人工智能在改变科学发现方式方面正发挥着日益关键的作用。我们介绍了AI Scientist-v2，这是一个端到端的智能体系统，能够产出第一篇完全由AI生成、并通过同行评审被接受的研讨会论文。该系统迭代地提出科学假设，设计并执行实验，分析和可视化数据，并自主撰写科学手稿。与其前身（v1，Lu et al., 2024 arXiv:2408.06292）相比，AI Scientist-v2消除了对人类编写代码模板的依赖，在多样化的机器学习领域中有效泛化，并利用了一种由专门的实验管理器智能体管理的新型渐进式智能体树搜索方法。此外，我们通过集成视觉语言模型（VLM）反馈回路来迭代优化图表的内容和美学效果，从而增强了AI审稿人组件。我们通过向一个同行评审的ICLR研讨会提交三篇完全自主撰写的手稿来评估AI Scientist-v2。值得注意的是，其中一篇手稿获得了足够高的分数，超过了人类平均接受阈值，这标志着完全由AI生成的论文首次成功通过同行评审。这一成就凸显了人工智能在开展科学研究各个方面日益增长的能力。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的生产，使研究生产力实现前所未有的可扩展性，并显著加速科学突破，从而极大地造福整个社会。我们已在https://github.com/SakanaAI/AI-Scientist-v2开源代码，以促进这一变革性技术的未来发展。我们还讨论了AI在科学中的作用，包括AI安全。
英文摘要：
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.
Can Agentic AI Match the Performance of Human Data Scientists?
School of Statistics, University of Minnesota（统计学系，明尼苏达大学）；Department of Electrical and Computer Engineering, University of Minnesota（电气与计算机工程系，明尼苏达大学）；Data Science Institute, University of Chicago（数据科学研究所，芝加哥大学）；Carlson School of Management, University of Minnesota（卡尔森管理学院，明尼苏达大学）；Cisco Research, San Jose, CA, USA（Cisco研究，加州圣何塞）
Agentic Explainable Artificial Intelligence (Agentic XAI) Approach To Explore Better Explanation: A Case Study in Decision Support for Rice Cultivation in Japan
Iteris: Agentic Research Loops for Computational Mathematics
School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Beijing International Center for Mathematical Research and the New Cornerstone Science Laboratory, Peking University（北京大学北京国际数学研究中心和新基石科学实验室）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；Center for Intelligent Computing, Great Bay Institute for Advanced Study, Great Bay University（大湾研究院先进研究所智能计算中心）；Zhongguancun Academy（中关村学院）
ToolUniverse: An open platform for democratizing AI scientists
Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Harvard College, Harvard University（哈佛大学哈佛学院）；Massachusetts Institute of Technology（麻省理工学院）；MIT Lincoln Laboratory（MIT林肯实验室）；Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University（哈佛大学自然与人工智能研究学院）；Broad Institute of MIT and Harvard（MIT与哈佛联合广谱研究所）；Harvard Data Science Initiative（哈佛数据科学计划）
LARC: Towards Human-level Constrained Retrosynthesis Planning through an Agentic Framework
Department of Computer Science and Engineering, 2 Division of Medicinal Chemistry & Pharmacognosy, 3 Department of Biomedical Informatics, 4 Translational Data Analytics Institute（1 计算机科学与工程系，2 药物化学与药理学系，3 生物医学信息学系，4 转化数据分析研究所）
Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis
School of Electronic and Computer Engineering, Peking University（电子与计算机工程学院，北京大学）；Shenzhen Graduate School, Shenzhen, 518055, China（深圳研究生院，深圳，518055，中国）；International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；Peng Cheng Laboratory, Shenzhen, 518000, China（鹏城实验室，深圳，518000，中国）
CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution
Department of Materials Science and Engineering, University of California, Berkeley, California, United States（加州大学伯克利分校材料科学与工程系）；Materials Sciences Division, Lawrence Berkeley National Laboratory, California, United States（劳伦斯伯克利国家实验室材料科学部）；Laboratory of Artificial Chemical Intelligence (LIAC), Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland（日内瓦联邦理工学院化学科学与工程研究所人工化学智能实验室）；National Centre of Competence in Research (NCCR) Catalysis, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland（日内瓦联邦理工学院催化研究国家中心）
The 1st National workshop on Smart Farming and Data Analytics took place at Maynooth University in Ireland on June 12, 2019. The workshop included two invited keynote presentations, invited talks and breakout group discussions. The workshop attracted in the order of 50 participants, consisting of a mixture of computer scientists, general scientists, farmers, farm advisors, and agricultural business representatives. This allowed for lively discussion and cross-fertilization of ideas. And showed the significant interest in the smart farming domain, the many research challenges faced in the space…
Santa Fe Institute（圣塔菲研究所）；University of New Mexico（新墨西哥大学）
In August of 2021, the Santa Fe Institute hosted a workshop on collective intelligence as part of its Foundations of Intelligence project. This project seeks to advance the field of artificial intelligence by promoting interdisciplinary research on the nature of intelligence. The workshop brought together computer scientists, biologists, philosophers, social scientists, and others to share their insights about how intelligence can emerge from interactions among multiple agents--whether those agents be machines, animals, or human beings. In this report, we summarize each of the talks and the su…
Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery
Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（印度理工学院德里人工智能学院）；Department of Computer Science and Engineering, Indian Institute of Technology Delhi（印度理工学院德里计算机科学与工程系）；Department of Civil and Environmental Engineering, Indian Institute of Technology Delhi（印度理工学院德里土木与环境工程系）；Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学有机与大分子化学实验室）；Center for Energy and Environmental Chemistry Jena, Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学能源与环境化学中心）；Helmholtz Institute for Polymers in Energy Applications Jena (HIPOLE Jena)（耶拿海德堡聚合物能源应用研究所（HIPOLE耶拿））
Santa Fe Institute（圣塔菲研究所）；University of New Mexico（新墨西哥大学）
In April of 2022, the Santa Fe Institute hosted a workshop on embodied, situated, and grounded intelligence as part of the Institute's Foundations of Intelligence project. The workshop brought together computer scientists, psychologists, philosophers, social scientists, and others to discuss the science of embodiment and related issues in human intelligence, and its implications for building robust, human-level AI. In this report, we summarize each of the talks and the subsequent discussions. We also draw out a number of key themes and identify important frontiers for future research.
Large Knowledge Model: A Knowledge Foundation for Agentic Science at Scale
DP Technology；Institute of Theoretical Physics, Chinese Academy of Sciences（中国科学院理论物理研究所）；International Center for Quantum Materials, School of Physics, Peking University（北京大学物理学院量子材料科学中心）；Lanzhou Center for Theoretical Physics, Lanzhou University（兰州大学理论物理研究中心）；AI for Science Institute（AI for Science 研究院）；School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）
Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale
DP Technology Beijing China（北京DP技术有限公司）；AI for Science Institute Beijing China（北京AI for Science研究院）；Shanghai Jiao Tong University Shanghai China（上海交通大学）；Beihang University Beijing China（北京航空航天大学）；Peking University Beijing China（北京大学）；Institute of Theoretical Physics Chinese Academy of Sciences Beijing China（中国科学院理论物理研究所）；Shanghai Innovation Institute Shanghai China（上海创新研究院）；East China Normal University Shanghai China（华东师范大学）；Zhongguancun Academy Beijing China（中关村学院）；University of Science and Technology of China Hefei China（中国科学技术大学）；Tongji University Shanghai China（同济大学）；The Hong Kong University of Science and Technology Hong Kong China（香港科技大学）
La Agente Óptima: Towards Agentic Self-Driving Laboratories
University of Toronto（多伦多大学）；Acceleration Consortium（加速联盟）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Canadian Institute for Advanced Research (CIFAR)（加拿大高级研究院）；NVIDIA（英伟达）；University of Amsterdam（阿姆斯特丹大学）；Instituto de Micro y Nanotecnología, IMN-CNM, CSIC（西班牙国家研究委员会微纳米技术研究所）；Sungkyunkwan University（成均馆大学）；Merck KGaA（默克集团）
From Prompts to Protocols: An AI Agent for Laboratory Automation
Department of Computer Science, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校计算机科学系）；Department of Chemistry, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校化学系）
Harnessing AtomisticSkills for Agentic Atomistic Research
Department of Materials Science；Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA；Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA；Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea；Harvard University, Department of Chemistry；Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA；Department of Nuclear Science；Shell Information Technology International Inc., Texas 77082, United States；Shell International Exploration \& Production Inc., Texas 77079, United States
