fix(rectification): use candidate dates for cross-midnight dasha scoring

Add date-isolated caches and regression coverage, align scoring identity, and freeze full research reruns while preserving historical artifacts. Record unresolved cache/receipt identity and end-to-end acceptance gaps for branch review only.

Co-Authored-By: Claude Code <noreply@anthropic.com>

tests/test_reported_offset_research.py

@@ -68,25 +68,33 @@ def test_shifted_window_preserves_dates_across_midnight(clock, offset):
     assert metrics["delivery_width_minutes"] == 31
 
 
-def test_cross_midnight_real_engine_scores_match_candidate_date_replay():
+def test_cross_midnight_real_engine_scores_match_candidate_date_replay(monkeypatch):
     case = json.loads(DATASET.read_text(encoding="utf-8"))["cases"][5]
     request, candidates = sweep.shifted_window(case, 0, 60)
     assert len({candidate.date() for candidate in candidates}) == 2
     contexts = sweep.compute_candidate_static_contexts(request, candidates=candidates)
-    grouped = sweep.score_window(request, contexts)
+    calls = []
+    build = sweep.build_event_contribution_matrix
+    def traced(request, **kwargs):
+        calls.append(len(kwargs["static_contexts"]))
+        return build(request, **kwargs)
+    with monkeypatch.context() as patch:
+        patch.setattr(sweep, "build_event_contribution_matrix", traced)
+        native = sweep.score_window(request, contexts)
+    assert calls == [len(contexts)]
     expected = []
     for context in contexts:
         dated = {**request, "birth_date": context["candidate_at"].date().isoformat()}
         built = sweep.build_event_contribution_matrix(dated, static_contexts=[context])
         expected.extend(sweep.score_from_matrix(dated, built))
-    assert grouped == expected
-    old_matrix = sweep.build_event_contribution_matrix(request, static_contexts=contexts)
-    old_rows = sweep.score_from_matrix(request, old_matrix)
-    assert old_rows != expected
+    assert native == expected
+    native_matrix = sweep.build_event_contribution_matrix(request, static_contexts=contexts)
+    native_rows = sweep.score_from_matrix(request, native_matrix)
+    assert native_rows == expected
 
 
 def test_recorded_specification_and_all_prespecified_cells():
-    report = json.loads((sweep.ROOT / "docs/research/reported_offset_2026_09_20.json").read_text(encoding="utf-8"))
+    report = json.loads(sweep.REPORT.read_text(encoding="utf-8"))
     spec = report["specification"]
     assert spec["ayanamsa"] == "raman"
     assert spec["node_mode"] == "mean"
@@ -98,7 +106,17 @@ def test_recorded_specification_and_all_prespecified_cells():
     assert spec["evaluator_sha256"] == sweep.file_sha256(sweep.ROOT / "scripts/research/reported_offset_sweep.py")
     assert spec["production_scoring_sha256"] == sweep.implementation_sha256(spec["production_scoring_files"])
     assert spec["research_implementation_sha256"] == sweep.implementation_sha256(spec["research_files"])
-    assert spec["replay_revision"] == "candidate_date_grouped_v2"
+    assert spec["replay_revision"] == "native_candidate_date_v3"
+    frozen = json.loads(sweep.FREEZE.read_text(encoding="utf-8"))
+    assert report["frozen_record"] == spec == frozen
+    sweep.verify_frozen_record(frozen, sweep.freeze_record())
+    assert report["implementation_hash_matches_at_replay"] is True
+    assert report["dataset_hash_matches_at_replay"] is True
+    assert frozen["frozen_at_utc"] <= report["replay_started_at_utc"] <= report["replay_finished_at_utc"]
+    assert spec["official_valid_independent_blind"] is False
+    assert spec["official_blind_trial_count"] == 0
+    assert spec["results_previously_seen"] is True
+    assert spec["must_not_use_for_tuning"] is True
     assert spec["truth_hidden_from_ranker"] is True
     assert spec["is_blind_evaluation"] is False
     assert report["trial_count"] == 20 * len(sweep.RADII) * len(sweep.OFFSETS)
@@ -115,3 +133,19 @@ def test_recorded_specification_and_all_prespecified_cells():
         assert row["truth_in_window_rate"] == expected
         assert row["delivery_coverage_rate"] <= expected
         assert row["top_1_rate"] <= expected
+    assert report["historical_comparison"] == sweep.historical_comparison(
+        sweep.LEGACY_REPORT, report["trials"], ("case_ordinal", "radius_minutes", "offset_minutes"),
+    )
+
+
+@pytest.mark.parametrize("key", ["dataset_sha256", "production_scoring_sha256", "research_implementation_sha256", "evaluator_sha256"])
+def test_sweep_changed_frozen_identity_fails_before_scoring(tmp_path, monkeypatch, key):
+    frozen = sweep.freeze_record()
+    frozen[key] = "0" * 64
+    path = tmp_path / "bad-freeze.json"
+    path.write_text(json.dumps(frozen), encoding="utf-8")
+    def unexpected(*args, **kwargs):
+        raise AssertionError("must reject identity before scoring")
+    monkeypatch.setattr(sweep, "compute_candidate_static_contexts", unexpected)
+    with pytest.raises(ValueError, match=f"frozen_record_mismatch:{key}"):
+        sweep.run(freeze_path=path)