Sakana AI 的 The AI Scientist 登上 Nature

ID: digest_2026-06-16_02
原文链接: https://x.com/hardmaru/status/2036841736702767135
作者: hardmaru
日期: 2026-03-25
分类: agents
标签: ai-research, sakana-ai, automation, scientific-discovery
质量评分: 5/5
抓取时间: 2026-06-17T04:32:14

The AI Scientist 登上 Nature

中文翻译

Sakana AI 的 "The AI Scientist" 项目正式登上 Nature。hardmaru 团队一开始就在追问一个根本问题：基础模型能不能跑完整个科研生命周期——从提出假设、设计实验到写论文？现在 Nature 的同行评审给了这条路学术层面的背书。Sakana 之前的 v1/v2 都在 arXiv，这次跨过的不是算法层级，而是学术界认可。这意味着 "AI 自动化做科研" 从实验演示变成了正式研究范式。Hardmaru 在推文里说自己相信 AI 将永远改变科学发现的格局。这条很可能带动一批 "AI 自动化研究" 团队跟进。

English Original

@hardmaru

I’m incredibly proud of The AI Scientist team for this milestone publication in @Nature. We started this project to explore if foundation models could execute the entire research lifecycle. Seeing this work validated at this level is a special moment. I truly believe AI will forever change the landscape of how scientific discoveries and scientific progress are made.

Quoted @SakanaAILabs: The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature

Nature: https://t.co/nNfpSV5e5I Blog: https://t.co/i6h8LVQOdl

When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.

From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.

Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.

Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!

This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.

Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.

Building upon our previous open-source releases (https://t.co/H1tBT14Yx8), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.

This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!

@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune