R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912619735023616 |
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| author | Yang, Xu Yang, Xiao Fang, Shikai Zhang, Yifei Wang, Jian Xian, Bowen Li, Qizheng Li, Jingyuan Xu, Minrui Li, Yuante Pan, Haoran Zhang, Yuge Liu, Weiqing Shen, Yelong Chen, Weizhu Bian, Jiang |
| author_facet | Yang, Xu Yang, Xiao Fang, Shikai Zhang, Yifei Wang, Jian Xian, Bowen Li, Qizheng Li, Jingyuan Xu, Minrui Li, Yuante Pan, Haoran Zhang, Yuge Liu, Weiqing Shen, Yelong Chen, Weizhu Bian, Jiang |
| contents | Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sourcing platforms alleviate some challenges, high-level machine learning engineering (MLE) tasks remain labor-intensive and iterative. We introduce R&D-Agent, a comprehensive, decoupled, and extensible framework that formalizes the MLE process. R&D-Agent defines the MLE workflow into two phases and six components, turning agent design for MLE from ad-hoc craftsmanship into a principled, testable process. Although several existing agents report promising gains on their chosen components, they can mostly be summarized as a partial optimization from our framework's simple baseline. Inspired by human experts, we designed efficient and effective agents within this framework that achieve state-of-the-art performance. Evaluated on MLE-Bench, the agent built on R&D-Agent ranks as the top-performing machine learning engineering agent, achieving 35.1% any medal rate, demonstrating the ability of the framework to speed up innovation and improve accuracy across a wide range of data science applications. We have open-sourced R&D-Agent on GitHub: https://github.com/microsoft/RD-Agent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14738 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science Yang, Xu Yang, Xiao Fang, Shikai Zhang, Yifei Wang, Jian Xian, Bowen Li, Qizheng Li, Jingyuan Xu, Minrui Li, Yuante Pan, Haoran Zhang, Yuge Liu, Weiqing Shen, Yelong Chen, Weizhu Bian, Jiang Artificial Intelligence Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sourcing platforms alleviate some challenges, high-level machine learning engineering (MLE) tasks remain labor-intensive and iterative. We introduce R&D-Agent, a comprehensive, decoupled, and extensible framework that formalizes the MLE process. R&D-Agent defines the MLE workflow into two phases and six components, turning agent design for MLE from ad-hoc craftsmanship into a principled, testable process. Although several existing agents report promising gains on their chosen components, they can mostly be summarized as a partial optimization from our framework's simple baseline. Inspired by human experts, we designed efficient and effective agents within this framework that achieve state-of-the-art performance. Evaluated on MLE-Bench, the agent built on R&D-Agent ranks as the top-performing machine learning engineering agent, achieving 35.1% any medal rate, demonstrating the ability of the framework to speed up innovation and improve accuracy across a wide range of data science applications. We have open-sourced R&D-Agent on GitHub: https://github.com/microsoft/RD-Agent. |
| title | R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.14738 |