mlsanityData to 7 Oct 2026

ML-Dev-Bench: Comparative Analysis of AI Agents on ML development workflows

arXiv 2502.00964 ↗First posted 3 Feb 2025cs.SE cs.AIcited by 16

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.

Builds on

4 of 8 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation5 Oct

Other references · 3

  1. 2021Program Synthesis with Large Language Modelscited by 3,509
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  3. 2023ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Codecited by 63

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Citing papers per year: 2025: 12; 2026: 4.

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  1. 2024MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
  2. 2025MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research
  3. 2025ML-Tool-Bench: Tool-Augmented Planning for ML Tasks
  4. 2026DeltaML-Bench: Evaluating Machine Learning Agents on Real-World Research Repositories
  5. 2025Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
  6. 2026ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development
  7. 2026MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI
  8. 2025MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?