From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery
Paper · 2025 · Jiaqi Wei, Yuejin Yang, Xiang Zhang et al.
Summary
AI's role in research is moving from narrow predictive tools toward systems that can run much of the scientific cycle on their own. The literature on this shift is scattered: some surveys follow the research workflow, others rank systems by degree of autonomy, and others dissect agent architectures. This survey by Wei et al. (Shanghai Artificial Intelligence Laboratory and partner universities) merges those three angles into one framework. The framework links agent capabilities, discovery processes, and domain applications. The authors propose a four-level ladder for AI in science and place "Agentic Science" mainly at Level 3, with Level 2 as its forerunner. They then break scientific agents into five capabilities, describe discovery as a flexible four-stage loop, and review agent systems in life sciences, chemistry, materials science, and physics/astronomy. The paper presents no new experiments. Its contribution is an organizing map of the field plus a catalogue of open challenges and future directions. This makes it a useful reference for anyone building or assessing research agents.
Key points
- Four-level evolution of AI for Science:
- Level 1: computational oracle (expert tools).
- Level 2: automated research assistant (partial agentic discovery).
- Level 3: autonomous scientific partner (full agentic discovery).
- Level 4: generative architect, a future prospect.
- Five core agent capabilities: reasoning and planning, tool integration, memory mechanisms, multi-agent collaboration, and optimization/evolution.
- Four-stage discovery workflow: observation and hypothesis generation; experimental planning and execution; data and result analysis; synthesis, validation, and evolution. Agents may recombine these stages dynamically.
- Domain coverage: the review spans more than a dozen subfields, including multi-omics, protein engineering, drug discovery, organic synthesis, quantum chemistry, materials design, astronomy, fluid dynamics, and quantum computing.
- Challenges, outlook, and resources:
- Challenges: reproducibility, verifying novelty, transparent reasoning, and ethics.
- Proposed frontiers: autonomous invention, cross-disciplinary synthesis, and global networks of cooperating agents.
- The "Nobel-Turing Test" is proposed as an ultimate benchmark.
- Resources: the Awesome-Agent-Scientists GitHub list. The Intern-Discovery platform and Intern-S1 model are cited as enablers.
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksDecision Sciences · 306 worksInformation Systems and Management · 96 worksMaterials Science · 58 worksMaterials Chemistry · 58 works
Cite
Cite as Jiaqi Wei, Yuejin Yang, Xiang Zhang, Yuhan Chen, Xiang Zhuang, Zhangyang Gao, Dongzhan Zhou, Guangshuai Wang, Zhiqiang Gao, Juntai Cao, Zijie Qiu, Ming Hu, Chenglong Ma, Shixiang Tang, Junjun He, Chunfeng Song, Xuming He, Qiang Zhang, Chenyu You, Shuangjia Zheng, Ning Ding, Wanli Ouyang, Nanqing Dong, Yu Cheng, Siqi Sun, Lei Bai and Bowen Zhou. 2025. From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery. arXiv:2508.14111.
@misc{wei2025ai,
title = {{From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery}},
author = {Jiaqi Wei, Yuejin Yang, Xiang Zhang, Yuhan Chen, Xiang Zhuang, Zhangyang Gao, Dongzhan Zhou, Guangshuai Wang, Zhiqiang Gao, Juntai Cao, Zijie Qiu, Ming Hu, Chenglong Ma, Shixiang Tang, Junjun He, Chunfeng Song, Xuming He, Qiang Zhang, Chenyu You, Shuangjia Zheng, Ning Ding, Wanli Ouyang, Nanqing Dong, Yu Cheng, Siqi Sun, Lei Bai and Bowen Zhou},
year = {2025},
doi = {10.48550/arxiv.2508.14111},
eprint = {2508.14111},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2508.14111}
}References
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