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TML-Bench: Benchmark for Data Science Agents on Tabular ML Tasks
🤖AI Summary
Researchers introduced TML-Bench, a new benchmark for evaluating AI coding agents on tabular machine learning tasks similar to Kaggle competitions. The study tested 10 open-source language models across four competitions with different time budgets, finding that MiniMax-M2.1 achieved the best overall performance.
Key Takeaways
- →TML-Bench provides a standardized way to evaluate AI agents on data science tasks with real-world time constraints.
- →MiniMax-M2.1 outperformed other open-source language models across all four Kaggle-style competitions tested.
- →Performance generally improved with longer time budgets (240s, 600s, 1200s), though scaling varied by model.
- →Success rates and run-to-run variability were measured alongside median performance for comprehensive evaluation.
- →The benchmark focuses on end-to-end correctness and practical reliability rather than just code generation quality.
#artificial-intelligence#machine-learning#benchmark#data-science#automation#llm#tabular-data#kaggle#research#open-source
Read Original →via arXiv – CS AI
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