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

Harshith Padigela, Chintan Shah, Dinkar Juyal

Track: tiny / short paper (up to 4 pages)

Keywords: AI agents, code, eval, machine learning, tool call

TL;DR: In this report, we present ML-Dev-Bench, a benchmark aimed at testing agen- tic capabilities on applied Machine Learning development tasks

Abstract: 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.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 41