Use probe sequence bottlenecks in harness

tests/test_core_flow.py

@@ -532,6 +532,83 @@ class CoreFlowTests(unittest.TestCase):
                 "\n".join(context["proposal_rules"]),
             )
 
+    def test_harness_uses_prior_infeasible_probe_for_active_bottleneck(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            tmp_path = Path(tmp)
+            study_path = _write_study_assets(
+                tmp_path,
+                trace_overrides={"request_mode": "decode_only"},
+                slo_overrides={
+                    "ttft_rule": None,
+                    "tpot_rule": {"kind": "fixed_ms", "threshold_ms": 20},
+                },
+                engine_overrides={
+                    "tunable_flags": [
+                        "tensor-parallel-size",
+                        "data-parallel-size",
+                        "max-num-seqs",
+                    ]
+                },
+            )
+            result_path = tmp_path / "trial-0001-result.json"
+            result_path.write_text(
+                json.dumps(
+                    {
+                        "status": "completed",
+                        "best_request_rate": 1.0,
+                        "best_pass_rate": 1.0,
+                        "probes": [
+                            {
+                                "threshold": 0.1,
+                                "feasible": False,
+                                "payload": {
+                                    "request_rate": 2.0,
+                                    "pass_rate": 0.1,
+                                    "early_stop_reason": "slo_pass_rate_unrecoverable",
+                                    "latency_summary": {
+                                        "failed_reason_counts": {"tpot_ms>20.0": 20}
+                                    },
+                                },
+                            },
+                            {
+                                "threshold": 0.01,
+                                "feasible": True,
+                                "payload": {
+                                    "request_rate": 1.0,
+                                    "pass_rate": 1.0,
+                                    "latency_summary": {"failed_reason_counts": {}},
+                                },
+                            },
+                        ],
+                    }
+                ),
+                encoding="utf-8",
+            )
+            study = load_study_spec(study_path)
+            context = build_harness_context(
+                study=study,
+                window_summary={},
+                state=StudyState(
+                    study_id=study.study_id,
+                    trials=[
+                        TrialSummary(
+                            trial_id="trial-0001",
+                            status="completed",
+                            result_path=str(result_path),
+                        )
+                    ],
+                ),
+            )
+            diagnostics = context["recent_trial_diagnostics"]
+            self.assertEqual(diagnostics[0]["active_bottleneck"], "decode_tpot")
+            active = {
+                harness["knob_family"]
+                for harness in context["knob_harnesses"]
+                if harness["active_now"]
+            }
+            self.assertIn("data-parallel-size", active)
+            self.assertIn("max-num-seqs", active)
+
     def test_load_study_spec_rejects_mismatched_served_model_name(self) -> None:
         with tempfile.TemporaryDirectory() as tmp:
             tmp_path = Path(tmp)