feat(recall): add proof_count boost to combined scoring (#821)

* feat(recall): add proof_count boost to combined scoring

Observations with more supporting evidence now rank slightly higher
in recall results. proof_count is threaded through the retrieval
pipeline and applied as a multiplicative boost in reranking:

- types.py: add proof_count field to RetrievalResult
- retrieval.py: include proof_count in SELECT columns
- reranking.py: add log1p-normalized proof_count boost (alpha=0.1)

The boost uses the same multiplicative pattern as recency and temporal
signals. proof_count=1 is neutral, proof_count=50 gives ~+5% boost.
Non-observation fact types are unaffected (neutral 0.5).

* fix(retrieval): Apply proof_count boost to graph and temporal retrieval, normalize scaling

* fix(retrieval): correct proof_norm math to zero-center at count 1

* fix(retrieval): Apply proof_count boost to link_expansion retrieval

* fix: remove BFS zombie, clamp proof_norm to [0,1], fix test comment (log1p->math.log)

hindsight-api-slim/hindsight_api/engine/search/link_expansion_retrieval.py

@@ -59,7 +59,7 @@ async def _find_semantic_seeds(
     rows = await conn.fetch(
         f"""
         SELECT id, text, context, event_date, occurred_start, occurred_end,
-               mentioned_at, fact_type, document_id, chunk_id, tags,
+               mentioned_at, fact_type, document_id, chunk_id, tags, proof_count,
                1 - (embedding <=> $1::vector) AS similarity
         FROM {fq_table("memory_units")}
         WHERE bank_id = $2
@@ -274,7 +274,7 @@ class LinkExpansionRetriever(GraphRetriever):
                 -- Score = COUNT(DISTINCT shared entities), mapped to [0,1] via tanh.
                 SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                        mu.occurred_end, mu.mentioned_at,
-                       mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                       mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
                        COUNT(DISTINCT ue_seed.entity_id)::float AS score,
                        'entity'::text AS source
                 FROM {ue} ue_seed
@@ -298,14 +298,14 @@ class LinkExpansionRetriever(GraphRetriever):
                 SELECT
                     id, text, context, event_date, occurred_start,
                     occurred_end, mentioned_at,
-                    fact_type, document_id, chunk_id, tags,
+                    fact_type, document_id, chunk_id, tags, proof_count,
                     MAX(weight) AS score,
                     'semantic'::text AS source
                 FROM (
                     SELECT
                         mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                         mu.occurred_end, mu.mentioned_at,
-                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
                         ml.weight
                     FROM {ml} ml
                     JOIN {mu} mu ON mu.id = ml.to_unit_id
@@ -317,7 +317,7 @@ class LinkExpansionRetriever(GraphRetriever):
                     SELECT
                         mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                         mu.occurred_end, mu.mentioned_at,
-                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
                         ml.weight
                     FROM {ml} ml
                     JOIN {mu} mu ON mu.id = ml.from_unit_id
@@ -328,7 +328,7 @@ class LinkExpansionRetriever(GraphRetriever):
                 ) sem_raw
                 GROUP BY id, text, context, event_date, occurred_start,
                          occurred_end, mentioned_at,
-                         fact_type, document_id, chunk_id, tags
+                         fact_type, document_id, chunk_id, tags, proof_count
                 ORDER BY score DESC
                 LIMIT $3
             ),
@@ -339,7 +339,7 @@ class LinkExpansionRetriever(GraphRetriever):
                 SELECT DISTINCT ON (mu.id)
                     mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                     mu.occurred_end, mu.mentioned_at,
-                    mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                    mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
                     ml.weight AS score,
                     'causal'::text AS source
                 FROM {ml} ml
@@ -429,7 +429,7 @@ class LinkExpansionRetriever(GraphRetriever):
             SELECT
                 mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                 mu.occurred_end, mu.mentioned_at,
-                mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
                 (SELECT COUNT(DISTINCT s) FROM unnest(mu.source_memory_ids) s WHERE s = ANY(ca.source_ids))::float AS score
             FROM {fq_table("memory_units")} mu, connected_array ca
             WHERE mu.fact_type = 'observation'
@@ -453,13 +453,13 @@ class LinkExpansionRetriever(GraphRetriever):
                 SELECT
                     id, text, context, event_date, occurred_start,
                     occurred_end, mentioned_at,
-                    fact_type, document_id, chunk_id, tags,
+                    fact_type, document_id, chunk_id, tags, proof_count,
                     MAX(weight) AS score,
                     'semantic'::text AS source
                 FROM (
                     SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                            mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id,
-                           mu.chunk_id, mu.tags, ml.weight
+                           mu.chunk_id, mu.tags, mu.proof_count, ml.weight
                     FROM {ml} ml JOIN {mu} mu ON mu.id = ml.to_unit_id
                     WHERE ml.from_unit_id = ANY($1::uuid[])
                       AND ml.link_type = 'semantic' AND mu.fact_type = 'observation'
@@ -467,21 +467,21 @@ class LinkExpansionRetriever(GraphRetriever):
                     UNION ALL
                     SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                            mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id,
-                           mu.chunk_id, mu.tags, ml.weight
+                           mu.chunk_id, mu.tags, mu.proof_count, ml.weight
                     FROM {ml} ml JOIN {mu} mu ON mu.id = ml.from_unit_id
                     WHERE ml.to_unit_id = ANY($1::uuid[])
                       AND ml.link_type = 'semantic' AND mu.fact_type = 'observation'
                       AND mu.id != ALL($1::uuid[])
                 ) sem_raw
                 GROUP BY id, text, context, event_date, occurred_start, occurred_end,
-                         mentioned_at, fact_type, document_id, chunk_id, tags
+                         mentioned_at, fact_type, document_id, chunk_id, tags, proof_count
                 ORDER BY score DESC LIMIT $2
             ),
             causal_expanded AS (
                 SELECT DISTINCT ON (mu.id)
                     mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                     mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id,
-                    mu.chunk_id, mu.tags, ml.weight AS score, 'causal'::text AS source
+                    mu.chunk_id, mu.tags, mu.proof_count, ml.weight AS score, 'causal'::text AS source
                 FROM {ml} ml JOIN {mu} mu ON ml.to_unit_id = mu.id
                 WHERE ml.from_unit_id = ANY($1::uuid[])
                   AND ml.link_type IN ('causes', 'caused_by', 'enables', 'prevents')

hindsight-api-slim/hindsight_api/engine/search/reranking.py

@@ -2,6 +2,7 @@
 Cross-encoder neural reranking for search results.
 """
 
+import math
 from datetime import datetime, timezone
 
 from .types import MergedCandidate, ScoredResult
@@ -13,6 +14,7 @@ UTC = timezone.utc
 # so the max combined boost is (1 + alpha/2)^2 ≈ +21% and min is (1 - alpha/2)^2 ≈ -19%.
 _RECENCY_ALPHA: float = 0.2
 _TEMPORAL_ALPHA: float = 0.2
+_PROOF_COUNT_ALPHA: float = 0.1  # Conservative: max ±5% for evidence strength
 
 
 def apply_combined_scoring(
@@ -20,28 +22,40 @@ def apply_combined_scoring(
     now: datetime,
     recency_alpha: float = _RECENCY_ALPHA,
     temporal_alpha: float = _TEMPORAL_ALPHA,
+    proof_count_alpha: float = _PROOF_COUNT_ALPHA,
 ) -> None:
     """Apply combined scoring to a list of ScoredResults in-place.
 
-    Uses the cross-encoder score as the primary relevance signal, with recency
-    and temporal proximity applied as multiplicative boosts. This ensures the
-    influence of these secondary signals is always proportional to the base
-    relevance score, regardless of the cross-encoder model's score calibration.
+    Uses the cross-encoder score as the primary relevance signal, with recency,
+    temporal proximity, and proof count applied as multiplicative boosts. This
+    ensures the influence of these secondary signals is always proportional to
+    the base relevance score, regardless of the cross-encoder model's score
+    calibration.
 
     Formula::
 
         recency_boost     = 1 + recency_alpha     * (recency     - 0.5)   # in [1-α/2, 1+α/2]
         temporal_boost    = 1 + temporal_alpha    * (temporal    - 0.5)   # in [1-α/2, 1+α/2]
-        combined_score = cross_encoder_score_normalized * recency_boost * temporal_boost
+        proof_count_boost = 1 + proof_count_alpha * (proof_norm  - 0.5)   # in [1-α/2, 1+α/2]
+        combined_score    = CE_normalized * recency_boost * temporal_boost * proof_count_boost
+
+    proof_norm maps proof_count using a smooth logarithmic curve centered at 0.5,
+    clamped to [0, 1]:
+      proof_count=1 → 0.5 + 0 = 0.5 (neutral multiplier)
+      proof_count=150 → clamped to 1.0 (max +5% boost)
 
     Temporal proximity is treated as neutral (0.5) when not set by temporal retrieval,
     so temporal_boost collapses to 1.0 for non-temporal queries.
 
+    Proof count is treated as neutral (0.5) when not available (non-observation facts),
+    so proof_count_boost collapses to 1.0 for world/experience/opinion facts.
+
     Args:
         scored_results: Results from the cross-encoder reranker. Mutated in place.
         now: Current UTC datetime for recency calculation.
         recency_alpha: Max relative recency adjustment (default 0.2 → ±10%).
         temporal_alpha: Max relative temporal adjustment (default 0.2 → ±10%).
+        proof_count_alpha: Max relative proof count adjustment (default 0.1 → ±5%).
     """
     if now.tzinfo is None:
         now = now.replace(tzinfo=UTC)
@@ -59,13 +73,23 @@ def apply_combined_scoring(
         # Temporal proximity: meaningful only for temporal queries; neutral otherwise.
         sr.temporal = sr.retrieval.temporal_proximity if sr.retrieval.temporal_proximity is not None else 0.5
 
+        # Proof count: log-normalized evidence strength; neutral for non-observations.
+        proof_count = sr.retrieval.proof_count
+        if proof_count is not None and proof_count >= 1:
+            # Clamp to [0, 1] so extreme counts stay within documented ±5% range
+            proof_norm = min(1.0, max(0.0, 0.5 + (math.log(proof_count) / 10.0)))
+        else:
+            # Neutral baseline is precisely 0.5, ensuring neutral multiplier (1.0)
+            proof_norm = 0.5
+
         # RRF: kept at 0.0 for trace continuity but excluded from scoring.
         # RRF is batch-relative (min-max normalised) and redundant after reranking.
         sr.rrf_normalized = 0.0
 
         recency_boost = 1.0 + recency_alpha * (sr.recency - 0.5)
         temporal_boost = 1.0 + temporal_alpha * (sr.temporal - 0.5)
-        sr.combined_score = sr.cross_encoder_score_normalized * recency_boost * temporal_boost
+        proof_count_boost = 1.0 + proof_count_alpha * (proof_norm - 0.5)
+        sr.combined_score = sr.cross_encoder_score_normalized * recency_boost * temporal_boost * proof_count_boost
         sr.weight = sr.combined_score
 
 

hindsight-api-slim/hindsight_api/engine/search/retrieval.py

@@ -141,7 +141,7 @@ async def retrieve_semantic_bm25_combined(
 
     cols = (
         "id, text, context, event_date, occurred_start, occurred_end, mentioned_at, "
-        "fact_type, document_id, chunk_id, tags, metadata"
+        "fact_type, document_id, chunk_id, tags, metadata, proof_count"
     )
     table = fq_table("memory_units")
 
@@ -336,7 +336,7 @@ async def retrieve_temporal_combined(
               {groups_clause}
         ),
         sim_ranked AS (
-            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.metadata,
+            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.proof_count, mu.document_id, mu.chunk_id, mu.tags, mu.metadata,
                    1 - (mu.embedding <=> $1::vector) AS similarity,
                    ROW_NUMBER() OVER (PARTITION BY mu.fact_type ORDER BY mu.embedding <=> $1::vector) AS sim_rn
             FROM date_ranked dr
@@ -344,7 +344,7 @@ async def retrieve_temporal_combined(
             WHERE dr.rn <= 50
               AND (1 - (mu.embedding <=> $1::vector)) >= $6
         )
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags, metadata, similarity
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, proof_count, document_id, chunk_id, tags, metadata, similarity
         FROM sim_ranked
         WHERE sim_rn <= 10
         """,

hindsight-api-slim/hindsight_api/engine/search/types.py

@@ -48,6 +48,7 @@ class RetrievalResult:
     chunk_id: str | None = None
     tags: list[str] | None = None  # Visibility scope tags
     metadata: dict[str, str] | None = None  # User-provided metadata
+    proof_count: int | None = None  # Number of supporting memories (observations only)
 
     # Retrieval-specific scores (only one will be set depending on retrieval method)
     similarity: float | None = None  # Semantic retrieval
@@ -72,6 +73,7 @@ class RetrievalResult:
             chunk_id=row.get("chunk_id"),
             tags=row.get("tags"),
             metadata=row.get("metadata"),
+            proof_count=row.get("proof_count"),
             similarity=row.get("similarity"),
             bm25_score=row.get("bm25_score"),
             activation=row.get("activation"),

hindsight-api-slim/tests/test_reranking_proof_count.py

@@ -0,0 +1,96 @@
+"""
+Unit tests for proof_count boost in reranking.
+"""
+
+from datetime import datetime, timezone
+import pytest
+from uuid import uuid4
+
+from hindsight_api.engine.search.types import RetrievalResult, MergedCandidate, ScoredResult
+from hindsight_api.engine.search.reranking import apply_combined_scoring
+
+UTC = timezone.utc
+
+def create_mock_scored_result(proof_count: int | None = None, ce_score: float = 0.8) -> ScoredResult:
+    """Helper to create a minimal ScoredResult suitable for scoring tests."""
+    retrieval = RetrievalResult(
+        id=uuid4(),
+        text="Test mock fact",
+        fact_type="observation" if proof_count is not None else "world",
+        document_id=uuid4(),
+        chunk_id=uuid4(),
+        embedding=[0.1]*384,
+        similarity=0.9,
+        proof_count=proof_count,
+        # Default neutral dates for testing so only proof_count changes score
+        occurred_start=datetime.now(UTC),
+        occurred_end=datetime.now(UTC)
+    )
+    candidate = MergedCandidate(
+        id=retrieval.id,
+        retrieval=retrieval,
+        semantic_rank=1,
+        bm25_rank=1,
+        rrf_score=0.1
+    )
+    return ScoredResult(
+        candidate=candidate,
+        cross_encoder_score=ce_score,
+        cross_encoder_score_normalized=ce_score,
+        weight=ce_score,
+    )
+
+def test_proof_count_neutral_when_none():
+    """Test that when proof_count is None (e.g. non-observation), it gets neutral 0.5 norm."""
+    sr = create_mock_scored_result(proof_count=None, ce_score=0.8)
+    now = datetime.now(UTC)
+    
+    apply_combined_scoring([sr], now, proof_count_alpha=0.1)
+    
+    # Neutral multiplier means score shouldn't be boosted by proof_count
+    # Since recency is neutral (just created) and temporal is neutral, score should remain unchanged
+    assert sr.combined_score == pytest.approx(0.8, rel=1e-3)
+
+def test_proof_count_neutral_at_one():
+    """Test that proof_count=1 gives neutral multiplier."""
+    sr = create_mock_scored_result(proof_count=1, ce_score=0.8)
+    now = datetime.now(UTC)
+    
+    apply_combined_scoring([sr], now, proof_count_alpha=0.1)
+    
+    # proof_count=1 -> math.log(1) = 0 -> 0.5 + 0/10 = 0.5 (neutral) -> multiplier 1.0
+    assert sr.combined_score == pytest.approx(0.8, rel=1e-3)
+
+def test_proof_count_increases_with_higher_counts():
+    """Test that higher proof counts yield strictly higher scores."""
+    now = datetime.now(UTC)
+    
+    # Create results with increasing proof counts
+    sr_5 = create_mock_scored_result(proof_count=5, ce_score=0.8)
+    sr_50 = create_mock_scored_result(proof_count=50, ce_score=0.8)
+    sr_100 = create_mock_scored_result(proof_count=100, ce_score=0.8)
+    
+    # Process them
+    apply_combined_scoring([sr_5, sr_50, sr_100], now, proof_count_alpha=0.1)
+    
+    # Assure scores strictly increase
+    assert sr_5.combined_score > 0.8
+    assert sr_50.combined_score > sr_5.combined_score
+    assert sr_100.combined_score > sr_50.combined_score
+
+def test_proof_count_no_hardcoded_cap_at_100():
+    """Test that proof_count continues to scale within the clamped [0, 1] range."""
+    now = datetime.now(UTC)
+    
+    # Use values that stay below the clamp ceiling (proof_norm < 1.0)
+    # log(5)/10=0.16, log(20)/10=0.30, log(100)/10=0.46 → all below 0.5 headroom
+    sr_5 = create_mock_scored_result(proof_count=5, ce_score=0.8)
+    sr_20 = create_mock_scored_result(proof_count=20, ce_score=0.8)
+    sr_100 = create_mock_scored_result(proof_count=100, ce_score=0.8)
+    
+    apply_combined_scoring([sr_5, sr_20, sr_100], now, proof_count_alpha=0.1)
+    
+    # Must strictly increase within the valid range
+    assert sr_20.combined_score > sr_5.combined_score
+    assert sr_100.combined_score > sr_20.combined_score
+