MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?

• URL: http://arxiv.org/abs/2504.09702v2
• Date: Sun, 18 May 2025 20:31:28 GMT
• Title: MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?
• Authors: Yunxiang Zhang, Muhammad Khalifa, Shitanshu Bhushan, Grant D Murphy, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, Lu Wang,
• Abstract summary: MLRC-Bench is a benchmark designed to quantify how effectively language agents can tackle challenging Machine Learning (ML) Research Competitions.<n>Unlike prior work, MLRC-Bench measures the key steps of proposing and implementing novel research methods.<n>Even the best-performing tested agent closes only 9.3% of the gap between baseline and top human participant scores.
• Score: 64.62421656031128
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce MLRC-Bench, a benchmark designed to quantify how effectively language agents can tackle challenging Machine Learning (ML) Research Competitions, with a focus on open research problems that demand novel methodologies. Unlike prior work, e.g., AI Scientist, which evaluates the end-to-end agentic pipeline by using LLM-as-a-judge, MLRC-Bench measures the key steps of proposing and implementing novel research methods and evaluates them with rigorous protocol and objective metrics. Our curated suite of 7 competition tasks reveals significant challenges for LLM agents. Even the best-performing tested agent (gemini-exp-1206 under MLAB) closes only 9.3% of the gap between baseline and top human participant scores. Furthermore, our analysis reveals a misalignment between the LLM-judged innovation and actual performance on cutting-edge ML research problems. MLRC-Bench is a dynamic benchmark, designed to grow with new ML competitions and encourage rigorous, objective evaluations of AI research capabilities. Our leaderboard and code are available at: https://huggingface.co/spaces/launch/MLRC_Bench

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