title: The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via
    Agentic Tree Search
summary: 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.
paper_authors._id: 67fd9abb4f2e48847878c9e1
paper_authors.name: Yutaro Yamada
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-04-15T11:14:40.840Z
paper_authors._id: 63142272e29fb2e86d63db0a
paper_authors.avatarUrl: /avatars/5726b5c40cea190a6cd9907fa596b66b.svg
paper_authors.fullname: Yutaro
paper_authors.type: user
paper_authors.user: yyamada
paper_authors._id: 67fd9abb4f2e48847878c9e2
paper_authors.name: Robert Tjarko Lange
paper_authors._id: 67fd9abb4f2e48847878c9e3
paper_authors.name: Cong Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-15T07:55:14.712Z
paper_authors._id: 6311ec6ac7722fdac9a93f92
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1670192227454-6311ec6ac7722fdac9a93f92.jpeg
paper_authors.fullname: Cong Lu
paper_authors.type: user
paper_authors.user: conglu
paper_authors._id: 67fd9abb4f2e48847878c9e4
paper_authors.name: Shengran Hu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:51:03.607Z
paper_authors._id: 640530fd0ab5e22719fb199a
paper_authors.avatarUrl: /avatars/6b023f496a2f8cb5f37ec617d84179b7.svg
paper_authors.fullname: Shengran HU
paper_authors.type: user
paper_authors.user: Shengran
paper_authors._id: 67fd9abb4f2e48847878c9e5
paper_authors.name: Chris Lu
paper_authors._id: 67fd9abb4f2e48847878c9e6
paper_authors.name: Jakob Foerster
paper_authors._id: 67fd9abb4f2e48847878c9e7
paper_authors.name: Jeff Clune
paper_authors._id: 67fd9abb4f2e48847878c9e8
paper_authors.name: David Ha
paper_title: The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via
    Agentic Tree Search
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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