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

# The A I Sc ient ist -v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Yutaro Yamada1,\*, Robert Tjarko Lange1,\*, Cong $\\mathbf { L u } ^ { 1 , 2 , 3 , \\ast }$ , Shengran $\\mathbf { H } \\mathbf { u } ^ { 1 , 2 , 3 }$ , Chris $\\mathbf { L u } ^ { 4 }$ , Jakob Foerster4, Jeff Clune2,3,5,† and David $\\mathbf { H } \\mathbf { a } ^ { 1 , \\dagger }$

\*Equal Contribution, 1Sakana AI, 2University of British Columbia, 3Vector Institute, 4FLAIR, University of Oxford, 5Canada CIFAR AI Chair, †Equal Advising

AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The A I Sc ient ist -v2, an end-to-end agentic system capable of producing the first entirely AIgenerated 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), The A I Sc ient ist -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 A I Sc ient ist -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](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.

# 1\. Introduction

Automated scientific discovery empowered by artificial intelligence (AI) has garnered considerable attention in recent years (Cornelio et al., 2023; Gil et al., 2014; King et al., 2009; Kitano, 2021; Wang et al., 2023; Xu et al., 2021). The development of end-to-end frameworks capable of autonomously formulating hypotheses, performing experiments, analyzing results, and authoring manuscripts could fundamentally transform the scientific process. A notable recent advance in this direction is The A I Sc ient ist -v1 (Lu et al., 2024), which demons

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