MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research
Authors: name: Hui Chen, name: Miao Xiong, name: Yujie Lu, name: Wei Han, name: Ailin Deng, name: Yufei He, name: Jiaying Wu, name: Yibo Li, name: Yue Liu, name: Bryan Hooi

Recent advancements in AI agents have demonstrated their growing potential to
drive and support scientific discovery. In this work, we introduce MLR-Bench, a
comprehensive benchmark for evaluating AI agents on open-ended machine learning
research. MLR-Bench includes three key components: (1) 201 research tasks
sourced from NeurIPS, ICLR, and ICML workshops covering diverse ML topics; (2)
MLR-Judge, an automated evaluation framework combining LLM-based reviewers with
carefully designed review rubrics to assess research quality; and (3)
MLR-Agent, a modular agent scaffold capable of completing research tasks
through four stages: idea generation, proposal formulation, experimentation,
and paper writing. Our framework supports both stepwise assessment across these
distinct research stages, and end-to-end evaluation of the final research
paper. We then use MLR-Bench to evaluate six frontier LLMs and an advanced
coding agent, finding that while LLMs are effective at generating coherent
ideas and well-structured papers, current coding agents frequently (e.g., in
80% of the cases) produce fabricated or invalidated experimental
results--posing a major barrier to scientific reliability. We validate
MLR-Judge through human evaluation, showing high agreement with expert
reviewers, supporting its potential as a scalable tool for research evaluation.
We open-source MLR-Bench to help the community benchmark, diagnose, and improve
AI research agents toward trustworthy and transparent scientific discovery.