@inproceedings{schmidgall-etal-2025-agent,
title = "Agent Laboratory: Using {LLM} Agents as Research Assistants",
author = "Schmidgall, Samuel and
Su, Yusheng and
Wang, Ze and
Sun, Ximeng and
Wu, Jialian and
Yu, Xiaodong and
Liu, Jiang and
Moor, Michael and
Liu, Zicheng and
Barsoum, Emad",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.320/",
pages = "5977--6043",
ISBN = "979-8-89176-335-7",
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{--}in order to produce research, 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) Incorporating human involvement improves the overall quality of research; (4) Agent Laboratory 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."
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<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–in order to produce research, 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) Incorporating human involvement improves the overall quality of research; (4) Agent Laboratory 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.</abstract>
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%0 Conference Proceedings
%T Agent Laboratory: Using LLM Agents as Research Assistants
%A Schmidgall, Samuel
%A Su, Yusheng
%A Wang, Ze
%A Sun, Ximeng
%A Wu, Jialian
%A Yu, Xiaodong
%A Liu, Jiang
%A Moor, Michael
%A Liu, Zicheng
%A Barsoum, Emad
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F schmidgall-etal-2025-agent
%X 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–in order to produce research, 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) Incorporating human involvement improves the overall quality of research; (4) Agent Laboratory 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.
%U https://aclanthology.org/2025.findings-emnlp.320/
%P 5977-6043
Markdown (Informal)
[Agent Laboratory: Using LLM Agents as Research Assistants](https://aclanthology.org/2025.findings-emnlp.320/) (Schmidgall et al., Findings 2025)
ACL
- Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, and Emad Barsoum. 2025. Agent Laboratory: Using LLM Agents as Research Assistants. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5977–6043, Suzhou, China. Association for Computational Linguistics.