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Add rerank module: bi-encoder retrieval plus an ONNX Runtime cross-encoder reranker behind a Spring AI DocumentPostProcessor; hit@k and MRR by pool size and question kind, latency by candidates, batch size, threads and int8, and five reproduced traps. Local MiniLM models, no database or API key.

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Co-Authored-By: Claude Sonnet 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01Cfo2K4u4rF18QsPXPFnytf
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Claude
2026-10-24 18:00:05 +00:00
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rerank/output/12-trap-score-scale.txt
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# Raw cross-encoder scores for the gold passage and for the best wrong passage
gold passage score : min -11.5, median 2.1, max 9.0
best wrong passage score : min -11.4, median -1.4, max 7.8
questions whose best-scoring passage is below 0: 26 of 60
a fixed cut-off of 0 would drop the gold passage for 26 of 60 questions
a fixed cut-off of 2 would drop the gold passage for 30 of 60 questions
a fixed cut-off of 4 would drop the gold passage for 38 of 60 questions