Agent Laboratory: Using LLM Agents as Research Assistants
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.
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Samuel Schmidgall
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Yusheng Su
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2025-01-09T20:22:29.469Z
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Yusheng Su
user
yushengsu
677f4d709e9ddbcae5ce5366
Ze Wang
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Ximeng Sun
677f4d709e9ddbcae5ce5368
Jialian Wu
admin_assigned
2025-01-09T20:24:01.650Z
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Jialian Wu
user
jialianww
677f4d709e9ddbcae5ce5369
Xiaodong Yu
admin_assigned
2025-01-09T20:23:43.262Z
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Xiaodong Yu
user
xiaodongyu-amd
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Jiang Liu
admin_assigned
2025-01-09T20:23:35.567Z
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JiangLiu
user
JiangLiu
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Zicheng Liu
admin_assigned
2025-01-09T20:23:28.447Z
6569f6cb44ce94a70187f407
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Zicheng Liu
user
ZCLiu35
677f4d709e9ddbcae5ce536c
Emad Barsoum
admin_assigned
2025-01-09T20:23:21.569Z
65adc9d086f88a686be41215
https://cdn-avatars.huggingface.co/v1/production/uploads/65adc9d086f88a686be41215/xizVHuZPkE0Gu8_ulx0Fm.jpeg
Emad Barsoum
user
ebarsoum
Agent Laboratory: Using LLM Agents as Research Assistants
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.
Agent Laboratory, an LLM-based framework, accelerates scientific research by automating the entire process from idea to report, resulting in high-quality outputs with reduced expenses.
LLM-based framework literature review experimentation report writing o1-preview machine learning code state-of-the-art performance human feedback