ML-Dev-Bench: Comparative Analysis of AI Agents on ML development workflows
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
In this report, we present ML-Dev-Bench, a benchmark aimed at testing agentic capabilities on applied Machine Learning development tasks. While existing benchmarks focus on isolated coding tasks or Kaggle-style competitions, ML-Dev-Bench tests agents' ability to handle the full complexity of ML development workflows. The benchmark assesses performance across critical aspects including dataset handling, model training, improving existing models, debugging, and API integration with popular ML tools. We evaluate three agents - ReAct, Openhands, and AIDE - on a diverse set of 30 tasks, providing insights into their strengths and limitations in handling practical ML development challenges. We open source the benchmark for the benefit of the community at hrefhttps://github.com/ml-dev-bench/ml-dev-benchhttps://github.com/ml-dev-bench/ml-dev-bench.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 4 of its 8 references that are in the map. First posted on arXiv 3 Feb 2025.
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4 of 8 references.
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Other references · 3
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Citing papers per year: 2025: 12; 2026: 4.
16 papers.
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- 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 167
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- 2025AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?cited by 24
- 2025RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedbackcited by 24
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- 2026ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Developmentcited by 6
- 2026FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamicscited by 6
- 2025FML-bench: Benchmarking Machine Learning Agents for Scientific Researchcited by 3
4 more
- 2025ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challengescited by 3
- 2025ML2B: Benchmarking LLMs on Cross-Lingual ML Pipeline Generationcited by 0
- 2026Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientistscited by 0
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environmentscited by 0
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environments20 Aug 2026
- 2026Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists3 Jun 2026
- 2026FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics17 May 2026
- 2026ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development16 Jan 2026
- 2025ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challenges19 Dec 2025
- 2025FML-bench: Benchmarking Machine Learning Agents for Scientific Research12 Oct 2025
- 2025RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback7 Oct 2025
- 2025ML2B: Benchmarking LLMs on Cross-Lingual ML Pipeline Generation26 Sep 2025
- 2025ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents19 Aug 2025
- 2025Large Language Model-based Data Science Agent: A Survey2 Aug 2025
- 2025How Far Are AI Scientists from Changing the World?31 Jul 2025
- 2025AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?19 Jul 2025
4 more
- 2025RExBench: Can coding agents autonomously implement AI research extensions?27 Jun 2025
- 2025AI Scientists Fail Without Strong Implementation Capability2 Jun 2025
- 2025TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents19 May 2025
- 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges15 May 2025
- 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 167
- 2025ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agentscited by 49
- 2025Large Language Model-based Data Science Agent: A Surveycited by 22
- 2025RExBench: Can coding agents autonomously implement AI research extensions?cited by 17
- 2025TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agentscited by 14
- 2026ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Developmentcited by 6
- 2026FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamicscited by 6
- 2025FML-bench: Benchmarking Machine Learning Agents for Scientific Researchcited by 3
- 2025ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challengescited by 3
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environmentscited by 0
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environments20 Aug 2026
- 2026FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics17 May 2026
- 2026ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development16 Jan 2026
- 2025ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challenges19 Dec 2025
- 2025FML-bench: Benchmarking Machine Learning Agents for Scientific Research12 Oct 2025
- 2025ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents19 Aug 2025
- 2025Large Language Model-based Data Science Agent: A Survey2 Aug 2025
- 2025RExBench: Can coding agents autonomously implement AI research extensions?27 Jun 2025
- 2025TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents19 May 2025
- 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges15 May 2025
- 2025AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?cited by 24
- 2025AI Scientists Fail Without Strong Implementation Capabilitycited by 20
- 2025How Far Are AI Scientists from Changing the World?cited by 15
- 2025ML2B: Benchmarking LLMs on Cross-Lingual ML Pipeline Generationcited by 0
- 2026Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientistscited by 0
- 2026Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists3 Jun 2026
- 2025ML2B: Benchmarking LLMs on Cross-Lingual ML Pipeline Generation26 Sep 2025
- 2025How Far Are AI Scientists from Changing the World?31 Jul 2025
- 2025AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?19 Jul 2025
- 2025AI Scientists Fail Without Strong Implementation Capability2 Jun 2025
Similar, not cited
Close in wording, no citation either way
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