_companions",
+    "tests/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_is_batch_invariant",
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+    "tests/check_qwen3_next_conv_bridge.py::test_empty_sequence_preserves_cache_and_gradients",
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+    "tests/check_qwen3_next_conv_bridge.py::test_varlen_matches_repeated_decode_and_preserves_inputs[lengths2]",
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+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-1]",
+    "tests/check_qwen3_next_core_matrix.py::test_full_chunked_reordered_shared_core[8-4]",
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+    "tests/check_qwen3_next_core_matrix.py::test_shared_core_one_hundred_repetitions[4]",
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train/reload.json b/docs/usage/evidence/qwen3-next-chain-b200/train/reload.json
new file mode 100644
index 000000000..a4863e79b
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train/reload.json
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train/result.json b/docs/usage/evidence/qwen3-next-chain-b200/train/result.json
new file mode 100644
index 000000000..5eada5e19
--- /dev/null
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+      "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[64]",
+      "tests/check_qwen3_next_attention.py::test_d256_full_chunk_decode_and_batch_are_raw_bit_equal[8]",
+      "tests/check_qwen3_next_attention.py::test_d256_input_device_guard_and_nondefault_stream",
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+      "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype0]",
+      "tests/check_qwen3_next_batch_invariance.py::test_causal_conv1d_update_is_batch_invariant[state_dtype1]",
+      "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[0]",
+      "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[1]",
+      "tests/check_qwen3_next_batch_invariance.py::test_chunk_metadata_partition_is_batch_invariant[2]",
+      "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype0]",
+      "tests/check_qwen3_next_batch_invariance.py::test_packed_recurrent_decode_is_batch_invariant[state_dtype1]",
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+      "tests/check_qwen3_next_forward.py::test_moe_vjp_matches_independent_double_formula",
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+      "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-16]",
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+      "tests/check_qwen3_next_forward.py::test_qwen_linear_shapes_batch_chunk_and_reorder[2048-37984]",
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+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_shape]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_matches_hf_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_gated_shape_mismatch_raises",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype1]",
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+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[2048]",
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+      "tests/test_qwen3_next_norm.py::test_offset_applied_in_fp32_not_folded_into_bf16",
+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
+      "tests/test_qwen3_next_norm.py::test_weight_offset_is_one",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train/run.json b/docs/usage/evidence/qwen3-next-chain-b200/train/run.json
new file mode 100644
index 000000000..7c5e5250e
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train/run.json
@@ -0,0 +1,5252 @@
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train/updates.json b/docs/usage/evidence/qwen3-next-chain-b200/train/updates.json
new file mode 100644
index 000000000..a4863e79b
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train/updates.json
@@ -0,0 +1,2021 @@
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train1/environment.json b/docs/usage/evidence/qwen3-next-chain-b200/train1/environment.json
new file mode 100644
index 000000000..eceac4890
--- /dev/null
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train1/l2_matrix.json b/docs/usage/evidence/qwen3-next-chain-b200/train1/l2_matrix.json
new file mode 100644
index 000000000..0e16ea019
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train1/l2_matrix.json
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train1/operators.json b/docs/usage/evidence/qwen3-next-chain-b200/train1/operators.json
new file mode 100644
index 000000000..500251cce
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train1/operators.json
@@ -0,0 +1,304 @@
+{
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+  "status": "passed",
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+  "git_commit": "c67ec14401b0847ac96441a24acfaac0b17b77ec",
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train1/result.json b/docs/usage/evidence/qwen3-next-chain-b200/train1/result.json
new file mode 100644
index 000000000..19f2cd549
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train1/result.json
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train1/run.json b/docs/usage/evidence/qwen3-next-chain-b200/train1/run.json
new file mode 100644
index 000000000..855a12002
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train1/run.json
@@ -0,0 +1,5252 @@
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+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedOp]",
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+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
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+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
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+}
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train1/updates.json b/docs/usage/evidence/qwen3-next-chain-b200/train1/updates.json
new file mode 100644
index 000000000..52f6ae717
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train1/updates.json
@@ -0,0 +1,685 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train2/environment.json b/docs/usage/evidence/qwen3-next-chain-b200/train2/environment.json
new file mode 100644
index 000000000..d034669aa
--- /dev/null
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train2/l2_matrix.json b/docs/usage/evidence/qwen3-next-chain-b200/train2/l2_matrix.json
new file mode 100644
index 000000000..149f97305
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train2/l2_matrix.json
@@ -0,0 +1,2236 @@
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train2/operators.json b/docs/usage/evidence/qwen3-next-chain-b200/train2/operators.json
new file mode 100644
index 000000000..500251cce
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train2/operators.json
@@ -0,0 +1,304 @@
+{
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train2/result.json b/docs/usage/evidence/qwen3-next-chain-b200/train2/result.json
new file mode 100644
index 000000000..cc69a99db
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train2/result.json
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train2/run.json b/docs/usage/evidence/qwen3-next-chain-b200/train2/run.json
new file mode 100644
index 000000000..e4a4b124f
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train2/run.json
@@ -0,0 +1,5252 @@
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+      "tests/test_qwen3_next_norm.py::test_cuda_gated_weight_offset_is_applied_in_fp32",
+      "tests/test_qwen3_next_norm.py::test_cuda_gated_wrapper_rejects_equal_numel_wrong_shape",
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+      "tests/test_qwen3_next_norm.py::test_cuda_offset_equals_explicit_fp32_weight",
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+      "tests/test_qwen3_next_norm.py::test_cuda_zero_centred_within_tolerance_of_golden",
+      "tests/test_qwen3_next_norm.py::test_deterministic_repeat",
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+      "tests/test_qwen3_next_norm.py::test_eps_inside_sqrt",
+      "tests/test_qwen3_next_norm.py::test_gated_batch_invariance_slice",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedHFOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_call_matches_forward[Qwen3NextRMSNormGatedOp]",
+      "tests/test_qwen3_next_norm.py::test_gated_conventions_diverge_in_low_precision",
+      "tests/test_qwen3_next_norm.py::test_gated_cuda_op_rejects_bad_activation_at_construction",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_handles_empty_batch_and_nondefault_stream",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[dy_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[gate_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[rstd_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_dtype]",
+      "tests/test_qwen3_next_norm.py::test_gated_extension_rejects_invalid_tensor_contract[weight_shape]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_hf_witness_matches_hf_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_gated_shape_mismatch_raises",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_gated_strict_matches_vllm_formula_fp32",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[128]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_fp32[2048]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype0]",
+      "tests/test_qwen3_next_norm.py::test_matches_upstream_formula_low_precision[dtype1]",
+      "tests/test_qwen3_next_norm.py::test_offset_applied_in_fp32_not_folded_into_bf16",
+      "tests/test_qwen3_next_norm.py::test_op_batch_invariance_on_device",
+      "tests/test_qwen3_next_norm.py::test_plain_reference_is_unchanged_by_the_offset_plumbing",
+      "tests/test_qwen3_next_norm.py::test_weight_offset_is_one",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_cuda_constructor_rejects_missing_extension",
+      "tests/test_qwen3_next_norm.py::test_zero_centred_op_inherits_the_plain_reference",
+      "tests/test_qwen3_next_norm.py::test_zero_weight_is_identity_scaling"
+    ],
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+      "qwen3_next_rms_norm-gradient.json",
+      "rms_norm_gated-forward.json",
+      "rms_norm_gated-gradient.json"
+    ]
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+            "batch4-varlen-reorder",
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+            "routes-replicated-8",
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+            "routes-replicated-64",
+            "full-chunk-reorder-64",
+            "routes-replicated-256",
+            "full-chunk-reorder-256",
+            "routes-replicated-1024",
+            "full-chunk-reorder-1024",
+            "training-forward",
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+            "batch4-length1024",
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+      "p0064-b4-full",
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+      "gdn/cases/prompt-gradient",
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+      "heads/cases/entropy-interface",
+      "heads/cases/hf-embedding-roundtrip",
+      "heads/cases/hf-lm-head-roundtrip",
+      "heads/cases/logprob-original-and-promoted-logits",
+      "heads/cases/nonzero-finite-embedding-head-gradient",
+      "heads/cases/replicated-norm-gradient",
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+      "moe/moe.cases/hf-load-export-all512experts",
+      "moe/moe.cases/replicated-router-gradient",
+      "moe/moe.cases/routes-replicated-1024",
+      "moe/moe.cases/routes-replicated-256",
+      "moe/moe.cases/routes-replicated-64",
+      "moe/moe.cases/routes-replicated-8",
+      "moe/moe.cases/training-forward"
+    ],
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+    "sha256": "bf3a74423b3636145cad903d5cf9d70f2c3c777b019a24d125c2ca79f8b1e8d9"
+  }
+}
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/train2/updates.json b/docs/usage/evidence/qwen3-next-chain-b200/train2/updates.json
new file mode 100644
index 000000000..62e6678ac
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/train2/updates.json
@@ -0,0 +1,1695 @@
+{
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+  "status": "passed",
+  "scope": "qwen3_next_training_update2",
+  "identity": {
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+      "path": "<RUN>/result.json",
+      "sha256": "bf3a74423b3636145cad903d5cf9d70f2c3c777b019a24d125c2ca79f8b1e8d9"
+    },
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+    "environment_sha256": "118384371a883b84289a48b5322a96b6563aec0584f74cee4a38777cfe7ff7ca",
+    "checkpoint": "<CHECKPOINT>",
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+    "checkpoint_identity": {
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+            },
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+              "scope": "shared_full_forward_implementation",
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+              "sources": {
+                "rl_engine/integrations/qwen3_next_provider.py": "a182a280820cb8395166c7e6ab6b91c18f8840807e3867ab9f7a0f4cf350d7d2",
+                "rl_engine/integrations/qwen3_next_conv.py": "882122df605cab6b7837c52f5a774cabd72f22dde65dadd9244c5c799936ad99",
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+                "rl_engine/integrations/qwen3_next_profile.py": "3f6b9ef3d4a581bb8b058cd27a7156fcd70e7b82233c273970624ca3e21a1f50",
+                "rl_engine/integrations/qwen3_next_tp.py": "e2be3bb9995e2168326aa3e564a960f090f43eff8175af41e138c817ae90e188",
+                "rl_engine/integrations/qwen3_next_tp_gdn.py": "1a33958513597345287b6feca269289d953a44c2aa5c3beb880875bd86962313",
+                "rl_engine/integrations/qwen3_next_tp_blocks.py": "75a304d042b15d02e8d97b5016106555b7bcf8fd73786509661a27e6be958813",
+                "rl_engine/integrations/qwen3_next_forward.py": "9a44c6fcfa960f3013ead17ecd31046fbdac7e0814ab04a5c219b4a7bb7e6420",
+                "rl_engine/integrations/qwen3_next_embedding.py": "9695c4dcd6e83d94fcb34919ba85e0ee462c111624063568afdb9072e9fe367e",
+                "rl_engine/integrations/qwen3_next_logprob.py": "9bb1803e7324d460c36413906e8b0161d5166bc8a588f185fb0f016e6ba82f8d",
+                "rl_engine/integrations/qwen3_next_vime.py": "a34d3836587bffe8f8e4bf81c8d7dca9565046b659b2ee7536244352c7383b44",
+                "rl_engine/integrations/qwen3_next_vllm.py": "6a143215b2963e0f6a5a51856d7d5d7ecdad984cd488a89f9a6d9e0385cc7b58",
+                "rl_engine/integrations/qwen3_next_vllm_attention.py": "af322104f2b290cb942386139a591e0d7e42665a11f7bd8893a4f2ef7928a395",
+                "rl_engine/integrations/qwen3_next_vllm_model.py": "f03a3a81f316d1b3839832c482d6329ee195c57a829442e223082ccd744738f8",
+                "rl_engine/kernels/ops/cuda/norm/rmsnorm.py": "8d1a21113bd7161a46a6dc6d8721bb613d4f900330cac008890b055efb576fea",
+                "csrc/cuda/rmsnorm.cu": "ad6ac9cba603e1c0aa7d33b2a348e6b69d8e5bd848fc93248eb2a3f0de690110"
+              },
+              "weights": "bfloat16",
+              "recurrent_state": "float32",
+              "mtp": false,
+              "prefix_reuse": false,
+              "dropout": false,
+              "dynamic_tuning": false,
+              "cuda_graph": false,
+              "sequence_parallel": false,
+              "fp32_residual_connection": false,
+              "vocab_size": 151936,
+              "padded_vocab_size": 151936,
+              "expert_tensor_parallel_size": 4,
+              "native_rollout_kv_cache_memory_bytes": 4294967296,
+              "runtime_env": {
+                "TRITON_F32_DEFAULT": "ieee",
+                "VLLM_USE_V2_MODEL_RUNNER": "0"
+              }
+            },
+            "topology": {
+              "tp": 4,
+              "pp": 1,
+              "cp": 1,
+              "ep": 1
+            },
+            "architecture_layers": 48,
+            "weights_dtype": "bfloat16",
+            "recurrent_state_dtype": "float32",
+            "run_id": "20261009T041838Z-train1-c67ec144-39e7db9f",
+            "execution_id": "39e7db9f488142f4b52872e05728c390",
+            "fixture_sha256": "7c98e4e17cc24bd0c5f65f6515d58a9dfc947afcdb6d4956bd3fa05f24bb28b6"
+          },
+          "step": 2,
+          "sample_position": 8
+        }
+      }
+    }
+  ],
+  "checkpoint": {
+    "path": "<RUN>/training-state/checkpoints/step-00000002-3bce5b5d91b045a1bbdb4f8ef235d1b6",
+    "sha256": "8906eaf4e2bee9d1aec783a6086e1a09b89b7da1a37525c6464aa922fec0e691"
+  },
+  "attempt": "0"
+}

From 56462e74c62862341dbca58e2b8d1aca4180a805 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 11 Oct 2026 01:32:55 +0000
Subject: [PATCH 65/70] docs(qwen3-next): chain evidence README gives commands
 in the migrated layout

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 docs/usage/evidence/qwen3-next-chain-b200/README.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/docs/usage/evidence/qwen3-next-chain-b200/README.md b/docs/usage/evidence/qwen3-next-chain-b200/README.md
index 177077593..3fb76586c 100644
--- a/docs/usage/evidence/qwen3-next-chain-b200/README.md
+++ b/docs/usage/evidence/qwen3-next-chain-b200/README.md
@@ -5,15 +5,15 @@ stage after another on nodes with four B200 GPUs, in one environment
 (`environment_sha256` `118384371a88…` in every `run.json`), on the official
 `Qwen/Qwen3-Next-80B-A3B-Instruct` revision
 `9c7f2fbe84465e40164a94cc16cd30b6999b0cc7`. Each stage was launched with the
-launcher of that commit, `python scripts/qwen3_next_run.py --stage <stage>
+launcher, `python tools/validation/models/qwen3_next_run.py --stage <stage>
 --previous <run of the stage before> --runs-root <runs> --checkpoint <checkpoint>
 --cuda-home <cuda-13.0> --python <venv>/bin/python` (see
 `docs/usage/qwen3-next-chain.md`), and the reports were made with:
 
 ```bash
-python scripts/qwen3_next_chain_report.py --last <runs>/<...-train-...> --out chain-report.json
-bash ci/run_qwen3_next_chain_gate.sh chain-report.json
-python scripts/qwen3_next_publish_evidence.py --last <runs>/<...-train-...> \
+python tools/validation/models/qwen3_next_chain_report.py --last <runs>/<...-train-...> --out chain-report.json
+bash ci/scripts/run_qwen3_next_chain_gate.sh chain-report.json
+python tools/validation/models/qwen3_next_publish_evidence.py --last <runs>/<...-train-...> \
     --chain-report chain-report.json --out docs/usage/evidence/qwen3-next-chain-b200
 ```
 
@@ -26,7 +26,8 @@ python scripts/qwen3_next_publish_evidence.py --last <runs>/<...-train-...> \
 | `train/` | checkpoint reload, L2 on the reloaded weights, continuous and resumed controls | passed, 16/16; reload == update 2 |
 | `chain-report.json` | chain-gate report (`qwen3-next-chain-v1`, compatible with `ws1-c10-c11-v5`) | passed, 64 cells, `first_drift: null` |
 
-The stages ran the code of `c67ec14`; the copy in this directory was made by
+Commands use the current repository layout; at `c67ec14` these runners were
+in `scripts/` and `ci/`. The stages ran the code of `c67ec14`; the copy in this directory was made by
 the publisher of `3025497`, which only adds redaction rules (`redaction.json`
 counts the placeholders) and changes no measured byte. `SHA256SUMS` covers
 every file except this README.

From 5bfd0ede4724aac9636c346395e4c2803e351455 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 11 Oct 2026 01:33:38 +0000
Subject: [PATCH 66/70] build(pre-commit): leave the byte-exact Qwen3-Next
 chain evidence untouched

The published chain evidence is a byte-exact copy covered by SHA256SUMS; a
pytest JUnit XML without a final newline would otherwise be rewritten by
end-of-file-fixer and break the checksum. Same treatment as reports/archive/.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .pre-commit-config.yaml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3e37f8bd1..9ef274150 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,9 +4,10 @@ repos:
     hooks:
       - id: trailing-whitespace
         # Unified diffs include significant context markers and pinned hashes.
-        exclude: '(\.patch$|^reports/archive/)'
+        # Published Qwen3-Next chain evidence is byte-exact (SHA256SUMS).
+        exclude: '(\.patch$|^reports/archive/|^docs/usage/evidence/qwen3-next-chain-b200/)'
       - id: end-of-file-fixer
-        exclude: ^reports/archive/
+        exclude: '^(reports/archive/|docs/usage/evidence/qwen3-next-chain-b200/)'
       - id: check-yaml
         exclude: 'mkdocs\.yaml$'
       - id: check-added-large-files

From 9ce25d04826a4178c33500395bc5a711bea2bc06 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 11 Oct 2026 01:34:26 +0000
Subject: [PATCH 67/70] build(pre-commit): leave the byte-exact
 batch-invariance evidence untouched

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .pre-commit-config.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9ef274150..a38433e48 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,10 +4,10 @@ repos:
     hooks:
       - id: trailing-whitespace
         # Unified diffs include significant context markers and pinned hashes.
-        # Published Qwen3-Next chain evidence is byte-exact (SHA256SUMS).
-        exclude: '(\.patch$|^reports/archive/|^docs/usage/evidence/qwen3-next-chain-b200/)'
+        # Published Qwen3-Next chain and batch-invariance evidence is byte-exact (SHA256SUMS).
+        exclude: '(\.patch$|^reports/archive/|^docs/usage/evidence/qwen3-next-(chain|batch-invariance)-b200/)'
       - id: end-of-file-fixer
-        exclude: '^(reports/archive/|docs/usage/evidence/qwen3-next-chain-b200/)'
+        exclude: '^(reports/archive/|docs/usage/evidence/qwen3-next-(chain|batch-invariance)-b200/)'
       - id: check-yaml
         exclude: 'mkdocs\.yaml$'
       - id: check-added-large-files

From 4bd3c7f0014adfd84df392ed1a5ede97e1b08793 Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 11 Oct 2026 01:51:37 +0000
Subject: [PATCH 68/70] test(qwen3-next): pin the case-manifest subprocess
 rootdir to its temp directory

Node ids are relative to pytest's rootdir. When the temp directory sits under
another checkout, the subprocess picked that checkout as rootdir and the
manifest's case id no longer matched.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 tests/models/qwen3_next/test_qwen3_next_case_manifest.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tests/models/qwen3_next/test_qwen3_next_case_manifest.py b/tests/models/qwen3_next/test_qwen3_next_case_manifest.py
index d3897b648..7d78a236f 100644
--- a/tests/models/qwen3_next/test_qwen3_next_case_manifest.py
+++ b/tests/models/qwen3_next/test_qwen3_next_case_manifest.py
@@ -81,6 +81,9 @@ def test_plugin_enforces_real_pytest_outcomes(tmp_path, outcome):
         "-q",
         "-p",
         "no:cacheprovider",
+        # Node ids are relative to the rootdir; keep it here even under another checkout.
+        "--rootdir",
+        str(tmp_path),
         "-p",
         "rl_engine.validation.models.pytest_case_manifest",
         "--case-manifest",

From e79c499b337b529cfa3925f0bcef563bc331b05e Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 11 Oct 2026 15:34:53 +0000
Subject: [PATCH 69/70] docs(qwen3-next): figure for the full-model chain
 evidence

tools/validation/models/plot_qwen3_next_chain.py draws, from the published
evidence only, the rollout-vs-recompute raw-bit matrix (16 cases x 4 weight
versions x tokens/logits/logprobs/layer digests) and the per-layer digest
change between weight versions.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../evidence/qwen3-next-chain-b200/README.md  |   4 +-
 .../evidence/qwen3-next-chain-b200/figure.png | Bin 0 -> 181469 bytes
 .../models/plot_qwen3_next_chain.py           | 152 ++++++++++++++++++
 3 files changed, 155 insertions(+), 1 deletion(-)
 create mode 100644 docs/usage/evidence/qwen3-next-chain-b200/figure.png
 create mode 100644 tools/validation/models/plot_qwen3_next_chain.py

diff --git a/docs/usage/evidence/qwen3-next-chain-b200/README.md b/docs/usage/evidence/qwen3-next-chain-b200/README.md
index 3fb76586c..c46986e28 100644
--- a/docs/usage/evidence/qwen3-next-chain-b200/README.md
+++ b/docs/usage/evidence/qwen3-next-chain-b200/README.md
@@ -30,7 +30,9 @@ Commands use the current repository layout; at `c67ec14` these runners were
 in `scripts/` and `ci/`. The stages ran the code of `c67ec14`; the copy in this directory was made by
 the publisher of `3025497`, which only adds redaction rules (`redaction.json`
 counts the placeholders) and changes no measured byte. `SHA256SUMS` covers
-every file except this README.
+every file except this README and `figure.png`, which
+`python tools/validation/models/plot_qwen3_next_chain.py docs/usage/evidence/qwen3-next-chain-b200`
+draws from the published files.
 
 `train` was preempted and restarted several times. The training journal lets a
 restarted run continue from its last durable step, and one restarted run reused
diff --git a/docs/usage/evidence/qwen3-next-chain-b200/figure.png b/docs/usage/evidence/qwen3-next-chain-b200/figure.png
new file mode 100644
index 0000000000000000000000000000000000000000..9fbe01b47e5984620abb0900581076114854ca1f
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diff --git a/tools/validation/models/plot_qwen3_next_chain.py b/tools/validation/models/plot_qwen3_next_chain.py
new file mode 100644
index 000000000..f3eab8718
--- /dev/null
+++ b/tools/validation/models/plot_qwen3_next_chain.py
@@ -0,0 +1,152 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Render a published Qwen3-Next full-model chain as a PNG.
+
+Needs only the published evidence directory and matplotlib:
+
+    python tools/validation/models/plot_qwen3_next_chain.py \\
+        docs/usage/evidence/qwen3-next-chain-b200
+
+writes ``figure.png`` into that directory. Left: for each L2 case and weight
+version, whether the vLLM rollout and the VIME recompute agree bit for bit on
+response tokens, logits, logprobs and the per-layer digests. Right: for each
+decoder layer, the share of cases whose layer digest changes from one weight
+version to the next, which shows the updates reach every layer and the reload
+reproduces update 2 exactly.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+import numpy as np  # noqa: E402
+from matplotlib.colors import ListedColormap  # noqa: E402
+
+INK = "#2b2b29"
+MUTED = "#6f6e69"
+SURFACE = "#fcfcfb"
+PASS = "#1baf7a"
+FAIL = "#d64545"
+CHANGE = ("#e4e3dd", "#2a78d6")
+
+STAGES = (("l2", "baseline"), ("train1", "update 1"), ("train2", "update 2"), ("train", "reload"))
+COMPARISONS = (
+    ("response_tokens", "tokens"),
+    ("logits", "logits"),
+    ("logprobs", "logprobs"),
+    ("layer_states", "layer digests"),
+)
+STEPS = (("baseline", "update1"), ("update1", "update2"), ("update2", "reload"))
+
+
+def _node_order(name):
+    return (0, 0) if name == "embedding" else (1, int(name.removeprefix("layer")))
+
+
+def plot(evidence: Path, out: Path) -> None:
+    matrices = {
+        label: json.loads((evidence / stage / "l2_matrix.json").read_text())
+        for stage, label in STAGES
+    }
+    cases = matrices["baseline"]["case_ids"]
+    grid = np.array(
+        [
+            [
+                bool(matrices[label]["cases"][case]["comparisons"][key])
+                for _, label in STAGES
+                for key, _ in COMPARISONS
+            ]
+            for case in cases
+        ]
+    )
+    report = json.loads((evidence / "chain-report.json").read_text())
+    cells = report["cells"]
+    nodes = sorted(cells[f"baseline/{cases[0]}"]["node_digests"], key=_node_order)
+    change = np.array(
+        [
+            [
+                np.mean(
+                    [
+                        cells[f"{a}/{case}"]["node_digests"][node]
+                        != cells[f"{b}/{case}"]["node_digests"][node]
+                        for case in cases
+                    ]
+                )
+                for node in nodes
+            ]
+            for a, b in STEPS
+        ]
+    )
+
+    fig, (left, right) = plt.subplots(
+        1, 2, figsize=(16, 6.2), gridspec_kw={"width_ratios": [1, 1.55]}, facecolor=SURFACE
+    )
+    left.imshow(grid, cmap=ListedColormap([FAIL, PASS]), vmin=0, vmax=1, aspect="auto")
+    left.set_yticks(range(len(cases)), cases, fontsize=8, color=INK)
+    names = [name for _, name in COMPARISONS]
+    left.set_xticks(range(grid.shape[1]), names * len(STAGES), rotation=90, fontsize=8)
+    for i, (_, label) in enumerate(STAGES):
+        left.text(i * 4 + 1.5, -1.2, label, ha="center", fontsize=9, color=INK)
+        if i:
+            left.axvline(i * 4 - 0.5, color=SURFACE, linewidth=3)
+    left.set_title(
+        f"vLLM rollout vs VIME recompute, raw bits\n{int(grid.sum())}/{grid.size} equal",
+        fontsize=10,
+        color=INK,
+        pad=24,
+    )
+
+    right.imshow(change, cmap=ListedColormap(CHANGE), vmin=0, vmax=1, aspect="auto")
+    right.set_yticks(range(len(STEPS)), [f"{a} → {b}" for a, b in STEPS], fontsize=9, color=INK)
+    labels = ["emb" if n == "embedding" else n.removeprefix("layer") for n in nodes]
+    right.set_xticks(range(len(nodes)), labels, fontsize=6, color=MUTED)
+    right.set_xlabel("embedding and decoder layers 0-47", fontsize=9, color=MUTED)
+    for row in range(len(STEPS)):
+        right.text(
+            len(nodes) + 0.6,
+            row,
+            f"{change[row].mean():.0%} of digests change",
+            va="center",
+            fontsize=8,
+            color=INK,
+        )
+    right.set_xlim(-0.5, len(nodes) + 9)
+    right.set_title(
+        "Per-layer digest change between weight versions (share of the 16 cases)",
+        fontsize=10,
+        color=INK,
+    )
+    for axis in (left, right):
+        for spine in axis.spines.values():
+            spine.set_visible(False)
+    commit = report["measured_commit"][:7]
+    gpu = f" on {report['gpu_name']}" if report.get("gpu_name") else ""
+    fig.suptitle(
+        f"Qwen3-Next-80B-A3B full-model chain, TP4{gpu}, commit {commit}: "
+        f"passed={report['passed']}, first drift={report['first_drift']}",
+        fontsize=11,
+        color=INK,
+    )
+    fig.tight_layout()
+    fig.savefig(out, dpi=160)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("evidence", type=Path)
+    args = parser.parse_args()
+    out = args.evidence / "figure.png"
+    plot(args.evidence, out)
+    print(f"wrote {out}")
+
+
+if __name__ == "__main__":
+    main()

From 0a5c15c1a2bbd33cefc5f6752f143b8df01a663b Mon Sep 17 00:00:00 2001
From: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Date: Sun, 11 Oct 2026 15:35:41 +0000
Subject: [PATCH 70/70] docs(qwen3-next): figure for the batch-invariance
 evidence

tools/validation/models/plot_qwen3_next_batch_invariance.py draws, from the
published evidence only, one panel per cell family (concurrency, chunk
boundaries, interference, packing); a cell is green when tokens, logits,
logprobs and every layer digest are bitwise equal to its reference.

Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
---
 .../README.md                                 |   4 +-
 .../figure.png                                | Bin 0 -> 174172 bytes
 .../plot_qwen3_next_batch_invariance.py       | 144 ++++++++++++++++++
 3 files changed, 147 insertions(+), 1 deletion(-)
 create mode 100644 docs/usage/evidence/qwen3-next-batch-invariance-b200/figure.png
 create mode 100644 tools/validation/models/plot_qwen3_next_batch_invariance.py

diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/README.md b/docs/usage/evidence/qwen3-next-batch-invariance-b200/README.md
index f3e81ad66..a909c31cf 100644
--- a/docs/usage/evidence/qwen3-next-batch-invariance-b200/README.md
+++ b/docs/usage/evidence/qwen3-next-batch-invariance-b200/README.md
@@ -18,7 +18,9 @@ Commands use the current repository layout; at `759755e` these runners were
 in `scripts/`. The stages ran the code of `759755e`; the copy in this directory was made by
 the publisher of `3025497`, which only adds redaction rules (`redaction.json`
 counts the placeholders) and changes no measured byte. `SHA256SUMS` covers
-every file.
+every file except this README and `figure.png`, which
+`python tools/validation/models/plot_qwen3_next_batch_invariance.py docs/usage/evidence/qwen3-next-batch-invariance-b200`
+draws from the published files.
 
 | Stage | Result |
 | --- | --- |
diff --git a/docs/usage/evidence/qwen3-next-batch-invariance-b200/figure.png b/docs/usage/evidence/qwen3-next-batch-invariance-b200/figure.png
new file mode 100644
index 0000000000000000000000000000000000000000..4984a896163149415831a33786a87039aca8c01f
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diff --git a/tools/validation/models/plot_qwen3_next_batch_invariance.py b/tools/validation/models/plot_qwen3_next_batch_invariance.py
new file mode 100644
index 000000000..21a128a9f
--- /dev/null
+++ b/tools/validation/models/plot_qwen3_next_batch_invariance.py
@@ -0,0 +1,144 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Render a published Qwen3-Next batch-invariance matrix as a PNG.
+
+Needs only the published evidence directory and matplotlib:
+
+    python tools/validation/models/plot_qwen3_next_batch_invariance.py \\
+        docs/usage/evidence/qwen3-next-batch-invariance-b200
+
+writes ``figure.png`` into that directory: one panel per cell family. A cell is
+green when the target request's response tokens, logits, logprobs and every
+layer digest are bitwise equal to its reference, red otherwise.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+import numpy as np  # noqa: E402
+from matplotlib.colors import ListedColormap  # noqa: E402
+
+INK = "#2b2b29"
+MUTED = "#6f6e69"
+SURFACE = "#fcfcfb"
+NA = "#e4e3dd"
+COLORS = ListedColormap([NA, "#d64545", "#1baf7a"])  # 0 absent, 1 differs, 2 bitwise equal
+
+
+def _value(cell):
+    return 2 if cell["passed"] and all(cell["comparisons"].values()) else 1
+
+
+def _grid(axis, values, rows, cols, title, xlabel=None, ylabel=None):
+    axis.imshow(values, cmap=COLORS, vmin=0, vmax=2, aspect="auto")
+    axis.set_yticks(range(len(rows)), rows, fontsize=8, color=INK)
+    axis.set_xticks(range(len(cols)), cols, fontsize=8, color=INK, rotation=0)
+    axis.set_xticks(np.arange(-0.5, len(cols)), minor=True)
+    axis.set_yticks(np.arange(-0.5, len(rows)), minor=True)
+    axis.grid(which="minor", color=SURFACE, linewidth=2)
+    axis.tick_params(which="minor", length=0)
+    passed = int((values == 2).sum())
+    total = int((values > 0).sum())
+    axis.set_title(f"{title}: {passed}/{total} bitwise", fontsize=10, color=INK)
+    if xlabel:
+        axis.set_xlabel(xlabel, fontsize=9, color=MUTED)
+    if ylabel:
+        axis.set_ylabel(ylabel, fontsize=9, color=MUTED)
+    for spine in axis.spines.values():
+        spine.set_visible(False)
+
+
+def plot(evidence: Path, out: Path) -> None:
+    matrix = json.loads((evidence / "bi" / "bi_matrix.json").read_text())
+    cells = matrix["cells"]
+
+    conc = {}
+    for cid, cell in cells.items():
+        m = re.fullmatch(r"concurrency-n(\d+)-(first|last)", cid)
+        if m:
+            conc[(int(m.group(1)), m.group(2))] = _value(cell)
+    ns = sorted({n for n, _ in conc})
+    conc_grid = np.array([[conc.get((n, p), 0) for n in ns] for p in ("first", "last")])
+
+    chunk = {}
+    for cid, cell in cells.items():
+        m = re.fullmatch(r"chunk-p(\d+)-c(\d+)-f(\d+)", cid)
+        if m:
+            chunk[(int(m.group(1)), int(m.group(2)), int(m.group(3)))] = _value(cell)
+    prompts = sorted({p for p, _, _ in chunk})
+    plans = sorted({(c, f) for _, c, f in chunk})
+    chunk_grid = np.array([[chunk.get((p, c, f), 0) for c, f in plans] for p in prompts])
+
+    def listed(prefix):
+        ids = [cid for cid in matrix["cell_ids"] if cid.startswith(prefix)]
+        return ids, np.array([[_value(cells[cid])] for cid in ids])
+
+    inter_ids, inter_grid = listed("interference-")
+    pack_ids, pack_grid = listed("packing-")
+
+    fig = plt.figure(figsize=(16, 8.5), facecolor=SURFACE)
+    spec = fig.add_gridspec(2, 3, width_ratios=[1.5, 0.9, 0.9], height_ratios=[1, 2.6])
+    _grid(
+        fig.add_subplot(spec[0, 0]),
+        conc_grid,
+        ["target first", "target last"],
+        [str(n) for n in ns],
+        "Concurrency (N requests, vs. the target alone)",
+        xlabel="N",
+    )
+    _grid(
+        fig.add_subplot(spec[1, 0]),
+        chunk_grid,
+        [str(p) for p in prompts],
+        [f"c{c}\nfirst {f}" for c, f in plans],
+        "Chunk boundaries (vs. the replay engine)",
+        xlabel="chunk size / first chunk",
+        ylabel="prompt tokens",
+    )
+    _grid(
+        fig.add_subplot(spec[:, 1]),
+        inter_grid,
+        [cid.removeprefix("interference-") for cid in inter_ids],
+        [""],
+        "Interference",
+    )
+    _grid(
+        fig.add_subplot(spec[:, 2]),
+        pack_grid,
+        [cid.removeprefix("packing-") for cid in pack_ids],
+        [""],
+        "Packing",
+    )
+    total = len(cells)
+    passed = sum(_value(cell) == 2 for cell in cells.values())
+    fig.suptitle(
+        f"Qwen3-Next-80B-A3B batch invariance, TP4, suite {matrix.get('suite', '')}: "
+        f"{passed}/{total} cells bitwise (tokens, logits, logprobs, layer digests)",
+        fontsize=11,
+        color=INK,
+    )
+    fig.tight_layout()
+    fig.savefig(out, dpi=160)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("evidence", type=Path)
+    args = parser.parse_args()
+    out = args.evidence / "figure.png"
+    plot(args.evidence, out)
+    print(f"wrote {out}")
+
+
+if __name__ == "__main__":
+    main()