Fusion min-max normalises over every keyword match, so hybrid_search asked
BM25 for a ranked list of the whole corpus: a hash insert per posting, then a
sort of every match, then the merge sorted every candidate again to keep k.
- BM25Index::scores returns every match unsorted, accumulated in a dense array
(contributions are strictly positive, so zero means untouched). search() is
built on it with the bounded heap.
- merge_vector_keyword partitions out its top k (select_nth) and orders only
those, with the same score-then-id order.
- Both hybrid paths use scores().
Rankings are identical (equivalence tests for both changes). p50 0.24 -> 0.07
ms (1K), 2.1 -> 0.49 ms (10K), 23 -> 4.65 ms (100K).
The harness gains --fusion-study, which measured the alternative — capping the
keyword pool — and found it changes the top-10 for most queries (overlap
0.83-0.92, different #1 for 10-35%) for only a 2x saving. Not adopted.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
