Stop harness when feasible probe reaches search high

src/aituner/harness.py

@@ -798,10 +798,6 @@ def _search_high_saturation_guard(
         float(study.search.tolerance),
         (float(study.search.high) - float(study.search.low)) / float(2 ** max(study.search.max_probes, 1)),
     )
-    latency_summary = last_probe.get("latency_summary")
-    failed = latency_summary.get("failed_reason_counts") if isinstance(latency_summary, dict) else {}
-    if not isinstance(failed, dict):
-        failed = {}
     if not last_probe.get("feasible"):
         return {
             **default,
@@ -817,20 +813,12 @@ def _search_high_saturation_guard(
             "threshold_gap_to_high": threshold_gap,
             "binary_probe_resolution": binary_probe_resolution,
         }
-    if failed:
-        return {
-            **default,
-            "reason": "incumbent_high_probe_has_slo_failures",
-            "last_threshold": last_threshold,
-            "threshold_gap_to_high": threshold_gap,
-            "failed_reason_counts": failed,
-        }
     return {
         "saturated": True,
         "reason": "search_high_saturated_by_incumbent",
         "summary": (
-            "The incumbent's highest measured probe is feasible, has no SLO failures, "
+            "The incumbent's highest measured probe is feasible and is within "
-            "and is within the configured binary-search resolution of search.high."
+            "the configured binary-search resolution of search.high."
         ),
         "incumbent_trial_id": state.best_trial_id,
         "search_high": study.search.high,

tests/test_core_flow.py

@@ -600,6 +600,68 @@ class CoreFlowTests(unittest.TestCase):
             self.assertIsNotNone(proposal)
             self.assertTrue(proposal.should_stop)
 
+    def test_harness_stop_allows_feasible_high_probe_with_some_failures(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            tmp_path = Path(tmp)
+            study_path = _write_study_assets(tmp_path)
+            study = load_study_spec(study_path)
+            result_path = tmp_path / "trial-0004.json"
+            result_path.write_text(
+                json.dumps(
+                    {
+                        "status": "completed",
+                        "best_sampling_u": 0.99609375,
+                        "best_request_rate": 1.77,
+                        "best_pass_rate": 0.968,
+                        "probes": [
+                            {
+                                "threshold": 0.99609375,
+                                "feasible": True,
+                                "payload": {
+                                    "request_count": 1063,
+                                    "pass_rate": 0.968,
+                                    "request_rate": 1.77,
+                                    "early_stopped": False,
+                                    "early_stop_reason": "",
+                                    "latency_summary": {
+                                        "failed_reason_counts": {
+                                            "tpot_ms>50.0": 34,
+                                        }
+                                    },
+                                },
+                            }
+                        ],
+                    }
+                ),
+                encoding="utf-8",
+            )
+            state = StudyState(
+                study_id=study.study_id,
+                best_trial_id="trial-0004",
+                best_request_rate=1.77,
+                best_request_rate_per_gpu=0.4425,
+                trials=[
+                    TrialSummary(
+                        trial_id="trial-0004",
+                        status="completed",
+                        best_request_rate=1.77,
+                        best_request_rate_per_gpu=0.4425,
+                        result_path=str(result_path),
+                        config_patch={
+                            "env_patch": {},
+                            "flag_patch": {"tensor-parallel-size": 4},
+                        },
+                    )
+                ],
+            )
+            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"], "search_high_saturated_by_incumbent")
+
     def test_harness_guided_first_tp_probe_for_latency_bottleneck(self) -> None:
         with tempfile.TemporaryDirectory() as tmp:
             tmp_path = Path(tmp)