arXiv:2504.08066cs.AIcs.CL2025-04被引 379

AI独立完成论文写作与实验,首次通过同行评审。

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

  • 用自研树搜索机制自主设计实验、分析数据、撰写论文。
  • 提交3篇全由AI生成的论文,1篇得分超人类平均水平并被接收。
  • 适合关注AI科研自动化与未来学术范式变革的研究者。

人工智能正深刻改变科学发现方式。我们提出 The AI Scientist-v2,一个端到端的智能体系统,可生成首个完全由AI撰写的、经同行评审接受的研讨会论文。该系统迭代提出科学假设,设计并执行实验,分析与可视化数据,并自主撰写科研论文。相比前代(v1, Lu et al., 2024 arXiv:2408.06292),v2 不再依赖人工代码模板,能有效泛化至多个机器学习领域,并采用由专用实验管理智能体调度的新颖渐进式智能体树搜索方法。此外,通过引入视觉-语言模型(VLM)反馈环,提升了审稿组件对内容与图表美学的迭代优化能力。我们通过向 ICLR 研讨会提交三篇全自主论文进行评估,其中一篇得分超过平均人类接受阈值,标志着首个完全由AI生成的论文成功通过同行评审。这一成果凸显了AI在科研全流程中的日益增强能力。我们预计,自主科学发现技术的进一步发展将深刻影响人类知识生产,显著提升研究效率,加速科学突破,惠及社会。代码已开源:https://github.com/SakanaAI/AI-Scientist-v2。同时讨论了AI在科学中的角色,包括安全问题。

原文摘要 · Abstract (English)

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.

AI科研自动发现智能体系统

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