Harness stop uses full state baseline

src/aituner/harness.py

@@ -607,6 +607,15 @@ def stateful_history_limit() -> int:
     return 8
 
 
+def _state_completed_trials_with_rates(state: StudyState) -> list[TrialSummary]:
+    return [
+        trial
+        for trial in state.trials
+        if trial.status == "completed"
+        and isinstance(trial.best_request_rate_per_gpu, (int, float))
+    ]
+
+
 def _load_result(trial: TrialSummary) -> dict[str, Any] | None:
     if not trial.result_path:
         return None
@@ -1960,15 +1969,19 @@ def _validation_exhausted_guard(
     }
     if not state.best_trial_id or not isinstance(state.best_request_rate_per_gpu, (int, float)):
         return default
-    completed = [
-        item
-        for item in recent_diagnostics
-        if item.get("status") == "completed"
-        and isinstance(item.get("best_request_rate_per_gpu"), (int, float))
-    ]
-    if not completed:
-        return default
-    baseline_rate = _as_float(completed[0].get("best_request_rate_per_gpu"))
+    state_completed = _state_completed_trials_with_rates(state)
+    if state_completed:
+        baseline_rate = float(state_completed[0].best_request_rate_per_gpu)
+    else:
+        completed = [
+            item
+            for item in recent_diagnostics
+            if item.get("status") == "completed"
+            and isinstance(item.get("best_request_rate_per_gpu"), (int, float))
+        ]
+        if not completed:
+            return default
+        baseline_rate = _as_float(completed[0].get("best_request_rate_per_gpu"))
     incumbent_rate = _as_float(state.best_request_rate_per_gpu)
     if baseline_rate <= 0 or incumbent_rate <= 0:
         return default
@@ -2084,16 +2097,11 @@ def _strong_incumbent_guard(
     }
     if state.best_trial_id is None or state.best_request_rate_per_gpu is None:
         return default
-    completed = [
-        item
-        for item in recent_diagnostics
-        if item.get("status") == "completed"
-        and isinstance(item.get("best_request_rate_per_gpu"), (int, float))
-    ]
+    completed = _state_completed_trials_with_rates(state)
     if len(completed) < 2:
         return default
     baseline = completed[0]
-    baseline_rate = float(baseline["best_request_rate_per_gpu"])
+    baseline_rate = float(baseline.best_request_rate_per_gpu)
     incumbent_rate = float(state.best_request_rate_per_gpu)
     if baseline_rate <= 0:
         return default
@@ -2103,7 +2111,7 @@ def _strong_incumbent_guard(
         return {
             "guard_active": True,
             "reason": "incumbent_exceeds_baseline_by_1_8x_and_latest_trial_is_best_enter_validation_phase",
-            "baseline_trial_id": baseline.get("trial_id"),
+            "baseline_trial_id": baseline.trial_id,
             "baseline_request_rate_per_gpu": baseline_rate,
             "incumbent_gain_vs_baseline": gain,
             "recommended_next_action": (
@@ -2113,7 +2121,7 @@ def _strong_incumbent_guard(
         }
     return {
         **default,
-        "baseline_trial_id": baseline.get("trial_id"),
+        "baseline_trial_id": baseline.trial_id,
         "baseline_request_rate_per_gpu": baseline_rate,
         "incumbent_gain_vs_baseline": gain,
         "reason": "need_more_evidence_before_strong_incumbent_stop",

tests/test_core_flow.py

@@ -1068,6 +1068,119 @@ class CoreFlowTests(unittest.TestCase):
             self.assertTrue(context["harness_stop"]["should_stop"])
             self.assertEqual(context["harness_stop"]["reason"], "post_incumbent_validation_exhausted")
 
+    def test_harness_validation_uses_full_state_baseline_when_history_window_moves(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            tmp_path = Path(tmp)
+            study_path = _write_study_assets(
+                tmp_path,
+                engine_overrides={"tunable_flags": ["max-num-seqs"]},
+            )
+            study = load_study_spec(study_path)
+            state = StudyState(
+                study_id=study.study_id,
+                best_trial_id="trial-0006",
+                best_parallel_size=8,
+                best_request_rate=2.4,
+                best_request_rate_per_gpu=0.3,
+                trials=[
+                    TrialSummary(
+                        trial_id="trial-0001",
+                        status="completed",
+                        parallel_size=8,
+                        best_request_rate=0.8,
+                        best_request_rate_per_gpu=0.1,
+                        config_patch={"env_patch": {}, "flag_patch": {}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0002",
+                        status="completed",
+                        parallel_size=8,
+                        best_request_rate=0.88,
+                        best_request_rate_per_gpu=0.11,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 16}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0003",
+                        status="completed",
+                        parallel_size=8,
+                        best_request_rate=0.96,
+                        best_request_rate_per_gpu=0.12,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 24}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0004",
+                        status="completed",
+                        parallel_size=8,
+                        best_request_rate=1.04,
+                        best_request_rate_per_gpu=0.13,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 32}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0005",
+                        status="completed",
+                        parallel_size=8,
+                        best_request_rate=2.24,
+                        best_request_rate_per_gpu=0.28,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 40}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0006",
+                        status="completed",
+                        parallel_size=8,
+                        best_request_rate=2.4,
+                        best_request_rate_per_gpu=0.3,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 48}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0007",
+                        status="completed",
+                        parallel_size=8,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 56}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0008",
+                        status="completed",
+                        parallel_size=8,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 64}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0009",
+                        status="completed",
+                        parallel_size=8,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 72}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0010",
+                        status="completed",
+                        parallel_size=8,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 80}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0011",
+                        status="failed",
+                        parallel_size=8,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 88}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0012",
+                        status="completed",
+                        parallel_size=8,
+                        config_patch={"env_patch": {}, "flag_patch": {"max-num-seqs": 96}},
+                    ),
+                ],
+            )
+            context = build_harness_context(
+                study=study,
+                window_summary={"prompt_tokens_p95": 2048},
+                state=state,
+            )
+            self.assertTrue(context["harness_stop"]["should_stop"])
+            self.assertEqual(context["harness_stop"]["reason"], "post_incumbent_validation_exhausted")
+            self.assertGreater(
+                context["harness_stop"]["evidence"]["incumbent_gain_vs_baseline"],
+                2.9,
+            )
+
     def test_harness_does_not_stop_immediately_after_strong_incumbent(self) -> None:
         with tempfile.TemporaryDirectory() as tmp:
             tmp_path = Path(tmp)