向量搜索老是调参？QASP 一次预测整条召回曲线，不用反复试，还省 80% 数据访问。做检索的朋友可以看看。
QASP 通过一次监督回归预测每条查询的完整召回曲线，省去搜索中的迭代模型调用。它使用缩放不变特征和搜索前推理，可跨召回目标、索引配置和数据集泛化。实验显示，QASP 达到 99% 召回率时数据访问减少 80%，并显著降低召回方差和偏差。论文还证明其训练样本量不随数据规模增长，数据访问节省随内在维度指数增长。
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 generalizes across recall targets, index configurations, and datasets. Its fine-grained progress predictions further enable a lightweight reactive complement that adjusts search depth based on predicted-versus-observed deviations without additional inference. We prove that QASP requires a finite training sample independent of dataset size and dimensionality, that its loss exceeds the irreducible lower bound of any fixed policy by a vanishing margin, and that its data access savings over fixed probing grow exponentially in intrinsic dimensionality. Experimentally, QASP achieves significantly lower recall variance and deviation from target, higher query satisfaction rate, and scales to large data and hierarchical indices without retraining, achieving 99% recall with 80% less data access.