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Computer Science > Artificial Intelligence

arXiv:2505.14738 (cs)
[Submitted on 20 May 2025]

Title:R&D-Agent: Automating Data-Driven AI Solution Building Through LLM-Powered Automated Research, Development, and Evolution

Authors:Xu Yang, Xiao Yang, Shikai Fang, Bowen Xian, Yuante Li, Jian Wang, Minrui Xu, Haoran Pan, Xinpeng Hong, Weiqing Liu, Yelong Shen, Weizhu Chen, Jiang Bian
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Abstract:Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. While crowdsourcing platforms alleviate some challenges, high-level data science tasks remain labor-intensive and iterative. To overcome these limitations, we introduce R&D-Agent, a dual-agent framework for iterative exploration. The Researcher agent uses performance feedback to generate ideas, while the Developer agent refines code based on error feedback. By enabling multiple parallel exploration traces that merge and enhance one another, R&D-Agent narrows the gap between automated solutions and expert-level performance. Evaluated on MLE-Bench, R&D-Agent emerges as the top-performing machine learning engineering agent, demonstrating its potential to accelerate innovation and improve precision across diverse data science applications. We have open-sourced R&D-Agent on GitHub: this https URL.
Comments: 7 pages, 1 figure, 1 table
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2505.14738 [cs.AI]
  (or arXiv:2505.14738v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2505.14738

Submission history

From: Weiqing Liu [view email]
[v1] Tue, 20 May 2025 06:07:00 UTC (542 KB)
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