diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 4634569cd..0500dd057 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -5,9 +5,9 @@ name: CI-Pipeline
 
 on:
   push:
-    branches: [ main, test ]
+    branches: [ main, test, test-qwennext ]
   pull_request:
-    branches: [ main, test ]
+    branches: [ main, test, test-qwennext ]
 
 permissions:
   contents: read
@@ -73,9 +73,34 @@ jobs:
 
       - name: Run Mocked Hardware Discovery Tests
         run: |
-          python -m pytest tests/runtime/test_dispatch.py -v
+          python -m pytest tests/runtime/test_dispatch.py tests/validation/common/test_file_digest.py -v
           PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 python -m pytest tests/ops/attention/test_attention_correctness.py -q -rs
-          python -m pytest tests/validation/operators/test_forward_invariance.py tests/contracts/test_tolerance_contract.py tests/validation/operators/test_ws1_workload.py tests/validation/operators/test_gradient_invariance.py tests/validation/operators/test_elementwise_inventory.py tests/validation/operators/test_four_judgment_matrix.py tests/validation/operators/test_op_checks.py tests/validation/operators/test_operator_inputs.py tests/benchmarks/test_profiler.py tests/validation/operators/test_kv_consistency.py tests/models/qwen3/test_ws1_qwen3_dense.py tests/validation/models/test_ws1_chain_integration.py -q
+          python -m pytest tests/validation/operators/test_forward_invariance.py tests/contracts/test_tolerance_contract.py tests/validation/operators/test_ws1_workload.py tests/validation/operators/test_gradient_invariance.py tests/validation/operators/test_elementwise_inventory.py tests/validation/operators/test_four_judgment_matrix.py tests/validation/operators/test_op_checks.py tests/validation/operators/test_operator_inputs.py tests/benchmarks/test_profiler.py tests/validation/operators/test_kv_consistency.py tests/models/qwen3/test_ws1_qwen3_dense.py tests/validation/models/test_ws1_chain_integration.py tests/models/qwen3_next/test_qwen3_next_norm.py tests/models/qwen3_next/test_gdn_state_contract.py tests/validation/common/test_tensor_identity.py tests/models/qwen3_next/test_qwen3_next_forward_contract.py tests/models/qwen3_next/test_qwen3_next_tp_blocks.py tests/models/qwen3_next/test_qwen3_next_tp_gdn.py -q
+          python -m pytest -q \
+            tests/models/qwen3_next/test_qwen3_next_binary_evidence.py \
+            tests/models/qwen3_next/test_qwen3_next_case_manifest.py \
+            tests/models/qwen3_next/test_qwen3_next_checkpoint.py \
+            tests/models/qwen3_next/test_qwen3_next_engine_weights.py \
+            tests/models/qwen3_next/test_qwen3_next_environment_identity.py \
+            tests/models/qwen3_next/test_qwen3_next_failure.py \
+            tests/models/qwen3_next/test_qwen3_next_imported_binary.py \
+            tests/models/qwen3_next/test_qwen3_next_installed_provider_identity.py \
+            tests/models/qwen3_next/test_qwen3_next_l2.py \
+            tests/models/qwen3_next/test_qwen3_next_layers.py \
+            tests/models/qwen3_next/test_qwen3_next_logprob_adapter.py \
+            tests/models/qwen3_next/test_qwen3_next_native_recompute.py \
+            tests/models/qwen3_next/test_qwen3_next_operator_gates.py \
+            tests/models/qwen3_next/test_qwen3_next_profile.py \
+            tests/models/qwen3_next/test_qwen3_next_rl_fixture.py \
+            tests/models/qwen3_next/test_qwen3_next_rotary_dtype.py \
+            tests/models/qwen3_next/test_qwen3_next_storage.py \
+            tests/models/qwen3_next/test_qwen3_next_training_audit.py \
+            tests/models/qwen3_next/test_qwen3_next_training_closeout.py \
+            tests/models/qwen3_next/test_qwen3_next_training_export.py \
+            tests/models/qwen3_next/test_qwen3_next_subnode.py \
+            tests/models/qwen3_next/test_qwen3_next_chain_report.py \
+            tests/models/qwen3_next/test_qwen3_next_run.py \
+            tests/models/qwen3_next/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
new file mode 100644
index 000000000..896f077bc
--- /dev/null
+++ b/.github/workflows/qwen3-next-provider-gpu.yml
@@ -0,0 +1,96 @@
+# SPDX-License-Identifier: Apache-2.0
+# RFC #428 Qwen3-Next provider comparisons: the C1/C3/C4 norm providers, the C6
+# 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/integrations/common/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:
+  pull_request:
+    branches: [main, test-qwennext]
+    paths:
+      - "rl_engine/backends/**"
+      - "rl_engine/reference/**"
+      - "csrc/cuda/attention/**"
+      - "rl_engine/models/qwen3_next/**"
+      - "rl_engine/integrations/engines/train/vllm/qwen3_next*"
+      - "rl_engine/validation/models/qwen3_next*"
+      - "tools/validation/models/*qwen3_next*"
+      - "tests/models/qwen3_next/**"
+      - "tests/models/qwen3_next/check_gdn_recurrent_golden.py"
+      - ".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:
+    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: 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
+        # The FP32 router needs IEEE Triton dots from process start.
+        env:
+          TRITON_F32_DEFAULT: ieee
+        run: |
+          python3 -m pytest -q tests/models/qwen3_next/check_qwen3_next_norm_providers.py tests/models/qwen3_next/check_gdn_recurrent_golden.py \
+            tests/models/qwen3_next/check_qwen3_next_forward.py tests/models/qwen3_next/check_qwen3_next_attention.py \
+            tests/models/qwen3_next/check_qwen3_next_gdn_bridge.py tests/models/qwen3_next/check_qwen3_next_gdn_sequence.py \
+            tests/models/qwen3_next/check_qwen3_next_conv_bridge.py tests/models/qwen3_next/check_qwen3_next_core_matrix.py \
+            tests/models/qwen3_next/check_qwen3_next_shared_core.py \
+            tests/models/qwen3_next/check_qwen3_next_paged_attention.py tests/models/qwen3_next/check_qwen3_next_batch_invariance.py
diff --git a/.github/workflows/ws1-gtest-gpu.yml b/.github/workflows/ws1-gtest-gpu.yml
index a0d8ac0c4..b853b9df0 100644
--- a/.github/workflows/ws1-gtest-gpu.yml
+++ b/.github/workflows/ws1-gtest-gpu.yml
@@ -10,7 +10,7 @@ name: WS1-gtest-GPU
 
 on:
   pull_request:
-    branches: [ main ]
+    branches: [ main, test-qwennext ]
     paths:
       - "rl_engine/validation/**"
       - "rl_engine/backends/**"
@@ -28,6 +28,7 @@ on:
       - "tools/validation/operators/ws1_candidate_evidence.py"
       - "tests/validation/**"
       - "tests/models/**"
+      - "tests/ops/norm/**"
       - "tests/validation/operators/test_forward_invariance.py"
       - "tests/validation/operators/test_gradient_invariance.py"
       - "tests/validation/operators/test_four_judgment_matrix.py"
@@ -36,7 +37,7 @@ on:
       - "ci/scripts/run_gpu_ci.sh"
       - ".github/workflows/ws1-gtest-gpu.yml"
   push:
-    branches: [ main ]
+    branches: [ main, test-qwennext ]
     paths:
       - "rl_engine/validation/**"
       - "rl_engine/backends/**"
@@ -54,6 +55,7 @@ on:
       - "tools/validation/operators/ws1_candidate_evidence.py"
       - "tests/validation/**"
       - "tests/models/**"
+      - "tests/ops/norm/**"
       - "tests/validation/operators/test_forward_invariance.py"
       - "tests/validation/operators/test_gradient_invariance.py"
       - "tests/validation/operators/test_four_judgment_matrix.py"
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$'
       - id: check-added-large-files
diff --git a/ci/scripts/run_qwen3_next_chain_gate.sh b/ci/scripts/run_qwen3_next_chain_gate.sh
new file mode 100755
index 000000000..8c042ea64
--- /dev/null
+++ b/ci/scripts/run_qwen3_next_chain_gate.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-Next chain-report adapter checks; with a report path, also validate that report.
+#   bash ci/scripts/run_qwen3_next_chain_gate.sh [chain-report.json]
+set -euo pipefail
+ROOT="$(cd "$(dirname "$0")/.." && pwd)"
+cd "$ROOT"
+PY="${PY:-python3}"
+export PYTHONPATH="$ROOT${PYTHONPATH:+:$PYTHONPATH}"
+"$PY" -m pytest -q tests/models/qwen3_next/test_qwen3_next_chain_report.py
+if [ "$#" -ge 1 ]; then
+  "$PY" - "$1" <<'PY'
+import dataclasses, json, sys
+
+from rl_engine.validation.models.chain_gate import ChainGateReport
+from rl_engine.validation.models.qwen3_next_chain_report import COMPATIBLE_SCHEMA, SCHEMA_VERSION
+
+report = json.load(open(sys.argv[1]))
+missing = {f.name for f in dataclasses.fields(ChainGateReport)} - set(report)
+assert not missing, f"chain report lacks ChainGateReport fields: {sorted(missing)}"
+assert report["schema_version"] == SCHEMA_VERSION, report["schema_version"]
+assert report["compatible_schema"] == COMPATIBLE_SCHEMA, report["compatible_schema"]
+print(f"passed={report['passed']} first_drift={report['first_drift']} "
+      f"family={report['first_drift_family']} cells={len(report['cells'])}")
+sys.exit(0 if report["passed"] else 1)
+PY
+fi
diff --git a/ci/scripts/run_qwen3_next_operator_gates.sh b/ci/scripts/run_qwen3_next_operator_gates.sh
new file mode 100644
index 000000000..ceba42dd5
--- /dev/null
+++ b/ci/scripts/run_qwen3_next_operator_gates.sh
@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+# Required operator acceptance and diagnostics use separate declared case sets.
+set -euo pipefail
+ROOT="$(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" tools/validation/models/qwen3_next_operator_gates.py --out "$OUT" \
+  --suite "${QWEN3_NEXT_OPERATOR_SUITE:-all}"
diff --git a/ci/scripts/run_ws1_gtest.sh b/ci/scripts/run_ws1_gtest.sh
index cc47dd678..1c5a2be53 100755
--- a/ci/scripts/run_ws1_gtest.sh
+++ b/ci/scripts/run_ws1_gtest.sh
@@ -19,6 +19,7 @@ echo "[ws1-gtest] interpreter=$PY out=$OUT"
 
 "$PY" -m pytest -q \
   tests/validation/operators/test_ws1_gtest_gpu.py \
+  tests/models/qwen3_next/test_qwen3_next_norm.py \
   tests/ops/attention/test_triton_batch_invariant_attention.py \
   tests/validation/operators/test_four_judgment_matrix.py \
   tests/validation/operators/test_ws1_candidate_evidence.py \
@@ -75,3 +76,14 @@ for cell in required:
         )
 print("[ws1-gtest] C8 gate passed")
 PY
+
+echo "[ws1-gtest] Qwen3-Next C3/C4 norms (operator scope)"
+QWEN3_NEXT_NORM_MANIFEST=rl_engine/validation/models/qwen3_next_norm_manifest.json
+"$PY" tools/validation/operators/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" tools/validation/operators/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" tools/validation/operators/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" tools/validation/operators/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/bindings/ops.cpp b/csrc/bindings/ops.cpp
index 0c7c3e7f4..a6e0d52f0 100644
--- a/csrc/bindings/ops.cpp
+++ b/csrc/bindings/ops.cpp
@@ -263,14 +263,36 @@ 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_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,
@@ -296,23 +318,39 @@ 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,
-  double eps)
+  double eps,
+  double weight_offset)
 {
   rmsnorm_check_input(x, "x");
   rmsnorm_check_input(weight, "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)");
+  rmsnorm_check_weight(x, weight);
 
   auto T = x.size(0);
   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};
 }
@@ -321,22 +359,89 @@ 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");
   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]");
-  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)");
 
   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;
+}
+
+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_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(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");
+  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]");
+
+  auto dx = torch::empty_like(x);
+
+  rmsnorm_gated_backward_dx_cuda(
+    dy, x, weight, gate, rstd, dx, weight_offset, activation);
 
   return dx;
 }
@@ -350,6 +455,7 @@ 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");
@@ -357,6 +463,8 @@ 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)");
 
@@ -718,9 +826,24 @@ 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");
+    // 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);
+    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");
+    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/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/csrc/cuda/norm/rmsnorm.cu b/csrc/cuda/norm/rmsnorm.cu
index b32bc5af4..81a02cd2d 100644
--- a/csrc/cuda/norm/rmsnorm.cu
+++ b/csrc/cuda/norm/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 +136,17 @@ __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);
+        // 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/models/qwen3_next/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);
     }
@@ -153,7 +161,8 @@ __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;
@@ -168,6 +177,7 @@ __global__ void rmsnorm_bwd_dx_kernel(
         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);
+        if (weight_offset != 0.0f) wv += weight_offset;
         local_dot += dyv * wv * xv;
     }
 
@@ -180,6 +190,7 @@ __global__ void rmsnorm_bwd_dx_kernel(
         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);
+        if (weight_offset != 0.0f) wv += weight_offset;
 
         float out = r * dyv * wv - xv * coeff;
         store_from_float<scalar_t>(dx_row + col, out);
@@ -187,6 +198,133 @@ __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);
+        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);
+    }
+}
+
+
+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);
+        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;
+    }
+
+    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);
+        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;
+        store_from_float<scalar_t>(dx_row + col, out);
+    }
+}
+
 template <typename scalar_t>
 __global__ void rmsnorm_partial_dw_kernel(
     const scalar_t* __restrict__ dy,
@@ -247,12 +385,14 @@ 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.
     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);
@@ -270,10 +410,12 @@ void rmsnorm_forward_cuda(
                 rstd.data_ptr<float>(),
                 T,
                 H,
-                static_cast<float>(eps)
+                static_cast<float>(eps),
+                static_cast<float>(weight_offset)
             );
         });
     });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 
@@ -282,10 +424,12 @@ 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);
+    if (T == 0) return;
     int H = x.size(1);
     int threads = choose_threads(H);
     size_t smem = threads * sizeof(float);
@@ -303,13 +447,94 @@ void rmsnorm_backward_dx_cuda(
                 rstd.data_ptr<float>(),
                 dx.data_ptr<x_t>(),
                 T,
-                H
+                H,
+                static_cast<float>(weight_offset)
             );
         });
     });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
+
+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
+) {
+    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);
+
+    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));
+            }
+        });
+    });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 
+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
+) {
+    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);
+
+    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));
+            }
+        });
+    });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
 void rmsnorm_backward_partial_dw_cuda(
     torch::Tensor dy,
     torch::Tensor x,
@@ -319,6 +544,7 @@ 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;
@@ -338,6 +564,7 @@ void rmsnorm_backward_partial_dw_cuda(
             H
         );
     });
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 
@@ -360,6 +587,7 @@ void rmsnorm_backward_reduce_dw_cuda(
         chunks,
         H
     );
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 
 #if !defined(USE_ROCM)
diff --git a/docs/.nav.yml b/docs/.nav.yml
index 29b5ead37..4e04fd061 100644
--- a/docs/.nav.yml
+++ b/docs/.nav.yml
@@ -29,6 +29,8 @@ nav:
       - operators/sampling.md
       - 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
     - Contributor Guide: contributing/contributor-guide.md
diff --git a/docs/archive/design/rfc428-c6-gdn-recurrent-replay.md b/docs/archive/design/rfc428-c6-gdn-recurrent-replay.md
new file mode 100644
index 000000000..84a977547
--- /dev/null
+++ b/docs/archive/design/rfc428-c6-gdn-recurrent-replay.md
@@ -0,0 +1,303 @@
+# 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,
+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/reference/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 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:
+
+| 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
+  (`build_tools/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/models/qwen3_next/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
+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.
+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.
+
+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: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.
+
+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
+
+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
+- the accumulator is fp32 for the whole step; the store rounds to the state tensor's
+  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 |
+
+A negative conv index is therefore inactive in the golden
+(`tests/models/qwen3_next/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: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`.
+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), bf16 I/O, `use_qk_l2norm_in_kernel=True`,
+random inputs, B ∈ {1, 4, 17, 64}, one seed per batch. Bounds asserted by
+`tests/models/qwen3_next/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/contracts/profiles/precision/ws1.json` is atol = rtol = 1e-4 for float32
+and atol = 5e-2, rtol = 2e-2 for bfloat16.
+
+| | max\|diff\| out | max\|diff\| state |
+|---|---|---|
+| fp32 state | ≤ 1e-3 | ≤ 1e-5 |
+| bf16 state | ≤ 1e-3 | ≤ 5e-3 |
+
+Measured values for the same inputs, from `tools/validation/models/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`;
+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/models/qwen3_next/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 (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:
+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 outputs differ.** There are two mechanisms, both on the provider
+side, and which one applies depends on the Triton specialization. Measured by
+`tools/validation/models/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
+
+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.
+
+**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 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
+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.
+
+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
+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: 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
+
+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, **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 |
+| 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. 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.
+
+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. *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/docs/operators/README.md b/docs/operators/README.md
index 00f4cbb45..be74cb6d0 100644
--- a/docs/operators/README.md
+++ b/docs/operators/README.md
@@ -32,4 +32,7 @@ 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)
+- [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/attention.md b/docs/operators/attention.md
index 52f8517ea..be71c7e7e 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/operators/qwen3-next-moe-route-combine.md b/docs/operators/qwen3-next-moe-route-combine.md
new file mode 100644
index 000000000..07804b8bc
--- /dev/null
+++ b/docs/operators/qwen3-next-moe-route-combine.md
@@ -0,0 +1,146 @@
+# 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.models.qwen3_next.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.models.qwen3_next.qwen3_next_forward import shared_moe, stable_top10_routes
+from rl_engine.models.qwen3_next.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 |
+| 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,
+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/models/qwen3_next/check_qwen3_next_forward.py::test_grouped_*`), so this is a speed change
+with no change in bits.
+
+## Results
+
+Measured on B200 with `tools/validation/models/qwen3_next_moe_prior_art.py` and the TP4 gate
+`tools/validation/models/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:
+
+| | 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): 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
+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/models/qwen3_next/test_qwen3_next_forward_contract.py` | CPU | CPU rejection, vLLM pin, fixed-order sum/softmax row independence |
+| `tests/models/qwen3_next/test_qwen3_next_tp_blocks.py` | CPU | HF shard/assemble round trips, replica drift rejection, TP4 parameter ownership |
+| `tests/validation/common/test_tensor_identity.py` | CPU | raw-bit identity (signed zero, NaN/Inf, dtype) |
+| `tests/models/qwen3_next/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 |
+| `tools/validation/models/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/operators/qwen3-next-rms-norm-gated.md b/docs/operators/qwen3-next-rms-norm-gated.md
new file mode 100644
index 000000000..217fa45ed
--- /dev/null
+++ b/docs/operators/qwen3-next-rms-norm-gated.md
@@ -0,0 +1,164 @@
+# 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.reference.norm.qwen3_next_rms_norm import (
+    Qwen3NextRMSNormGatedOp,      # strict / vLLM convention
+    Qwen3NextRMSNormGatedHFOp,    # transformers convention, kept as a witness
+)
+from rl_engine.backends.cuda.norm.rmsnorm import (
+    Qwen3NextRMSNormGatedCudaOp,
+    rmsnorm_gated_cuda,
+)
+
+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")
+```
+
+## 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 | 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"` | 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
+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
+
+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 levels:
+
+- **L1** for `Qwen3NextRMSNormGatedCudaOp`: prefix slices in
+  `tests/models/qwen3_next/test_qwen3_next_norm.py`, and batch size, chunking, padding and permutation
+  in the C3/C4 gates (`ci/scripts/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/models/qwen3_next/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. `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/models/qwen3_next/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
+`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 tools/validation/operators/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
+`tools/validation/models/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 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.
+
+## Tests
+
+```bash
+python -m pytest tests/models/qwen3_next/test_qwen3_next_norm.py -v
+# imports real vLLM, so it is not collected by default:
+python -m pytest tests/models/qwen3_next/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
new file mode 100644
index 000000000..2fbd8baa5
--- /dev/null
+++ b/docs/operators/qwen3-next-rms-norm.md
@@ -0,0 +1,145 @@
+# 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; 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`.
+
+## Entry Point
+
+```python
+from rl_engine.reference.norm.qwen3_next_rms_norm import Qwen3NextRMSNormOp
+from rl_engine.backends.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 levels:
+
+- **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 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.
+
+Accuracy tests resolve their tolerances from `tolerance_contract.json`
+(`forward_accuracy`, `reduction` × dtype). The bounds in
+`tests/models/qwen3_next/check_qwen3_next_norm_providers.py` are provider-gap bounds, not contract
+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.
+
+## 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
+`tools/validation/models/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
+python -m pytest tests/models/qwen3_next/test_qwen3_next_norm.py -v
+python tools/validation/operators/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 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 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/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..c46986e28
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/README.md
@@ -0,0 +1,44 @@
+# 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, `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 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
+```
+
+| 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` |
+
+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 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
+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
+44cac38375a7598919a6d00d3bcc1651d8da5b04be767d53dc9aa1694f817879  l2/environment.json
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diff --git a/docs/usage/evidence/qwen3-next-chain-b200/chain-report.json b/docs/usage/evidence/qwen3-next-chain-b200/chain-report.json
new file mode 100644
index 000000000..729ecd2ac
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-chain-b200/chain-report.json
@@ -0,0 +1,8083 @@
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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",
+  "git_dirty": false,
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+    "transformers": "5.17.0",
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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",
+    "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]",
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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
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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
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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",
+      "rms_norm_gated-forward.json",
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+            "full-chunk-reorder-64",
+            "routes-replicated-256",
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+            "routes-replicated-1024",
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+            "training-forward",
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+      "p0064-b4-full",
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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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+              },
+              "weights": "bfloat16",
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+              "cp": 1,
+              "ep": 1
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+            "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"
+}
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..0a86096b4
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/README.md
@@ -0,0 +1,47 @@
+# Qwen3-Next routed MoE: prior art and TP4 gate (B200)
+
+`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 tools/validation/models/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 tools/validation/models/plot_qwen3_next_moe_prior_art.py report.json` |
+| `tp4-moe/rank-{0..3}.json` | `TRITON_F32_DEFAULT=ieee torchrun --nproc-per-node 4 tools/validation/models/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/models/qwen3_next/check_qwen3_next_forward.py tests/validation/common/test_tensor_identity.py
+tests/models/qwen3_next/test_qwen3_next_forward_contract.py tests/models/qwen3_next/test_qwen3_next_tp_blocks.py`
+passed (65 tests). `tests/integrations/common/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
new file mode 100644
index 000000000..e09ec93ac
Binary files /dev/null and b/docs/usage/evidence/qwen3-next-moe-route-b200/figure.png differ
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..5a9fa5056
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-moe-route-b200/report.json
@@ -0,0 +1,381 @@
+{
+  "kind": "qwen3_next_prior_art_report",
+  "rfc": 428,
+  "rl_kernel_commit": "c58816bf54940195b543beda14e0de65a71182c6",
+  "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,
+      "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",
+  "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
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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 @@
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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 @@
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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 @@
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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
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diff --git a/docs/usage/evidence/qwen3-next-rms-norm-b200/figure.png b/docs/usage/evidence/qwen3-next-rms-norm-b200/figure.png
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index 000000000..0fc8452ae
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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
new file mode 100644
index 000000000..eaf59c418
Binary files /dev/null and b/docs/usage/evidence/qwen3-next-rms-norm-gated-b200/figure-gated_rmsnorm.png differ
diff --git a/docs/usage/evidence/qwen3-next-rms-norm-gated-b200/figure-zero_centred_rmsnorm.png b/docs/usage/evidence/qwen3-next-rms-norm-gated-b200/figure-zero_centred_rmsnorm.png
new file mode 100644
index 000000000..5cd70b5cf
Binary files /dev/null and b/docs/usage/evidence/qwen3-next-rms-norm-gated-b200/figure-zero_centred_rmsnorm.png differ
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
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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..a21252c17
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/README.md
@@ -0,0 +1,71 @@
+# 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 tools/validation/models/qwen3_next_tp_gdn_check.py --checkpoint <ckpt> --output tp4-gdn` |
+| `tp4-attention/` | `9e6c4f2` | `torchrun --nproc-per-node 4 tools/validation/models/qwen3_next_tp_attention_check.py --checkpoint <ckpt> --output tp4-attention` |
+| `tp4-moe/` | `9e6c4f2` | `TRITON_F32_DEFAULT=ieee torchrun --nproc-per-node 4 tools/validation/models/qwen3_next_tp_moe_check.py --checkpoint <ckpt> --output tp4-moe` |
+| `attention/report.json`, `attention/figure.png` | `703c6dc` | two processes, then `tools/validation/models/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. `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
+(`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/integrations/common/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_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 tools/validation/models/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 tools/validation/models/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
new file mode 100644
index 000000000..cb0c65b02
Binary files /dev/null and b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/figure.png differ
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
new file mode 100644
index 000000000..3d55ea2aa
--- /dev/null
+++ b/docs/usage/evidence/qwen3-next-tp4-mixers-b200/attention/report.json
@@ -0,0 +1,346 @@
+{
+  "kind": "qwen3_next_prior_art_report",
+  "rfc": 428,
+  "rl_kernel_commit": "703c6dca23da2f42b288fefe70f2073ef6a97986",
+  "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": "0.16.0rc0",
+      "megatron_core": "0.16.0rc0",
+      "transformer_engine": "2.16.1+c9877beb",
+      "vime": "0.3.2"
+    }
+  },
+  "op": "qwen3_next_tp4_attention",
+  "shape": {
+    "q_heads": 4,
+    "kv_heads": 1,
+    "head_dim": 256,
+    "scale": 0.0625
+  },
+  "target_tokens": 1000,
+  "companion_tokens": [
+    777,
+    1500,
+    64
+  ],
+  "candidates": [
+    {
+      "key": "rl_kernel_cuda",
+      "name": "RL-Kernel deterministic attention (one launch per sequence)",
+      "source": "rl_engine.integrations.qwen3_next_forward.shared_attention",
+      "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": 0.0016090058011337504,
+        "max_abs_vs_fp64": 0.0066821561991154965
+      },
+      "batch_invariant": true
+    },
+    {
+      "key": "torch_sdpa",
+      "name": "torch SDPA (default backend, one launch per sequence)",
+      "source": "torch 2.13.0+cu130 scaled_dot_product_attention",
+      "batch_bitwise": {
+        "first": true,
+        "last": true
+      },
+      "prefill_decode_bitwise": {
+        "last_64": false,
+        "last_1": false
+      },
+      "repeatable": true,
+      "backward_batch_bitwise": {
+        "dq": false,
+        "dk": true,
+        "dv": true
+      },
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.0019992834566479067,
+        "max_abs_vs_fp64": 0.0066821561991154965
+      },
+      "batch_invariant": false
+    },
+    {
+      "key": "vllm_fa2_auto",
+      "name": "vLLM FlashAttention-2 varlen, num_splits=0",
+      "source": "vllm 0.30.0 vllm_flash_attn.flash_attn_varlen_func",
+      "batch_bitwise": {
+        "first": true,
+        "last": true
+      },
+      "prefill_decode_bitwise": {
+        "last_64": true,
+        "last_1": false
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.0020096983122931318,
+        "max_abs_vs_fp64": 0.0066821561991154965
+      },
+      "batch_invariant": false
+    },
+    {
+      "key": "vllm_fa2_split1",
+      "name": "vLLM FlashAttention-2 varlen, num_splits=1",
+      "source": "vllm 0.30.0 vllm_flash_attn.flash_attn_varlen_func",
+      "batch_bitwise": {
+        "first": true,
+        "last": true
+      },
+      "prefill_decode_bitwise": {
+        "last_64": true,
+        "last_1": true
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.0020096983122931318,
+        "max_abs_vs_fp64": 0.0066821561991154965
+      },
+      "batch_invariant": true
+    },
+    {
+      "key": "vllm_triton_2d",
+      "name": "vLLM Triton unified attention (2D kernel)",
+      "source": "vllm 0.30.0 v1/attention/ops/triton_unified_attention",
+      "batch_bitwise": {
+        "first": true,
+        "last": true
+      },
+      "prefill_decode_bitwise": {
+        "last_64": true,
+        "last_1": false
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.002021002108737795,
+        "max_abs_vs_fp64": 0.0066821561991154965
+      },
+      "batch_invariant": false
+    },
+    {
+      "key": "flashinfer",
+      "name": "FlashInfer BatchPrefillWithRaggedKVCache",
+      "source": "flashinfer 0.6.18.post1",
+      "batch_bitwise": {
+        "first": false,
+        "last": false
+      },
+      "prefill_decode_bitwise": {
+        "last_64": false,
+        "last_1": false
+      },
+      "repeatable": true,
+      "accuracy": {
+        "rel_l2_vs_fp64": 0.002146056864137061,
+        "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": {
+        "dq": false,
+        "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
+      },
+      "batch_invariant": true
+    }
+  ],
+  "latency": {
+    "prefill_us": {
+      "512": {
+        "rl_kernel_cuda": 725.02401471138,
+        "torch_sdpa": 66.0800002515316,
+        "vllm_fa2_auto": 118.46399679780006,
+        "vllm_fa2_split1": 118.71999874711037,
+        "vllm_triton_2d": 263.88800144195557,
+        "flashinfer": 237.88800090551376,
+        "fa4_cute": 210.09600162506104,
+        "te_training": 2039.6959781646729,
+        "megatron_local": 324.5760053396225,
+        "te_fused": 336.70400083065033
+      },
+      "2048": {
+        "rl_kernel_cuda": 10141.616344451904,
+        "torch_sdpa": 115.9840002655983,
+        "vllm_fa2_auto": 152.8479978442192,
+        "vllm_fa2_split1": 156.78400546312332,
+        "vllm_triton_2d": 324.6240019798279,
+        "flashinfer": 293.16800832748413,
+        "fa4_cute": 211.31199598312378,
+        "te_training": 5458.016157150269,
+        "megatron_local": 320.8480030298233,
+        "te_fused": 323.0559974908829
+      },
+      "8192": {
+        "rl_kernel_cuda": 159770.19500732422,
+        "torch_sdpa": 607.344001531601,
+        "vllm_fa2_auto": 661.2959802150726,
+        "vllm_fa2_split1": 657.3439836502075,
+        "vllm_triton_2d": 1134.160041809082,
+        "flashinfer": 764.3359899520874,
+        "fa4_cute": 312.0640069246292,
+        "te_training": 22656.047821044922,
+        "megatron_local": 3245.0079917907715,
+        "te_fused": 612.496018409729
+      }
+    },
+    "decode_us_kv8192": {
+      "rl_kernel_cuda": 757.8720152378082,
+      "torch_sdpa": 314.2559975385666,
+      "vllm_fa2_auto": 164.39999639987946,
+      "vllm_fa2_split1": 336.4799916744232,
+      "vllm_triton_2d": 564.3199980258942,
+      "flashinfer": 235.24799942970276,
+      "fa4_cute": 256.49599730968475,
+      "te_training": 4540.832042694092,
+      "megatron_local": 304.56000566482544,
+      "te_fused": 443.83999705314636
+    },
+    "forward_plus_backward_us_2048": {
+      "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"
+      }
+    }
+  }
+}
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 @@
+{
+  "status": "passed",
+  "scope": "tp4_full_attention_layer3",
+  "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"
+  },
+  "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"
+    ],
+    "gradient_max_abs": {
+      "q_proj.weight": 7.748603820800781e-06,
+      "k_proj.weight": 1.52587890625e-05,
+      "v_proj.weight": 3.075599670410156e-05,
+      "o_proj.weight": 1.2099742889404297e-05,
+      "q_norm.weight": 1.8835067749023438e-05,
+      "k_norm.weight": 1.8715858459472656e-05
+    }
+  }
+}
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 @@
+{
+  "status": "passed",
+  "scope": "tp4_full_attention_layer3",
+  "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"
+  },
+  "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"
+    ],
+    "gradient_max_abs": {
+      "q_proj.weight": 8.761882781982422e-06,
+      "k_proj.weight": 1.52587890625e-05,
+      "v_proj.weight": 3.075599670410156e-05,
+      "o_proj.weight": 1.2099742889404297e-05,
+      "q_norm.weight": 1.8835067749023438e-05,
+      "k_norm.weight": 1.8715858459472656e-05
+    }
+  }
+}
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 @@
+{
+  "status": "passed",
+  "scope": "tp4_full_attention_layer3",
+  "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"
+  },
+  "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"
+    ],
+    "gradient_max_abs": {
+      "q_proj.weight": 1.633167266845703e-05,
+      "k_proj.weight": 2.1338462829589844e-05,
+      "v_proj.weight": 2.3126602172851562e-05,
+      "o_proj.weight": 2.2292137145996094e-05,
+      "q_norm.weight": 1.8835067749023438e-05,
+      "k_norm.weight": 1.8715858459472656e-05
+    }
+  }
+}
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 @@
+{
+  "status": "passed",
+  "scope": "tp4_full_attention_layer3",
+  "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"
+  },
+  "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"
+    ],
+    "gradient_max_abs": {
+      "q_proj.weight": 1.2993812561035156e-05,
+      "k_proj.weight": 2.1338462829589844e-05,
+      "v_proj.weight": 2.3126602172851562e-05,
+      "o_proj.weight": 9.894371032714844e-06,
+      "q_norm.weight": 1.8835067749023438e-05,
+      "k_norm.weight": 1.8715858459472656e-05
+    }
+  }
+}
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 @@
+{
+  "status": "passed",
+  "scope": "tp4_gdn_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"
+  },
+  "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": 4.462208380573429e-12,
+    "dt_bias": 3.595346242946107e-12,
+    "in_proj_qkvz.weight": 6.948539521545172e-10,
+    "in_proj_ba.weight": 3.6834535421803594e-11,
+    "conv1d.weight": 3.128661774098873e-09,
+    "norm.weight": 3.91155481338501e-08,
+    "out_proj.weight": 3.255991032347083e-10
+  }
+}
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
@@ -0,0 +1,32 @@
+{
+  "status": "passed",
+  "scope": "tp4_gdn_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"
+  },
+  "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": 1.0550138540565968e-10,
+    "dt_bias": 9.458744898438454e-11,
+    "in_proj_qkvz.weight": 3.055902197957039e-10,
+    "in_proj_ba.weight": 3.296918293926865e-11,
+    "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
+    }
+  }
+}
diff --git a/docs/usage/qwen3-next-chain.md b/docs/usage/qwen3-next-chain.md
new file mode 100644
index 000000000..c5d5801aa
--- /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 tools/validation/models/qwen3_next_run.py --stage all \
+    --runs-root <runs> --checkpoint <checkpoint> --cuda-home <cuda-13.0> \
+    --python <venv>/bin/python --until l2
+python tools/validation/models/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 tools/validation/models/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/validation/models/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 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-<date>
+```
+
+`chain-report.json` has every field of the Qwen3-8B chain gate's report
+(`rl_engine.validation.models.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/models/qwen3_next/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.
+
+`tools/validation/models/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/models/qwen3_next/check_qwen3_next_*.py` import vLLM and run in the self-hosted
+`Qwen3-Next-provider-GPU` workflow and in `preflight`.
+`tests/models/qwen3_next/check_qwen3_next_vime_weight_hooks.py`,
+`tests/models/qwen3_next/test_qwen3_next_training_checkpoint.py` and
+`tests/models/qwen3_next/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 207606282..457685c48 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]
@@ -33,6 +33,14 @@ rl_kernel = "rl_engine.integrations.engines.rollout.vllm.runtime:register_vllm_p
 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/_C.pyi b/rl_engine/_C.pyi
index fb3c4379c..c0f9cb02c 100644
--- a/rl_engine/_C.pyi
+++ b/rl_engine/_C.pyi
@@ -256,16 +256,38 @@ 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,
     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_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,
diff --git a/rl_engine/backends/cuda/attention/deterministic_attn.py b/rl_engine/backends/cuda/attention/deterministic_attn.py
index 2873c0c8e..8b6d9fc4c 100644
--- a/rl_engine/backends/cuda/attention/deterministic_attn.py
+++ b/rl_engine/backends/cuda/attention/deterministic_attn.py
@@ -27,8 +27,8 @@
 )
 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/rl_engine/backends/cuda/norm/rmsnorm.py b/rl_engine/backends/cuda/norm/rmsnorm.py
index ec3c65f77..60cdbffd3 100644
--- a/rl_engine/backends/cuda/norm/rmsnorm.py
+++ b/rl_engine/backends/cuda/norm/rmsnorm.py
@@ -4,9 +4,21 @@
 from rl_engine.ops.autograd.backward_runtime import record_backward
 from rl_engine.ops.autograd.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 _fold_dweight_rows(rows: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+    """The single left-fold entrypoint for this backend's dweight reductions.
+
+    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.
+    """
+    return reduce_rows_fp32(rows).to(dtype)
+
+
+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 +27,21 @@ 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/norm/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/norm/rmsnorm.cu "
+            "for weight_offset support."
+        )
 
 
 class RMSNormCuda(torch.autograd.Function):
@@ -30,20 +50,23 @@ 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]
         """
+        _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"
@@ -51,10 +74,6 @@ def forward(ctx, x, weight, mask=None, eps=1e-6):
         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:
@@ -64,10 +83,11 @@ 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 +101,7 @@ 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.
@@ -89,7 +109,7 @@ 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=(
@@ -101,16 +121,18 @@ 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:
@@ -119,11 +141,11 @@ 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.
+    weight_offset = 0.0
 
     def __call__(self, x, weight, *, eps=1e-6):
         return self.forward(x, weight, eps=eps)
@@ -131,12 +153,236 @@ 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):
-        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]
+        # 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(),
+            float(eps),
+            float(self.weight_offset),
+        )
+        rows = rmsnorm_dweight_rows_fp32(x, grad_output, rstd=rstd.reshape(x.shape[:-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
+
+
+# --------------------------------------------------------------------------- #
+# 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/norm/rmsnorm.cu."
+        )
+
+
+#: 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()
+    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/norm/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)
+        # 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(
+            "rms_norm_gated",
+            kernel_id=(
+                "rl_engine._C.rmsnorm_gated_backward_dx"
+                "+rl_engine.ops.autograd.vjp_fp32.rmsnorm_dweight_rows_fp32"
+                "+rl_engine.ops.autograd.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")
+    """
+    act = _check_gate_activation(activation)
+    return RMSNormGatedCuda.apply(x, weight, gate, eps, weight_offset, act)
+
+
+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 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"
+
+    #: 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
+
+    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"
+        )
+
+    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)
+        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):
+        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]
+        _, 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/config/workload.py b/rl_engine/config/workload.py
index 69b21c20f..79f93208a 100644
--- a/rl_engine/config/workload.py
+++ b/rl_engine/config/workload.py
@@ -264,10 +264,30 @@ 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.validation.models.qwen3_next_workload import validate_norm_manifest
+
+        validate_norm_manifest(raw)
+    else:
+        validate_manifest(raw)
     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]
@@ -292,20 +312,25 @@ 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/rl_engine/integrations/common/qwen3_next_binary.py b/rl_engine/integrations/common/qwen3_next_binary.py
new file mode 100644
index 000000000..e883959af
--- /dev/null
+++ b/rl_engine/integrations/common/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/engines/rollout/vllm/qwen3_next_native_rollout.py b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_native_rollout.py
new file mode 100644
index 000000000..3e0dbda76
--- /dev/null
+++ b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_native_rollout.py
@@ -0,0 +1,796 @@
+# 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.common.qwen3_next_binary import (
+    imported_provider_identity,
+    validate_provider_identity,
+)
+from rl_engine.models.qwen3_next import qwen3_next_subnode as subnode
+from rl_engine.validation.common.tensor_identity import assert_tensor_bitwise_equal as exact
+from rl_engine.validation.models.qwen3_next_l2 import (
+    embedding_digest,
+    json_digest,
+    layer_digest,
+    load_tensor_artifact,
+    save_tensor_artifact,
+    validate_debug_capture,
+    validate_model_artifact,
+)
+
+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.engines.rollout.vllm.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.engines.rollout.vllm.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.engines.rollout.vllm.qwen3_next_vllm import metadata_sequences
+        from rl_engine.integrations.engines.rollout.vllm.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.contracts.operators.logprob import LogprobRole
+        from rl_engine.models.qwen3_next.qwen3_next_forward import shared_logprob
+        from rl_engine.models.qwen3_next.qwen3_next_logprob import qwen_logprob_contract
+
+        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.engines.rollout.vllm.qwen3_next_vllm_model import (
+        register_shared_model,
+    )
+    from rl_engine.integrations.orchestrators.vime.experiments.qwen3_next.profile import (
+        activate_for_identity,
+    )
+
+    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.engines.rollout.vllm.qwen3_next_native_rollout"
+            ".ExactPrefillScheduler"
+        ),
+        async_scheduling=False,
+        worker_cls=(
+            "rl_engine.integrations.engines.rollout.vllm.qwen3_next_vllm_worker" ".SharedQwenWorker"
+        ),
+        logits_processors=[
+            "rl_engine.integrations.engines.rollout.vllm.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/engines/rollout/vllm/qwen3_next_vllm.py b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm.py
new file mode 100644
index 000000000..462f07198
--- /dev/null
+++ b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm.py
@@ -0,0 +1,210 @@
+# 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.engines.train.vllm.qwen3_next_provider import (
+    GDNProviderConfig,
+    GDNState,
+)
+from rl_engine.models.qwen3_next.qwen3_next_tp import _ReduceFromTP
+from rl_engine.models.qwen3_next.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.orchestrators.vime.experiments.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.engines.rollout.vllm.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.models.qwen3_next.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/engines/rollout/vllm/qwen3_next_vllm_attention.py b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm_attention.py
new file mode 100644
index 000000000..c8bd85335
--- /dev/null
+++ b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm_attention.py
@@ -0,0 +1,241 @@
+# 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.models.qwen3_next.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.orchestrators.vime.experiments.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.engines.rollout.vllm.qwen3_next_vllm_attention"
+        ".SharedAttentionBackend",
+    )
diff --git a/rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm_model.py b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm_model.py
new file mode 100644
index 000000000..89e154677
--- /dev/null
+++ b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm_model.py
@@ -0,0 +1,372 @@
+# 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.contracts.operators.logprob import LogprobRole
+from rl_engine.distributed.algorithms.collectives import collective_for_group
+from rl_engine.models.qwen3_next.qwen3_next_forward import (
+    FORWARD_PROVIDER_ID,
+    shared_linear,
+    shared_logprob,
+    shared_moe,
+)
+from rl_engine.models.qwen3_next.qwen3_next_logprob import qwen_logprob_contract
+from rl_engine.models.qwen3_next.qwen3_next_tp import _ReduceFromTP
+from rl_engine.models.qwen3_next.qwen3_next_tp_blocks import TP4FullAttention
+
+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.backends.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.models.qwen3_next.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.engines.rollout.vllm.qwen3_next_vllm import SharedQwenGDN
+    from rl_engine.integrations.engines.rollout.vllm.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.engines.rollout.vllm.qwen3_next_vllm import (
+            register_shared_gdn_adapter,
+        )
+        from rl_engine.integrations.engines.rollout.vllm.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.engines.rollout.vllm.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/engines/rollout/vllm/qwen3_next_vllm_worker.py b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm_worker.py
new file mode 100644
index 000000000..45d0adee0
--- /dev/null
+++ b/rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm_worker.py
@@ -0,0 +1,55 @@
+# 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.common.qwen3_next_binary import imported_provider_identity
+from rl_engine.integrations.engines.rollout.vllm.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.orchestrators.vime.experiments.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.engines.rollout.vllm.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/integrations/engines/train/vllm/__init__.py b/rl_engine/integrations/engines/train/vllm/__init__.py
new file mode 100644
index 000000000..988131360
--- /dev/null
+++ b/rl_engine/integrations/engines/train/vllm/__init__.py
@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0
diff --git a/rl_engine/integrations/engines/train/vllm/qwen3_next_conv.py b/rl_engine/integrations/engines/train/vllm/qwen3_next_conv.py
new file mode 100644
index 000000000..2e2da8329
--- /dev/null
+++ b/rl_engine/integrations/engines/train/vllm/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.reference.linear_attn.causal_conv1d import CausalConv1dUpdateOp
+from rl_engine.reference.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/engines/train/vllm/qwen3_next_gdn.py b/rl_engine/integrations/engines/train/vllm/qwen3_next_gdn.py
new file mode 100644
index 000000000..c9f136f57
--- /dev/null
+++ b/rl_engine/integrations/engines/train/vllm/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.reference.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/engines/train/vllm/qwen3_next_provider.py b/rl_engine/integrations/engines/train/vllm/qwen3_next_provider.py
new file mode 100644
index 000000000..af296f413
--- /dev/null
+++ b/rl_engine/integrations/engines/train/vllm/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.engines.train.vllm.qwen3_next_conv import causal_conv_sequence
+from rl_engine.integrations.engines.train.vllm.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.backends.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/orchestrators/vime/experiments/qwen3_next/__init__.py b/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/__init__.py
new file mode 100644
index 000000000..efe5f73d2
--- /dev/null
+++ b/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/__init__.py
@@ -0,0 +1 @@
+"""rl_engine.integrations.orchestrators.vime.experiments.qwen3_next package."""
diff --git a/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/native_recompute.py b/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/native_recompute.py
new file mode 100644
index 000000000..cb868323e
--- /dev/null
+++ b/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/native_recompute.py
@@ -0,0 +1,319 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Independent native VIME full-model recomputation from frozen tokens only."""
+
+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.models.qwen3_next import qwen3_next_subnode as subnode
+from rl_engine.validation.common.file_digest import file_sha256
+from rl_engine.validation.common.tensor_identity import assert_tensor_bitwise_equal as exact
+from rl_engine.validation.models.qwen3_next_l2 import (
+    embedding_digest,
+    layer_digest,
+    load_tensor_artifact,
+    save_tensor_artifact,
+    validate_model_artifact,
+    validate_native_worker_traces,
+)
+
+
+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.common.qwen3_next_binary import imported_provider_identity
+        from rl_engine.integrations.orchestrators.vime.providers.qwen3_next_vime_runtime import (
+            create_actor,
+        )
+        from rl_engine.models.qwen3_next.qwen3_next_forward import shared_logprob
+        from rl_engine.models.qwen3_next.qwen3_next_logprob import qwen_logprob_contract
+
+        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):
+            digest = file_sha256(path)
+            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/orchestrators/vime/experiments/qwen3_next/native_training.py b/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/native_training.py
new file mode 100644
index 000000000..c3e2ae3fc
--- /dev/null
+++ b/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/native_training.py
@@ -0,0 +1,392 @@
+# 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.validation.models.qwen3_next_l2 import load_tensor_artifact, validate_fixture
+from rl_engine.validation.models.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.orchestrators.vime.providers.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.orchestrators.vime.providers.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.orchestrators.vime.providers.qwen3_next_vime_runtime import (
+            create_actor,
+        )
+        from rl_engine.validation.models.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/orchestrators/vime/experiments/qwen3_next/native_workers.py b/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/native_workers.py
new file mode 100644
index 000000000..55aa521e1
--- /dev/null
+++ b/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/native_workers.py
@@ -0,0 +1,55 @@
+# 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.engines.rollout.vllm import qwen3_next_native_rollout as rollout
+    from rl_engine.integrations.orchestrators.vime.experiments.qwen3_next import (
+        native_recompute as recompute,
+    )
+
+    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.engines.rollout.vllm.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.orchestrators.vime.experiments.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/orchestrators/vime/experiments/qwen3_next/profile.py b/rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/profile.py
new file mode 100644
index 000000000..8e2e99fe0
--- /dev/null
+++ b/rl_engine/integrations/orchestrators/vime/experiments/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[6]
+
+
+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.validation.models.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 / "tools/validation/models"))
+    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/orchestrators/vime/providers/qwen3_next_training.py b/rl_engine/integrations/orchestrators/vime/providers/qwen3_next_training.py
new file mode 100644
index 000000000..8b60fc415
--- /dev/null
+++ b/rl_engine/integrations/orchestrators/vime/providers/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.validation.models.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/orchestrators/vime/providers/qwen3_next_vime.py b/rl_engine/integrations/orchestrators/vime/providers/qwen3_next_vime.py
new file mode 100644
index 000000000..046959593
--- /dev/null
+++ b/rl_engine/integrations/orchestrators/vime/providers/qwen3_next_vime.py
@@ -0,0 +1,403 @@
+# 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.orchestrators.vime.providers.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.engines.train.vllm.qwen3_next_provider import GDNProviderConfig
+from rl_engine.models.qwen3_next.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.models.qwen3_next.qwen3_next_tp import _CopyToTP
+from rl_engine.models.qwen3_next.qwen3_next_tp_blocks import TP4FullAttention, TP4MoE
+from rl_engine.models.qwen3_next.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.backends.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.models.qwen3_next.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.models.qwen3_next.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/orchestrators/vime/providers/qwen3_next_vime_runtime.py b/rl_engine/integrations/orchestrators/vime/providers/qwen3_next_vime_runtime.py
new file mode 100644
index 000000000..a7481150a
--- /dev/null
+++ b/rl_engine/integrations/orchestrators/vime/providers/qwen3_next_vime_runtime.py
@@ -0,0 +1,187 @@
+# 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.validation.models.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.orchestrators.vime.providers.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.orchestrators.vime.providers.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.orchestrators.vime.providers.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.models.qwen3_next.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/models/qwen3_next/__init__.py b/rl_engine/models/qwen3_next/__init__.py
new file mode 100644
index 000000000..988131360
--- /dev/null
+++ b/rl_engine/models/qwen3_next/__init__.py
@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0
diff --git a/rl_engine/models/qwen3_next/qwen3_next_embedding.py b/rl_engine/models/qwen3_next/qwen3_next_embedding.py
new file mode 100644
index 000000000..f62feceb9
--- /dev/null
+++ b/rl_engine/models/qwen3_next/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.models.qwen3_next.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/models/qwen3_next/qwen3_next_engine_weights.py b/rl_engine/models/qwen3_next/qwen3_next_engine_weights.py
new file mode 100644
index 000000000..c1fa7eba9
--- /dev/null
+++ b/rl_engine/models/qwen3_next/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.validation.common.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/models/qwen3_next/qwen3_next_forward.py b/rl_engine/models/qwen3_next/qwen3_next_forward.py
new file mode 100644
index 000000000..e9de7d0ab
--- /dev/null
+++ b/rl_engine/models/qwen3_next/qwen3_next_forward.py
@@ -0,0 +1,409 @@
+# 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
+
+from rl_engine.contracts.operators.logprob import LogprobContract
+
+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
+
+
+@lru_cache(maxsize=1)
+def _attention_op():
+    from rl_engine.backends.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)
+
+
+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.backends.cuda.logprob.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/models/qwen3_next/qwen3_next_logprob.py b/rl_engine/models/qwen3_next/qwen3_next_logprob.py
new file mode 100644
index 000000000..5fb827aec
--- /dev/null
+++ b/rl_engine/models/qwen3_next/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.contracts.operators.logprob import (
+    LogprobContract,
+    LogprobDType,
+    LogprobRole,
+    MaskSpec,
+    ReductionSpec,
+    ShardingSpec,
+)
+from rl_engine.models.qwen3_next.qwen3_next_forward import shared_logprob
+
+
+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/models/qwen3_next/qwen3_next_subnode.py b/rl_engine/models/qwen3_next/qwen3_next_subnode.py
new file mode 100644
index 000000000..cc9fc79cd
--- /dev/null
+++ b/rl_engine/models/qwen3_next/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/models/qwen3_next/qwen3_next_tp.py b/rl_engine/models/qwen3_next/qwen3_next_tp.py
new file mode 100644
index 000000000..876d373ff
--- /dev/null
+++ b/rl_engine/models/qwen3_next/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.algorithms.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/models/qwen3_next/qwen3_next_tp_blocks.py b/rl_engine/models/qwen3_next/qwen3_next_tp_blocks.py
new file mode 100644
index 000000000..950130a73
--- /dev/null
+++ b/rl_engine/models/qwen3_next/qwen3_next_tp_blocks.py
@@ -0,0 +1,380 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Official Qwen3-Next TP4 full-attention and MoE blocks for shared adapters.
+
+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.models.qwen3_next.qwen3_next_forward import (
+    shared_attention,
+    shared_linear,
+    shared_moe,
+)
+from rl_engine.models.qwen3_next.qwen3_next_tp import (
+    _CopyToTP,
+    _parallel_parameter,
+    _rank,
+    _ReduceFromTP,
+    _tp_group,
+    _weight,
+)
+from rl_engine.validation.common.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.backends.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 = {
+        "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."""
+
+    # 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)
+        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, 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)
+        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/models/qwen3_next/qwen3_next_tp_gdn.py b/rl_engine/models/qwen3_next/qwen3_next_tp_gdn.py
new file mode 100644
index 000000000..07abaea20
--- /dev/null
+++ b/rl_engine/models/qwen3_next/qwen3_next_tp_gdn.py
@@ -0,0 +1,163 @@
+# 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.engines.train.vllm.qwen3_next_provider import (
+    GDNProviderConfig,
+    GDNState,
+    shared_gdn_core,
+)
+from rl_engine.models.qwen3_next.qwen3_next_tp import _CopyToTP, _rank, _ReduceFromTP
+from rl_engine.validation.common.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.models.qwen3_next.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/reference/linear_attn/__init__.py b/rl_engine/reference/linear_attn/__init__.py
new file mode 100644
index 000000000..80a2a86b9
--- /dev/null
+++ b/rl_engine/reference/linear_attn/__init__.py
@@ -0,0 +1,18 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Linear-attention operators (Gated DeltaNet and friends)."""
+
+from rl_engine.reference.linear_attn.causal_conv1d import CausalConv1dUpdateOp
+from rl_engine.reference.linear_attn.gated_delta_rule import (
+    NULL_BLOCK_ID,
+    SOFTPLUS_THRESHOLD,
+    GatedDeltaRuleRecurrentStepOp,
+)
+
+__all__ = [
+    "CausalConv1dUpdateOp",
+    "GatedDeltaRuleRecurrentStepOp",
+    "NULL_BLOCK_ID",
+    "SOFTPLUS_THRESHOLD",
+]
diff --git a/rl_engine/reference/linear_attn/causal_conv1d.py b/rl_engine/reference/linear_attn/causal_conv1d.py
new file mode 100644
index 000000000..97886c8c1
--- /dev/null
+++ b/rl_engine/reference/linear_attn/causal_conv1d.py
@@ -0,0 +1,184 @@
+# 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.
+
+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
+
+from rl_engine.reference.linear_attn.gated_delta_rule import NULL_BLOCK_ID, _validate_state_indices
+
+__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 (vLLM: ``== 0``)
+    ============== ========================== ==========================
+
+    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]
+        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)}")
+        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]
+
+        # Match the provider's product rounding and bias-before-taps order.
+        acc = torch.zeros(len(rows), dim, dtype=torch.float32, device=x.device)
+        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 = acc / (1.0 + torch.exp(-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/reference/linear_attn/gated_delta_rule.py b/rl_engine/reference/linear_attn/gated_delta_rule.py
new file mode 100644
index 000000000..442f1f6ac
--- /dev/null
+++ b/rl_engine/reference/linear_attn/gated_delta_rule.py
@@ -0,0 +1,334 @@
+# 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. 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).
+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.reference.norm.rms_norm.shape_invariant_rstd`
+#: pins the RMSNorm statistic.
+_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.
+
+    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:
+        # 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)
+    )
+
+
+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``."""
+    small = x <= threshold
+    safe = torch.where(small, x, torch.zeros_like(x))
+    return torch.where(small, torch.log1p(torch.exp(safe)), 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)}")
+
+        batch = mixed_qkv.shape[0]
+        hv, v_dim, k_dim = state.shape[-3:]
+        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):
+            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]}"
+            )
+
+        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/rl_engine/reference/norm/qwen3_next_rms_norm.py b/rl_engine/reference/norm/qwen3_next_rms_norm.py
new file mode 100644
index 000000000..14ecdcfa6
--- /dev/null
+++ b/rl_engine/reference/norm/qwen3_next_rms_norm.py
@@ -0,0 +1,161 @@
+# 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. 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 +`` 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 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`` and, for the gated
+pair, ``docs/operators/qwen3-next-rms-norm-gated.md``.
+"""
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+
+from rl_engine.reference.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 gated convention is the strict
+    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.
+
+    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)}"
+            )
+        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
+
+    @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/reference/norm/rms_norm.py b/rl_engine/reference/norm/rms_norm.py
index 76dc13b49..e08738a2e 100644
--- a/rl_engine/reference/norm/rms_norm.py
+++ b/rl_engine/reference/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/rl_engine/runtime/registry.py b/rl_engine/runtime/registry.py
index a190bbca1..9a6494d5d 100644
--- a/rl_engine/runtime/registry.py
+++ b/rl_engine/runtime/registry.py
@@ -159,6 +159,18 @@ 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.reference.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.reference.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],
@@ -694,6 +716,8 @@ 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,
@@ -722,6 +746,8 @@ 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],
@@ -762,6 +788,12 @@ 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/validation/common/file_digest.py b/rl_engine/validation/common/file_digest.py
new file mode 100644
index 000000000..f8858a956
--- /dev/null
+++ b/rl_engine/validation/common/file_digest.py
@@ -0,0 +1,16 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Content identities for validation artifacts, including large tensor files."""
+
+import hashlib
+from pathlib import Path
+
+
+def file_sha256(path: str | Path) -> str:
+    """Hash a file in bounded chunks on every supported Python version."""
+    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()
diff --git a/rl_engine/validation/common/tensor_identity.py b/rl_engine/validation/common/tensor_identity.py
new file mode 100644
index 000000000..5d05c0fe9
--- /dev/null
+++ b/rl_engine/validation/common/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/rl_engine/validation/models/pytest_case_manifest.py b/rl_engine/validation/models/pytest_case_manifest.py
new file mode 100644
index 000000000..a25e41769
--- /dev/null
+++ b/rl_engine/validation/models/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.validation.models.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/validation/models/qwen3_next_attention_prior_art.py b/rl_engine/validation/models/qwen3_next_attention_prior_art.py
new file mode 100644
index 000000000..db705b56f
--- /dev/null
+++ b/rl_engine/validation/models/qwen3_next_attention_prior_art.py
@@ -0,0 +1,601 @@
+# 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, 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;
+* **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: 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]))
+    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.models.qwen3_next.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.models.qwen3_next.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 _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),
+        _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),
+        _guard("fa4_cute", _fa4),
+        _guard("te_fused", lambda: _te(False)),
+        _guard("te_training", lambda: _te(True)),
+        _guard("megatron_local", _megatron_local),
+    ]
+
+
+# --------------------------------------------------------------------------- #
+# 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 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)
+
+    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]:
+    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)
+        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 has_backward(cand):
+            out["backward_batch_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_batch_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: grad_batch(c, [(q, k, v)], [dy]))
+            for c in ready
+            if has_backward(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(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 = [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},
+        "target_tokens": TARGET,
+        "companion_tokens": list(COMPANIONS),
+        "candidates": [_one(c, target, others) for c in cands],
+        "latency": _latency(cands),
+    }
diff --git a/rl_engine/validation/models/qwen3_next_binary.py b/rl_engine/validation/models/qwen3_next_binary.py
new file mode 100644
index 000000000..929c6a6c5
--- /dev/null
+++ b/rl_engine/validation/models/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 json
+import os
+import shutil
+from pathlib import Path
+
+from rl_engine.validation.common.file_digest import file_sha256
+
+
+def _digest(path):
+    return file_sha256(Path(path))
+
+
+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.common.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/validation/models/qwen3_next_cases.py b/rl_engine/validation/models/qwen3_next_cases.py
new file mode 100644
index 000000000..72e1794c7
--- /dev/null
+++ b/rl_engine/validation/models/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/validation/models/qwen3_next_chain_report.py b/rl_engine/validation/models/qwen3_next_chain_report.py
new file mode 100644
index 000000000..28cc0fae8
--- /dev/null
+++ b/rl_engine/validation/models/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 (``tools/validation/models/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.validation.models.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.models.qwen3_next.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.contracts.numerical import (
+    BackendProvenance,
+    compute_logprob_aggregates,
+    default_clip_interval,
+    judge_logprob_aggregates,
+    resolve_comparison_roles,
+)
+from rl_engine.validation.models.chain_gate import CellOutput, ChainGateReport
+from rl_engine.validation.operators.forward_invariance import TensorComparisonDetail
+
+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/validation/models/qwen3_next_export_schema.json b/rl_engine/validation/models/qwen3_next_export_schema.json
new file mode 100644
index 000000000..e1389c4f5
--- /dev/null
+++ b/rl_engine/validation/models/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/validation/models/qwen3_next_l2.py b/rl_engine/validation/models/qwen3_next_l2.py
new file mode 100644
index 000000000..d20a76a88
--- /dev/null
+++ b/rl_engine/validation/models/qwen3_next_l2.py
@@ -0,0 +1,1052 @@
+# 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.models.qwen3_next.qwen3_next_subnode import SUBNODES, subnode_layout
+from rl_engine.validation.common.file_digest import file_sha256
+from rl_engine.validation.common.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.orchestrators.vime.experiments.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.engines.rollout.vllm.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")
+    digest = file_sha256(path)
+    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.validation.models.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/validation/models/qwen3_next_layer_cases.json b/rl_engine/validation/models/qwen3_next_layer_cases.json
new file mode 100644
index 000000000..4e1af4c67
--- /dev/null
+++ b/rl_engine/validation/models/qwen3_next_layer_cases.json
@@ -0,0 +1,122 @@
+{
+  "schema_version": 1,
+  "gates": {
+    "gdn": {
+      "script": "tools/validation/models/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": "tools/validation/models/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": "tools/validation/models/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": "tools/validation/models/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": "tools/validation/models/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/validation/models/qwen3_next_moe_prior_art.py b/rl_engine/validation/models/qwen3_next_moe_prior_art.py
new file mode 100644
index 000000000..7c0cc33c9
--- /dev/null
+++ b/rl_engine/validation/models/qwen3_next_moe_prior_art.py
@@ -0,0 +1,688 @@
+# 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: 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]))
+    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.models.qwen3_next 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.models.qwen3_next.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 _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)),
+        _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)),
+        _guard("megatron_te", lambda: _megatron(w)),
+        _guard("sglang_triton", lambda: _sglang(w, False)),
+        _guard("sglang_deterministic", lambda: _sglang(w, True)),
+    ]
+
+
+# --------------------------------------------------------------------------- #
+# 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 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):
+        return cand_grads(cand, rows, 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 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
+        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 not has_backward(cand):
+                continue
+
+            def step(c=cand):
+                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(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)
+    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},
+        "probe_rows": PROBE,
+        "candidates": [_one(c, x, w) for c in cands],
+        "latency": _latency(cands, PERF_TOKENS),
+    }
diff --git a/rl_engine/validation/models/qwen3_next_norm_manifest.json b/rl_engine/validation/models/qwen3_next_norm_manifest.json
new file mode 100644
index 000000000..9cf5977ef
--- /dev/null
+++ b/rl_engine/validation/models/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": {
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+      "content_hash": "00a6a3b5573ccbac2e01fadb673c2cd7bca172d40ddd73f95ed7afa5cbdb9dcd",
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+    "execution_dtype": "bfloat16",
+    "reference_dtype": "float32",
+    "accumulation_dtype": "float32",
+    "temperature": 1.0,
+    "loss_reduction": "sum_over_active_tokens_then_optional_mean_by_active_count",
+    "logprob_selection": "selected_token_logprob_on_active_mask",
+    "active_token_policy": "active selected tokens only",
+    "aggregates": [
+      "max_abs_dlogp",
+      "approx_kl0",
+      "clipfrac0"
+    ],
+    "clip_interval": [
+      0.8,
+      1.2
+    ],
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+    "comparison_roles_source": "rl_engine/kernels/gtest/tolerance_contract.json",
+    "forbidden_comparison_roles": [
+      "baseline",
+      "singleton_aggregate"
+    ],
+    "singleton_aggregate_note": "singleton_aggregate is a C2 execution/aggregation mode only. It must never populate comparison_lhs_role or comparison_rhs_role.",
+    "tf32_policy_ref": "rl_engine/kernels/gtest/tolerance_contract.json#/policy/tf32",
+    "tf32_note": "WS1 TF32 enable/disable is owned by the C1 contract; C2 gates must not introduce a private TF32 policy.",
+    "report_naming": {
+      "comparison_lhs_role": "from_c1_by_report_kind",
+      "comparison_rhs_role": "from_c1_by_report_kind",
+      "forbidden_in_reports": [
+        "baseline",
+        "singleton_aggregate"
+      ],
+      "singleton_aggregate_is": "c2_execution_aggregation_mode_only",
+      "note": "C2 freezes naming rules; C3+ emit reports that must obey these roles."
+    },
+    "backend_actual_semantics": {
+      "c2_representative_actual_source": "scripts/ws1_candidate_evidence.py runtime execution",
+      "full_model_runtime_observed_actual_owner": [
+        "C3",
+        "C8",
+        "C10",
+        "C11"
+      ],
+      "note": "C2 executes every representative case and records runtime-observed actual backend/kernel provenance. Later children own full-model dispatch provenance."
+    }
+  },
+  "stochastic_policy": {
+    "dropout": 0.0,
+    "attention_dropout": 0.0,
+    "sampling_in_logprob_parity": false,
+    "canonical_gate_uses_dropout_zero": true,
+    "rng_source": "manifest_seed_plus_logical_sample_token_identity",
+    "undeclared_randomness": "hard_fail",
+    "retained_stochastic_ops": []
+  },
+  "primary_matrix": {
+    "description": "Fixed #150 Batch \u00d7 Chunked-Prefill matrix prerequisite workload cells.",
+    "N": 4,
+    "batch_size_bn": 4,
+    "sample_ids": [
+      "s0",
+      "s1",
+      "s2",
+      "s3"
+    ],
+    "sample_order_fixed": true,
+    "batch_permutation": {
+      "enabled": true,
+      "permutation": [
+        2,
+        0,
+        3,
+        1
+      ],
+      "target_sample_position_in_bn": 0,
+      "note": "Permutation exercises layout invariance; logical compare restores sample_id order."
+    },
+    "chunk": {
+      "chunk_size_tokens": 7,
+      "require_ge_2_chunks": true,
+      "non_divisible_case": true,
+      "note": "Longest primary seq_len=19 with chunk_size=7 yields chunks [7,7,5]."
+    },
+    "cells": [
+      {
+        "cell_id": "B1-singleton_aggregate/full",
+        "batch_mode": "singleton_aggregate",
+        "batch_size_per_run": 1,
+        "num_runs": 4,
+        "prefill_mode": "full",
+        "aggregation": {
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+      },
+      {
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+        "batch_mode": "batched",
+        "batch_size_per_run": 4,
+        "num_runs": 1,
+        "prefill_mode": "full",
+        "aggregation": {
+          "order": "sample_ids",
+          "denominator": "active_token_count_across_all_samples"
+        }
+      },
+      {
+        "cell_id": "B1-singleton_aggregate/chunked",
+        "batch_mode": "singleton_aggregate",
+        "batch_size_per_run": 1,
+        "num_runs": 4,
+        "prefill_mode": "chunked",
+        "aggregation": {
+          "order": "sample_ids",
+          "denominator": "active_token_count_across_all_samples"
+        }
+      },
+      {
+        "cell_id": "BN/chunked",
+        "batch_mode": "batched",
+        "batch_size_per_run": 4,
+        "num_runs": 1,
+        "prefill_mode": "chunked",
+        "aggregation": {
+          "order": "sample_ids",
+          "denominator": "active_token_count_across_all_samples"
+        }
+      }
+    ]
+  },
+  "fixtures": {
+    "prompt_template": "ws1_fixed_token_fixture",
+    "dtype_for_token_tensors": "int64",
+    "position_ids": {
+      "basis": "logical_zero_based_per_sample",
+      "reset_after_pack_boundary": true
+    },
+    "attention_mask": {
+      "active_value": 1,
+      "padding_value": 0,
+      "causal": true
+    },
+    "primary_seq_len": 19,
+    "primary_prompt_len": 8,
+    "short_seq_len": 8,
+    "long_seq_len": 32,
+    "varlen_seq_lens": [
+      11,
+      16,
+      13,
+      19
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+    "padding": {
+      "modes": [
+        "right",
+        "left"
+      ],
+      "pad_token_id": 151643,
+      "primary_padded_len": 20
+    },
+    "packing": {
+      "status": "supported",
+      "implementation": "rl_engine.kernels.ops.pytorch.packing.pack.NativePackOp",
+      "packed_fixture": {
+        "sample_order": [
+          "s0",
+          "s1",
+          "s2",
+          "s3"
+        ],
+        "segment_lengths": [
+          11,
+          16,
+          13,
+          19
+        ],
+        "total_tokens": 59,
+        "restore_key": [
+          "sample_id",
+          "token_position"
+        ]
+      }
+    },
+    "loss_mask": {
+      "prompt_tokens_active": false,
+      "completion_tokens_active": true
+    },
+    "samples": [
+      {
+        "sample_id": "s0",
+        "seq_len": 11,
+        "prompt_len": 8,
+        "token_ids": [
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+          101,
+          102,
+          103,
+          104,
+          105,
+          106,
+          107,
+          200,
+          201,
+          202
+        ]
+      },
+      {
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+        "seq_len": 16,
+        "prompt_len": 8,
+        "token_ids": [
+          110,
+          111,
+          112,
+          113,
+          114,
+          115,
+          116,
+          117,
+          210,
+          211,
+          212,
+          213,
+          214,
+          215,
+          216,
+          217
+        ]
+      },
+      {
+        "sample_id": "s2",
+        "seq_len": 13,
+        "prompt_len": 8,
+        "token_ids": [
+          120,
+          121,
+          122,
+          123,
+          124,
+          125,
+          126,
+          127,
+          220,
+          221,
+          222,
+          223,
+          224
+        ]
+      },
+      {
+        "sample_id": "s3",
+        "seq_len": 19,
+        "prompt_len": 8,
+        "token_ids": [
+          130,
+          131,
+          132,
+          133,
+          134,
+          135,
+          136,
+          137,
+          230,
+          231,
+          232,
+          233,
+          234,
+          235,
+          236,
+          237,
+          238,
+          239,
+          240
+        ]
+      }
+    ],
+    "short_full_model_fixture": {
+      "fixture_id": "short_full_model_seq8",
+      "seq_len": 8,
+      "prompt_len": 4,
+      "token_ids": [
+        310,
+        311,
+        312,
+        313,
+        410,
+        411,
+        412,
+        413
+      ],
+      "note": "Shorter sequence on full architecture+weights only; never shrinks layers/hidden/heads/vocab.",
+      "candidate_case_ids": [
+        "short_full_model_seq8_qwen3_next_rms_norm",
+        "short_full_model_seq8_rms_norm_gated"
+      ]
+    },
+    "long_full_model_fixture": {
+      "fixture_id": "long_full_model_seq32",
+      "seq_len": 32,
+      "prompt_len": 16,
+      "token_ids": [
+        500,
+        501,
+        502,
+        503,
+        504,
+        505,
+        506,
+        507,
+        508,
+        509,
+        510,
+        511,
+        512,
+        513,
+        514,
+        515,
+        600,
+        601,
+        602,
+        603,
+        604,
+        605,
+        606,
+        607,
+        608,
+        609,
+        610,
+        611,
+        612,
+        613,
+        614,
+        615
+      ],
+      "note": "Long fixed sequence on the same full architecture and pinned weight snapshot.",
+      "candidate_case_ids": [
+        "long_full_model_seq32_qwen3_next_rms_norm",
+        "long_full_model_seq32_rms_norm_gated"
+      ]
+    },
+    "representative_full_model_fixture": {
+      "fixture_id": "rep_full_model_seq16",
+      "seq_len": 16,
+      "prompt_len": 8,
+      "sample_ids": [
+        "s0",
+        "s1",
+        "s2",
+        "s3"
+      ],
+      "note": "Primary variable-length matrix fixture; full architecture+weights.",
+      "candidate_case_ids": [
+        "rep_full_model_seq16_qwen3_next_rms_norm",
+        "rep_full_model_seq16_rms_norm_gated"
+      ]
+    },
+    "prompt_lens": [
+      8,
+      8,
+      8,
+      8
+    ],
+    "completion_lens": [
+      3,
+      8,
+      5,
+      11
+    ],
+    "max_completion_len": 11
+  },
+  "logical_identity": {
+    "key": [
+      "sample_id",
+      "token_position"
+    ],
+    "token_position_basis": "logical_unpadded_index_in_sample",
+    "restore_before_compare_after": [
+      "pad",
+      "pack",
+      "chunk",
+      "batch_permute"
+    ],
+    "gradient_singleton_aggregate": {
+      "definition": "N independent B=1 runs of the same N logical samples, aggregated with fixed sample order and active-token denominator",
+      "compare_to": "single B=N run of the same logical sample/token multiset",
+      "forbid_different_sample_sets": true
+    }
+  },
+  "capabilities": {
+    "required_chain_ops": [
+      {
+        "op": "qwen3_next_rms_norm",
+        "status": "required"
+      },
+      {
+        "op": "rms_norm_gated",
+        "status": "required"
+      }
+    ],
+    "operator_spec_map": {
+      "qwen3_next_rms_norm": "qwen3_next_rms_norm",
+      "rms_norm_gated": "rms_norm_gated"
+    }
+  },
+  "backend_profiles": {
+    "cuda_bf16": {
+      "backend_family": "cuda",
+      "execution_dtype": "bfloat16",
+      "required_nodes": [
+        {
+          "node": "qwen3_next_rms_norm",
+          "status": "declared",
+          "expected_backend_id": "cuda",
+          "expected_kernel_config_id": "rl_engine.kernels.ops.cuda.norm.rmsnorm.Qwen3NextRMSNormCudaOp",
+          "algorithm_property": "fixed row reduction and ordered FP32 parameter fold"
+        },
+        {
+          "node": "rms_norm_gated",
+          "status": "declared",
+          "expected_backend_id": "cuda",
+          "expected_kernel_config_id": "rl_engine.kernels.ops.cuda.norm.rmsnorm.Qwen3NextRMSNormGatedCudaOp",
+          "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/validation/models/qwen3_next_operator_cases.json b/rl_engine/validation/models/qwen3_next_operator_cases.json
new file mode 100644
index 000000000..101ad30f1
--- /dev/null
+++ b/rl_engine/validation/models/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 tools/validation/models/qwen3_next_operator_cases.py; case changes require review before a frozen run.",
+  "suites": {
+    "norm": [
+      "tests/models/qwen3_next/check_qwen3_next_norm_providers.py::test_custom_op_has_distinct_native_and_cuda_paths",
+      "tests/models/qwen3_next/check_qwen3_next_norm_providers.py::test_gdn_decode_provider_env_defaults_are_recorded",
+      "tests/models/qwen3_next/check_qwen3_next_norm_providers.py::test_our_gated_op_is_batch_invariant",
+      "tests/models/qwen3_next/check_qwen3_next_norm_providers.py::test_vllm_gated_norm_is_batch_invariant[forward_cuda]",
+      "tests/models/qwen3_next/check_qwen3_next_norm_providers.py::test_vllm_gated_norm_is_batch_invariant[forward_native]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_bad_weight_shape_raises",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[16]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[1]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[2]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[32]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[48]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[64]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_across_concurrency[8]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_slice[128]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_slice[2048]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_batch_invariance_with_padding",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_call_matches_forward",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_chunked_reduction_is_slice_invariant_on_device[128]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_chunked_reduction_is_slice_invariant_on_device[2048]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_default_offset_preserves_plain_convention",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_backward_matches_autograd_golden",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[16]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[1]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[2]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[32]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[48]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[512]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[64]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_batch_invariance[8]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_dgate_keeps_signed_zero_weight[dtype0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_dgate_keeps_signed_zero_weight[dtype1]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_dgate_keeps_signed_zero_weight[dtype2]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_dweight_is_batch_invariant[1]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_dweight_is_batch_invariant[512]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_dweight_is_batch_invariant[64]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_dweight_is_batch_invariant[8]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_matches_golden_within_contract",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_parameter_vjp_contributions_match_the_fold[sigmoid-dtype0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_parameter_vjp_contributions_match_the_fold[sigmoid-dtype1]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_parameter_vjp_contributions_match_the_fold[silu-dtype0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_parameter_vjp_contributions_match_the_fold[silu-dtype1]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rejects_bad_activation",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rejects_mismatched_gate",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[bf16-128-sigmoid-0.0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[bf16-128-sigmoid-1.0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[bf16-128-silu-0.0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[bf16-128-silu-1.0]",
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+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[bf16-2048-sigmoid-1.0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[bf16-2048-silu-0.0]",
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+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[bf16-5120-sigmoid-1.0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[bf16-5120-silu-0.0]",
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+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp16-2048-sigmoid-0.0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp16-2048-sigmoid-1.0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp16-2048-silu-0.0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp16-2048-silu-1.0]",
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+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp16-5120-silu-0.0]",
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+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_rstd_is_bitwise_identical_to_plain_kernel[fp32-5120-silu-1.0]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_sigmoid_activation",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_sigmoid_backward_uses_the_sigmoid_derivative",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_signed_zero_weight_is_preserved[dtype0]",
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+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_signed_zero_weight_is_preserved[dtype2]",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_swish_is_an_alias_for_silu",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_unit_weight_is_plain_norm_times_silu",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_weight_offset_is_applied_in_fp32",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_wrapper_rejects_equal_numel_wrong_shape",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_gated_zero_gate_zeroes_output",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_offset_equals_explicit_fp32_weight",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_offset_is_not_folded_through_bfloat16",
+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_parameter_contributions_reproduce_backward[dtype0-shape0-0.0]",
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+      "tests/models/qwen3_next/test_qwen3_next_norm.py::test_cuda_parameter_contributions_reproduce_backward[dtype1-shape1-1.0]",
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+      "tests/models/qwen3_next/check_gdn_recurrent_golden.py::test_recurrent_forward_fp32_widens_the_state[state_dtype0]",
+      "tests/models/qwen3_next/check_gdn_recurrent_golden.py::test_recurrent_forward_fp32_widens_the_state[state_dtype1]",
+      "tests/models/qwen3_next/check_gdn_recurrent_golden.py::test_recurrent_without_in_kernel_l2norm_differs",
+      "tests/models/qwen3_next/check_qwen3_next_batch_invariance.py::test_flashinfer_chunked_prefill_deviation_stays_rounding_scale",
+      "tests/models/qwen3_next/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_split_matches_one_pass[100]",
+      "tests/models/qwen3_next/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_split_matches_one_pass[128]",
+      "tests/models/qwen3_next/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_split_matches_one_pass[192]",
+      "tests/models/qwen3_next/check_qwen3_next_batch_invariance.py::test_triton_chunked_prefill_split_matches_one_pass[64]",
+      "tests/models/qwen3_next/check_qwen3_next_norm_providers.py::test_ours_tracks_each_vllm_path_within_bounds[forward_cuda]",
+      "tests/models/qwen3_next/check_qwen3_next_norm_providers.py::test_ours_tracks_each_vllm_path_within_bounds[forward_native]",
+      "tests/models/qwen3_next/check_qwen3_next_norm_providers.py::test_vllm_paths_disagree_only_within_bounds[dtype0-0.02-0.05]",
+      "tests/models/qwen3_next/check_qwen3_next_norm_providers.py::test_vllm_paths_disagree_only_within_bounds[dtype1-1e-05-1.0]"
+    ]
+  }
+}
diff --git a/rl_engine/validation/models/qwen3_next_rl_fixture.py b/rl_engine/validation/models/qwen3_next_rl_fixture.py
new file mode 100644
index 000000000..7942c7801
--- /dev/null
+++ b/rl_engine/validation/models/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.validation.common.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/rl_engine/validation/models/qwen3_next_workload.py b/rl_engine/validation/models/qwen3_next_workload.py
new file mode 100644
index 000000000..ee56f90da
--- /dev/null
+++ b/rl_engine/validation/models/qwen3_next_workload.py
@@ -0,0 +1,93 @@
+# 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
+from typing import Any
+
+from rl_engine.config.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/validation/operators/gradient_adapters.py b/rl_engine/validation/operators/gradient_adapters.py
index f22d80285..9831fb670 100644
--- a/rl_engine/validation/operators/gradient_adapters.py
+++ b/rl_engine/validation/operators/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/norm/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/backends/cuda/norm/rmsnorm.py", "csrc/cuda/norm/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/backends/cuda/norm/rmsnorm.py", "csrc/cuda/norm/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/validation/operators/operator_inputs.py b/rl_engine/validation/operators/operator_inputs.py
index ca3b7c120..1cd03d31f 100644
--- a/rl_engine/validation/operators/operator_inputs.py
+++ b/rl_engine/validation/operators/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/validation/operators/operator_specs.py b/rl_engine/validation/operators/operator_specs.py
index 2323f4787..7f17c369d 100644
--- a/rl_engine/validation/operators/operator_specs.py
+++ b/rl_engine/validation/operators/operator_specs.py
@@ -46,6 +46,30 @@ 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.reference.norm.qwen3_next_rms_norm.Qwen3NextRMSNormGatedOp"),
+        gold_method="forward_fp32",
+        candidate_paths={
+            "pytorch": ("rl_engine.reference.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.reference.norm.qwen3_next_rms_norm.Qwen3NextRMSNormOp"),
+        gold_method="forward_fp32",
+        candidate_paths={
+            "pytorch": ("rl_engine.reference.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/tests/models/qwen3_next/__init__.py b/tests/models/qwen3_next/__init__.py
new file mode 100644
index 000000000..988131360
--- /dev/null
+++ b/tests/models/qwen3_next/__init__.py
@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0
diff --git a/tests/models/qwen3_next/check_gdn_recurrent_golden.py b/tests/models/qwen3_next/check_gdn_recurrent_golden.py
new file mode 100644
index 000000000..043df421a
--- /dev/null
+++ b/tests/models/qwen3_next/check_gdn_recurrent_golden.py
@@ -0,0 +1,521 @@
+# 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. The integration isolation test
+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/models/qwen3_next/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.
+
+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
+
+import pytest
+import torch
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required")
+
+from rl_engine.reference.linear_attn import GatedDeltaRuleRecurrentStepOp  # noqa: E402
+from rl_engine.validation.models.qwen3_next_workload import FINGERPRINT  # noqa: E402
+
+# 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.contracts.numerical's
+# resolve_tolerance. For scale, the gate contract's
+# forward_accuracy/by_op_class/reduction row in
+# rl_engine/contracts/profiles/precision/ws1.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
+# tools/validation/models/ws1_gdn_provider_agreement.py, so both use the same bounds.
+_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")
+    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", 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)
+
+    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"
+    assert torch.equal(state_ref[0], inp["state"][0]), "provider must leave block 0 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_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()
+
+    # 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"
+    # 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
+
+
+# --------------------------------------------------------------------------- #
+# 4. The causal-conv1d state update, the other half of a decode step
+# --------------------------------------------------------------------------- #
+# 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():
+    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.reference.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("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.reference.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.
+
+    Measured by tools/validation/models/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.
+    """
+    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
+
+
+@pytest.mark.parametrize("batch", [1, 17])
+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
+
+
+def test_conv_null_block_id_is_skipped():
+    from rl_engine.reference.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.reference.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.reference.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.reference.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)
+
+
+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/models/qwen3_next/check_qwen3_next_attention.py b/tests/models/qwen3_next/check_qwen3_next_attention.py
new file mode 100644
index 000000000..9dfe6c267
--- /dev/null
+++ b/tests/models/qwen3_next/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.backends.cuda.attention.deterministic_attn import DeterministicAttentionOp
+from rl_engine.backends.extension import _C
+from rl_engine.validation.common.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/models/qwen3_next/check_qwen3_next_batch_invariance.py b/tests/models/qwen3_next/check_qwen3_next_batch_invariance.py
new file mode 100644
index 000000000..84b096f8b
--- /dev/null
+++ b/tests/models/qwen3_next/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/scripts/run_qwen3_next_operator_gates.sh``
+or explicitly:
+
+    CUDA_VISIBLE_DEVICES=1 pytest tests/models/qwen3_next/check_qwen3_next_batch_invariance.py -v
+"""
+
+from __future__ import annotations
+
+from functools import partial
+
+import pytest
+import torch
+
+from rl_engine.validation.common.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/models/qwen3_next/check_qwen3_next_conv_bridge.py b/tests/models/qwen3_next/check_qwen3_next_conv_bridge.py
new file mode 100644
index 000000000..963d8b630
--- /dev/null
+++ b/tests/models/qwen3_next/check_qwen3_next_conv_bridge.py
@@ -0,0 +1,99 @@
+# 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.engines.train.vllm.qwen3_next_conv import (
+    _provider,
+    causal_conv_sequence,
+)
+from rl_engine.validation.common.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/models/qwen3_next/check_qwen3_next_core_matrix.py b/tests/models/qwen3_next/check_qwen3_next_core_matrix.py
new file mode 100644
index 000000000..823471772
--- /dev/null
+++ b/tests/models/qwen3_next/check_qwen3_next_core_matrix.py
@@ -0,0 +1,101 @@
+# 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.engines.train.vllm.qwen3_next_provider import (
+    GDNProviderConfig,
+    GDNState,
+    shared_gdn_core,
+)
+from rl_engine.validation.common.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/models/qwen3_next/check_qwen3_next_engine_metadata.py b/tests/models/qwen3_next/check_qwen3_next_engine_metadata.py
new file mode 100644
index 000000000..2dd7ce0f5
--- /dev/null
+++ b/tests/models/qwen3_next/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.engines.rollout.vllm.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.engines.rollout.vllm.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/models/qwen3_next/check_qwen3_next_forward.py b/tests/models/qwen3_next/check_qwen3_next_forward.py
new file mode 100644
index 000000000..ec089be31
--- /dev/null
+++ b/tests/models/qwen3_next/check_qwen3_next_forward.py
@@ -0,0 +1,291 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Mandatory shared CUDA GEMM/MoE/logprob checks; these do not claim model L2."""
+
+from dataclasses import replace
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from rl_engine.contracts.operators.logprob import (
+    LogprobContract,
+    LogprobDType,
+    LogprobRole,
+    MaskSpec,
+    ReductionSpec,
+    ShardingSpec,
+)
+from rl_engine.models.qwen3_next.qwen3_next_forward import (
+    combine_routes,
+    shared_linear,
+    shared_logprob,
+    shared_moe,
+    stable_top10_routes,
+)
+from rl_engine.validation.common.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
+
+
+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)
+    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.models.qwen3_next.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.models.qwen3_next.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.models.qwen3_next.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.models.qwen3_next 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/models/qwen3_next/check_qwen3_next_gdn_bridge.py b/tests/models/qwen3_next/check_qwen3_next_gdn_bridge.py
new file mode 100644
index 000000000..9656b80c7
--- /dev/null
+++ b/tests/models/qwen3_next/check_qwen3_next_gdn_bridge.py
@@ -0,0 +1,118 @@
+# 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.engines.train.vllm.qwen3_next_gdn import (
+    _provider,
+    packed_decode_training_step,
+)
+from rl_engine.reference.linear_attn import GatedDeltaRuleRecurrentStepOp
+from rl_engine.validation.common.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/models/qwen3_next/check_qwen3_next_gdn_sequence.py b/tests/models/qwen3_next/check_qwen3_next_gdn_sequence.py
new file mode 100644
index 000000000..6fa53afb2
--- /dev/null
+++ b/tests/models/qwen3_next/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.engines.train.vllm.qwen3_next_gdn import packed_recurrent_sequence
+from rl_engine.validation.common.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/models/qwen3_next/check_qwen3_next_multigpu.py b/tests/models/qwen3_next/check_qwen3_next_multigpu.py
new file mode 100644
index 000000000..ede9df2f7
--- /dev/null
+++ b/tests/models/qwen3_next/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/models/qwen3_next/check_qwen3_next_native_rollout.py b/tests/models/qwen3_next/check_qwen3_next_native_rollout.py
new file mode 100644
index 000000000..67e7cd08d
--- /dev/null
+++ b/tests/models/qwen3_next/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.engines.rollout.vllm import qwen3_next_native_rollout as rollout
+from rl_engine.validation.models.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/models/qwen3_next/check_qwen3_next_norm_providers.py b/tests/models/qwen3_next/check_qwen3_next_norm_providers.py
new file mode 100644
index 000000000..f4d25d2d8
--- /dev/null
+++ b/tests/models/qwen3_next/check_qwen3_next_norm_providers.py
@@ -0,0 +1,239 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""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
+The integration isolation test 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/models/qwen3_next/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 records two things so a vLLM upgrade cannot move them silently:
+
+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 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:
+
+===============================  ==================  ================
+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.reference.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. Provider facts -- recorded, not a runtime dispatch check
+# --------------------------------------------------------------------------- #
+def test_custom_op_has_distinct_native_and_cuda_paths(vllm_config_ctx):
+    """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():
+    """Record the GDN decode env defaults.
+
+    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
+
+    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",
+    [
+        # 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),
+        # 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),
+    ],
+)
+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)
+
+    # 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%}"
+
+
+# --------------------------------------------------------------------------- #
+# 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/models/qwen3_next/check_qwen3_next_paged_attention.py b/tests/models/qwen3_next/check_qwen3_next_paged_attention.py
new file mode 100644
index 000000000..6b2663fba
--- /dev/null
+++ b/tests/models/qwen3_next/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.engines.rollout.vllm.qwen3_next_vllm_attention import (
+    SharedAttentionImpl,
+    SharedAttentionMetadata,
+)
+from rl_engine.validation.common.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/models/qwen3_next/check_qwen3_next_shared_core.py b/tests/models/qwen3_next/check_qwen3_next_shared_core.py
new file mode 100644
index 000000000..f850b0bee
--- /dev/null
+++ b/tests/models/qwen3_next/check_qwen3_next_shared_core.py
@@ -0,0 +1,87 @@
+# 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.engines.train.vllm.qwen3_next_provider import (
+    GDNProviderConfig,
+    GDNState,
+    shared_gdn_core,
+)
+from rl_engine.validation.common.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/models/qwen3_next/check_qwen3_next_vime_weight_hooks.py b/tests/models/qwen3_next/check_qwen3_next_vime_weight_hooks.py
new file mode 100644
index 000000000..8931e2a2f
--- /dev/null
+++ b/tests/models/qwen3_next/check_qwen3_next_vime_weight_hooks.py
@@ -0,0 +1,95 @@
+# 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.orchestrators.vime.providers.qwen3_next_vime import (
+    SharedInputNorm,
+    install_vime_gdn_weight_hooks,
+)
+from rl_engine.validation.common.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/models/qwen3_next/check_qwen3_next_vllm_model_contract.py b/tests/models/qwen3_next/check_qwen3_next_vllm_model_contract.py
new file mode 100644
index 000000000..08862e02f
--- /dev/null
+++ b/tests/models/qwen3_next/check_qwen3_next_vllm_model_contract.py
@@ -0,0 +1,283 @@
+# 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.engines.rollout.vllm import qwen3_next_vllm_model as adapter
+from rl_engine.models.qwen3_next.qwen3_next_tp_blocks import shard_attention_weights
+from rl_engine.validation.common.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.engines.rollout.vllm.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.engines.rollout.vllm.qwen3_next_vllm import SharedQwenGDN
+    from rl_engine.integrations.engines.rollout.vllm.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.engines.rollout.vllm 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/models/qwen3_next/test_gdn_state_contract.py b/tests/models/qwen3_next/test_gdn_state_contract.py
new file mode 100644
index 000000000..c8aaec572
--- /dev/null
+++ b/tests/models/qwen3_next/test_gdn_state_contract.py
@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Provider-independent cache contracts, collected in the ordinary CPU suite."""
+
+import pytest
+import torch
+
+from rl_engine.reference.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
+
+
+def test_softplus_large_branch_has_finite_gradient():
+    from rl_engine.reference.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]))
+
+
+def test_chunked_sum_keeps_fixed_order_for_non_multiple_of_32_width():
+    from rl_engine.reference.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])
diff --git a/tests/models/qwen3_next/test_qwen3_next_binary_evidence.py b/tests/models/qwen3_next/test_qwen3_next_binary_evidence.py
new file mode 100644
index 000000000..19cf4309c
--- /dev/null
+++ b/tests/models/qwen3_next/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.validation.models.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.common.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/models/qwen3_next/test_qwen3_next_case_manifest.py b/tests/models/qwen3_next/test_qwen3_next_case_manifest.py
new file mode 100644
index 000000000..7d78a236f
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_case_manifest.py
@@ -0,0 +1,109 @@
+# 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.validation.models.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",
+        # 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",
+        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[3]))
+    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/models/qwen3_next/test_qwen3_next_chain_report.py b/tests/models/qwen3_next/test_qwen3_next_chain_report.py
new file mode 100644
index 000000000..1d3164404
--- /dev/null
+++ b/tests/models/qwen3_next/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.contracts.numerical import load_contract
+from rl_engine.validation.models import qwen3_next_chain_report as chain
+from rl_engine.validation.models.chain_gate import ChainGateReport
+
+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/models/qwen3_next/test_qwen3_next_checkpoint.py b/tests/models/qwen3_next/test_qwen3_next_checkpoint.py
new file mode 100644
index 000000000..95672f6a1
--- /dev/null
+++ b/tests/models/qwen3_next/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[3] / "tools/validation/models"))
+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/models/qwen3_next/test_qwen3_next_engine_weights.py b/tests/models/qwen3_next/test_qwen3_next_engine_weights.py
new file mode 100644
index 000000000..b8afd7661
--- /dev/null
+++ b/tests/models/qwen3_next/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.models.qwen3_next.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.validation.common.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/models/qwen3_next/test_qwen3_next_environment_identity.py b/tests/models/qwen3_next/test_qwen3_next_environment_identity.py
new file mode 100644
index 000000000..26d3e7231
--- /dev/null
+++ b/tests/models/qwen3_next/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[3] / "tools/validation/models"))
+
+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/models/qwen3_next/test_qwen3_next_evidence_redaction.py b/tests/models/qwen3_next/test_qwen3_next_evidence_redaction.py
new file mode 100644
index 000000000..5ceb298e3
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_evidence_redaction.py
@@ -0,0 +1,118 @@
+# 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[3]
+sys.path.insert(0, str(ROOT / "tools/validation/models"))
+
+import qwen3_next_publish_evidence as publisher  # noqa: E402
+
+PATTERNS = (
+    r"(?<![\w.>-])/data/",
+    r"(?<![\w.>-])/home/",
+    r"(?<![\w.>-])/raid/",
+    r"(?<![\w.>-])/scratch",
+    r"\bdgx-",
+    r"(?i)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": "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)
diff --git a/tests/models/qwen3_next/test_qwen3_next_failure.py b/tests/models/qwen3_next/test_qwen3_next_failure.py
new file mode 100644
index 000000000..21de8b2c1
--- /dev/null
+++ b/tests/models/qwen3_next/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[3] / "tools/validation/models"))
+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/models/qwen3_next/test_qwen3_next_forward_contract.py b/tests/models/qwen3_next/test_qwen3_next_forward_contract.py
new file mode 100644
index 000000000..2b8fdb5b3
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_forward_contract.py
@@ -0,0 +1,66 @@
+# 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.models.qwen3_next 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)),
+        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):
+    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/models/qwen3_next/test_qwen3_next_imported_binary.py b/tests/models/qwen3_next/test_qwen3_next_imported_binary.py
new file mode 100644
index 000000000..33116087c
--- /dev/null
+++ b/tests/models/qwen3_next/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.common 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/models/qwen3_next/test_qwen3_next_installed_provider_identity.py b/tests/models/qwen3_next/test_qwen3_next_installed_provider_identity.py
new file mode 100644
index 000000000..9bfb87677
--- /dev/null
+++ b/tests/models/qwen3_next/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[3] / "tools/validation/models"))
+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/models/qwen3_next/test_qwen3_next_l2.py b/tests/models/qwen3_next/test_qwen3_next_l2.py
new file mode 100644
index 000000000..9c213ff3f
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_l2.py
@@ -0,0 +1,725 @@
+# 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.validation.models.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.models.qwen3_next.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.validation.models.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.validation.models.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.validation.models.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[3] / "tools/validation/models"))
+    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[3] / "tools/validation/models"))
+    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[3] / "tools/validation/models"))
+    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.validation.models.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.validation.models.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.validation.models.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.engines.rollout.vllm.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.validation.models.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.validation.models.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.validation.models.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.validation.models.qwen3_next_binary import (
+        archive_provider_binary,
+        validate_binary_record,
+    )
+    from rl_engine.validation.models.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/models/qwen3_next/test_qwen3_next_layers.py b/tests/models/qwen3_next/test_qwen3_next_layers.py
new file mode 100644
index 000000000..b3bf66e88
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_layers.py
@@ -0,0 +1,138 @@
+# 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[3]
+sys.path.insert(0, str(ROOT / "tools/validation/models"))
+
+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.validation.models.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/models/qwen3_next/test_qwen3_next_logprob_adapter.py b/tests/models/qwen3_next/test_qwen3_next_logprob_adapter.py
new file mode 100644
index 000000000..db95b6492
--- /dev/null
+++ b/tests/models/qwen3_next/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.models.qwen3_next.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/models/qwen3_next/test_qwen3_next_native_recompute.py b/tests/models/qwen3_next/test_qwen3_next_native_recompute.py
new file mode 100644
index 000000000..00e0dd3a8
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_native_recompute.py
@@ -0,0 +1,235 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from pathlib import Path
+
+import pytest
+import torch
+
+from rl_engine.integrations.orchestrators.vime.experiments.qwen3_next.native_recompute import (
+    response_rows,
+)
+from rl_engine.integrations.orchestrators.vime.providers.qwen3_next_vime_runtime import runtime_argv
+from rl_engine.models.qwen3_next.qwen3_next_subnode import SUBNODES, subnode_layout
+from rl_engine.validation.common.file_digest import file_sha256
+
+
+@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
+
+    from rl_engine.integrations.orchestrators.vime.experiments.qwen3_next import (
+        native_recompute as recompute,
+    )
+    from rl_engine.validation.models.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)
+        record["sha256"] = file_sha256(record["path"])
+        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.validation.models.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/models/qwen3_next/test_qwen3_next_norm.py b/tests/models/qwen3_next/test_qwen3_next_norm.py
new file mode 100644
index 000000000..4497ff87f
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_norm.py
@@ -0,0 +1,884 @@
+# 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 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.
+
+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.contracts.numerical import load_contract, resolve_tolerance
+from rl_engine.reference.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
+
+_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)
+    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.reference.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.reference.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(), **_forward_tol(dtype))
+
+
+@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(), **_forward_tol(dtype))
+
+
+@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(), **_forward_tol(dtype))
+
+
+# --------------------------------------------------------------------------- #
+# 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.backends.extension import _C, _EXT_AVAILABLE
+
+        _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
+
+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.backends.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.backends.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.backends.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.backends.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.backends.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, **_forward_tol(torch.bfloat16))
+
+
+@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.backends.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
+
+
+# --------------------------------------------------------------------------- #
+# 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.backends.extension import _C as _C_probe
+        from rl_engine.backends.extension 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"
+)
+
+
+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.backends.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, **_forward_tol(torch.bfloat16))
+
+
+@requires_cuda_gated
+@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. 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.backends.extension import _C
+
+    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)
+
+
+@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.backends.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.backends.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.backends.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, **_forward_tol(torch.bfloat16))
+
+
+@requires_cuda_gated
+def test_cuda_gated_sigmoid_activation():
+    from rl_engine.backends.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()
+    ref = rmsnorm_cuda(x, w, eps=_EPS).float() * torch.sigmoid(gate.float())
+    torch.testing.assert_close(got, ref, **_forward_tol(torch.bfloat16))
+
+
+@requires_cuda_gated
+def test_cuda_gated_backward_matches_autograd_golden():
+    """dx, dweight and dgate against the fp32 reference's autograd."""
+    from rl_engine.backends.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.backends.cuda.norm.rmsnorm import rmsnorm_gated_cuda
+    from rl_engine.backends.extension import _C
+    from rl_engine.ops.autograd.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.backends.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
+@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.backends.cuda.norm.rmsnorm import Qwen3NextRMSNormGatedCudaOp, _fold_dweight_rows
+
+    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(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)
+
+
+@requires_cuda_gated
+def test_cuda_gated_wrapper_rejects_equal_numel_wrong_shape():
+    from rl_engine.backends.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
+def test_cuda_gated_sigmoid_backward_uses_the_sigmoid_derivative():
+    """The activation gradient has two branches; only silu was covered."""
+    from rl_engine.backends.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.backends.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")
+
+
+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.backends.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.backends.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.backends.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`
+# --------------------------------------------------------------------------- #
+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.reference.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.reference.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.reference.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))
+
+
+@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.backends.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.backends.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp, RMSNormCudaOp
+    from rl_engine.ops.autograd.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.backends.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()
+
+
+@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
+
+
+@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.backends.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))
+
+
+@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.backends.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/models/qwen3_next/test_qwen3_next_operator_gates.py b/tests/models/qwen3_next/test_qwen3_next_operator_gates.py
new file mode 100644
index 000000000..9f2832fa0
--- /dev/null
+++ b/tests/models/qwen3_next/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.validation.models.qwen3_next_cases import execution_verdict
+from tools.validation.models 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/models/qwen3_next/test_qwen3_next_profile.py b/tests/models/qwen3_next/test_qwen3_next_profile.py
new file mode 100644
index 000000000..7013c7f0b
--- /dev/null
+++ b/tests/models/qwen3_next/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.orchestrators.vime.experiments.qwen3_next import 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.validation.models.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/models/qwen3_next/test_qwen3_next_rl_fixture.py b/tests/models/qwen3_next/test_qwen3_next_rl_fixture.py
new file mode 100644
index 000000000..31a823368
--- /dev/null
+++ b/tests/models/qwen3_next/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.validation.models.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/models/qwen3_next/test_qwen3_next_rotary_dtype.py b/tests/models/qwen3_next/test_qwen3_next_rotary_dtype.py
new file mode 100644
index 000000000..919b44088
--- /dev/null
+++ b/tests/models/qwen3_next/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.models.qwen3_next.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/models/qwen3_next/test_qwen3_next_run.py b/tests/models/qwen3_next/test_qwen3_next_run.py
new file mode 100644
index 000000000..3b99d763e
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_run.py
@@ -0,0 +1,120 @@
+# 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[3] / "tools/validation/models"))
+
+import qwen3_next_run as run  # noqa: E402
+
+
+def manifest(tmp_path, stage="layers", script=None):
+    source = tmp_path / "source"
+    (source / "tools/validation/models").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 tools/validation/models/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/models/qwen3_next/test_qwen3_next_storage.py b/tests/models/qwen3_next/test_qwen3_next_storage.py
new file mode 100644
index 000000000..e897ab7af
--- /dev/null
+++ b/tests/models/qwen3_next/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[3] / "tools/validation/models"))
+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/models/qwen3_next/test_qwen3_next_subnode.py b/tests/models/qwen3_next/test_qwen3_next_subnode.py
new file mode 100644
index 000000000..00919e787
--- /dev/null
+++ b/tests/models/qwen3_next/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.models.qwen3_next 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/models/qwen3_next/test_qwen3_next_tp_blocks.py b/tests/models/qwen3_next/test_qwen3_next_tp_blocks.py
new file mode 100644
index 000000000..14de47b97
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_tp_blocks.py
@@ -0,0 +1,162 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""HF mappings, KV replica ownership and official Qwen3-Next attention/MoE dimensions."""
+
+import pytest
+import torch
+
+from rl_engine.models.qwen3_next import qwen3_next_tp
+from rl_engine.models.qwen3_next import qwen3_next_tp_blocks as blocks
+from rl_engine.validation.common.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.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",
+    [
+        "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)
+
+
+@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/models/qwen3_next/test_qwen3_next_tp_gdn.py b/tests/models/qwen3_next/test_qwen3_next_tp_gdn.py
new file mode 100644
index 000000000..953472169
--- /dev/null
+++ b/tests/models/qwen3_next/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.models.qwen3_next.qwen3_next_tp_gdn import (
+    _GLOBAL_SHAPES,
+    assemble_gdn_weights,
+    shard_gdn_weights,
+)
+from rl_engine.validation.common.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)
diff --git a/tests/models/qwen3_next/test_qwen3_next_training_audit.py b/tests/models/qwen3_next/test_qwen3_next_training_audit.py
new file mode 100644
index 000000000..72df6b490
--- /dev/null
+++ b/tests/models/qwen3_next/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.orchestrators.vime.providers.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/models/qwen3_next/test_qwen3_next_training_checkpoint.py b/tests/models/qwen3_next/test_qwen3_next_training_checkpoint.py
new file mode 100644
index 000000000..f486a09b5
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_training_checkpoint.py
@@ -0,0 +1,542 @@
+# 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[3] / "tools/validation/models"))
+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.orchestrators.vime.providers.qwen3_next_training import (  # noqa: E402
+    capture_rng_state,
+    state_digest,
+)
+from rl_engine.validation.models.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/models/qwen3_next/test_qwen3_next_training_closeout.py b/tests/models/qwen3_next/test_qwen3_next_training_closeout.py
new file mode 100644
index 000000000..4f0e41e0e
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_training_closeout.py
@@ -0,0 +1,434 @@
+# 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.models.qwen3_next.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[3] / "tools/validation/models"))
+import qwen3_next_training as runner  # noqa: E402
+
+from rl_engine.integrations.orchestrators.vime.experiments.qwen3_next import (  # noqa: E402
+    native_training as native,
+)
+from rl_engine.integrations.orchestrators.vime.providers import (  # noqa: E402
+    qwen3_next_training as observer,
+)
+
+
+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.validation.models 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/models/qwen3_next/test_qwen3_next_training_export.py b/tests/models/qwen3_next/test_qwen3_next_training_export.py
new file mode 100644
index 000000000..bec03f57a
--- /dev/null
+++ b/tests/models/qwen3_next/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[3] / "tools/validation/models"))
+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/models/qwen3_next/test_qwen3_next_vllm_attention_metadata.py b/tests/models/qwen3_next/test_qwen3_next_vllm_attention_metadata.py
new file mode 100644
index 000000000..c47aaa08d
--- /dev/null
+++ b/tests/models/qwen3_next/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.engines.rollout.vllm.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)
diff --git a/tests/models/qwen3_next/test_qwen3_next_workload.py b/tests/models/qwen3_next/test_qwen3_next_workload.py
new file mode 100644
index 000000000..9710f0151
--- /dev/null
+++ b/tests/models/qwen3_next/test_qwen3_next_workload.py
@@ -0,0 +1,66 @@
+# 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 pathlib import Path
+
+import pytest
+
+from rl_engine.config.workload import WorkloadError, load_manifest, manifest_identity_hash
+from rl_engine.validation.models.qwen3_next_workload import validate_norm_manifest
+from rl_engine.validation.operators.gradient_adapters import get_adapter, resolve_profile_candidate
+
+MANIFEST = (
+    Path(__file__).resolve().parents[3]
+    / "rl_engine/validation/models/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.validation.models.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/ops/norm/test_rms_norm.py b/tests/ops/norm/test_rms_norm.py
index 90d7bab23..352612805 100644
--- a/tests/ops/norm/test_rms_norm.py
+++ b/tests/ops/norm/test_rms_norm.py
@@ -17,10 +17,11 @@
 try:
     from rl_engine.backends.extension 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
@@ -251,7 +252,7 @@ def test_backward_batch_invariance_slice():
 # 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
+    from rl_engine.backends.cuda.norm import rmsnorm as cuda_rmsnorm
 
     monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", False)
     monkeypatch.setattr(cuda_rmsnorm, "_C", None)
@@ -260,10 +261,10 @@ def test_cuda_op_construction_fails_without_extension(monkeypatch):
 
 
 def test_cuda_op_construction_fails_when_symbols_missing(monkeypatch):
-    from rl_engine.kernels.ops.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.backends.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())
@@ -273,8 +274,8 @@ class _WithoutRMSNorm:  # a built extension that lacks the rmsnorm symbols
 
 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
+    from rl_engine.backends.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.runtime.registry import KernelRegistry
 
     monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", False)
     monkeypatch.setattr(cuda_rmsnorm, "_C", None)
@@ -286,30 +287,66 @@ def test_registry_falls_back_to_native_without_extension(monkeypatch):
 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
+    from rl_engine.backends.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.runtime.registry import KernelRegistry
 
     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.backends.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.runtime.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
-    from rl_engine.kernels.registry import KernelRegistry
+    from rl_engine.backends.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.runtime.registry import KernelRegistry
 
     monkeypatch.setattr(cuda_rmsnorm, "_EXT_AVAILABLE", True)
     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)
 
 
diff --git a/tests/runtime/test_dispatch.py b/tests/runtime/test_dispatch.py
index 3c47bbbcf..6517d3309 100644
--- a/tests/runtime/test_dispatch.py
+++ b/tests/runtime/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
@@ -258,3 +259,67 @@ def test_executor_flow():
         print("\n All infrastructure tests passed!")
     except Exception as e:
         print(f"\n Test failed with error: {e}")
+
+
+_QWEN3_NEXT_NORMS = [
+    pytest.param(
+        "rms_norm_gated",
+        OpBackend.CUDA_RMS_NORM_GATED,
+        OpBackend.PYTORCH_NATIVE_RMS_NORM_GATED,
+        "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][op_type] == [ref_backend], platform
+
+
+@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.backends.cuda.norm import rmsnorm as cuda_rmsnorm
+    from rl_engine.reference.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"):
+        getattr(cuda_rmsnorm, cuda_cls)()
+
+    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/tests/validation/common/test_file_digest.py b/tests/validation/common/test_file_digest.py
new file mode 100644
index 000000000..4131fd1b2
--- /dev/null
+++ b/tests/validation/common/test_file_digest.py
@@ -0,0 +1,22 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+import hashlib
+
+import pytest
+
+from rl_engine.validation.common.file_digest import file_sha256
+
+
+@pytest.mark.parametrize("size", [0, 17, 8 * 1024 * 1024 + 17])
+def test_artifact_sha256_without_python311_api(tmp_path, monkeypatch, size):
+    monkeypatch.delattr(hashlib, "file_digest", raising=False)
+    content = bytes(range(256)) * (size // 256) + bytes(range(size % 256))
+    path = tmp_path / "artifact.bin"
+    path.write_bytes(content)
+    assert file_sha256(path) == hashlib.sha256(content).hexdigest()
+
+
+def test_missing_artifact_is_not_a_valid_digest(tmp_path):
+    with pytest.raises(FileNotFoundError):
+        file_sha256(tmp_path / "missing.bin")
diff --git a/tests/validation/common/test_tensor_identity.py b/tests/validation/common/test_tensor_identity.py
new file mode 100644
index 000000000..dd3a1ed13
--- /dev/null
+++ b/tests/validation/common/test_tensor_identity.py
@@ -0,0 +1,44 @@
+# 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.validation.common.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())
diff --git a/tests/validation/operators/test_ws1_ascend_closeout.py b/tests/validation/operators/test_ws1_ascend_closeout.py
index 66c8ae4a6..b9b0fe331 100644
--- a/tests/validation/operators/test_ws1_ascend_closeout.py
+++ b/tests/validation/operators/test_ws1_ascend_closeout.py
@@ -187,9 +187,15 @@ 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
@@ -197,6 +203,22 @@ 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
diff --git a/tests/validation/operators/test_ws1_gtest_gpu.py b/tests/validation/operators/test_ws1_gtest_gpu.py
index bcaebc2f5..60ac5102c 100644
--- a/tests/validation/operators/test_ws1_gtest_gpu.py
+++ b/tests/validation/operators/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",
diff --git a/tools/validation/models/plot_qwen3_next_attention_prior_art.py b/tools/validation/models/plot_qwen3_next_attention_prior_art.py
new file mode 100644
index 000000000..6a3fd7a3f
--- /dev/null
+++ b/tools/validation/models/plot_qwen3_next_attention_prior_art.py
@@ -0,0 +1,152 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Render the TP4 attention prior-art report as a PNG.
+
+    python tools/validation/models/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",
+    "#6b2a0e",
+    "#c96a2c",
+    "#f0a878",
+)
+
+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",
+    "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_batch_bitwise", "dq", "bwd dq (packed)"),
+    ("backward_batch_bitwise", "dk", "bwd dk (packed)"),
+    ("backward_batch_bitwise", "dv", "bwd dv (packed)"),
+)
+
+
+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/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()
diff --git a/tools/validation/models/plot_qwen3_next_moe_prior_art.py b/tools/validation/models/plot_qwen3_next_moe_prior_art.py
new file mode 100644
index 000000000..21cee4fb9
--- /dev/null
+++ b/tools/validation/models/plot_qwen3_next_moe_prior_art.py
@@ -0,0 +1,162 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Render the routed-MoE prior-art report as a PNG.
+
+Needs only the report JSON and matplotlib, so it runs anywhere:
+
+    python tools/validation/models/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", "#f6bd94", "#d9773f", "#6b2a0e")
+
+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",
+    "megatron_te": "Megatron-core + TE (VIME config)",
+    "sglang_triton": "SGLang fused_moe (default)",
+    "sglang_deterministic": "SGLang fused_moe (deterministic)",
+}
+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/tools/validation/models/plot_qwen3_next_norm_evidence.py b/tools/validation/models/plot_qwen3_next_norm_evidence.py
new file mode 100644
index 000000000..5067d4d7c
--- /dev/null
+++ b/tools/validation/models/plot_qwen3_next_norm_evidence.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Plot a report from tools/validation/models/qwen3_next_norm_evidence.py: one 2x2 figure per op.
+
+    python tools/validation/models/plot_qwen3_next_norm_evidence.py report.json
+
+Writes figure[-<op>].png beside the report.
+"""
+
+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)", "*").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=(14, 10), layout="constrained")
+    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\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]
+        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.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.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"
+        hidden = data.get("hidden", report.get("hidden"))
+        title = (
+            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)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/validation/models/qwen3_next_adapter_check.py b/tools/validation/models/qwen3_next_adapter_check.py
new file mode 100644
index 000000000..6b15352fc
--- /dev/null
+++ b/tools/validation/models/qwen3_next_adapter_check.py
@@ -0,0 +1,326 @@
+#!/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.validation.common.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.engines.rollout.vllm.qwen3_next_vllm import (
+        SharedGDNMetadataBuilder,
+        SharedQwenGDN,
+    )
+    from rl_engine.integrations.orchestrators.vime.providers.qwen3_next_vime import (
+        get_qwen3_next_shared_spec,
+    )
+
+    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.common.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/tools/validation/models/qwen3_next_attention_prior_art.py b/tools/validation/models/qwen3_next_attention_prior_art.py
new file mode 100644
index 000000000..6f4f0e485
--- /dev/null
+++ b/tools/validation/models/qwen3_next_attention_prior_art.py
@@ -0,0 +1,122 @@
+#!/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.
+``tools/validation/models/plot_qwen3_next_attention_prior_art.py`` turns the report into a figure.
+
+    python tools/validation/models/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[3]
+sys.path.insert(0, str(ROOT))
+
+import torch  # noqa: E402
+
+from rl_engine.validation.models.qwen3_next_attention_prior_art import (  # noqa: E402
+    attention_report,
+)
+
+LIBRARIES = (
+    "vllm",
+    "flashinfer-python",
+    "transformers",
+    "triton",
+    "sglang",
+    "megatron-core",
+    "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 _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"):
+        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"] = _merge_latency(base["latency"], extra["latency"])
+    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")
+    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.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(
+        f"wrote {args.out} (commit {report['rl_kernel_commit'][:7]}, "
+        f"dirty={report['tracked_tree_dirty']})"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/validation/models/qwen3_next_chain_report.py b/tools/validation/models/qwen3_next_chain_report.py
new file mode 100644
index 000000000..99f19ea76
--- /dev/null
+++ b/tools/validation/models/qwen3_next_chain_report.py
@@ -0,0 +1,100 @@
+#!/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 tools/validation/models/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.validation.models.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[3]
+sys.path.insert(0, str(ROOT))
+
+from rl_engine.contracts.numerical import load_contract  # noqa: E402
+from rl_engine.validation.models.qwen3_next_chain_report import build_report  # noqa: E402
+from rl_engine.validation.models.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/tools/validation/models/qwen3_next_checkpoint.py b/tools/validation/models/qwen3_next_checkpoint.py
new file mode 100644
index 000000000..2d3e9036a
--- /dev/null
+++ b/tools/validation/models/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/tools/validation/models/qwen3_next_contract.py b/tools/validation/models/qwen3_next_contract.py
new file mode 100644
index 000000000..428746bfc
--- /dev/null
+++ b/tools/validation/models/qwen3_next_contract.py
@@ -0,0 +1,372 @@
+# 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[3]
+            / "rl_engine/validation/models/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/validation/models/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.validation.models.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.validation.models.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/tools/validation/models/qwen3_next_gate_common.py b/tools/validation/models/qwen3_next_gate_common.py
new file mode 100644
index 000000000..f1a8e94b5
--- /dev/null
+++ b/tools/validation/models/qwen3_next_gate_common.py
@@ -0,0 +1,135 @@
+# 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[3]
+
+
+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.models.qwen3_next.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")
+        from rl_engine.integrations.common.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()
+    finally:
+        dist.destroy_process_group()
diff --git a/tools/validation/models/qwen3_next_l2.py b/tools/validation/models/qwen3_next_l2.py
new file mode 100644
index 000000000..cb874ca0e
--- /dev/null
+++ b/tools/validation/models/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[3]
+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.validation.models.qwen3_next_binary import (  # noqa: E402
+    archive_provider_binary,
+    validate_binary_record,
+    verify_imported_provider,
+)
+from rl_engine.validation.models.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.validation.models.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/validation/models/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.validation.models.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/tools/validation/models/qwen3_next_layer_contract.py b/tools/validation/models/qwen3_next_layer_contract.py
new file mode 100644
index 000000000..67764821d
--- /dev/null
+++ b/tools/validation/models/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/validation/models/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.validation.models.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.validation.models.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/tools/validation/models/qwen3_next_layers.py b/tools/validation/models/qwen3_next_layers.py
new file mode 100644
index 000000000..ea3c62a8d
--- /dev/null
+++ b/tools/validation/models/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[3]
+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.validation.models.qwen3_next_binary import (  # noqa: E402
+    archive_provider_binary,
+    validate_binary_record,
+)
+from rl_engine.validation.models.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.engines.rollout.vllm.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 / "tools/validation/models/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/tools/validation/models/qwen3_next_moe_prior_art.py b/tools/validation/models/qwen3_next_moe_prior_art.py
new file mode 100644
index 000000000..c3c8d088a
--- /dev/null
+++ b/tools/validation/models/qwen3_next_moe_prior_art.py
@@ -0,0 +1,116 @@
+#!/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.
+``tools/validation/models/plot_qwen3_next_moe_prior_art.py`` turns the report into a figure.
+
+    TRITON_F32_DEFAULT=ieee python tools/validation/models/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[3]
+sys.path.insert(0, str(ROOT))
+
+import torch  # noqa: E402
+
+from rl_engine.validation.models.qwen3_next_moe_prior_art import moe_report  # noqa: E402
+
+LIBRARIES = (
+    "vllm",
+    "flashinfer-python",
+    "transformers",
+    "triton",
+    "sglang",
+    "sgl-kernel",
+    "megatron_core",
+    "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 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")
+    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.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(
+        f"wrote {args.out} (commit {report['rl_kernel_commit'][:7]}, "
+        f"dirty={report['tracked_tree_dirty']})"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/validation/models/qwen3_next_norm_evidence.py b/tools/validation/models/qwen3_next_norm_evidence.py
new file mode 100644
index 000000000..aaeba82c3
--- /dev/null
+++ b/tools/validation/models/qwen3_next_norm_evidence.py
@@ -0,0 +1,348 @@
+#!/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 tools/validation/models/qwen3_next_norm_evidence.py --out report.json
+    python tools/validation/models/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
+import torch.nn.functional as F
+
+REPO_ROOT = Path(__file__).resolve().parents[3]
+sys.path.insert(0, str(REPO_ROOT))
+
+from rl_engine.backends.cuda.norm.rmsnorm import (  # noqa: E402
+    Qwen3NextRMSNormCudaOp,
+    Qwen3NextRMSNormGatedCudaOp,
+)
+from rl_engine.reference.norm.qwen3_next_rms_norm import (  # noqa: E402
+    Qwen3NextRMSNormGatedOp,
+    Qwen3NextRMSNormOp,
+)
+
+EPS = 1e-6
+
+
+# --------------------------------------------------------------------------- #
+# 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(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},
+    }
+    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, 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(spec, rows: int, seed: int, dtype=torch.bfloat16):
+    g = torch.Generator(device="cuda").manual_seed(seed)
+    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, 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(), [t.grad for t in rl], wl.grad
+
+
+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, 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"](*row_inputs, 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(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:
+            row_inputs, w, up = _inputs(spec, rows, seed)
+            if c["backward"]:
+                full_out, full_drows, _ = _grads(c["fn"], row_inputs, w, up)
+            else:
+                with torch.no_grad():
+                    full_out = c["fn"](*row_inputs, w)
+            for i in range(0, rows, step):
+                part = [t[i : i + 1] for t in row_inputs]
+                if c["backward"]:
+                    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"](*part, 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(spec, cands) -> dict[str, Any]:
+    result: dict[str, Any] = {name: {} for name in cands}
+    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(*row_inputs, w))
+            if c["backward"]:
+                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, lv=leaves: torch.autograd.grad(o, lv, 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, spec) -> dict[str, Any]:
+    cands = spec["candidates"](spec["hidden"])
+    print(f"[{name}] candidates: {', '.join(cands)}", flush=True)
+    report = {
+        "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 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",
+        "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(),
+        "eps": EPS,
+        "dtype": "bfloat16",
+        "ops": {name: run_op(name, OPS[name]) for name in args.ops.split(",")},
+    }
+    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()
diff --git a/tools/validation/models/qwen3_next_operator_cases.py b/tools/validation/models/qwen3_next_operator_cases.py
new file mode 100644
index 000000000..d5784a5bd
--- /dev/null
+++ b/tools/validation/models/qwen3_next_operator_cases.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Regenerate ``rl_engine/validation/models/qwen3_next_operator_cases.json`` from pytest collection.
+
+    python tools/validation/models/qwen3_next_operator_cases.py          # check, exit 1 if stale
+    python tools/validation/models/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[3]
+REGISTRY = ROOT / "rl_engine/validation/models/qwen3_next_operator_cases.json"
+NORM_FILES = (
+    "tests/models/qwen3_next/test_qwen3_next_norm.py",
+    "tests/models/qwen3_next/check_qwen3_next_norm_providers.py",
+)
+CORE_FILES = (
+    "tests/models/qwen3_next/check_qwen3_next_attention.py",
+    "tests/models/qwen3_next/check_qwen3_next_batch_invariance.py",
+    "tests/models/qwen3_next/check_qwen3_next_conv_bridge.py",
+    "tests/models/qwen3_next/check_qwen3_next_core_matrix.py",
+    "tests/models/qwen3_next/check_qwen3_next_forward.py",
+    "tests/models/qwen3_next/check_qwen3_next_gdn_bridge.py",
+    "tests/models/qwen3_next/check_qwen3_next_gdn_sequence.py",
+    "tests/models/qwen3_next/check_qwen3_next_paged_attention.py",
+    "tests/models/qwen3_next/check_qwen3_next_shared_core.py",
+)
+DIAGNOSTIC_FILES = ("tests/models/qwen3_next/check_gdn_recurrent_golden.py",)
+# Approximate comparisons against alternative providers, collected from the
+# mandatory files above but recorded as diagnostics.
+DIAGNOSTIC_TESTS = {
+    "tests/models/qwen3_next/check_qwen3_next_norm_providers.py": (
+        "test_ours_tracks_each_vllm_path_within_bounds",
+        "test_vllm_paths_disagree_only_within_bounds",
+    ),
+    "tests/models/qwen3_next/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 tools/validation/models/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/tools/validation/models/qwen3_next_operator_gates.py b/tools/validation/models/qwen3_next_operator_gates.py
new file mode 100644
index 000000000..3af9522c9
--- /dev/null
+++ b/tools/validation/models/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.validation.models.qwen3_next_cases import case_digest, execution_verdict, load_cases
+
+ROOT = Path(__file__).resolve().parents[3]
+MANIFEST = ROOT / "rl_engine/validation/models/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.validation.models.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"tools/validation/operators/check_{gate}_invariance.py",
+                            "--manifest",
+                            "rl_engine/validation/models/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/tools/validation/models/qwen3_next_optimizer_checkpoint.py b/tools/validation/models/qwen3_next_optimizer_checkpoint.py
new file mode 100644
index 000000000..f99c54afe
--- /dev/null
+++ b/tools/validation/models/qwen3_next_optimizer_checkpoint.py
@@ -0,0 +1,310 @@
+# 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.orchestrators.vime.providers.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/tools/validation/models/qwen3_next_preflight.py b/tools/validation/models/qwen3_next_preflight.py
new file mode 100644
index 000000000..fcf312b96
--- /dev/null
+++ b/tools/validation/models/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/scripts/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/models/qwen3_next/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",
+                    "tools/validation/models/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/validation/models/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,
+                    "tools/validation/models/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.engines.train.vllm.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/tools/validation/models/qwen3_next_publish_evidence.py b/tools/validation/models/qwen3_next_publish_evidence.py
new file mode 100644
index 000000000..83ebd1e06
--- /dev/null
+++ b/tools/validation/models/qwen3_next_publish_evidence.py
@@ -0,0 +1,152 @@
+#!/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 tools/validation/models/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
+
+# Absolute site paths only: "cpu/data/" inside a package path is not a site path.
+_ABSOLUTE = r"(?<![\w.>-])"
+FORBIDDEN = (
+    _ABSOLUTE + r"/data/",
+    _ABSOLUTE + r"/home/",
+    _ABSOLUTE + r"/raid/",
+    _ABSOLUTE + r"/scratch",
+    r"\bdgx-",
+    r"(?i)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"/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:
+            label = placeholder.split("/", 1)[0]
+            counts[label] = counts.get(label, 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/tools/validation/models/qwen3_next_rollout_smoke.py b/tools/validation/models/qwen3_next_rollout_smoke.py
new file mode 100644
index 000000000..8ebb72c84
--- /dev/null
+++ b/tools/validation/models/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/tools/validation/models/qwen3_next_run.py b/tools/validation/models/qwen3_next_run.py
new file mode 100644
index 000000000..b36207e8f
--- /dev/null
+++ b/tools/validation/models/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 tools/validation/models/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[3]
+ORDER = ("preflight", "layers", "l2", "train1", "train2", "train")
+PROVIDER_SOURCES = (
+    "rl_engine/integrations/engines/train/vllm/qwen3_next_provider.py",
+    "rl_engine/integrations/engines/train/vllm/qwen3_next_conv.py",
+    "rl_engine/integrations/engines/train/vllm/qwen3_next_gdn.py",
+    "rl_engine/models/qwen3_next/qwen3_next_engine_weights.py",
+    "rl_engine/integrations/orchestrators/vime/experiments/qwen3_next/profile.py",
+    "rl_engine/models/qwen3_next/qwen3_next_tp.py",
+    "rl_engine/models/qwen3_next/qwen3_next_tp_gdn.py",
+    "rl_engine/models/qwen3_next/qwen3_next_tp_blocks.py",
+    "rl_engine/models/qwen3_next/qwen3_next_forward.py",
+    "rl_engine/models/qwen3_next/qwen3_next_embedding.py",
+    "rl_engine/models/qwen3_next/qwen3_next_logprob.py",
+    "rl_engine/integrations/orchestrators/vime/providers/qwen3_next_vime.py",
+    "rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm.py",
+    "rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm_attention.py",
+    "rl_engine/integrations/engines/rollout/vllm/qwen3_next_vllm_model.py",
+    "rl_engine/backends/cuda/norm/rmsnorm.py",
+    "csrc/cuda/norm/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/tools/validation/models/qwen3_next_runtime.py b/tools/validation/models/qwen3_next_runtime.py
new file mode 100644
index 000000000..8f801eae0
--- /dev/null
+++ b/tools/validation/models/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 ``tools/validation/models/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
+
+from rl_engine.validation.common.file_digest import file_sha256
+
+# --------------------------------------------------------------------------- #
+# Environment identity
+# --------------------------------------------------------------------------- #
+
+
+def file_digest(path):
+    return file_sha256(Path(path))
+
+
+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):
+    return file_sha256(path)
+
+
+_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, "tools/validation/models/qwen3_next_preflight.py"),
+    "layers": ("512G", "04:00:00", "preflight", "tools/validation/models/qwen3_next_layers.py"),
+    "l2": ("512G", "04:00:00", "layers", "tools/validation/models/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", "tools/validation/models/qwen3_next_training.py"),
+    "train2": ("1280G", "07:00:00", "train1", "tools/validation/models/qwen3_next_training.py"),
+    "train": ("1280G", "09:00:00", "train2", "tools/validation/models/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/tools/validation/models/qwen3_next_tp_attention_check.py b/tools/validation/models/qwen3_next_tp_attention_check.py
new file mode 100644
index 000000000..a94487e35
--- /dev/null
+++ b/tools/validation/models/qwen3_next_tp_attention_check.py
@@ -0,0 +1,145 @@
+#!/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 tools/validation/models/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.
+"""
+
+import sys
+from contextlib import ExitStack
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+import torch  # noqa: E402
+import torch.distributed as dist  # noqa: E402
+
+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 (  # noqa: E402
+    assert_tensor_bitwise_equal as exact,
+)
+from tools.validation.models.qwen3_next_gate_common import (  # noqa: E402
+    CheckpointWeights,
+    check_gradients,
+    gather,
+    run_gate,
+)
+
+
+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/tools/validation/models/qwen3_next_tp_gdn_check.py b/tools/validation/models/qwen3_next_tp_gdn_check.py
new file mode 100644
index 000000000..65fee3815
--- /dev/null
+++ b/tools/validation/models/qwen3_next_tp_gdn_check.py
@@ -0,0 +1,108 @@
+#!/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 tools/validation/models/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 sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+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 (  # 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 (  # 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):
+    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/tools/validation/models/qwen3_next_tp_head_check.py b/tools/validation/models/qwen3_next_tp_head_check.py
new file mode 100644
index 000000000..98fa8247f
--- /dev/null
+++ b/tools/validation/models/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.backends.cuda.norm.rmsnorm import Qwen3NextRMSNormCudaOp
+from rl_engine.models.qwen3_next.qwen3_next_embedding import shared_embedding
+from rl_engine.models.qwen3_next.qwen3_next_forward import shared_linear, shared_logprob
+from rl_engine.models.qwen3_next.qwen3_next_logprob import qwen_logprob_contract
+from rl_engine.models.qwen3_next.qwen3_next_tp import _CopyToTP
+from rl_engine.validation.common.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.common.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/tools/validation/models/qwen3_next_tp_moe_check.py b/tools/validation/models/qwen3_next_tp_moe_check.py
new file mode 100644
index 000000000..c18b5b1fd
--- /dev/null
+++ b/tools/validation/models/qwen3_next_tp_moe_check.py
@@ -0,0 +1,114 @@
+#!/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 \\
+        tools/validation/models/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 sys
+from contextlib import ExitStack
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+import torch  # noqa: E402
+import torch.distributed as dist  # noqa: E402
+
+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 (  # noqa: E402
+    assert_tensor_bitwise_equal as exact,
+)
+from tools.validation.models.qwen3_next_gate_common import (  # noqa: E402
+    CheckpointWeights,
+    check_gradients,
+    gather,
+    run_gate,
+    timed,
+)
+
+TOKEN_COUNTS = (8, 64, 256, 1024)
+
+
+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}
+
+
+if __name__ == "__main__":
+    run_gate(
+        __doc__,
+        "tp4_moe_block_layer0",
+        lambda checkpoint, generator: {"moe": moe_gate(checkpoint, generator)},
+    )
diff --git a/tools/validation/models/qwen3_next_tp_probe.py b/tools/validation/models/qwen3_next_tp_probe.py
new file mode 100644
index 000000000..fd55d6d56
--- /dev/null
+++ b/tools/validation/models/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/tools/validation/models/qwen3_next_training.py b/tools/validation/models/qwen3_next_training.py
new file mode 100644
index 000000000..dbedf2a4f
--- /dev/null
+++ b/tools/validation/models/qwen3_next_training.py
@@ -0,0 +1,1079 @@
+#!/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[3]
+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.orchestrators.vime.experiments.qwen3_next.native_training import (  # noqa: E402,E501
+    policy_fixture,
+    run_training_phase,
+    validate_active_parameter_names,
+)
+from rl_engine.integrations.orchestrators.vime.providers.qwen3_next_training import (  # noqa: E402
+    state_digest,
+)
+from rl_engine.validation.models.qwen3_next_binary import (  # noqa: E402
+    archive_provider_binary,
+    validate_binary_record,
+)
+from rl_engine.validation.models.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 / "tools/validation/models/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/tools/validation/models/qwen3_next_training_checkpoint.py b/tools/validation/models/qwen3_next_training_checkpoint.py
new file mode 100644
index 000000000..f25519a8a
--- /dev/null
+++ b/tools/validation/models/qwen3_next_training_checkpoint.py
@@ -0,0 +1,605 @@
+# 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.orchestrators.vime.providers.qwen3_next_training import (
+    capture_rng_state,
+    named_trainable_parameters,
+    restore_rng_state,
+    state_digest,
+)
+from rl_engine.validation.models.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/tools/validation/models/qwen3_next_training_export.py b/tools/validation/models/qwen3_next_training_export.py
new file mode 100644
index 000000000..f785e30e3
--- /dev/null
+++ b/tools/validation/models/qwen3_next_training_export.py
@@ -0,0 +1,511 @@
+# 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.orchestrators.vime.providers.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[3]
+        / "rl_engine/validation/models/qwen3_next_norm_manifest.json"
+    )
+    return json.loads(path.read_text())["model_identity"]
+
+
+def _schema_reference():
+    path = (
+        Path(__file__).resolve().parents[3]
+        / "rl_engine/validation/models/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.orchestrators.vime.providers.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/tools/validation/models/ws1_gdn_provider_agreement.py b/tools/validation/models/ws1_gdn_provider_agreement.py
new file mode 100644
index 000000000..5b2ee3567
--- /dev/null
+++ b/tools/validation/models/ws1_gdn_provider_agreement.py
@@ -0,0 +1,496 @@
+#!/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/models/qwen3_next/check_gdn_recurrent_golden.py`` asserts regression bounds; this runner
+prints the measured values behind them, so the numbers quoted in
+``docs/archive/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.
+
+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, 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``
+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,
+and write the result outside the checkout: an untracked file inside it would itself
+make the next run report ``git_dirty: true``.
+
+    python tools/validation/models/ws1_gdn_provider_agreement.py \\
+        > "${TMPDIR:-/tmp}/gdn_provider_agreement.json"
+"""
+
+from __future__ import annotations
+
+import argparse
+import importlib.util
+import json
+import math
+import os
+import platform
+import re
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+from typing import Any
+
+import torch
+
+REPO_ROOT = Path(__file__).resolve().parents[3]
+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}
+# 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 = {
+    "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",
+    # 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():
+    """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 | 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):
+        return None
+
+
+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__,
+        "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),
+    }
+
+
+def _measure_recurrent(check, batches: list[int]) -> list[dict[str, Any]]:
+    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():
+            # 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]) -> list[dict[str, Any]]:
+    import triton
+
+    fp_fusion = bool(triton.knobs.language.default_fp_fusion)  # this process's setting
+    rows = []
+    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 _count(patterns: dict[str, str], text: str) -> dict[str, int]:
+    return {name: len(re.findall(pattern, text)) for name, pattern in patterns.items()}
+
+
+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()
+    }
+
+
+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, 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]:
+    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]:
+    """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(
+        "--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",
+    )
+    # 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()
+
+
+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()
+    if args.conv_only:
+        print(json.dumps(_conv_report(check, batches), sort_keys=True))
+        return 0
+
+    report: dict[str, Any] = {
+        "runner": "tools/validation/models/ws1_gdn_provider_agreement.py",
+        "provenance": _provenance(),
+        "inputs": {
+            "source": (
+                "tests/models/qwen3_next/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",
+            "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["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
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/tools/validation/operators/check_forward_invariance.py b/tools/validation/operators/check_forward_invariance.py
index c7a836f2c..6830d8315 100644
--- a/tools/validation/operators/check_forward_invariance.py
+++ b/tools/validation/operators/check_forward_invariance.py
@@ -18,8 +18,9 @@
 if str(REPO_ROOT) not in sys.path:
     sys.path.insert(0, str(REPO_ROOT))
 
-from rl_engine.config.workload import load_manifest  # noqa: E402
+from rl_engine.config.workload import load_manifest, workload_report  # noqa: E402
 from rl_engine.contracts.numerical import resolve_dtype_policy  # noqa: E402
+from rl_engine.validation.models.qwen3_next_workload import validate_norm_dimensions  # noqa: E402
 from rl_engine.validation.operators import (  # noqa: E402
     BackendProvenance,
     assert_forward_batch_invariant,
@@ -113,6 +114,12 @@ 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,
+        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"
@@ -146,7 +153,8 @@ 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)
+    validate_norm_dimensions(manifest.raw, args.op, args.hidden, args.head_dim)
     adapter = get_adapter(args.op)
     if adapter.requirement == "layout_supported":
         raise SystemExit(
@@ -233,7 +241,9 @@ def main() -> None:
     )
 
     if args.json:
-        print(json.dumps(report.to_dict(), indent=2, default=str))
+        payload = report.to_dict()
+        payload["workload"] = workload_report(manifest)
+        print(json.dumps(payload, indent=2, default=str))
     else:
         _summarize(report)
     if not report.passed:
diff --git a/tools/validation/operators/check_gradient_invariance.py b/tools/validation/operators/check_gradient_invariance.py
index db81d8c58..178a5a4a6 100644
--- a/tools/validation/operators/check_gradient_invariance.py
+++ b/tools/validation/operators/check_gradient_invariance.py
@@ -18,8 +18,9 @@
 if str(REPO_ROOT) not in sys.path:
     sys.path.insert(0, str(REPO_ROOT))
 
-from rl_engine.config.workload import load_manifest  # noqa: E402
+from rl_engine.config.workload import load_manifest, workload_report  # noqa: E402
 from rl_engine.contracts.numerical import resolve_dtype_policy  # noqa: E402
+from rl_engine.validation.models.qwen3_next_workload import validate_norm_dimensions  # noqa: E402
 from rl_engine.validation.operators import (  # noqa: E402
     BackendProvenance,
     assert_gradient_batch_invariant,
@@ -125,6 +126,12 @@ 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,
+        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"
@@ -159,7 +166,8 @@ 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)
+    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
@@ -255,7 +263,9 @@ 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_report(manifest)
+        print(json.dumps(payload, indent=2, default=str))
     else:
         _summarize(report)
     if not report.passed: