![[2510.10472] FML-bench: Benchmarking Machine Learning Agents for Scientific Research](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2510.10472] FML-bench: Benchmarking Machine Learning Agents for Scientific Research
Summary
The paper introduces FML-bench, a new benchmark for evaluating machine learning agents in scientific research, focusing on exploration diversity and its impact on performance.
Why It Matters
FML-bench addresses the limitations of existing benchmarks by emphasizing the research processes of machine learning agents rather than just their final performance. This shift can lead to improved designs and better understanding of agent behavior in scientific contexts, which is crucial for advancing AI research.
Key Takeaways
- FML-bench includes 8 diverse ML research tasks for comprehensive evaluation.
- Exploration Diversity metric quantifies variance in proposals, influencing research outcomes.
- Agents with broader exploration strategies show higher performance and diversity.
- The benchmark aims to inform future designs of research agents.
- Findings highlight the importance of exploration patterns in achieving better results.
Computer Science > Computation and Language arXiv:2510.10472 (cs) [Submitted on 12 Oct 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:FML-bench: Benchmarking Machine Learning Agents for Scientific Research Authors:Qiran Zou, Hou Hei Lam, Wenhao Zhao, Yiming Tang, Tingting Chen, Samson Yu, Tianyi Zhang, Chang Liu, Xiangyang Ji, Dianbo Liu View a PDF of the paper titled FML-bench: Benchmarking Machine Learning Agents for Scientific Research, by Qiran Zou and 9 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments. However, existing benchmarks predominantly adopt an engineering-oriented perspective: they emphasize application-oriented tasks and evaluate primarily on final performance and computational cost, overlooking agents' research processes and limiting assessment of their capabilities in scientific research settings. To more comprehensively evaluate agents in scientific research settings, we introduce FML-bench, a benchmark comprising 8 diverse and fundamental ML research tasks, and further propose complementary metrics, notably Exploration Diversity, which quantifies the variance of proposals across iterations and reveals how exploration patterns influence research outcomes. We evaluate state-of-the-art research agents on FML-bench, showing that agents employing broad exploration strategies exhibit highe...
