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

2025-4-8
The AI Scientist-v2: Workshop-Level
Automated Scientific Discovery via Agentic
Tree Search
Yutaro Yamada1,*, Robert Tjarko Lange1,*, Cong Lu1,2,3,*, Shengran Hu1,2,3, Chris Lu4, Jakob Foerster4, Jeff
Clune2,3,5,† and David Ha1,†
*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 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), 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.
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 AI
Scientist-v1 (Lu et al., 2024), which demonstrated the feasibility of a fully automated scientific
workflow and downstream manuscript production. However, significant limitations constrained its
broad applicability and autonomy. Specifically, it relied heav

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