Merge pull request 'feat(halacha): #81.8 — כיול שער-האישור-האוטומטי על ה-gold-set (משמרים 0.80, מתועד)' (#191) from worktree-halacha-autoapprove-calibration into main

mcp-server/src/legal_mcp/config.py

@@ -138,12 +138,26 @@ BM25_HYBRID_ENABLED = (
 )
 
 # Halacha extraction — auto-approve threshold. Halachot with extractor
-# confidence >= this value are inserted with review_status='approved'
-# instead of 'pending_review' (so they immediately appear in
-# search_precedent_library). Set to a value > 1.0 to disable auto-approval.
-# 0.80 baseline: 89% of historical extractions land here, manual spot-check
-# of 10 random samples confirmed quality. Tunable via env if drift is
-# observed (e.g. raise to 0.90 if false-positives appear).
+# confidence >= this value AND no quality_flags are inserted
+# review_status='approved' (so they appear immediately in
+# search_precedent_library). Set > 1.0 to disable auto-approval.
+#
+# CALIBRATION (#81.8, 2026-06-11) against the 100-item human-labeled gold-set
+# (db.goldset_calibrate, ground_truth='chair'; 93 keep / 7 drop):
+#   conf>=0.80 -> precision 0.98, recall 0.53   <- current (errs safe)
+#   conf>=0.75 -> precision 0.96, recall 0.81
+#   conf>=0.70 -> precision 0.94, recall 0.94
+# 0.80 clears the >=0.90 precision target with margin, so we KEEP it — it errs
+# toward the chair (low recall = more items reviewed, never the reverse).
+# Two findings shape the policy:
+#  (a) self-confidence alone is well-calibrated for PRECISION; the rule-based
+#      validators do NOT discriminate keep/drop on the gold-set (P~0.1), so a
+#      "confidence x validators" combined score would only hurt — not adopted.
+#  (b) the real COVERAGE lever is the tri-model panel (halacha_panel_approve):
+#      unanimous-3/3 -> precision 0.988 at 95% coverage, dominating any single
+#      confidence threshold. Lowering this gate to ~0.75 is a governance
+#      tradeoff (more unreviewed auto-approvals, INV-G10) on thin evidence
+#      (7 negatives) -> deferred to chair/panel (TaskMaster #121), not changed here.
 HALACHA_AUTO_APPROVE_THRESHOLD = float(
     os.environ.get("HALACHA_AUTO_APPROVE_THRESHOLD", "0.80")
 )

mcp-server/src/legal_mcp/services/db.py

@@ -5025,6 +5025,68 @@ async def goldset_score(batch: str = "default") -> dict:
     }
 
 
+async def goldset_calibrate(
+    batch: str = "default", ground_truth: str = "chair",
+    thresholds: tuple[float, ...] = (0.70, 0.75, 0.80, 0.85, 0.90, 0.95),
+) -> dict:
+    """Calibrate the halacha auto-approve gate against the gold-set (#81.8).
+
+    Against the gold-set ``is_holding`` labels, measures:
+      - confidence-threshold gate: for each T, precision (P[is_holding|conf≥T])
+        and recall (share of true keeps approved) — calibrates
+        ``HALACHA_AUTO_APPROVE_THRESHOLD``;
+      - panel policies: precision + coverage of auto-approving on the stored
+        tri-model votes (majority 2/3, unanimous 3/3).
+
+    ``ground_truth='chair'`` scores against HUMAN labels only — the panel votes
+    are an input to the panel-policy rows, so scoring them against the consensus
+    they produced would be circular; the human labels are the independent truth.
+    Returns a structured dict (INV-LRN3); no DB writes (read-only).
+    """
+    items = await goldset_list(batch)
+    if ground_truth == "chair":
+        rows = [r for r in items if r.get("tagged_by") == "chair" and r.get("is_holding") is not None]
+    else:
+        rows = [r for r in items if r.get("is_holding") is not None]
+    conf_rows = [r for r in rows if r.get("confidence") is not None]
+    pos = sum(1 for r in conf_rows if r["is_holding"])
+
+    def _gate(t: float) -> dict:
+        appr = [r for r in conf_rows if float(r["confidence"]) >= t]
+        tp = sum(1 for r in appr if r["is_holding"])
+        prec = tp / len(appr) if appr else 0.0
+        rec = tp / pos if pos else 0.0
+        return {"threshold": t, "approved": len(appr),
+                "precision": round(prec, 3), "recall": round(rec, 3)}
+
+    def _votes(r: dict) -> list[bool]:
+        return [r[k] for k in ("ai_is_holding", "ds_is_holding", "gm_is_holding")
+                if r.get(k) is not None]
+
+    def _policy(unanimous: bool) -> dict:
+        decided = approved = tp = 0
+        for r in rows:
+            v = _votes(r)
+            if len(v) < (3 if unanimous else 2):
+                continue
+            keep = all(v) if unanimous else (sum(v) > len(v) - sum(v))
+            decided += 1
+            if keep:
+                approved += 1
+                tp += int(bool(r["is_holding"]))
+        return {"approved": approved, "precision": round(tp / approved, 3) if approved else 0.0,
+                "coverage": round(decided / len(rows), 3) if rows else 0.0}
+
+    return {
+        "batch": batch, "ground_truth": ground_truth,
+        "n": len(conf_rows), "positives": pos, "negatives": len(conf_rows) - pos,
+        "confidence_sweep": [_gate(t) for t in thresholds],
+        "current_threshold": config.HALACHA_AUTO_APPROVE_THRESHOLD,
+        "panel_majority_2of3": _policy(unanimous=False),
+        "panel_unanimous_3of3": _policy(unanimous=True),
+    }
+
+
 async def list_corroboration_for_halacha(halacha_id: UUID) -> list[dict]:
     """Return all corroboration rows for one halacha, ordered by match_score DESC."""
     pool = await get_pool()

mcp-server/tests/test_goldset_calibrate.py

@@ -0,0 +1,79 @@
+"""Tests for #81.8 — db.goldset_calibrate (auto-approve gate calibration).
+
+Verifies the confidence-threshold sweep and the panel-policy precision/coverage
+against synthetic gold-set rows. Fully OFFLINE — monkeypatches db.goldset_list,
+no Postgres.
+"""
+
+from __future__ import annotations
+
+import asyncio
+
+import pytest
+
+from legal_mcp.services import db
+
+
+def _item(tag, keep, conf, votes):
+    c, d, g = votes
+    return {
+        "tagged_by": tag, "is_holding": keep, "confidence": conf,
+        "ai_is_holding": c, "ds_is_holding": d, "gm_is_holding": g,
+    }
+
+
+# A,B,C,D are chair-labeled; E is panel-labeled (excluded under ground_truth='chair').
+ITEMS = [
+    _item("chair", True, 0.90, (True, True, True)),     # A unanimous keep
+    _item("chair", True, 0.80, (True, True, False)),    # B majority keep
+    _item("chair", False, 0.60, (False, False, False)),  # C unanimous drop
+    _item("chair", True, 0.75, (True, True, True)),     # D unanimous keep
+    _item("panel:opus+deepseek+gemini", False, 0.99, (False, False, False)),  # E excluded
+]
+
+
+@pytest.fixture()
+def patched(monkeypatch: pytest.MonkeyPatch):
+    async def _fake(batch="default"):
+        return list(ITEMS)
+    monkeypatch.setattr(db, "goldset_list", _fake)
+
+
+def _run(coro):
+    loop = asyncio.new_event_loop()
+    try:
+        return loop.run_until_complete(coro)
+    finally:
+        loop.close()
+
+
+def test_ground_truth_chair_excludes_panel_rows(patched):
+    r = _run(db.goldset_calibrate("default", ground_truth="chair"))
+    assert r["n"] == 4 and r["positives"] == 3 and r["negatives"] == 1
+
+
+def test_confidence_sweep_precision_recall(patched):
+    r = _run(db.goldset_calibrate("default", ground_truth="chair"))
+    sweep = {round(g["threshold"], 2): g for g in r["confidence_sweep"]}
+    # T=0.80 approves A(0.90)+B(0.80) → both keep → P=1.0, recall 2/3
+    assert sweep[0.80]["approved"] == 2
+    assert sweep[0.80]["precision"] == 1.0
+    assert sweep[0.80]["recall"] == pytest.approx(0.667, abs=0.01)
+    # T=0.70 approves A,B,D (C's 0.60 excluded) → all keep → P=1.0, recall 1.0
+    assert sweep[0.70]["approved"] == 3
+    assert sweep[0.70]["recall"] == 1.0
+
+
+def test_panel_policies(patched):
+    r = _run(db.goldset_calibrate("default", ground_truth="chair"))
+    # majority: A,B,D keep / C drop → approves 3, all keep, full coverage
+    maj = r["panel_majority_2of3"]
+    assert maj["approved"] == 3 and maj["precision"] == 1.0 and maj["coverage"] == 1.0
+    # unanimous: A,D approved (B is T,T,F → not unanimous), all decided → P=1.0
+    un = r["panel_unanimous_3of3"]
+    assert un["approved"] == 2 and un["precision"] == 1.0 and un["coverage"] == 1.0
+
+
+def test_current_threshold_surfaced(patched):
+    r = _run(db.goldset_calibrate("default"))
+    assert r["current_threshold"] == db.config.HALACHA_AUTO_APPROVE_THRESHOLD