QASP: Query-Adaptive Robust Vector Search Policy
A fundamental challenge of vector search is achieving consistently high recall while minimizing computational costs. Fixed search parameters cause significant performance variance across queries, and conventional evaluation on average recall masks these per-query disparities. We introduce QASP (Query-Adaptive robust vector Search Policy), which predicts the complete recall progression curve per query via a single upfront supervised regression, from which a search policy is derived for any recall target; this avoids iterative model invocations during search or separate predictors per target. By predicting normalized recall values with scale-invariant features and pre-search inference, QASP...
| Authors | Hakan Ferhatosmanoglu, Kushal Kumar, Tal Wagner et al. |
|---|---|
| Primary category | cs.IR |
| Published | 31 July 2026 |
| Last updated at source | 14 October 2026 |
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