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.
@article{arxiv.2603.01570,
title = {Adversarial Query Synthesis via Bayesian Optimization},
author = {Jeffrey Tao and Yimeng Zeng and Haydn Thomas Jones and Natalie Maus and Osbert Bastani and Jacob R. Gardner and Ryan Marcus},
journal= {arXiv preprint arXiv:2603.01570},
year = {2026}
}