feat(corroboration): aggregator — distinct positive + negative-flag (INV-COR4, X11)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

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

@@ -33,6 +33,23 @@ def accept_match(best: tuple[str, float] | None, floor: float = config.HALACHA_C
     return halacha_id if sim >= floor else None
 
 
+def aggregate(links: list[dict], min_cites: int = config.HALACHA_CORROBORATION_MIN_CITES) -> dict:
+    """Aggregate per-halacha corroboration (INV-COR4/COR2).
+
+    links: [{"source_id": str, "treatment": str}, ...] already matched to ONE halacha.
+    positive_sources = count of DISTINCT source_id whose treatment is positive.
+    has_negative = any negative treatment present.
+    corroborated = positive_sources >= min_cites AND not has_negative.
+    """
+    positive = {l["source_id"] for l in links if is_positive(l["treatment"])}
+    has_negative = any(is_negative(l["treatment"]) for l in links)
+    return {
+        "positive_sources": len(positive),
+        "has_negative": has_negative,
+        "corroborated": len(positive) >= min_cites and not has_negative,
+    }
+
+
 _TREATMENT_PROMPT = """אתה משפטן בכיר. נתון ציטוט של פסק/החלטה קודמים בתוך החלטה מאוחרת.
 סווג כיצד ההחלטה המאוחרת **מטפלת** בתקדים המצוטט, לפי אחת מהקטגוריות:
 - followed   — אימצה והחילה את ההלכה.

mcp-server/tests/test_corroboration.py

@@ -24,3 +24,23 @@ def test_match_rejects_below_threshold():
 
 def test_match_rejects_empty():
     assert cor.accept_match(None, floor=0.50) is None
+
+def _link(src, treatment):
+    return {"source_id": src, "treatment": treatment}
+
+def test_aggregate_counts_distinct_positive():
+    links = [_link("d1","followed"), _link("d1","explained"), _link("d2","followed")]
+    agg = cor.aggregate(links, min_cites=2)
+    assert agg["positive_sources"] == 2          # d1 counted once (INV-COR4 independence)
+    assert agg["has_negative"] is False
+    assert agg["corroborated"] is True
+
+def test_aggregate_negative_blocks():
+    links = [_link("d1","followed"), _link("d2","followed"), _link("d3","distinguished")]
+    agg = cor.aggregate(links, min_cites=2)
+    assert agg["has_negative"] is True
+    assert agg["corroborated"] is False          # any negative -> not corroborated (INV-COR2)
+
+def test_aggregate_below_threshold():
+    agg = cor.aggregate([_link("d1","followed")], min_cites=2)
+    assert agg["corroborated"] is False           # single source insufficient (INV-COR4)