FML-Bench: A Controlled Study of AI Research Agent Strategies
The article discusses the introduction of FML-Bench, a benchmark designed to evaluate AI research agent strategies in machine learning. It aims to differentiate agent strategy from execution infrastructure to better understand performance drivers. The study finds that strategy complexity does not always correlate with performance and highlights the importance of exploration behaviors in achieving better results.
- ▪FML-Bench includes 18 fundamental ML research tasks across 10 domains.
- ▪The benchmark separates agent strategy from execution infrastructure and defines 12 process-level behavioral metrics.
- ▪A simple greedy hill-climber nearly matches the performance of the best tree-search agent, indicating that strategy complexity alone does not guarantee strong performance.
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Computer Science > Machine Learning arXiv:2605.17373 (cs) [Submitted on 17 May 2026] Title:FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics Authors:Qiran Zou, Hou Hei Lam, Wenhao Zhao, Tingting Chen, Yiming Tang, Samson Yu, Yingtao Zhu, Srinivas Anumasa, Zufeng Zhang, Tianyi Zhang, Chang Liu, Zhengyao Jiang, Anirudh Goyal, Dianbo Liu View a PDF of the paper titled FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics, by Qiran Zou and 13 other authors View PDF HTML (experimental) Abstract:AI research agents accelerate ML research by automating hypothesis generation, experimentation, and empirical refinement.
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