Per-Query Recall Guarantees for Approximate Nearest Neighbor Search in Dynamic Vector Databases

2026-11-06来源 arXiv cs.DBEnglish

arXiv:2611.07579v1 Announce Type: new Abstract: Vector search runs as an operator in database systems, where a missed neighbor can make a filter, join, or aggregate wrong, yet approximate nearest neighbor (ANN) indexes are tuned only for an average recall target. In our measurements, indexes tuned to an average recall of 0.99 left 5-19% of in-distribution queries and, tuned without a query log, up to 99.6% of real out-of-distribution queries below this target, and the misses grew under updates. We present PQR-Search, a k-ANN search method that guarantees $\Pr[\mathrm{Recall}(q)

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Per-Query Recall Guarantees for Approximate Nearest Neighbor Search in Dynamic Vector Databases

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