feat: add novelty bonus to pilot priority formula

Add +0.05×novelty term to _pilot_priority() so that within a seed
family, higher-novelty variants are preferred during pilot panel
selection. SEED-005 variants (novelty=0.467, the pool maximum) gain
up to +0.023 priority — a tiebreaker that pushes the most novel
sequence variants into Wave 1 without overriding the ensemble score.

Adds three new tests: higher-novelty-higher-priority, missing-novelty-
defaults-to-0, and formula contribution verification.

Author: cschanhniem

src/openamp_foundry/selection/pilot.py

@@ -14,15 +14,17 @@ def _seed_from_source(source: str) -> str:
 
 
 def _pilot_priority(scores: dict) -> float:
-    """Higher is better: reward ensemble, reward serum stability, penalise disagreement.
+    """Higher is better: reward ensemble, stability, and novelty; penalise disagreement.
 
-    Serum stability is weighted lightly (0.05) so it acts as a tiebreaker without
-    overriding the ensemble score.  A candidate with stability=1.0 gains at most +0.05.
+    Within a seed family, higher-novelty variants are preferred as tiebreakers.
+    Novelty is weighted lightly (0.05) so the ensemble score remains dominant.
+    A candidate with novelty=0.467 (SEED-005 max) gains at most +0.023.
     """
     ensemble = scores.get("ensemble", 0.0)
     disagreement = scores.get("disagreement", 0.5)
     stability = scores.get("serum_stability", 0.5)
-    return round(ensemble - 0.3 * disagreement + 0.05 * stability, 6)
+    novelty = scores.get("novelty", 0.0)
+    return round(ensemble - 0.3 * disagreement + 0.05 * stability + 0.05 * novelty, 6)
 
 
 def select_pilot_panel(

tests/test_pilot_panel.py

@@ -90,6 +90,22 @@ def test_higher_stability_higher_priority(self):
         p2 = _pilot_priority({"ensemble": 0.70, "disagreement": 0.10, "serum_stability": 0.0})
         assert p1 > p2
 
+    def test_higher_novelty_higher_priority(self):
+        p1 = _pilot_priority({"ensemble": 0.70, "disagreement": 0.10, "novelty": 0.467})
+        p2 = _pilot_priority({"ensemble": 0.70, "disagreement": 0.10, "novelty": 0.10})
+        assert p1 > p2
+
+    def test_missing_novelty_defaults_to_0(self):
+        p_absent = _pilot_priority({"ensemble": 0.70, "disagreement": 0.10})
+        p_zero = _pilot_priority({"ensemble": 0.70, "disagreement": 0.10, "novelty": 0.0})
+        assert p_absent == pytest.approx(p_zero, abs=1e-9)
+
+    def test_novelty_formula_contribution(self):
+        # novelty bonus = 0.05 * 0.40 = 0.02; stability absent → 0.5
+        p = _pilot_priority({"ensemble": 0.70, "disagreement": 0.10, "novelty": 0.40})
+        expected = 0.70 - 0.3 * 0.10 + 0.05 * 0.5 + 0.05 * 0.40
+        assert p == pytest.approx(expected, abs=1e-6)
+
 
 class TestSelectPilotPanel:
     def test_empty_input_returns_empty(self):