RoboPhD: Self-Improving Text-to-SQL Through Autonomous Agent Evolution

• URL: http://arxiv.org/abs/2601.01126v1
• Date: Sat, 03 Jan 2026 09:16:07 GMT
• Title: RoboPhD: Self-Improving Text-to-SQL Through Autonomous Agent Evolution
• Abstract summary: RoboPhD is a system where AI agents autonomously conduct research to improve Text-to-adaptive performance.<n>Central to the framework is an ELO-based selection mechanism enabling survival-of-the-fittest dynamics.<n>The system achieves 73.67% accuracy on the BIRD test set, demonstrating that AI can autonomously build a strong agentic system with only a trivial human-provided starting point.
• Score: 0.2389431394996565
• License: http://creativecommons.org/licenses/by/4.0/
• 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 two coordinated components: a SQL Generation agent composed of a database analysis script and SQL generation instructions, and an Evolution agent that designs new versions based on performance feedback. Central to the framework is an ELO-based selection mechanism enabling survival-of-the-fittest dynamics while handling non-transitivity in performance. Starting from a naive 70-line baseline, RoboPhD evolves agents through iterative cross-pollination, discovering effective techniques without any external guidance on the Text-to-SQL domain. Our best agent, evolved to 1500 lines over 18 iterations, autonomously discovered strategies such as size-adaptive database analysis that adjusts depth based on schema complexity and SQL generation patterns for column selection, evidence interpretation, and aggregation. Evolution provides the largest gains on cheaper models: while we improve by 2.3 points over a strong Claude Opus 4.5 naive baseline, we show an improvement of 8.9 points over the weaker Claude Haiku model. This enables 'skip a tier' deployment: evolved Haiku exceeds naive Sonnet accuracy, and evolved Sonnet exceeds naive Opus, both at lower cost. The full system achieves 73.67% accuracy on the BIRD test set, demonstrating that AI can autonomously build a strong agentic system with only a trivial human-provided starting point.

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