Amazon's QASP Vector Search Policy Slashes Data Access by 80%
_reachsumit · x · 2026-08-03
Amazon's team proposed QASP (Query-Adaptive Robust Vector Search Policy), a query-adaptive vector search strategy.
Traditional fixed-parameter searches suffer from massive performance variance across queries. QASP predicts the complete recall progression curve per query via a single upfront supervised regression, from which a search policy is derived. This avoids iterative model invocations during search.
Key Advantages:
- Massive Data Reduction: Cuts data access by up to 80% while maintaining 99% recall.
- Strong Generalization: Uses scale-invariant features, generalizing across recall targets, index configurations, and datasets without retraining.
- Lightweight Adjustment: Offers a reactive complement that dynamically adjusts search depth based on predicted-vs-observed deviations without extra inference.
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