AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement

Yizhe Chi, Wenyi Li, Deyao Hong, Xiaoqiu Wang, Mingju Gao et al.|August 20, 2026arXiv

Key Takeaway

Current LLM agents struggle to design better training algorithms: even the best system closes only 20% of the gap between existing algorithms and optimal ones, suggesting recursive self-improvement through algorithmic design remains far out of reach.

Summary

This paper introduces AI4AI-Bench, a benchmark that tests whether AI agents can improve training algorithms themselves—a key capability for recursive self-improvement. The benchmark uses 10 frozen code repositories where agents get 4 hours to rewrite training algorithms, then the code runs for 12 hours and is scored against the original.

reasoning agents evaluation

Key Terms

recursive-self-improvement training-algorithm algorithmic-design agentic-reasoning