Ananta Shahane

Optimizing for Difference: LLM-Driven Benchmark Design for Maximum Solver Discriminability

This work proposes an automated framework that generates and refines benchmark suites using large language models (LLMs) and evolutionary search and shows that this combined approach substantially increases discriminability, improving scores from approximately 2.04 to 2.58.