AI-Driven Research for Databases
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. Buildi...
| Authors | Audrey Cheng, Harald Ng, Aaron Kabcenell et al. |
|---|---|
| Primary category | cs.DB |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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