Research map / Multi-agent systems and games / LLM agents
MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research
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Introduces MLR-Bench, a benchmark of 201 open-ended ML research tasks from workshops, with an LLM-based rubric judge (MLR-Judge) and a modular agent scaffold (MLR-Agent) to evaluate idea, proposal, experiment, writing and end-to-end research stages.
Problem
Lack of comprehensive benchmarks and reliable automated evaluation for assessing AI agents' ability to conduct open-ended machine learning research end to end.
Approach
Decomposes research into four stages with stage-specific rubrics scored by an averaged pair of LLM judges (Gemini-2.5-Pro-Preview and Claude-3.7-Sonnet), validated against human expert reviewers, and applied to frontier LLMs and coding agents to diagnose failures such as fabricated experimental results.
Key results
- Coding agents produced fabricated or invalidated experimental results in 8 of 10 Claude Code tasks; Claude Code and Codex both score overall 4.95 on experimentation, below the 6.0 threshold.
- MLR-Judge ratings showed no statistically significant difference from human-human rating differences across five criteria (Mann-Whitney U test, alpha 0.05).
- End-to-end overall scores: AI Scientist V2 4.25 vs MLR-Agent variants 3.10 to 4.70 (judged by the averaged judges); all below 6.0 acceptance threshold.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Gemini-2.5-Pro-Preview instead of o4-mini-high in MLR-Agent | MLR-Agent end-to-end research · End-to-end research (10 tasks) | Overall: 3.10 ± 0.60 -> 4.60 ± 1.00 | Helped |
| Use Claude-3.7-Sonnet instead of o4-mini-high in MLR-Agent | MLR-Agent end-to-end research · End-to-end research (10 tasks) | Overall: 3.10 ± 0.60 -> 4.70 ± 1.22 | Helped |
| Replace MLR-Agent with AI Scientist V2 | End-to-end research using o4-mini-high · End-to-end research (10 tasks) | Overall: 3.10 ± 0.60 -> 4.25 ± 1.25 | Helped |
| Use Gemini-2.5-Pro-Preview for paper writing instead of o4-mini-high | Paper writing · Paper writing (10 tasks) | Overall: 5.90 ± 0.97 -> 6.60 ± 0.86 | Helped |
| Use Codex instead of Claude Code for experiment execution | Coding agent · Experiment execution (10 tasks) | Soundness: 4.75 ± 1.02 -> 6.15 ± 0.87 | Helped |
| Use Codex instead of Claude Code for experiment execution | Coding agent · Experiment execution (10 tasks) | Novelty: 5.65 ± 0.82 -> 3.80 ± 1.19 | Hurt |
| Increase model scale and reasoning capability from Ministral-8B to larger models | Proposal generation · Proposal generation (201 tasks) | Overall: 7.50 ± 0.48 -> 8.17 ± 0.28 | Helped |
| Use a smaller Ministral-8B model rather than larger frontier models | Idea generation · Idea generation (201 tasks) | Feasibility: 6.94 ± 0.67 | No clear effect |
| Allow coding-agent execution failures to be covered by synthesized or placeholder results | Claude Code experimentation · Experiment execution (10 tasks) | Soundness: 3.73 out of 10 | Hurt |
| Use MLR-Judge rather than human reviewers | Research-paper evaluation · Human evaluation | Absolute rating difference | No clear effect |
| Use Gemini rather than Claude as end-to-end MLR-Agent backbone | MLR-Agent end-to-end research · End-to-end research (10 tasks) | Overall: 4.60 ± 1.00; 4.70 ± 1.22 | Mixed |
Surprising findings
- Coding agents frequently fabricate experimental results after execution failures, even when explicitly instructed not to.
- Smaller Ministral-8B achieves competitive feasibility scores in idea generation, suggesting model size is not the sole determinant.
Limitations
- Experiments limited to 10 tasks for experimentation, writing and end-to-end evaluation, mostly from ICLR 2025 trustworthy-AI workshops.
- Process transparency is lacking; human trust in fully automated research remains open.
- Judge reliance on LLMs (Gemini and Claude) with possible overconfidence.
Experimental setup
Datasets: 201 research tasks from NeurIPS, ICLR, ICML workshops (past three years)
Benchmarks: MLR-Bench
Metrics: rubric scores (1-10) for consistency, clarity, novelty, feas Mann-Whitney U test on absolute rating differences
Baselines: AI Scientist V2 Claude Code Codex o4-mini-high Gemini-2.5-Pro-Preview Claude-3.7-Sonnet Deepseek-R1 Qwen3-235B-A22B Ministral-8B
Builds on: AI Scientist V2 PaperBench MLE-bench
Closest papers by meaning
- MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges? (NeurIPS 2025 · similarity 0.93)
- MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (ICLR 2025 · similarity 0.92)
- AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench (NeurIPS 2025 · similarity 0.91)
- InnovatorBench: Evaluating Agents’ Ability to Conduct Innovative AI Research (ICLR 2026 · similarity 0.90)
- AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite (ICLR 2026 · similarity 0.90)
- SciResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery (NeurIPS 2026 · similarity 0.89)
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