TML-Bench: Benchmark for Data Science Agents on Tabular ML Tasks
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915838551916544 |
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| author | Pinchuk, Mykola |
| author_facet | Pinchuk, Mykola |
| contents | Autonomous coding agents can produce strong tabular baselines quickly on Kaggle-style tasks. Practical value depends on end-to-end correctness and reliability under time limits. This paper introduces TML-Bench, a tabular benchmark for data science agents on Kaggle-style tasks. This paper evaluates 10 OSS LLMs on four Kaggle competitions and three time budgets (240s, 600s, and 1200s). Each model is run five times per task and budget. A run is successful if it produces a valid submission and a private-holdout score on hidden labels that are not accessible to the agent. This paper reports median performance, success rates, and run-to-run variability. MiniMax-M2.1 model achieves the best aggregate performance score on all four competitions under the paper's primary aggregation. Average performance improves with larger time budgets. Scaling is noisy for some individual models at the current run count. Code and materials are available at https://github.com/MykolaPinchuk/TML-bench/tree/master. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05764 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TML-Bench: Benchmark for Data Science Agents on Tabular ML Tasks Pinchuk, Mykola Machine Learning Artificial Intelligence I.2.6; I.2.11; I.2.1; D.2.8 Autonomous coding agents can produce strong tabular baselines quickly on Kaggle-style tasks. Practical value depends on end-to-end correctness and reliability under time limits. This paper introduces TML-Bench, a tabular benchmark for data science agents on Kaggle-style tasks. This paper evaluates 10 OSS LLMs on four Kaggle competitions and three time budgets (240s, 600s, and 1200s). Each model is run five times per task and budget. A run is successful if it produces a valid submission and a private-holdout score on hidden labels that are not accessible to the agent. This paper reports median performance, success rates, and run-to-run variability. MiniMax-M2.1 model achieves the best aggregate performance score on all four competitions under the paper's primary aggregation. Average performance improves with larger time budgets. Scaling is noisy for some individual models at the current run count. Code and materials are available at https://github.com/MykolaPinchuk/TML-bench/tree/master. |
| title | TML-Bench: Benchmark for Data Science Agents on Tabular ML Tasks |
| topic | Machine Learning Artificial Intelligence I.2.6; I.2.11; I.2.1; D.2.8 |
| url | https://arxiv.org/abs/2603.05764 |