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arXiv · 2603.01570

Adversarial Query Synthesis via Bayesian Optimization

Jeffrey Tao·Yimeng Zeng·Haydn Thomas Jones·Natalie Maus·Osbert Bastani·Jacob R. Gardner·Ryan Marcus
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Abstract

Benchmark workloads are extremely important to the database management research community, especially as more machine learning components are integrated into database systems. Here, we propose a Bayesian optimization technique to automatically search for difficult benchmark queries, significantly reducing the amount of manual effort usually required. In preliminary experiments, we show that our approach can generate queries with more than double the optimization headroom compared to existing benchmarks.

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Jeffrey Tao, Yimeng Zeng, Haydn Thomas Jones, Natalie Maus, Osbert Bastani, Jacob R. Gardner, Ryan Marcus. 2026-03-02. Adversarial Query Synthesis via Bayesian Optimization. https://arxiv.org/abs/2603.01570

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