26794aab09
* 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) |
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| .. | ||
| __init__.py | ||
| fusion.py | ||
| graph_retrieval.py | ||
| link_expansion_retrieval.py | ||
| reranking.py | ||
| retrieval.py | ||
| tags.py | ||
| temporal_extraction.py | ||
| think_utils.py | ||
| trace.py | ||
| tracer.py | ||
| types.py | ||