Title: RoboPhD: Self-Improving Text-to-SQL Through Autonomous Agent Evolution
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Text-to-SQL, evolutionary algorithms, autonomous AI research, automated prompt engineering, Program synthesis

Abstract: We present RoboPhD, a system where AI agents autonomously conduct research to improve Text-to-SQL performance. RoboPhD implements a closed-loop evolution cycle with three coordinated components: a Database Analysis agent that profiles schemas without seeing test questions, a SQL Evaluation agent that generates executable queries conditioned on analysis and evidence, and an Evolution agent that designs new versions of the other two agents based on performance feedback. The Evolution agent employs weighted random sampling from multiple evolutionary strategies—including refinement, research-driven adaptation, and error-focused improvement—ensuring diverse exploration paths. Central to the framework is an ELO-based selection mechanism, which enables survival-of-the-fittest dynamics across competing agents while handling non-transitivity in performance. 
    Starting from a simple baseline, RoboPhD evolves agents through iterative refinement, discovering how to analyze databases to facilitate Text-to-SQL, while also evolving a sophisticated prompt for SQL Evaluation that covers numerous common pitfalls such as proper ordering of columns, when to use COUNT() vs SUM(), etc.-—without being explicitly programmed for them.
    The full system achieves 68.6\% execution accuracy on the BIRD test benchmark, demonstrating that AI can autonomously generate hypotheses, run experiments, and refine techniques.

TL;DR: RoboPhD introduces an ELO-driven evolutionary framework where autonomous agents co-evolve database analysis and SQL generation strategies to improve Text-to-SQL.