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MLR-Bench
Comprehensive benchmark for evaluating AI agents on open-ended machine learning research tasks from top ML conferences.
In 30 seconds
- What
- Standardized benchmark with 201 ML research tasks from top conferences and automated evaluation across methodology, implementation, and analysis dimensions.
- When to use
- Assessing whether your research agent can handle open-ended ML problems comparable to published conference work across multiple subfields.
- Watch out
- Scores reflect narrow task performance; agents may overfit to benchmark patterns without generalizing to novel research directions outside these 9 areas.
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MLR-Bench: Overview
Comprehensive benchmark for evaluating AI agents on open-ended machine learning research tasks from top ML conferences.
- 201 research tasks from NeurIPS/ICLR/ICML
- MLR-Judge automated evaluation
- Covers 9 core ML research areas
- Real workshop paper tasks
- Modular research agent scaffold
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References
The papers, specifications, and repositories this pattern is based on.
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