The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via
  Agentic Tree Search
AI is increasingly playing a pivotal role in transforming how scientific
discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic
system capable of producing the first entirely AI generated
peer-review-accepted workshop paper. This system iteratively formulates
scientific hypotheses, designs and executes experiments, analyzes and
visualizes data, and autonomously authors scientific manuscripts. Compared to
its predecessor (v1, Lu et al., 2024 arXiv:2408.06292), The AI Scientist-v2
eliminates the reliance on human-authored code templates, generalizes
effectively across diverse machine learning domains, and leverages a novel
progressive agentic tree-search methodology managed by a dedicated experiment
manager agent. Additionally, we enhance the AI reviewer component by
integrating a Vision-Language Model (VLM) feedback loop for iterative
refinement of content and aesthetics of the figures. We evaluated The AI
Scientist-v2 by submitting three fully autonomous manuscripts to a
peer-reviewed ICLR workshop. Notably, one manuscript achieved high enough
scores to exceed the average human acceptance threshold, marking the first
instance of a fully AI-generated paper successfully navigating a peer review.
This accomplishment highlights the growing capability of AI in conducting all
aspects of scientific research. We anticipate that further advancements in
autonomous scientific discovery technologies will profoundly impact human
knowledge generation, enabling unprecedented scalability in research
productivity and significantly accelerating scientific breakthroughs, greatly
benefiting society at large. We have open-sourced the code at
https://github.com/SakanaAI/AI-Scientist-v2 to foster the future development of
this transformative technology. We also discuss the role of AI in science,
including AI safety.
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Yutaro Yamada
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2025-04-15T11:14:40.840Z
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Yutaro
user
yyamada
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Robert Tjarko Lange
67fd9abb4f2e48847878c9e3
Cong Lu
claimed_verified
2025-04-15T07:55:14.712Z
6311ec6ac7722fdac9a93f92
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Cong Lu
user
conglu
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Shengran Hu
claimed_verified
2025-06-02T07:51:03.607Z
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Shengran HU
user
Shengran
67fd9abb4f2e48847878c9e5
Chris Lu
67fd9abb4f2e48847878c9e6
Jakob Foerster
67fd9abb4f2e48847878c9e7
Jeff Clune
67fd9abb4f2e48847878c9e8
David Ha
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via
  Agentic Tree Search
AI is increasingly playing a pivotal role in transforming how scientific
discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic
system capable of producing the first entirely AI generated
peer-review-accepted workshop paper. This system iteratively formulates
scientific hypotheses, designs and executes experiments, analyzes and
visualizes data, and autonomously authors scientific manuscripts. Compared to
its predecessor (v1, Lu et al., 2024 arXiv:2408.06292), The AI Scientist-v2
eliminates the reliance on human-authored code templates, generalizes
effectively across diverse machine learning domains, and leverages a novel
progressive agentic tree-search methodology managed by a dedicated experiment
manager agent. Additionally, we enhance the AI reviewer component by
integrating a Vision-Language Model (VLM) feedback loop for iterative
refinement of content and aesthetics of the figures. We evaluated The AI
Scientist-v2 by submitting three fully autonomous manuscripts to a
peer-reviewed ICLR workshop. Notably, one manuscript achieved high enough
scores to exceed the average human acceptance threshold, marking the first
instance of a fully AI-generated paper successfully navigating a peer review.
This accomplishment highlights the growing capability of AI in conducting all
aspects of scientific research. We anticipate that further advancements in
autonomous scientific discovery technologies will profoundly impact human
knowledge generation, enabling unprecedented scalability in research
productivity and significantly accelerating scientific breakthroughs, greatly
benefiting society at large. We have open-sourced the code at
https://github.com/SakanaAI/AI-Scientist-v2 to foster the future development of
this transformative technology. We also discuss the role of AI in science,
including AI safety.
The AI Scientist-v2 autonomously proposes hypotheses, performs experiments, analyzes data, and writes peer-reviewed scientific papers, marking the first fully AI-generated paper accepted by a conference.
agentic system scientific hypotheses experiment design data analysis autonomous authoring machine learning domains progressive agentic tree-search experiment manager agent Vision-Language Model (VLM) peer-reviewed ICLR workshop AI safety