title: The AI Scientist: Towards Fully Automated Open-Ended Scientific
    Discovery
summary: One of the grand challenges of artificial general intelligence is developing
  agents capable of conducting scientific research and discovering new knowledge.
  While frontier models have already been used as aids to human scientists, e.g.
  for brainstorming ideas, writing code, or prediction tasks, they still conduct
  only a small part of the scientific process. This paper presents the first
  comprehensive framework for fully automatic scientific discovery, enabling
  frontier large language models to perform research independently and
  communicate their findings. We introduce The AI Scientist, which generates
  novel research ideas, writes code, executes experiments, visualizes results,
  describes its findings by writing a full scientific paper, and then runs a
  simulated review process for evaluation. In principle, this process can be
  repeated to iteratively develop ideas in an open-ended fashion, acting like the
  human scientific community. We demonstrate its versatility by applying it to
  three distinct subfields of machine learning: diffusion modeling,
  transformer-based language modeling, and learning dynamics. Each idea is
  implemented and developed into a full paper at a cost of less than $15 per
  paper. To evaluate the generated papers, we design and validate an automated
  reviewer, which we show achieves near-human performance in evaluating paper
  scores. The AI Scientist can produce papers that exceed the acceptance
  threshold at a top machine learning conference as judged by our automated
  reviewer. This approach signifies the beginning of a new era in scientific
  discovery in machine learning: bringing the transformative benefits of AI
  agents to the entire research process of AI itself, and taking us closer to a
  world where endless affordable creativity and innovation can be unleashed on
  the world's most challenging problems. Our code is open-sourced at
  https://github.com/SakanaAI/AI-Scientist
paper_authors._id: 66bac463046cedd837a748c7
paper_authors.name: Chris Lu
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paper_authors.fullname: Chris Lu
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paper_authors.user: chrlu
paper_authors._id: 66bac463046cedd837a748c8
paper_authors.name: Cong Lu
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paper_authors.fullname: Cong Lu
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paper_authors.user: conglu
paper_authors._id: 66bac463046cedd837a748c9
paper_authors.name: Robert Tjarko Lange
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paper_authors.fullname: Robert Lange
paper_authors.type: user
paper_authors.user: RobertTLange
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paper_authors.name: Jakob Foerster
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-15T10:11:55.520Z
paper_authors._id: 60b4acc69c978cce68723b34
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paper_authors.fullname: Jannes Stubbemann
paper_authors.type: user
paper_authors.user: stubbi
paper_authors._id: 66bac463046cedd837a748cb
paper_authors.name: Jeff Clune
paper_authors._id: 66bac463046cedd837a748cc
paper_authors.name: David Ha
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-13T08:29:11.154Z
paper_authors._id: 6335ea47b5a0e02f80538bf3
paper_authors.avatarUrl: /avatars/fce4e08ae86339952e35ff73680da7e9.svg
paper_authors.fullname: David
paper_authors.type: user
paper_authors.user: davidha
paper_title: The AI Scientist: Towards Fully Automated Open-Ended Scientific
    Discovery
paper_summary: One of the grand challenges of artificial general intelligence is developing
  agents capable of conducting scientific research and discovering new knowledge.
  While frontier models have already been used as aids to human scientists, e.g.
  for brainstorming ideas, writing code, or prediction tasks, they still conduct
  only a small part of the scientific process. This paper presents the first
  comprehensive framework for fully automatic scientific discovery, enabling
  frontier large language models to perform research independently and
  communicate their findings. We introduce The AI Scientist, which generates
  novel research ideas, writes code, executes experiments, visualizes results,
  describes its findings by writing a full scientific paper, and then runs a
  simulated review process for evaluation. In principle, this process can be
  repeated to iteratively develop ideas in an open-ended fashion, acting like the
  human scientific community. We demonstrate its versatility by applying it to
  three distinct subfields of machine learning: diffusion modeling,
  transformer-based language modeling, and learning dynamics. Each idea is
  implemented and developed into a full paper at a cost of less than $15 per
  paper. To evaluate the generated papers, we design and validate an automated
  reviewer, which we show achieves near-human performance in evaluating paper
  scores. The AI Scientist can produce papers that exceed the acceptance
  threshold at a top machine learning conference as judged by our automated
  reviewer. This approach signifies the beginning of a new era in scientific
  discovery in machine learning: bringing the transformative benefits of AI
  agents to the entire research process of AI itself, and taking us closer to a
  world where endless affordable creativity and innovation can be unleashed on
  the world's most challenging problems. Our code is open-sourced at
  https://github.com/SakanaAI/AI-Scientist
paper_ai_keywords: diffusion modeling transformer-based language modeling learning dynamics