Research Paper (preprint): “Agent Laboratory: Using LLM Agents as Research Assistants”

The article (preprint) linked below was recently shared on arXiv.

Title

Agent Laboratory: Using LLM Agents as Research Assistants

Author

Samuel Schmidgall
AMD
Johns Hopkins University

Yusheng Su
AMD

Ze Wang
AMD

Ximeng Sun
AMD

Jialian Wu
AMD

Xiaodong Yu
AMD

Jiang Liu
AMD

Zicheng Liu
AMD

Emad Barsoum
AMD

Source

via arXiv

DOI: 10.48550/arXiv.2501.04227

Abstract

Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce Agent Laboratory, an autonomous LLM-based framework capable of completing the entire research process. This framework accepts a human-provided research idea and progresses through three stages–literature review, experimentation, and report writing to produce comprehensive research outputs, including a code repository and a research report, while enabling users to provide feedback and guidance at each stage. We deploy Agent Laboratory with various state-of-the-art LLMs and invite multiple researchers to assess its quality by participating in a survey, providing human feedback to guide the research process, and then evaluate the final paper. We found that: (1) Agent Laboratory driven by o1-preview generates the best research outcomes; (2) The generated machine learning code is able to achieve state-of-the-art performance compared to existing methods; (3) Human involvement, providing feedback at each stage, significantly improves the overall quality of research; (4) Agent Laboratory significantly reduces research expenses, achieving an 84% decrease compared to previous autonomous research methods. We hope Agent Laboratory enables researchers to allocate more effort toward creative ideation rather than low-level coding and writing, ultimately accelerating scientific discovery.

Direct to Direct to Abstract + Link to Full Text Article

Direc to Agent Lab (via GitHub)

Update (1/10/2025): AIAgents Coming Soon to a Workplace Near You (via Axios)

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