As the complexity of modern workloads and hardware increasingly outpaces human research and engineering capacity, existing methods for database performance optimization struggle to keep pace. To address this gap, a new class of techniques, termed AI-Driven Research for Systems (ADRS), uses large language models to automate solution discovery. This approach shifts optimization from manual system design to automated code generation. The key obstacle, however, in applying ADRS is the evaluation pipeline. Since these frameworks rapidly generate hundreds of candidates without human supervision, they depend on fast and accurate feedback from evaluators to converge on effective solutions. Building such evaluators is especially difficult for complex database systems. To enable the practical application of ADRS in this domain, we propose automating the design of evaluators by co-evolving them with the solutions. We demonstrate the effectiveness of this approach through three case studies optimizing buffer management, query rewriting, and index selection. Our automated evaluators enable the discovery of novel algorithms that outperform state-of-the-art baselines (e.g., a deterministic query rewrite policy that achieves up to 6.8x lower latency), demonstrating that addressing the evaluation bottleneck unlocks the potential of ADRS to generate highly optimized, deployable code for next-generation data systems.

翻译：随着现代工作负载与硬件复杂度的提升逐渐超越人类研究与工程能力，现有数据库性能优化方法已难以跟上发展步伐。为弥补这一差距，一类称为"AI驱动的系统研究"（AI-Driven Research for Systems, ADRS）的新技术利用大语言模型自动发现解决方案，将系统优化从人工设计转向自动化代码生成。然而，ADRS应用的核心障碍在于评估流程。由于这些框架无需人工监督即可快速生成数百个候选方案，其收敛到有效解决方案依赖于评估器提供快速准确的反馈。针对复杂数据库系统构建此类评估器尤为困难。为实现ADRS在该领域的实际应用，我们提出通过将评估器与解决方案协同进化来实现评估器设计的自动化。通过缓冲区管理、查询重写和索引选择三个案例研究，我们验证了该方法的有效性。自动化评估器能够发现超越现有最优基线的新型算法（例如，确定性查询重写策略实现了高达6.8倍的延迟降低），证明解决评估瓶颈可充分释放ADRS的潜力，为下一代数据系统生成高度优化且可直接部署的代码。