← 研踪 PaperTrailarXiv:2607.00768 · v2
RACORN: Robust Predicate-Agnostic Filtered Vector Search under Extreme Selectivity
首次提交 2026-07-01 18:52
版本更新 2026-11-06 22:59
雷达观测 2026-11-09 13:22
摘要
Filtered vector search returns the nearest neighbors among the vectors that pass a predicate. The predicate is often selective: most tag queries of the Big ANN filtered track select less than 1% of YFCC 10M. Predicate-agnostic methods search one HNSW graph built without metadata and so take any predicate, but below a few percent selectivity they break down. ACORN-1 draws candidates only from passing neighbors and its recall collapses below 1%. HNSW keeps searching but spends about ef/s expansion cycles at selectivity s. We present RACORN-1, an extension of ACORN-1 that keeps its graph and filter-first traversal and adds Adaptive Search Fallback. When the two-hop expansion of a node falls short of a target, failing nodes enter the candidate queue as bridges, under a per-expansion budget, but never enter the result queue. RACORN-1 serves the selectivities ACORN-1 cannot: from 1% down ACORN-1 no longer reaches recall 0.9 at any ef, while RACORN-1 reaches 0.95 from 3% down on every dataset we sweep. It also reaches 0.9 on every natural filter that YFCC and LAION ship and on every conjunction and caption predicate we run, where ACORN-1 misses more than half. With a deadline equal to the time HNSW needs to fill its result queue, RACORN-1 returns the higher recall on all eight datasets at four selectivities. On eight indexes of 1M to 100M vectors, at 1% selectivity and recall 0.9 it is 1.3-8.4x faster than HNSW, and it is 2.2-10.5x faster under adverse correlation.
作者备注：13 pages, 8 figures, 4 tables. Code: https://github.com/naver/racorn
观测到的版本
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v2 · 2026-11-06 22:59 · 最新观测版本
RACORN: Robust Predicate-Agnostic Filtered Vector Search under Extreme Selectivity
Filtered vector search returns the nearest neighbors among the vectors that pass a predicate. The predicate is often selective: most tag queries of the Big ANN filtered track select less than 1% of YFCC 10M. Predicate-agnostic methods search one HNSW graph built without metadata and so take any predicate, but below a few percent selectivity they break down. ACORN-1 draws candidates only from passing neighbors and its recall collapses below 1%. HNSW keeps searching but spends about ef/s expansion cycles at selectivity s. We present RACORN-1, an extension of ACORN-1 that keeps its graph and filter-first traversal and adds Adaptive Search Fallback. When the two-hop expansion of a node falls short of a target, failing nodes enter the candidate queue as bridges, under a per-expansion budget, but never enter the result queue. RACORN-1 serves the selectivities ACORN-1 cannot: from 1% down ACORN-1 no longer reaches recall 0.9 at any ef, while RACORN-1 reaches 0.95 from 3% down on every dataset we sweep. It also reaches 0.9 on every natural filter that YFCC and LAION ship and on every conjunction and caption predicate we run, where ACORN-1 misses more than half. With a deadline equal to the time HNSW needs to fill its result queue, RACORN-1 returns the higher recall on all eight datasets at four selectivities. On eight indexes of 1M to 100M vectors, at 1% selectivity and recall 0.9 it is 1.3-8.4x faster than HNSW, and it is 2.2-10.5x faster under adverse correlation.
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