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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.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

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.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2508.14111 · 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

455 references, 142 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Mahyar Abbasian, Iman Azimi, Amir M Rahmani, and Ramesh Jain. Conversational health agents: A personalized llm-powered agent framework. arXiv preprint arXiv:2310.02374, 2023. — [arXiv:2310.02374](https://arxiv.org/abs/2310.02374) find
  2. Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos, and Michalis Vazirgiannis. Prot2text: Multimodal protein’s function generation with gnns and transformers. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 10757–10765, 2024. doi: 10.1609/aaai.v38i10.28948. — [doi:10.1609/aaai.v38i10.28948](https://doi.org/10.1609/aaai.v38i10.28948) find
  3. Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al. Accurate structure prediction of biomolecular interactions with alphafold 3. Nature, 630(8016):493–500, 2024. find
  4. Shubham Agarwal, Gaurav Sahu, Abhay Puri, Issam H Laradji, Krishnamurthy DJ Dvijotham, Jason Stanley, Laurent Charlin, and Christopher Pal. Litllm: A toolkit for scientific literature review. arXiv preprint arXiv:2402.01788, 2024.
  5. Pegah Ahadian and Qiang Guan. Ai trustworthy challenges in drug discovery. In Hao Chen, Yuyin Zhou, Daguang Xu, and Varut Vince Vardhanabhuti, editors, Trustworthy Artificial Intelligence for Healthcare, pages 1–12, Cham, 2024. Springer Nature Switzerland. ISBN 978-3-031-67751-9. find
  6. Samuel Alber, Bowen Chen, Eric Sun, Alina Isakova, Aaron James Wilk, and James Zou. Cellvoyager: Ai compbio agent generates new insights by autonomously analyzing biological data. bioRxiv, pages 2025–06, 2025. find
  7. Mehrad Ansari and Seyed Mohamad Moosavi. Agent-based learning of materials datasets from the scientific literature. Digital Discovery, 3(12):2607–2617, 2024. find
  8. Mehrad Ansari, Jeffrey Watchorn, Carla E Brown, and Joseph S Brown. dziner: Rational inverse design of materials with ai agents. arXiv preprint arXiv:2410.03963, 2024. find
  9. Luis M. Antunes, Keith T. Butler, and Ricardo Grau-Crespo. Crystal structure generation with autoregressive large language modeling. Nature Communications, 15(1):10570, Dec 2024. ISSN 2041-1723. doi: 10.1038/s41467-024-54639-7. URL https://doi.org/10.1038/s41467-024-54639-7. — [doi:10.1038/s41467-024-54639-7](https://doi.org/10.1038/s41467-024-54639-7) ↗
  10. Jicong Ao, Fan Wu, Yansong Wu, Abdalla Swikir, and Sami Haddadin. Llm as bt-planner: Leveraging llms for behavior tree generation in robot task planning. arXiv preprint arXiv:2409.10444, 2024. — [arXiv:2409.10444](https://arxiv.org/abs/2409.10444) find
  11. Reza Averly, Frazier N Baker, and Xia Ning. Liddia: Language-based intelligent drug discovery agent. arXiv preprint arXiv:2502.13959, 2025. find
  12. Žiga Avsec, Natasha Latysheva, Jun Cheng, Guido Novati, Kyle R Taylor, Tom Ward, Clare Bycroft, Lauren Nicolaisen, Eirini Arvaniti, Joshua Pan, et al. Alphagenome: advancing regulatory variant effect prediction with a unified dna sequence model. bioRxiv, pages 2025–06, 2025. find
  13. Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, and Sung Ju Hwang. Researchagent: Iterative research idea generation over scientific literature with large language models. arXiv preprint arXiv:2404.07738, 2024.
  14. Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, and Sung Ju Hwang. Researchagent: Iterative research idea generation over scientific literature with large language models, 2025. URL https://arxiv.org/abs/2404.07738. ↗
  15. Viraj Bagal, Rishal Aggarwal, PK Vinod, and U Deva Priyakumar. Molgpt: molecular generation using a transformer-decoder model. Journal of chemical information and modeling, 62(9):2064–2076, 2021. find
  16. Viraj Bagal, Rishal Aggarwal, P. K. Vinod, and U. Deva Priyakumar. MolGPT: Molecular Generation Using a Transformer-Decoder Model. Journal of Chemical Information and Modeling, 62(9):2064–2076, May 2022. ISSN 1549-9596. doi: 10.1021/acs.jcim.1c00600. URL https://doi.org/10.1021/acs.jcim.1c00600. — [doi:10.1021/acs.jcim.1c00600](https://doi.org/10.1021/acs.jcim.1c00600) ↗
  17. Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023.
  18. Lei Bai, Zhongrui Cai, Maosong Cao, Weihan Cao, Chiyu Chen, Haojiong Chen, Kai Chen, Pengcheng Chen, Ying Chen, Yongkang Chen, Yu Cheng, Yu Cheng, Pei Chu, Tao Chu, Erfei Cui, Ganqu Cui, Long Cui, Ziyun Cui, Nianchen Deng, Ning Ding, Nanqin Dong, Peijie Dong, Shihan Dou, Sinan Du, Haodong Duan, Caih… — Intern-S1: A Scientific Multimodal Foundation Model
  19. Xuefeng Bai, Song He, Yi Li, Yabo Xie, Xin Zhang, Wenli Du, and Jian-Rong Li. Construction of a knowledge graph for framework material enabled by large language models and its application. npj Computational Materials, 11(1):51, Feb 2025. ISSN 2057-3960. doi: 10.1038/s41524-025-01540-6. URL https://d… — [doi:10.1038/s41524-025-01540-6](https://doi.org/10.1038/s41524-025-01540-6) find
  20. SD Bakshi, P Barry, C Bissolotti, I Cloet, S Corrodi, Z Djurcic, S Habib, K Heitmann, TJ Hobbs, W Hopkins, et al. Argoloom: agentic ai for fundamental physics from quarks to cosmos. arXiv preprint arXiv:2510.02426, 2025. — [arXiv:2510.02426](https://arxiv.org/abs/2510.02426) find
  21. Suryanarayanan Balaji, Rishikesh Magar, Yayati Jadhav, and Amir Barati Farimani. Gpt-molberta: Gpt molecular features language model for molecular property prediction, 2023. URL https://arxiv.org/abs/2310.03030. — [arXiv:2310.03030](https://arxiv.org/abs/2310.03030) ↗
  22. Soumya Banerjee et al. arXiv:2411.19211, 2024. On the ethical considerations of generative agents. — [arXiv:2411.19211](https://arxiv.org/abs/2411.19211) find
  23. Muneera Bano, Didar Zowghi, Pip Shea, and Georgina Ibarra. Investigating responsible ai for scientific research: an empirical study. arXiv preprint arXiv:2312.09561, 2023. — [arXiv:2312.09561](https://arxiv.org/abs/2312.09561) find
  24. Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M Elena, Dávid P Kovács, Janosh Riebesell, Xavier R Advincula, Mark Asta, Matthew Avaylon, William J Baldwin, et al. A foundation model for atomistic materials chemistry. arXiv preprint arXiv:2401.00096, 2023. — [arXiv:2401.00096](https://arxiv.org/abs/2401.00096) find
  25. Adib Bazgir, Yuwen Zhang, et al. Multicrossmodal automated agent for integrating diverse materials science data. arXiv preprint arXiv:2505.15132, 2025. — [arXiv:2505.15132](https://arxiv.org/abs/2505.15132) find
  26. Jonas Belouadi, Anne Lauscher, and Steffen Eger. Automatikz: Text-guided synthesis of scientific vector graphics with tikz, 2024. URL https://arxiv.org/abs/2310.00367. ↗
  27. Yoshua Bengio, Michael Cohen, Damiano Fornasiere, Joumana Ghosn, Pietro Greiner, Matt MacDermott, Sören Mindermann, Adam Oberman, Jesse Richardson, Oliver Richardson, et al. Superintelligent agents pose catastrophic risks: Can scientist ai offer a safer path? arXiv preprint arXiv:2502.15657, 2025.
  28. Yoshua Bengio, Michael Cohen, Damiano Fornasiere, Joumana Ghosn, Pietro Greiner, Matt Mac-Dermott, Sören Mindermann, Adam Oberman, Jesse Richardson, Oliver Richardson, Marc-Antoine Rondeau, Pierre-Luc St-Charles, and David Williams-King. Superintelligent agents pose catastrophic risks: Can scientist… — Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
  29. Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, and Farshad Khorrami. Grounding llms for robot task planning using closed-loop state feedback. arXiv preprint arXiv:2402.08546, 2024. — [arXiv:2402.08546](https://arxiv.org/abs/2402.08546) find
  30. Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes. Autonomous chemical research with large language models. Nature, 624(7992):570–578, 2023.
  31. Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, and Christopher D. Manning. Biomedlm: A 2.7b parameter language model trained on biomedical text, 2024. URL https://arxiv.org/abs/2403.18421. ↗
  32. Jannis Born and Matteo Manica. Regression transformer enables concurrent sequence regression and generation for molecular language modelling. Nature Machine Intelligence, 5(4):432–444, April 2023. ISSN 2522-5839. doi: 10.1038/s42256-023-00639-z. URL http://dx.doi.org/10.1038/s42256-023-00639-z. — [doi:10.1038/s42256-023-00639-z](https://doi.org/10.1038/s42256-023-00639-z) ↗
  33. Albert Bou, Morgan Thomas, Sebastian Dittert, Carles Navarro, Maciej Majewski, Ye Wang, Shivam Patel, Gary Tresadern, Mazen Ahmad, Vincent Moens, et al. Acegen: Reinforcement learning of generative chemical agents for drug discovery. Journal of Chemical Information and Modeling, 64(15): 5900–5911, 2… find
  34. Andres Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D. White, and Philippe Schwaller. Augmenting large language models with chemistry tools. Nature Machine Intelligence, 6(5):525–535, May 2024. ISSN 2522-5839. doi: 10.1038/s42256-024-00832-8. URL https://doi.org/10.1038/s42256-024-00832-… — [doi:10.1038/s42256-024-00832-8](https://doi.org/10.1038/s42256-024-00832-8) ↗
  35. Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller. Chemcrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.05376, 2023.
  36. Garyk Brixi, Matthew G Durrant, Jerome Ku, Michael Poli, Greg Brockman, Daniel Chang, Gabriel A Gonzalez, Samuel H King, David B Li, Aditi T Merchant, et al. Genome modeling and design across all domains of life with evo 2. BioRxiv, pages 2025–02, 2025. find
  37. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, et al. Language models are few-shot learners. In Advances in Neural Information Processing Systems, 2020.
  38. Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton. A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 4 (1):1–43, 201… find
  39. Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, et al. Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. arXiv preprint arXiv:2004.07213, 2020.
  40. Markus J Buehler. MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines, and modalities. Applied Mechanics Reviews, 76(2): 021001, 2024. find
  41. Tiffany J Callahan, Nathaniel H Park, and Sara Capponi. Agentic mixture-of-workflows for multi-modal chemical search. arXiv preprint arXiv:2502.19629, 2025. — [arXiv:2502.19629](https://arxiv.org/abs/2502.19629) find
  42. Askery Canabarro, Felipe Fernandes Fanchini, André Luiz Malvezzi, Rodrigo Pereira, and Rafael Chaves. Unveiling phase transitions with machine learning. Physical Review B, 100(4):045129, 2019. find
  43. He Cao, Zijing Liu, Xingyu Lu, Yuan Yao, and Yu Li. Instructmol: Multi-modal integration for building a versatile and reliable molecular assistant in drug discovery. arXiv preprint arXiv:2311.16208, 2023.
  44. Shuxiang Cao, Zijian Zhang, Mohammed Alghadeer, Simone D Fasciati, Michele Piscitelli, Mustafa Bakr, Peter Leek, and Alán Aspuru-Guzik. Agents for self-driving laboratories applied to quantum computing. arXiv preprint arXiv:2412.07978, 2024. — [arXiv:2412.07978](https://arxiv.org/abs/2412.07978) find
  45. Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. Grounding large language models in interactive environments with online reinforcement learning. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarle… find
  46. Jingyi Chai, Shuo Tang, Rui Ye, Yuwen Du, Xinyu Zhu, Mengcheng Zhou, Yanfeng Wang, Siheng Chen, et al. Scimaster: Towards general-purpose scientific ai agents, part i. x-master as foundation: Can we lead on humanity’s last exam? arXiv preprint arXiv:2507.05241, 2025.
  47. Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Lilian Weng, and Aleksander Mądry. Mle-bench: Evaluating machine learning agents on machine learning engineering, 2025. URL https://arxiv.org/abs/2410.07095. ↗
  48. Xinhao Che, Yujing Zhao, Qilei Liu, Fang Yu, Hanyu Gao, and Lei Zhang. Csstep: Step-by-step exploration of the chemical space of drug molecules via multi-agent and multi-stage reinforcement learning. Chemical Engineering Science, page 122048, 2025. find
  49. Bei Chen, Gaolei Li, Xi Lin, Zheng Wang, and Jianhua Li. Blockagents: Towards byzantine-robust llm-based multi-agent coordination via blockchain. In ACM Turing Award Celebration Conference, pages 187–192, 2024. find
  50. Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou, and Benyou Wang. Huatuogpt-o1, towards medical complex reasoning with llms. arXiv preprint arXiv:2412.18925, 2024.
  51. Junying Chen, Chi Gui, Ruyi Ouyang, Anningzhe Gao, Shunian Chen, Guiming Hardy Chen, Xidong Wang, Ruifei Zhang, Zhenyang Cai, Ke Ji, Guangjun Yu, Xiang Wan, and Benyou Wang. Huatuogpt-vision, towards injecting medical visual knowledge into multimodal llms at scale, 2024. URL https://arxiv.org/abs/2… ↗
  52. Junying Chen, Xidong Wang, Ke Ji, Anningzhe Gao, Feng Jiang, Shunian Chen, Hongbo Zhang, Dingjie Song, Wenya Xie, Chuyi Kong, Jianquan Li, Xiang Wan, Haizhou Li, and Benyou Wang. Huatuogpt-ii, one-stage training for medical adaption of llms. Proceedings of COLM (arXiv:2311.09774v2), 2024. URL https:… — [arXiv:2311.09774](https://arxiv.org/abs/2311.09774) find
  53. Justin Chih-Yao Chen, Swarnadeep Saha, and Mohit Bansal. Reconcile: Round-table conference improves reasoning via consensus among diverse llms. arXiv preprint arXiv:2309.13007, 2023.
  54. Kexin Chen, Junyou Li, Kunyi Wang, Yuyang Du, Jiahui Yu, Jiamin Lu, Lanqing Li, Jiezhong Qiu, Jianzhang Pan, Yi Huang, et al. Chemist-x: Large language model-empowered agent for reaction condition recommendation in chemical synthesis. arXiv preprint arXiv:2311.10776, 2023. — [arXiv:2311.10776](https://arxiv.org/abs/2311.10776) find
  55. Kexin Chen, Hanqun Cao, Junyou Li, Yuyang Du, Menghao Guo, Xin Zeng, Lanqing Li, Jiezhong Qiu, Pheng Ann Heng, and Guangyong Chen. An autonomous large language model agent for chemical literature data mining. arXiv preprint arXiv:2402.12993, 2024. — [arXiv:2402.12993](https://arxiv.org/abs/2402.12993) find
  56. Qiguang Chen, Mingda Yang, Libo Qin, Jinhao Liu, Zheng Yan, Jiannan Guan, Dengyun Peng, Yiyan Ji, Hanjing Li, Mengkang Hu, et al. Ai4research: A survey of artificial intelligence for scientific research. arXiv preprint arXiv:2507.01903, 2025.
  57. Anoop Cherian, Radu Corcodel, Siddarth Jain, and Diego Romeres. LLMPhy: Complex physical reasoning using large language models and world models. arXiv preprint arXiv:2411.08027, 2024. — [arXiv:2411.08027](https://arxiv.org/abs/2411.08027) find
  58. Yuan Chiang, Elvis Hsieh, Chia-Hong Chou, and Janosh Riebesell. Llamp: Large language model made powerful for high-fidelity materials knowledge retrieval and distillation. arXiv preprint arXiv:2401.17244, 2024.
  59. Jae-Woo Choi, Hyungmin Kim, Hyobin Ong, Youngwoo Yoon, Minsu Jang, Jaehong Kim, et al. Reactree: Hierarchical task planning with dynamic tree expansion using llm agent nodes. 2025. find
  60. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, et al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arXiv preprint a…
  61. Haotian Cui, Chloe Wang, Hassaan Maan, Kuan Pang, Fengning Luo, Nan Duan, and Bo Wang. scgpt: toward building a foundation model for single-cell multi-omics using generative ai. Nature methods, 21(8):1470–1480, 2024. find
  62. Allan Dafoe et al. Open problems in cooperative ai, 2020. find
  63. F. Dai et al. Toward de novo protein design from natural language. bioRxiv, 2025. doi: 10.1101/2024.08.01.606258. URL https://www.biorxiv.org/content/10.1101/2024.08.01.606258v4. — [doi:10.1101/2024.08.01.606258](https://doi.org/10.1101/2024.08.01.606258) ↗
  64. Tianwei Dai, Sriram Vijayakrishnan, Filip T Szczypi ’nski, Jean-Fran ccois Ayme, Ehsan Simaei, Thomas Fellowes, Rob Clowes, Lyubomir Kotopanov, Caitlin E Shields, Zhengxue Zhou, et al. Autonomous mobile robots for exploratory synthetic chemistry. Nature, 635 (8040):890–897, 2024. find
  65. Hugo Dalla-Torre, Liam Gonzalez, Javier Mendoza-Revilla, Nicolas Lopez Carranza, Adam Henryk Grzywaczewski, Francesco Oteri, Christian Dallago, Evan Trop, Bernardo P de Almeida, Hassan Sirelkhatim, et al. Nucleotide transformer: building and evaluating robust foundation models for human genomics. Na… find
  66. Mike D’Arcy et al. Marg: Multi-agent review generation for scientific papers, 2024.
  67. Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, et al. Organa: A robotic assistant for automated chemistry experimentation and characterization. arXiv preprint arXiv:2401.06949, 2024. — [arXiv:2401.06949](https://arxiv.org/abs/2401.06949) find
  68. Ayushman Das et al. Enabling synergistic knowledge sharing and reasoning in large language models with collaborative multi-agents. In IEEE International Conference on Collaboration and Internet Computing, 2023. find
  69. Bernardo P de Almeida, Guillaume Richard, Hugo Dalla-Torre, Christopher Blum, Lorenz Hexemer, Priyanka Pandey, Stefan Laurent, Chandana Rajesh, Marie Lopez, Alexandre Laterre, et al. A multimodal conversational agent for dna, rna and protein tasks. Nature Machine Intelligence, pages 1–14, 2025. find
  70. José Antonio Siqueira de Cerqueira, Mamia Agbese, Rebekah Rousi, Nannan Xi, Juho Hamari, and Pekka Abrahamsson. Can we trust ai agents? an experimental study towards trustworthy llm-based multi-agent systems for ai ethics. arXiv preprint arXiv:2411.08881, 2024. — [arXiv:2411.08881](https://arxiv.org/abs/2411.08881) find
  71. Ning Ding, Shang Qu, Linhai Xie, Yifei Li, Zaoqu Liu, Kaiyan Zhang, Yibai Xiong, Yuxin Zuo, Zhangren Chen, Ermo Hua, et al. Automating exploratory proteomics research via language models. arXiv preprint arXiv:2411.03743, 2024.
  72. Zhehao Dong, Zhen Lu, and Yue Yang. Fine-tuning a large language model for automating computational fluid dynamics simulations. Theoretical and Applied Mechanics Letters, page 100594, 2025. find
  73. Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. Improving factuality and reasoning in language models through multiagent debate. 2023.
  74. Edmund H Durfee. Distributed problem solving and planning. In ECCAI Advanced Course on Artificial Intelligence, pages 118–149. Springer, 2001. find
  75. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. From local to global: A graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130, 2024. find
  76. Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji. Translation between molecules and natural language. In 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, pages 375–413. Association for Computational Linguistics (ACL), 2022.
  77. Yao Fehlis, Charles Crain, Aidan Jensen, Michael Watson, James Juhasz, Paul Mandel, Betty Liu, Shawn Mahon, Daren Wilson, Nick Lynch-Jonely, et al. Accelerating drug discovery through agentic ai: A multi-agent approach to laboratory automation in the dmta cycle. arXiv preprint arXiv:2507.09023, 2025… — [arXiv:2507.09023](https://arxiv.org/abs/2507.09023) find
  78. Jingsen Feng, Ran Xu, and Xu Chu. Openfoamgpt 2.0: end-to-end, trustworthy automation for computational fluid dynamics. arXiv preprint arXiv:2504.19338, 2025. find
  79. Mohamed Amine Ferrag, Norbert Tihanyi, and Merouane Debbah. From llm reasoning to autonomous ai agents: A comprehensive review. arXiv preprint arXiv:2504.19678, 2025. find
  80. Daniel Flam-Shepherd and Alán Aspuru-Guzik. Language models can generate molecules, materials, and protein binding sites directly in three dimensions as xyz, cif, and pdb files, 2023. URL https://arxiv.org/abs/2305.05708. — [arXiv:2305.05708](https://arxiv.org/abs/2305.05708) ↗
  81. Shubham Gandhi, Dhruv Shah, Manasi Patwardhan, Lovekesh Vig, and Gautam Shroff. Researchcodeagent: An llm multi-agent system for automated codification of research methodologies. In International Workshop on AI for Transportation, pages 3–37. Springer, 2025. find
  82. Bowen Gao, Yanwen Huang, Yiqiao Liu, Wenxuan Xie, Wei-Ying Ma, Ya-Qin Zhang, and Yanyan Lan. Pharmagents: Building a virtual pharma with large language model agents. arXiv preprint arXiv:2503.22164, 2025. — [arXiv:2503.22164](https://arxiv.org/abs/2503.22164) find
  83. Changnan Gao, Wenjie Bao, Shuang Wang, Jianyang Zheng, Lulu Wang, Yongqi Ren, Linfang Jiao, Jianmin Wang, and Xun Wang. Dockingga: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation. Briefings in Functional Genomics, 23 (5):595–606,… — [doi:10.1093/bfgp/elae011](https://doi.org/10.1093/bfgp/elae011) find
  84. Huan-ang Gao, Jiayi Geng, Wenyue Hua, Mengkang Hu, Xinzhe Juan, Hongzhang Liu, Shilong Liu, Jiahao Qiu, Xuan Qi, Yiran Wu, et al. A survey of self-evolving agents: On path to artificial super intelligence. arXiv preprint arXiv:2507.21046, 2025.
  85. Jialin Gao, Jianyu Chen, Jiaqi Wei, Bin Jiang, and A-Li Luo. Deep multimodal networks for m-type star classification with paired spectrum and photometric image. Publications of the Astronomical Society of the Pacific, 135(1046):044503, 2023. find
  86. Muhan Gao, Jash Shah, Weiqi Wang, and Daniel Khashabi. Science hierarchography: Hierarchical organization of science literature, 2025. URL https://arxiv.org/abs/2504.13834. — [arXiv:2504.13834](https://arxiv.org/abs/2504.13834) ↗
  87. Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori, Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic, and Marinka Zitnik. Empowering biomedical discovery with ai agents. Cell, 187(22):6125–6151, 2024.
  88. Shanghua Gao, Richard Zhu, Zhenglun Kong, Ayush Noori, Xiaorui Su, Curtis Ginder, Theodoros Tsiligkaridis, and Marinka Zitnik. Txagent: An ai agent for therapeutic reasoning across a universe of tools. arXiv preprint arXiv:2503.10970, 2025.
  89. Yubin Ge, Neeraja Kirtane, Hao Peng, and Dilek Hakkani-Tür. Llms are vulnerable to malicious prompts disguised as scientific language. arXiv preprint arXiv:2501.14073, 2025.
  90. Alireza Ghafarollahi and Markus J Buehler. Atomagents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence. arXiv preprint arXiv:2407.10022, 2024. — [arXiv:2407.10022](https://arxiv.org/abs/2407.10022) find
  91. Alireza Ghafarollahi and Markus J Buehler. Protagents: protein discovery via large language model multi-agent collaborations combining physics and machine learning. Digital Discovery, 2024. find
  92. Alireza Ghafarollahi and Markus J Buehler. Rapid and automated alloy design with graph neural network-powered llm-driven multi-agent systems. arXiv preprint arXiv:2410.13768, 2024. — [arXiv:2410.13768](https://arxiv.org/abs/2410.13768) find
  93. Alireza Ghafarollahi and Markus J. Buehler. Sciagents: Automating scientific discovery through multi-agent intelligent graph reasoning, 2024. URL https://arxiv.org/abs/2409.05556. ↗
  94. Alireza Ghafarollahi and Markus J Buehler. Automating alloy design and discovery with physics-aware multimodal multiagent ai. Proceedings of the National Academy of Sciences, 122(4):e2414074122, 2025. find
  95. Alireza Ghafarollahi and Markus J Buehler. Sciagents: automating scientific discovery through bioinspired multi-agent intelligent graph reasoning. Advanced Materials, 37(22):2413523, 2025. find
  96. Alireza Ghafarollahi and Markus J Buehler. Sparks: Multi-agent artificial intelligence model discovers protein design principles. arXiv preprint arXiv:2504.19017, 2025. — [arXiv:2504.19017](https://arxiv.org/abs/2504.19017) find
  97. Ali Essam Ghareeb, Benjamin Chang, Ludovico Mitchener, Angela Yiu, Caralyn J Szostkiewicz, Jon M Laurent, Muhammed T Razzak, Andrew D White, Michaela M Hinks, and Samuel G Rodriques. Robin: A multi-agent system for automating scientific discovery. arXiv preprint arXiv:2505.13400, 2025.
  98. Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic, Artiom Myaskovsky, Felix Weissenberger, Keran Rong, Ryutaro Tanno, et al. Towards an ai co-scientist, 2025. URL https://arxiv.org/abs/2502.18864. ↗
  99. Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic, Artiom Myaskovsky, Felix Weissenberger, Keran Rong, Ryutaro Tanno, et al. Towards an ai co-scientist. arXiv preprint arXiv:2502.18864, 2025.
  100. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Minlie Huang, Nan Duan, and Weizhu Chen. Tora: A tool-integrated reasoning agent for mathematical problem solving. arXiv preprint arXiv:2309.17452, 2023.
  101. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Minlie Huang, Nan Duan, and Weizhu Chen. Tora: A tool-integrated reasoning agent for mathematical problem solving. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenRevie…
  102. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yujiu Yang, Nan Duan, Weizhu Chen, et al. Critic: Large language models can self-correct with tool-interactive critiquing. 2024.
  103. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024.
  104. Eliska Greplova, Agnes Valenti, Gregor Boschung, Frank Schäfer, Niels Lörch, and Sebastian D Huber. Unsupervised identification of topological phase transitions using predictive models. New Journal of Physics, 22(4):045003, 2020. find
  105. Felix Grezes, Sergi Blanco-Cuaresma, Alberto Accomazzi, Michael J Kurtz, Golnaz Shapurian, Edwin Henneken, Carolyn S Grant, Donna M Thompson, Roman Chyla, Stephen McDonald, et al. Building astrobert, a language model for astronomy & astrophysics. arXiv preprint arXiv:2112.00590, 2021. — [arXiv:2112.00590](https://arxiv.org/abs/2112.00590) find
  106. Mourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine, Lenon Mendes, and Christina Mack. Agentic ai for scientific discovery: A survey of progress, challenges, and future directions. arXiv preprint arXiv:2503.08979, 2025. — [arXiv:2503.08979](https://arxiv.org/abs/2503.08979) find
  107. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025.
  108. Hongyi Guo, Zhihan Liu, Yufeng Zhang, and Zhaoran Wang. Can large language models play games? a case study of a self-play approach. arXiv preprint arXiv:2403.05632, 2024. — [arXiv:2403.05632](https://arxiv.org/abs/2403.05632) find
  109. Siyuan Guo, Cheng Deng, Ying Wen, Hechang Chen, Yi Chang, and Jun Wang. Ds-agent: Automated data science by empowering large language models with case-based reasoning. arXiv preprint arXiv:2402.17453, 2024.
  110. Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V Chawla, Olaf Wiest, and Xiangliang Zhang. Large language model based multi-agents: A survey of progress and challenges. arXiv preprint arXiv:2402.01680, 2024.
  111. Siwei Han, Peng Xia, Ruiyi Zhang, Tong Sun, Yun Li, Hongtu Zhu, and Huaxiu Yao. Mdocagent: A multi-modal multi-agent framework for document understanding. arXiv preprint arXiv:2503.13964, 2025. — [arXiv:2503.13964](https://arxiv.org/abs/2503.13964) find
  112. Tianyu Han, Lisa C Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K Bressem. Medalpaca–an open-source collection of medical conversational ai models and training data. arXiv preprint arXiv:2304.08247, 2023.
  113. Minsheng Hao, Jing Gong, Xin Zeng, Chiming Liu, Yucheng Guo, Xingyi Cheng, Taifeng Wang, Jianzhu Ma, Xuegong Zhang, and Le Song. Large-scale foundation model on single-cell transcriptomics. Nature methods, 21(8):1481–1491, 2024. find
  114. Minsheng Hao, Yongju Lee, Hanchen Wang, Gabriele Scalia, and Aviv Regev. Perturboagent: A self-planning agent for boosting sequential perturb-seq experiments. bioRxiv, pages 2025–05, 2025. find
  115. Kenneth D Harris. Airus: a simple workflow for ai-assisted exploration of scientific data. bioRxiv, pages 2025–02, 2025. find
  116. Kan Hatakeyama-Sato, Naoki Yamane, Yasuhiko Igarashi, Yuta Nabae, and Teruaki Hayakawa. Prompt engineering of gpt-4 for chemical research: what can/cannot be done? Science and Technology of Advanced Materials: Methods, 3(1):2260300, 2023. find
  117. Thomas Hayes, Roshan Rao, Halil Akin, Nicholas J. Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q. Tran, Jonathan Deaton, Marius Wiggert, Rohil Badkundri, Irhum Shafkat, Jun Gong, Alexander Derry, Raul S. Molina, Neil Thomas, Yousuf A. Khan, Chetan Mishra, Carolyn Kim, Liam J. Bartie,… — [doi:10.1126/science.ads0018](https://doi.org/10.1126/science.ads0018) find
  118. Thomas Hayes, Roshan Rao, Halil Akin, Nicholas J Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q Tran, Jonathan Deaton, Marius Wiggert, et al. Simulating 500 million years of evolution with a language model. Science, 387(6736):850–858, 2025. find
  119. Jiyan He, Weitao Feng, Yaosen Min, Jingwei Yi, Kunsheng Tang, Shuai Li, Jie Zhang, Kejiang Chen, Wenbo Zhou, Xing Xie, Weiming Zhang, Nenghai Yu, and Shuxin Zheng. Control risk for potential misuse of artificial intelligence in science, 2023. URL https://arxiv.org/abs/2312.06632. — [arXiv:2312.06632](https://arxiv.org/abs/2312.06632) ↗
  120. Zhitao He et al. LEGO: A multi-agent collaborative framework with role-playing and iterative feedback for causality explanation generation. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9142–9163, Singapore, Decemb… find
  121. Thorsten Hellert, Drew Bertwistle, Simon C Leemann, Antonin Sulc, and Marco Venturini. Agentic ai for multi-stage physics experiments at a large-scale user facility particle accelerator. arXiv preprint arXiv:2509.17255, 2025. — [arXiv:2509.17255](https://arxiv.org/abs/2509.17255) find
  122. Maximilian Herde, Bogdan Raonic, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, and Siddhartha Mishra. Poseidon: Efficient foundation models for PDEs. Advances in Neural Information Processing Systems, 37:72525–72624, 2024. find
  123. Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al. Metagpt: Meta programming for a multi-agent collaborative framework. 2024.
  124. Julien Horwood and Emmanuel Noutahi. Molecular design in synthetically accessible chemical space via deep reinforcement learning. ACS omega, 5(51):32984–32994, 2020. find
  125. Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville, Alessandro Sordoni, and Rishabh Agarwal. V-star: Training verifiers for self-taught reasoners. arXiv preprint arXiv:2402.06457, 2024.
  126. Jinming Hu, Hassan Nawaz, Yuting Rui, Lijie Chi, Arif Ullah, and Pavlo O Dral. Aitomia: Your intelligent assistant for ai-driven atomistic and quantum chemical simulations. arXiv preprint arXiv:2505.08195, 2025. — [arXiv:2505.08195](https://arxiv.org/abs/2505.08195) find
  127. Mengkang Hu, Yao Mu, Xinmiao Yu, Mingyu Ding, Shiguang Wu, Wenqi Shao, Qiguang Chen, Bin Wang, Yu Qiao, and Ping Luo. Tree-planner: Efficient close-loop task planning with large language models. arXiv preprint arXiv:2310.08582, 2023. — [arXiv:2310.08582](https://arxiv.org/abs/2310.08582) find
  128. Ming Hu, Kun Yuan, Yaling Shen, Feilong Tang, Xiaohao Xu, Lin Zhou, Wei Li, Ying Chen, Zhongxing Xu, Zelin Peng, et al. Ophclip: Hierarchical retrieval-augmented learning for ophthalmic surgical video-language pretraining. arXiv preprint arXiv:2411.15421, 2024. — [arXiv:2411.15421](https://arxiv.org/abs/2411.15421) find
  129. Ming Hu, Zhengdi Yu, Feilong Tang, Kaiwen Chen, Yulong Li, Imran Razzak, Junjun He, Tolga Birdal, Kaijing Zhou, and Zongyuan Ge. Towards dynamic 3d reconstruction of hand-instrument interaction in ophthalmic surgery. arXiv preprint arXiv:2505.17677, 2025. — [arXiv:2505.17677](https://arxiv.org/abs/2505.17677) find
  130. Shengguo Hu, Mingyi Li, Jiawen Xu, Hongrui Zhang, Shanghang Zhang, Tie Jun Cui, Philipp Del Hougne, and Lianlin Li. Electromagnetic metamaterial agent. Light: Science & Applications, 14(1):12, 2025. find
  131. Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu, Yu Lu, Yaochu Jin, Lili Pan, and Zhenzhong Lan. Nova: An iterative planning and search approach to enhance novelty and diversity of llm generated ideas. arXiv preprint arXiv:2410.14255, 2024.
  132. Zhaolin Hu, Yixiao Zhou, Zhongan Wang, Xin Li, Weimin Yang, Hehe Fan, and Yi Yang. OSDA agent: Leveraging large language models for de novo design of organic structure directing agents. In The Thirteenth International Conference on Learning Representations, 2025. URL \<https://openreview. net/forum?i… find
  133. Changwu Huang, Zeqi Zhang, Bifei Mao, and Xin Yao. An overview of artificial intelligence ethics. IEEE Transactions on Artificial Intelligence, 4(4):799–819, 2022. find
  134. Di Huang, Hao Li, Wenyu Li, Heming Zhang, Patricia Dickson, Ming Zhan, J Philip Miller, Carlos Cruchaga, Michael Province, Yixin Chen, Philip Payne, and Fuhai Li. Omnicellagent: Towards ai co-scientists for scientific discovery in precision medicine. August 2025. doi: 10.1101/2025.07.31.667797. URL… — [doi:10.1101/2025.07.31.667797](https://doi.org/10.1101/2025.07.31.667797) find
  135. Kaixuan Huang, Yuanhao Qu, Henry Cousins, William A Johnson, Di Yin, Mihir Shah, Denny Zhou, Russ Altman, Mengdi Wang, and Le Cong. Crispr-gpt: An llm agent for automated design of gene-editing experiments. arXiv preprint arXiv:2404.18021, 2024.
  136. Kexin Huang, Serena Zhang, Hanchen Wang, Yuanhao Qu, Yingzhou Lu, Yusuf Roohani, Ryan Li, Lin Qiu, Junze Zhang, Yin Di, et al. Biomni: A general-purpose biomedical ai agent. bioRxiv, pages 2025–05, 2025. find
  137. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems, 43 (2):1–55,…
  138. Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Hao Wang, Defu Lian, Yasheng Wang, Ruiming Tang, and Enhong Chen. Understanding the planning of llm agents: A survey. arXiv preprint arXiv:2402.02716, 2024.
  139. Mingjia Huo, Han Guo, Xingyi Cheng, Digvijay Singh, Hamidreza Rahmani, Shen Li, Philipp Gerlof, Trey Ideker, Danielle A Grotjahn, Elizabeth Villa, et al. Multi-modal large language model enables protein function prediction. bioRxiv, pages 2024–08, 2024. find
  140. Theo Jaffrelot Inizan, Sherry Yang, Aaron Kaplan, Yen-hsu Lin, Jian Yin, Saber Mirzaei, Mona Abdelgaid, Ali H Alawadhi, KwangHwan Cho, Zhiling Zheng, et al. System of agentic ai for the discovery of metal-organic frameworks. arXiv preprint arXiv:2504.14110, 2025. find
  141. Yoshitaka Inoue, Tianci Song, and Tianfan Fu. Drugagent: Explainable drug repurposing agent with large language model-based reasoning. arXiv preprint arXiv:2408.13378, 2024. find
  142. Kartheik G Iyer, Mikaeel Yunus, Charles O’Neill, Christine Ye, Alina Hyk, Kiera Mccormick, Ioana Ciucă, John F Wu, Alberto Accomazzi, Simone Astarita, et al. pathfinder: A semantic framework for literature review and knowledge discovery in astronomy. The Astrophysical Journal Supplement Series, 275(… find
  143. Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti Badhwar, Joshua D Bocarsly, Andres M Bran, Stefan Bringuier, L Catherine Brinson, Kamal Choudhary, Defne Circi, et al. 14 examples of how llms can transform materials science and chemistry: a reflection on a large language model hackath… find
  144. Masoud Jafaripour, Shadan Golestan, Shotaro Miwa, Yoshihiro Mitsuka, and Osmar Zaiane. Adaptive iterative feedback prompting for obstacle-aware path planning via llms. In AAAI Workshop, 2025. find
  145. Raj Jaiswal, Dhruv Jain, Harsh Parimal Popat, Avinash Anand, Abhishek Dharmadhikari, Atharva Marathe, and Rajiv Ratn Shah. Improving physics reasoning in large language models using mixture of refinement agents. arXiv preprint arXiv:2412.00821, 2024.
  146. Peter Jansen, Marc-Alexandre Côté, Tushar Khot, Erin Bransom, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Oyvind Tafjord, and Peter Clark. Discoveryworld: A virtual environment for developing and evaluating automated scientific discovery agents. Advances in Neural Information Processing Syste…
  147. Peter Jansen, Oyvind Tafjord, Marissa Radensky, Pao Siangliulue, Tom Hope, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Daniel S Weld, and Peter Clark. Codescientist: End-to-end semi-automated scientific discovery with code-based experimentation. arXiv preprint arXiv:2503.22708, 2025.
  148. Shankar Kumar Jeyakumar, Alaa Alameer Ahmad, and Adrian Garret Gabriel. Advancing agentic systems: Dynamic task decomposition, tool integration and evaluation using novel metrics and dataset. In NeurIPS 2024 Workshop on Open-World Agents, 2024. find
  149. Shuyi Jia, Chao Zhang, and Victor Fung. Llmatdesign: Autonomous materials discovery with large language models. arXiv preprint arXiv:2406.13163, 2024. — [arXiv:2406.13163](https://arxiv.org/abs/2406.13163) find
  150. Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. Llmlingua: Compressing prompts for accelerated inference of large language models. pages 13358–13376, 2023.
  151. Jinhao Jiang, Zhipeng Chen, Yingqian Min, Jie Chen, Xiaoxue Cheng, Jiapeng Wang, Yiru Tang, Haoxiang Sun, Jia Deng, Wayne Xin Zhao, et al. Technical report: Enhancing llm reasoning with reward-guided tree search. arXiv preprint arXiv:2411.11694, 2024. — [arXiv:2411.11694](https://arxiv.org/abs/2411.11694) find
  152. Lei Jiang, Shuzhou Sun, Biqing Qi, Yuchen Fu, Xiaohua Xu, Yuqiang Li, Dongzhan Zhou, and Tianfan Fu. Chem3dllm: 3d multimodal large language models for chemistry. arXiv preprint, 2025. find
  153. Shuyang Jiang, Yuhao Wang, and Yu Wang. Selfevolve: A code evolution framework via large language models. arXiv preprint arXiv:2306.02907, 2023. — [arXiv:2306.02907](https://arxiv.org/abs/2306.02907) find
  154. Zhengyao Jiang, Dominik Schmidt, Dhruv Srikanth, Dixing Xu, Ian Kaplan, Deniss Jacenko, and Yuxiang Wu. Aide: Ai-driven exploration in the space of code, 2025. URL https://arxiv.org/abs/2502.13138. ↗
  155. Ruofan Jin, Zaixi Zhang, Mengdi Wang, and Le Cong. Stella: Self-evolving llm agent for biomedical research. arXiv preprint arXiv:2507.02004, 2025.
  156. Sebastian Antony Joseph, Syed Murtaza Husain, Stella SR Offner, Stéphanie Juneau, Paul Torrey, Adam S Bolton, Juan P Farias, Niall Gaffney, Greg Durrett, and Junyi Jessy Li. Astrovisbench: A code benchmark for scientific computing and visualization in astronomy. arXiv preprint arXiv:2505.20538, 2025… — [arXiv:2505.20538](https://arxiv.org/abs/2505.20538) find
  157. John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al. Highly accurate protein structure prediction with alphafold. nature, 596(7873):583–589, 2021.
  158. A Jun, Xiang Zhang, Xiaofan Zhang, Jiaqi Wei, Te Zhang, Yamin Deng, Pu Liu, Zongxiang Nie, Yi Chen, Nanqin Dong, et al. Massnet: billion-scale ai-friendly mass spectral corpus enables robust de novo peptide sequencing. bioRxiv, 2025. find
  159. Yeonghun Kang and Jihan Kim. Chatmof: An autonomous ai system for predicting and generating metal-organic frameworks. arXiv preprint arXiv:2308.01423, 2023. — [arXiv:2308.01423](https://arxiv.org/abs/2308.01423) find
  160. Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R Bowman, Tim Rocktäschel, and Ethan Perez. Debating with more persuasive llms leads to more truthful answers. arXiv preprint arXiv:2402.06782, 2024.
  161. Byeonghwi Kim, Minhyuk Seo, and Jonghyun Choi. Online continual learning for interactive instruction following agents, 2024. URL https://arxiv.org/abs/2403.07548. — [arXiv:2403.07548](https://arxiv.org/abs/2403.07548) ↗
  162. Hyomin Kim, Yunhui Jang, and Sungsoo Ahn. Mt-mol: Multi agent system with tool-based reasoning for molecular optimization. arXiv preprint arXiv:2505.20820, 2025. find
  163. Kyungha Kim, Sangyun Lee, Kung-Hsiang Huang, Hou Pong Chan, Manling Li, and Heng Ji. Can llms produce faithful explanations for fact-checking? towards faithful explainable fact-checking via multi-agent debate. arXiv preprint arXiv:2402.07401, 2024. — [arXiv:2402.07401](https://arxiv.org/abs/2402.07401) find
  164. Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, Xuhai Xu, Daniel McDuff, Hyeonhoon Lee, Marzyeh Ghassemi, Cynthia Breazeal, Hae Park, et al. Mdagents: An adaptive collaboration of llms for medical decision-making. 37:79410–79452, 2024. find
  165. James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114(13): 3521–3526… find
  166. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. 35:22199–22213, 2022.
  167. Nikolay Koldunov and Thomas Jung. Local climate services for all, courtesy of large language models. Communications Earth & Environment, 5(1):13, Jan 2024. ISSN 2662-4435. doi: 10.1038/s43247-023-01199-1. URL https://doi.org/10.1038/s43247-023-01199-1. — [doi:10.1038/s43247-023-01199-1](https://doi.org/10.1038/s43247-023-01199-1) ↗
  168. Vadim Korolev and Pavel Protsenko. Accurate, interpretable predictions of materials properties within transformer language models. Patterns, 4(10):100803, October 2023. ISSN 2666-3899. doi: 10.1016/j.patter.2023.100803. URL http://dx.doi.org/10.1016/j.patter.2023.100803. — [doi:10.1016/j.patter.2023.100803](https://doi.org/10.1016/j.patter.2023.100803) ↗
  169. Dmitriy Kostunin, Vladimir Sotnikov, Sergo Golovachev, and Alexandre Strube. Ai agents for ground-based gamma astronomy. arXiv preprint arXiv:2503.00821, 2025. — [arXiv:2503.00821](https://arxiv.org/abs/2503.00821) find
  170. Christopher Kuenneth and Rampi Ramprasad. polybert: a chemical language model to enable fully machine-driven ultrafast polymer informatics. Nature Communications, 14(1), July 2023. ISSN 2041-1723. doi: 10.1038/s41467-023-39868-6. URL http://dx.doi.org/10.1038/s41467-023-39868-6. — [doi:10.1038/s41467-023-39868-6](https://doi.org/10.1038/s41467-023-39868-6) ↗
  171. Varun Kumar, Leonard Gleyzer, Adar Kahana, Khemraj Shukla, and George Em Karniadakis. Mycrunchgpt: A llm assisted framework for scientific machine learning. Journal of Machine Learning for Modeling and Computing, 4(4), 2023. find
  172. Shrinidhi Kumbhar, Venkatesh Mishra, Kevin Coutinho, Divij Handa, Ashif Iquebal, and Chitta Baral. Hypothesis generation for materials discovery and design using goal-driven and constraint-guided llm agents. arXiv preprint arXiv:2501.13299, 2025. find
  173. Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, and Richard Dufour. Biomistral: A collection of open-source pretrained large language models for medical domains, 2024.
  174. Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen tau Yih, Daniel Fried, Sida Wang, and Tao Yu. Ds-1000: A natural and reliable benchmark for data science code generation, 2022. URL https://arxiv.org/abs/2211.11501. ↗
  175. Zheyuan Lai and Yingming Pu. Prim: Principle-inspired material discovery through multi-agent collaboration. arXiv preprint arXiv:2504.08810, 2025. — [arXiv:2504.08810](https://arxiv.org/abs/2504.08810) find
  176. Jakub Lála, Odhran O’Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G Rodriques, and Andrew D White. Paperqa: Retrieval-augmented generative agent for scientific research. 2023.
  177. Alireza Rashidi Laleh and Majid Nili Ahmadabadi. A survey on enhancing reinforcement learning in complex environments: Insights from human and llm feedback. arXiv preprint arXiv:2411.13410, 2024. — [arXiv:2411.13410](https://arxiv.org/abs/2411.13410) find
  178. Andrew Laverick, Kristen Surrao, Inigo Zubeldia, Boris Bolliet, Miles Cranmer, Antony Lewis, Blake Sherwin, and Julien Lesgourgues. Multi-agent system for cosmological parameter analysis. arXiv preprint arXiv:2412.00431, 2024. — [arXiv:2412.00431](https://arxiv.org/abs/2412.00431) find
  179. Namkyeong Lee, Edward De Brouwer, Ehsan Hajiramezanali, Tommaso Biancalani, Chanyoung Park, and Gabriele Scalia. Rag-enhanced collaborative llm agents for drug discovery. arXiv preprint arXiv:2502.17506, 2025. — [arXiv:2502.17506](https://arxiv.org/abs/2502.17506) find
  180. Seowoo Lee, Jiwon Youn, Hyungjin Kim, Mansu Kim, and Soon Ho Yoon. Cxr-llava: a multimodal large language model for interpreting chest x-ray images, 2024. URL https://arxiv.org/abs/2310.18341. — [arXiv:2310.18341](https://arxiv.org/abs/2310.18341) ↗
  181. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33:9459–9474, 2020.
  182. Bingxuan Li, Yiwei Wang, Jiuxiang Gu, Kai-Wei Chang, and Nanyun Peng. Metal: A multi-agent framework for chart generation with test-time scaling. arXiv preprint arXiv:2502.17651, 2025.
  183. Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao. LLaVA-Med: Training a large language-and-vision assistant for biomedicine in one day. Advances in Neural Information Processing Systems, 36:28541–28564, 2023.
  184. Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. Camel: Communicative agents for "mind" exploration of large language model society. 2023.
  185. Haoyuan Li, Hao Jiang, Tianke Zhang, Zhelun Yu, Aoxiong Yin, Hao Cheng, Siming Fu, Yuhao Zhang, and Wanggui He. Traineragent: Customizable and efficient model training through llm-powered multi-agent system. arXiv preprint arXiv:2311.06622, 2023. — [arXiv:2311.06622](https://arxiv.org/abs/2311.06622) find
  186. Jie Li, Fuyong Zhao, Panfeng Chen, Jiafu Xie, Xiangrui Zhang, Hui Li, Mei Chen, Yanhao Wang, and Ming Zhu. An astronomical question answering dataset for evaluating large language models. Scientific Data, 12(1):447, 2025. find
  187. Junkai Li, Yunghwei Lai, Weitao Li, Jingyi Ren, Meng Zhang, Xinhui Kang, Siyu Wang, Peng Li, Ya-Qin Zhang, Weizhi Ma, et al. Agent hospital: A simulacrum of hospital with evolvable medical agents. arXiv preprint arXiv:2405.02957, 2024. — [arXiv:2405.02957](https://arxiv.org/abs/2405.02957) find
  188. Junyi Li, Yongqiang Chen, Chenxi Liu, Qianyi Cai, Tongliang Liu, Bo Han, Kun Zhang, and Hui Xiong. Can large language models help experimental design for causal discovery?, 2025. URL https://arxiv.org/abs/2503.01139. — [arXiv:2503.01139](https://arxiv.org/abs/2503.01139) ↗
  189. Rui Li, Zixuan Hu, Wenxi Qu, Jinouwen Zhang, Zhenfei Yin, Sha Zhang, Xuantuo Huang, Hanqing Wang, Tai Wang, Jiangmiao Pang, et al. Labutopia: High-fidelity simulation and hierarchical benchmark for scientific embodied agents. arXiv preprint arXiv:2505.22634, 2025. — [arXiv:2505.22634](https://arxiv.org/abs/2505.22634) find
  190. Shimin Li, Tianxiang Sun, Qinyuan Cheng, and Xipeng Qiu. Agent alignment in evolving social norms, 2024. URL https://arxiv.org/abs/2401.04620. — [arXiv:2401.04620](https://arxiv.org/abs/2401.04620) ↗
  191. Tianbin Li, Yanzhou Su, Wei Li, Bin Fu, Zhe Chen, Ziyan Huang, Guoan Wang, Chenglong Ma, Ying Chen, Ming Hu, et al. Gmai-vl & gmai-vl-5.5 m: A large vision-language model and a comprehensive multimodal dataset towards general medical ai. arXiv preprint arXiv:2411.14522, 2024. — [arXiv:2411.14522](https://arxiv.org/abs/2411.14522) find
  192. Tianbin Li, Yanzhou Su, Wei Li, Bin Fu, Zhe Chen, Ziyan Huang, Guoan Wang, Chenglong Ma, Ying Chen, Ming Hu, Yanjun Li, Pengcheng Chen, Xiaowei Hu, Zhongying Deng, Yuanfeng Ji, Jin Ye, Yu Qiao, and Junjun He. GMAI-VL & GMAI-VL-5.5m: A large vision-language model and a comprehensive multimodal datase… — [arXiv:2411.14522](https://arxiv.org/abs/2411.14522) find
  193. Weichen Li and Weimin Pan. Enhancing chain-of-thought reasoning in large language models through text style diversity and prompt fusion. In EIBDCT, volume 13181, pages 226–232. SPIE, 2024. find
  194. Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, et al. Autosdt: Scaling data-driven discovery tasks toward open co-scientists. arXiv preprint arXiv:2506.08140, 2025. find
  195. Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng, and Wenjun Zhang. Learning distilled collaboration graph for multi-agent perception. 34:29541–29552, 2021. find
  196. Zhucong Li, Bowei Zhang, Jin Xiao, Zhijian Zhou, Fenglei Cao, Jiaqing Liang, and Yuan Qi. Chemhas: Hierarchical agent stacking for enhancing chemistry tools. arXiv preprint arXiv:2505.21569, 2025. — [arXiv:2505.21569](https://arxiv.org/abs/2505.21569) find
  197. Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, and Zhaopeng Tu. Encouraging divergent thinking in large language models through multi-agent debate. arXiv preprint arXiv:2305.19118, 2023.
  198. Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, and Zhaopeng Tu. Encouraging divergent thinking in large language models through multi-agent debate. pages 17889–17904, 2024.
  199. Wang Liang. LLaMA-Gene: A general-purpose gene task large language model based on instruction fine-tuning. arXiv preprint arXiv:2412.00471, 2024. — [arXiv:2412.00471](https://arxiv.org/abs/2412.00471) find
  200. Xuechen Liang, Yangfan He, Yinghui Xia, Xinyuan Song, Jianhui Wang, Meiling Tao, Li Sun, Xinhang Yuan, Jiayi Su, Keqin Li, et al. Self-evolving agents with reflective and memory-augmented abilities. arXiv preprint arXiv:2409.00872, 2024. — [arXiv:2409.00872](https://arxiv.org/abs/2409.00872) find
  201. Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, et al. Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv, 2022. find
  202. Mario Lino, Stathi Fotiadis, Anil A Bharath, and Chris D Cantwell. Current and emerging deep-learning methods for the simulation of fluid dynamics. Proceedings of the Royal Society A, 479(2275):20230058, 2023. find
  203. Bang Liu, Xinfeng Li, Jiayi Zhang, Jinlin Wang, Tanjin He, Sirui Hong, Hongzhang Liu, Shaokun Zhang, Kaitao Song, Kunlun Zhu, et al. Advances and challenges in foundation agents: From brain-inspired intelligence to evolutionary, collaborative, and safe systems. arXiv preprint arXiv:2504.01990, 2025. — [arXiv:2504.01990](https://arxiv.org/abs/2504.01990) find
  204. Haoyang Liu, Yijiang Li, Jinglin Jian, Yuxuan Cheng, Jianrong Lu, Shuyi Guo, Jinglei Zhu, Mianchen Zhang, Miantong Zhang, and Haohan Wang. Toward a team of ai-made scientists for scientific discovery from gene expression data. arXiv preprint arXiv:2402.12391, 2024. — [arXiv:2402.12391](https://arxiv.org/abs/2402.12391) find
  205. Jiachen Liu, Ziheng Geng, Ran Cao, Lu Cheng, Paolo Bocchini, and Minghui Cheng. A large language model-empowered agent for reliable and robust structural analysis. arXiv preprint arXiv:2507.02938, 2025. — [arXiv:2507.02938](https://arxiv.org/abs/2507.02938) find
  206. Junhua Liu, Fanfan Lin, Xinze Li, Kwan Hui Lim, and Shuai Zhao. Physics-informed llm-agent for automated modulation design in power electronics systems. arXiv preprint arXiv:2411.14214, 2024. — [arXiv:2411.14214](https://arxiv.org/abs/2411.14214) find
  207. Ruibo Liu, Jason Wei, Shixiang Shane Gu, Te-Yen Wu, Soroush Vosoughi, Claire Cui, Denny Zhou, and Andrew M. Dai. Mind’s eye: Grounded language model reasoning through simulation. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenRevie… find
  208. Shengchao Liu, Yanjing Li, Zhuoxinran Li, Anthony Gitter, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Arvind Ramanathan, Chaowei Xiao, Jian Tang, Hongyu Guo, and Anima Anandkumar. A text-guided protein design framework. arXiv preprint, 2023. doi: 10.48550/arXiv.2302.04611. v4, 2025. — [doi:10.48550/arxiv.2302.04611](https://doi.org/10.48550/arxiv.2302.04611) find
  209. Sizhe Liu, Yizhou Lu, Siyu Chen, Xiyang Hu, Jieyu Zhao, Yingzhou Lu, and Yue Zhao. Drugagent: Automating ai-aided drug discovery programming through llm multi-agent collaboration. arXiv preprint arXiv:2411.15692, 2024.
  210. Wanhao Liu, Zonglin Yang, Jue Wang, Lidong Bing, Di Zhang, Dongzhan Zhou, Yuqiang Li, Houqiang Li, Erik Cambria, and Wanli Ouyang. Moose-chem3: Toward experiment-guided hypothesis ranking via simulated experimental feedback. arXiv preprint arXiv:2505.17873, 2025. find
  211. Yang Liu, Peng Sun, and Hang Li. Large language models as agents in two-player games. arXiv preprint arXiv:2402.08078, 2024. — [arXiv:2402.08078](https://arxiv.org/abs/2402.08078) find
  212. Yuyan Liu, Sirui Ding, Sheng Zhou, Wenqi Fan, and Qiaoyu Tan. Moleculargpt: Open large language model (llm) for few-shot molecular property prediction. arXiv preprint arXiv:2406.12950, 2024. — [arXiv:2406.12950](https://arxiv.org/abs/2406.12950) find
  213. Zequn Liu, Wei Zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang, and Tie-Yan Liu. Molxpt: Wrapping molecules with text for generative pre-training, 2023. URL https://arxiv.org/abs/2305.10688. ↗
  214. Zhengliang Liu, Yiwei Li, Peng Shu, Aoxiao Zhong, Longtao Yang, Chao Ju, Zihao Wu, Chong Ma, Jie Luo, Cheng Chen, Sekeun Kim, Jiang Hu, Haixing Dai, Lin Zhao, Dajiang Zhu, Jun Liu, Wei Liu, Dinggang Shen, Tianming Liu, Quanzheng Li, and Xiang Li. Radiology-llama2: Best-in-class large language model… — [arXiv:2309.06419](https://arxiv.org/abs/2309.06419) find
  215. Zijun Liu, Kaiming Liu, Yiqi Zhu, Xuanyu Lei, Zonghan Yang, Zhenhe Zhang, Peng Li, and Yang Liu. Aigs: Generating science from ai-powered automated falsification. arXiv preprint arXiv:2411.11910, 2024. — [arXiv:2411.11910](https://arxiv.org/abs/2411.11910) find
  216. Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, and Diyi Yang. A dynamic llm-powered agent network for task-oriented agent collaboration. In COLM, 2024.
  217. Zijun Liu et al. A dynamic LLM-powered agent network for task-oriented agent collaboration. In First Conference on Language Modeling, Oct. 2024.
  218. Jieyi Long. Large language model guided tree-of-thought. arXiv preprint arXiv:2305.08291, 2023. find
  219. Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292v3, 2024. URL https://www.arxiv.org/abs/2408.06292v3. ↗
  220. Darui Lu, Jordan M Malof, and Willie J Padilla. An agentic framework for autonomous metamaterial modeling and inverse design. arXiv preprint arXiv:2506.06935, 2025. — [arXiv:2506.06935](https://arxiv.org/abs/2506.06935) find
  221. Yi Luo, Linghang Shi, Yihao Li, Aobo Zhuang, Yeyun Gong, Ling Liu, and Chen Lin. From intention to implementation: automating biomedical research via llms. Science China Information Sciences, 68(7): 1–18, 2025. find
  222. Ziming Luo, Zonglin Yang, Zexin Xu, Wei Yang, and Xinya Du. Llm4sr: A survey on large language models for scientific research. arXiv preprint arXiv:2501.04306, 2025.
  223. Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, and Yonghong Tian. Prollama: A protein language model for multi-task protein language processing. arXiv preprint, 2024. doi: 10.48550/arXiv.2402.16445. — [doi:10.48550/arxiv.2402.16445](https://doi.org/10.48550/arxiv.2402.16445) find
  224. Artem Lykov, Maria Dronova, Nikolay Naglov, Mikhail Litvinov, Sergei Satsevich, Artem Bazhenov, Vladimir Berman, Aleksei Shcherbak, and Dzmitry Tsetserukou. Llm-mars: Large language model for behavior tree generation and nlp-enhanced dialogue in multi-agent robot systems. arXiv preprint arXiv:2312.0… — [arXiv:2312.09348](https://arxiv.org/abs/2312.09348) find
  225. Chengdong Ma, Ziran Yang, Hai Ci, Jun Gao, Minquan Gao, Xuehai Pan, and Yaodong Yang. Evolving diverse red-team language models in multi-round multi-agent games. arXiv preprint arXiv:2310.00322, 2023. — [arXiv:2310.00322](https://arxiv.org/abs/2310.00322) find
  226. Hao Ma, Tianyi Hu, Zhiqiang Pu, Liu Boyin, Xiaolin Ai, Yanyan Liang, and Min Chen. Coevolving with the other you: Fine-tuning llm with sequential cooperative multi-agent reinforcement learning. 37:15497–15525, 2024. find
  227. Kangyong Ma. Ai agents in chemical research: Gvim–an intelligent research assistant system. Digital Discovery, 4(2):355–375, 2025. find
  228. Yubo Ma, Zhibin Gou, Junheng Hao, Ruochen Xu, Shuohang Wang, Liangming Pan, Yujiu Yang, Yixin Cao, and Aixin Sun. Sciagent: Tool-augmented language models for scientific reasoning. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 Conference on Empirical Methods…
  229. Yubo Ma, Zhibin Gou, Junheng Hao, Ruochen Xu, Shuohang Wang, Liangming Pan, Yujiu Yang, Yixin Cao, Aixin Sun, Hany Awadalla, et al. Sciagent: Tool-augmented language models for scientific reasoning. arXiv preprint arXiv:2402.11451, 2024.
  230. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback. 36:46534–46594, 2023. find
  231. Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen, Oyvind Tafjord, Niket Tandon, Li Zhang, Chris Callison-Burch, and Peter Clark. Clin: A continually learning language agent for rapid task adaptation and generalization, 2023. URL https://arxiv.org/abs/2310.10134. ↗
  232. Indrajeet Mandal, Jitendra Soni, Mohd Zaki, Morten M Smedskjaer, Katrin Wondraczek, Lothar Wondraczek, Nitya Nand Gosvami, and NM Krishnan. Autonomous microscopy experiments through large language model agents. arXiv preprint arXiv:2501.10385, 2024. — [arXiv:2501.10385](https://arxiv.org/abs/2501.10385) find
  233. David Maranto. Llmsat: A large language model-based goal-oriented agent for autonomous space exploration. arXiv preprint arXiv:2405.01392, 2024. — [arXiv:2405.01392](https://arxiv.org/abs/2405.01392) find
  234. Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning, 2022. URL https://arxiv.org/abs/2203.10244. ↗
  235. Tula Masterman, Sandi Besen, Mason Sawtell, and Alex Chao. The landscape of emerging ai agent architectures for reasoning, planning, and tool calling: A survey. arXiv preprint arXiv:2404.11584, 2024.
  236. Andrew D McNaughton, Gautham Krishna Sankar Ramalaxmi, Agustin Kruel, Carter R Knutson, Rohith A Varikoti, and Neeraj Kumar. Cactus: Chemistry agent connecting tool usage to science. ACS omega, 9(46):46563–46573, 2024. find
  237. Nikita Mehandru, Amanda K Hall, Olesya Melnichenko, Yulia Dubinina, Daniel Tsirulnikov, David Bamman, Ahmed Alaa, Scott Saponas, and Venkat S Malladi. Bioagents: Democratizing bioinformatics analysis with multi-agent systems. arXiv preprint arXiv:2501.06314, 2025. find
  238. Lingrui Mei, Jiayu Yao, Yuyao Ge, Yiwei Wang, Baolong Bi, Yujun Cai, Jiazhi Liu, Mingyu Li, Zhong-Zhi Li, Duzhen Zhang, et al. A survey of context engineering for large language models. arXiv preprint arXiv:2507.13334, 2025. — [arXiv:2507.13334](https://arxiv.org/abs/2507.13334) find
  239. Siddharth Mishra-Sharma, Yiding Song, and Jesse Thaler. Paperclip: Associating astronomical observations and natural language with multi-modal models. arXiv preprint arXiv:2403.08851, 2024. — [arXiv:2403.08851](https://arxiv.org/abs/2403.08851) find
  240. Michael Moor, Qian Huang, Shirley Wu, Michihiro Yasunaga, Cyril Zakka, Yash Dalmia, Eduardo Pontes Reis, Pranav Rajpurkar, and Jure Leskovec. Med-flamingo: A multimodal medical few-shot learner. arXiv preprint arXiv:2307.15189, 2023.
  241. Adam Moss. The ai cosmologist i: An agentic system for automated data analysis. arXiv preprint arXiv:2504.03424, 2025.
  242. Vladimir Naumov, Diana Zagirova, Sha Lin, Yupeng Xie, Wenhao Gou, Anatoly Urban, Nina Tikhonova, Khadija Alawi, Mike Durymanov, Fedor Galkin, et al. Dora ai scientist: Multi-agent virtual research team for scientific exploration discovery and automated report generation. bioRxiv, 2025. find
  243. Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel S. Weld, Joseph Chee Chang, and Kyle Lo. Arxivdigestables: Synthesizing scientific literature into tables using language models, 2024. URL https://arxiv.org/abs/2410.22360. ↗
  244. Eric Nguyen, Michael Poli, Matthew G Durrant, Brian Kang, Dhruva Katrekar, David B Li, Liam J Bartie, Armin W Thomas, Samuel H King, Garyk Brixi, et al. Sequence modeling and design from molecular to genome scale with evo. Science, 386(6723):eado9336, 2024. find
  245. Tuan Dung Nguyen, Yuan-Sen Ting, Ioana Ciucă, Charlie O’Neill, Ze-Chang Sun, Maja Jabłońska, Sandor Kruk, Ernest Perkowski, Jack Miller, Jason Li, et al. Astrollama: Towards specialized foundation models in astronomy. arXiv preprint arXiv:2309.06126, 2023. find
  246. Tuan Dung Nguyen, Yuan-Sen Ting, Ioana Ciucă, Charlie O’Neill, Ze-Chang Sun, Maja Jabłońska, Sandor Kruk, Ernest Perkowski, Jack Miller, Jason Li, et al. Astrollama: Towards specialized foundation models in astronomy. arXiv preprint arXiv:2309.06126, 2023. find
  247. Bo Ni and Markus J Buehler. Mechagents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge. Extreme Mechanics Letters, 67: 102131, 2024. find
  248. Ziqi Ni, Yahao Li, Kaijia Hu, Kunyuan Han, Ming Xu, Xingyu Chen, Fengqi Liu, Yicong Ye, and Shuxin Bai. Matpilot: an llm-enabled ai materials scientist under the framework of human-machine collaboration. arXiv preprint arXiv:2411.08063, 2024. — [arXiv:2411.08063](https://arxiv.org/abs/2411.08063) find
  249. Seyednami Niyakan and Xiaoning Qian. Phenograph: A multi-agent framework for phenotype-driven discovery in spatial transcriptomics data augmented with knowledge graphs. bioRxiv, pages 2025–06, 2025. find
  250. Alexander Novikov, Ngân Vũ, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov, Borislav Kozlovskii, Francisco JR Ruiz, Abbas Mehrabian, et al. Alphaevolve: A coding agent for scientific and algorithmic discovery. arXiv preprint arXiv:2506.13131, 2025.
  251. Janghoon Ock, Radheesh Sharma Meda, Srivathsan Badrinarayanan, Neha S Aluru, Achuth Chandrasekhar, and Amir Barati Farimani. Large language model agent for modular task execution in drug discovery. arXiv preprint arXiv:2507.02925, 2025. — [arXiv:2507.02925](https://arxiv.org/abs/2507.02925) find
  252. Odhran O’Donoghue, Aleksandar Shtedritski, John Ginger, Ralph Abboud, Ali Essa Ghareeb, Justin Booth, and Samuel G Rodriques. Bioplanner: Automatic evaluation of llms on protocol planning in biology, 2023. URL https://arxiv.org/abs/2310.10632. ↗
  253. Ryotaro Okabe, Zack West, Abhijatmedhi Chotrattanapituk, Mouyang Cheng, Denisse Córdova Car-rizales, Weiwei Xie, Robert J. Cava, and Mingda Li. Large language model-guided prediction toward quantum materials synthesis, 2024. URL https://arxiv.org/abs/2410.20976. — [arXiv:2410.20976](https://arxiv.org/abs/2410.20976) ↗
  254. Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, and Benjamin Van Durme. Claimcheck: How grounded are llm critiques of scien… find
  255. Charles Packer, Vivian Fang, Shishir G Patil, Kevin Lin, Sarah Wooders, and Joseph E Gonzalez. Memgpt: Towards llms as operating systems. CoRR, 2023. find
  256. Haining Pan, Nayantara Mudur, William Taranto, Maria Tikhanovskaya, Subhashini Venugopalan, Yasaman Bahri, Michael P Brenner, and Eun-Ah Kim. Quantum many-body physics calculations with large language models. Communications Physics, 8(1):49, 2025. find
  257. Rui Pan, Tuan Dung Nguyen, Hardik Arora, Alberto Accomazzi, Tirthankar Ghosal, and Yuan-Sen Ting. Astromlab 2: Astrollama-2-70b model and benchmarking specialised llms for astronomy. In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis… find
  258. Sandeep Pandey, Ran Xu, Wenkang Wang, and Xu Chu. Openfoamgpt: a rag-augmented llm agent for openfoam-based computational fluid dynamics. arXiv preprint arXiv:2501.06327, 2025. find
  259. Jing-Cheng Pang, Pengyuan Wang, Kaiyuan Li, Xiong-Hui Chen, Jiacheng Xu, Zongzhang Zhang, and Yang Yu. Language model self-improvement by reinforcement learning contemplation. 2024.
  260. J Gregory Pauloski, Yadu Babuji, Ryan Chard, Mansi Sakarvadia, Kyle Chard, and Ian Foster. Empowering scientific workflows with federated agents. arXiv preprint arXiv:2505.05428, 2025. — [arXiv:2505.05428](https://arxiv.org/abs/2505.05428) find
  261. Ernest Perkowski, Rui Pan, Tuan Dung Nguyen, Yuan-Sen Ting, Sandor Kruk, Tong Zhang, Charlie O’Neill, Maja Jablonska, Zechang Sun, Michael J Smith, et al. Astrollama-chat: Scaling astrollama with conversational and diverse datasets. Research Notes of the AAS, 8(1):7, 2024. find
  262. Thang D Pham, Aditya Tanikanti, and Murat Keçeli. Chemgraph: An agentic framework for computational chemistry workflows. arXiv preprint arXiv:2506.06363, 2025. — [arXiv:2506.06363](https://arxiv.org/abs/2506.06363) find
  263. Can Polat, Mehmet Tuncel, Mustafa Kurban, Erchin Serpedin, and Hasan Kurban. xchemagents: Agentic ai for explainable quantum chemistry. arXiv preprint arXiv:2505.20574, 2025. — [arXiv:2505.20574](https://arxiv.org/abs/2505.20574) find
  264. Evangelos Pournaras. Science in the era of chatgpt, large language models and generative ai. KI-Kritik/AI Critique Volume 6, page 275, 2023. find
  265. Vignesh Prabhakar, Md Amirul Islam, Adam Atanas, Yao-Ting Wang, Joah Han, Aastha Jhunjhunwala, Rucha Apte, Robert Clark, Kang Xu, Zihan Wang, et al. Omniscience: A domain-specialized llm for scientific reasoning and discovery. arXiv preprint arXiv:2503.17604, 2025.
  266. Yingming Pu, Tao Lin, and Hongyu Chen. Piflow: Principle-aware scientific discovery with multi-agent collaboration. arXiv preprint arXiv:2505.15047, 2025. — [arXiv:2505.15047](https://arxiv.org/abs/2505.15047) find
  267. Biqing Qi, Kaiyan Zhang, Haoxiang Li, Kai Tian, Sihang Zeng, Zhang-Ren Chen, and Bowen Zhou. Large language models are zero shot hypothesis proposers. arXiv preprint arXiv:2311.05965, 2023.
  268. Chen Qian et al. ChatDev: Communicative agents for software development. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, Aug. 2024.
  269. Shuofei Qiao, Honghao Gui, Chengfei Lv, Qianghuai Jia, Huajun Chen, and Ningyu Zhang. Making language models better tool learners with execution feedback. arXiv preprint arXiv:2305.13068, 2023. find
  270. Shuofei Qiao, Runnan Fang, Ningyu Zhang, Yuqi Zhu, Xiang Chen, Shumin Deng, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. Agent planning with world knowledge model. 37:114843–114871, 2024. find
  271. Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Eleanor Jiang, Chengfei Lv, and Huajun Chen. Autoact: Automatic agent learning from scratch for qa via self-planning. arXiv preprint arXiv:2401.05268, 2024. find
  272. Zijie Qiu, Jiaqi Wei, Xiang Zhang, Sheng Xu, Kai Zou, Zhi Jin, Zhiqiang Gao, Nanqing Dong, and Siqi Sun. Universal biological sequence reranking for improved de novo peptide sequencing. arXiv preprint arXiv:2505.17552, 2025. — [arXiv:2505.17552](https://arxiv.org/abs/2505.17552) find
  273. Shang Qu, Ning Ding, Linhai Xie, Yifei Li, Zaoqu Liu, Kaiyan Zhang, Yibai Xiong, Yuxin Zuo, Zhangren Chen, Ermo Hua, et al. Automating exploratory multiomics research via language models. arXiv preprint arXiv:2506.07591, 2025. — [arXiv:2506.07591](https://arxiv.org/abs/2506.07591) find
  274. Xin Quan, Marco Valentino, Louise A. Dennis, and André Freitas. Verification and refinement of natural language explanations through llm-symbolic theorem proving, 2024. URL https://arxiv.org/abs/2405.01379. ↗
  275. Gollam Rabby, Diyana Muhammed, Prasenjit Mitra, and Sören Auer. Iterative hypothesis generation for scientific discovery with monte carlo nash equilibrium self-refining trees, 2025. URL https://arxiv.org/abs/2503.19309. ↗
  276. Mayk Caldas Ramos, Christopher J Collison, and Andrew D White. A review of large language models and autonomous agents in chemistry. Chemical Science, 2025.
  277. Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, and Alexander Rives. Msa transformer. In International conference on machine learning, pages 8844–8856. PMLR, 2021. find
  278. Shuo Ren, Pu Jian, Zhenjiang Ren, Chunlin Leng, Can Xie, and Jiajun Zhang. Towards scientific intelligence: A survey of llm-based scientific agents. arXiv preprint arXiv:2503.24047, 2025.
  279. Corban Rivera, Grayson Byrd, William Paul, Tyler Feldman, Meghan Booker, Emma Holmes, David Handelman, Bethany Kemp, Andrew Badger, Aurora Schmidt, et al. Conceptagent: Llm-driven precondition grounding and tree search for robust task planning and execution. arXiv preprint arXiv:2410.06108, 2024. — [arXiv:2410.06108](https://arxiv.org/abs/2410.06108) find
  280. Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences, 118(15), 2021. find
  281. Yusuf Roohani, Andrew Lee, Qian Huang, Jian Vora, Zachary Steinhart, Kexin Huang, Alexander Marson, Percy Liang, and Jure Leskovec. Biodiscoveryagent: An ai agent for designing genetic perturbation experiments. arXiv preprint arXiv:2405.17631, 2024.
  282. Yixiang Ruan, Chenyin Lu, Ning Xu, Yuchen He, Yixin Chen, Jian Zhang, Jun Xuan, Jianzhang Pan, Qun Fang, Hanyu Gao, et al. An automatic end-to-end chemical synthesis development platform powered by large language models. Nature communications, 15(1):10160, 2024. find
  283. Yixiang Ruan, Chenyin Lu, Ning Xu, Jian Zhang, Jun Xuan, Jianzhang Pan, Qun Fang, Hanyu Gao, Xiaodong Shen, Ning Ye, et al. Accelerated end-to-end chemical synthesis development with large language models. doi:10.26434/chemrxiv-2024-6wmg4, 2024. — [doi:10.26434/chemrxiv-2024-6wmg4](https://doi.org/10.26434/chemrxiv-2024-6wmg4) find
  284. Daniel Saeedi, Denise Buckner, Jose C Aponte, and Amirali Aghazadeh. Astroagents: A multi-agent ai for hypothesis generation from mass spectrometry data. arXiv preprint arXiv:2503.23170, 2025. — [arXiv:2503.23170](https://arxiv.org/abs/2503.23170) find
  285. Andreas WM Sauter, Erman Acar, and Vincent Francois-Lavet. A meta-reinforcement learning algorithm for causal discovery. In Conference on Causal Learning and Reasoning, pages 602–619. PMLR, 2023. find
  286. Samuel Schmidgall and Michael Moor. Agentrxiv: Towards collaborative autonomous research. arXiv preprint arXiv:2503.18102, 2025.
  287. Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Zicheng Liu, and Emad Barsoum. Agent laboratory: Using llm agents as research assistants. arXiv preprint arXiv:2501.04227, 2025.
  288. Johannes Schneider. Generative to agentic ai: Survey, conceptualization, and challenges. arXiv preprint arXiv:2504.18875, 2025. — [arXiv:2504.18875](https://arxiv.org/abs/2504.18875) find
  289. Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, Atilla Kiraly, Madeleine Traverse, Timo Kohlberger, Shawn Xu, Fayaz Jamil, Cían Hughes, Charles Lau, et al. Medgemma technical report. arXiv preprint arXiv:2507.05201, 2025.
  290. SeungWon Seo, Junhyeok Lee, SeongRae Noh, and HyeongYeop Kang. Llm-based cooperative agents using information relevance and plan validation. arXiv preprint arXiv:2405.16751, 2024. — [arXiv:2405.16751](https://arxiv.org/abs/2405.16751) find
  291. Haiyang Shen, Yue Li, Desong Meng, Dongqi Cai, Sheng Qi, Li Zhang, Mengwei Xu, and Yun Ma. Shortcutsbench: A large-scale real-world benchmark for api-based agents. In The Thirteenth International Conference on Learning Representations, 2025. find
  292. Zhengliang Shi, Shen Gao, Lingyong Yan, Yue Feng, Xiuyi Chen, Zhumin Chen, Dawei Yin, Suzan Verberne, and Zhaochun Ren. Tool learning in the wild: Empowering language models as automatic tool agents. In Proceedings of the ACM on Web Conference 2025, pages 2222–2237, 2025. find
  293. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. 36:8634–8652, 2023.
  294. Chenglei Si, Diyi Yang, and Tatsunori Hashimoto. Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers. arXiv preprint arXiv:2409.04109, 2024.
  295. Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl, Heather Cole-Lewis, Darlene Neal, Mike Schaekermann, Amy Wang, Mohamed Amin, Sami Lachgar, Philip Mansfield, Sushant Prakash, Bradley Green, Ewa Dominowska, Blaise Aguera y Arcas, Nenad Tomasev, Y… — Towards Expert-Level Medical Question Answering with Large Language Models
  296. Khachik Smbatyan, Tsolak Ghukasyan, Tigran Aghajanyan, Hovhannes Dabaghyan, Sergey Adamyan, Aram Bughdaryan, Vahagn Altunyan, Gagik Navasardyan, Aram Davtyan, Anush Hakobyan, et al. Can ai agents design and implement drug discovery pipelines? arXiv preprint arXiv:2504.19912, 2025. — [arXiv:2504.19912](https://arxiv.org/abs/2504.19912) find
  297. Michael J Smith, Ryan J Roberts, Eirini Angeloudi, and Marc Huertas-Company. Astropt: Scaling large observation models for astronomy. arXiv preprint arXiv:2405.14930, 2024. — [arXiv:2405.14930](https://arxiv.org/abs/2405.14930) find
  298. Tao Song, Man Luo, Linjiang Chen, Yan Huang, Qing Zhu, Daobin Liu, Baicheng Zhang, Gang Zou, Fei Zhang, Weiwei Shang, Jun Jiang, and Yi Luo. A multi-agent-driven robotic ai chemist enabling autonomous chemical research on demand. ChemRxiv, July 2024. doi: 10.26434/chemrxiv-2024-w953h-v2. URL https:/… — [doi:10.26434/chemrxiv-2024-w953h-v2](https://doi.org/10.26434/chemrxiv-2024-w953h-v2) find
  299. Tao Song, Man Luo, Xiaolong Zhang, Linjiang Chen, Yan Huang, Jiaqi Cao, Qing Zhu, Daobin Liu, Baicheng Zhang, Gang Zou, et al. A multiagent-driven robotic ai chemist enabling autonomous chemical research on demand. Journal of the American Chemical Society, 147(15):12534–12545, 2025. find
  300. Yifan Song, Da Yin, Xiang Yue, Jie Huang, Sujian Li, and Bill Yuchen Lin. Trial and error: Exploration-based trajectory optimization of llm agents. pages 7584–7600, 2024.
  301. Zhilong Song, Shuaihua Lu, Minggang Ju, Qionghua Zhou, and Jinlan Wang. Is large language model all you need to predict the synthesizability and precursors of crystal structures?, 2024. URL https://arxiv.org/abs/2407.07016. — [arXiv:2407.07016](https://arxiv.org/abs/2407.07016) ↗
  302. Henry W Sprueill, Carl Edwards, Khushbu Agarwal, Mariefel V Olarte, Udishnu Sanyal, Conrad Johnston, Hongbin Liu, Heng Ji, and Sutanay Choudhury. Chemreasoner: Heuristic search over a large language model’s knowledge space using quantum-chemical feedback. arXiv preprint arXiv:2402.10980, 2024.
  303. Sakhinana Sagar Srinivas, Shivam Gupta, and Venkataramana Runkana. Autochemschematic ai: A closed-loop, physics-aware agentic framework for auto-generating chemical process and instrumentation diagrams. arXiv preprint arXiv:2505.24584, 2025. — [arXiv:2505.24584](https://arxiv.org/abs/2505.24584) find
  304. Felix Strieth-Kalthoff, Han Hao, Vandana Rathore, Joshua Derasp, Théophile Gaudin, Nicholas H Angello, Martin Seifrid, Ekaterina Trushina, Mason Guy, Junliang Liu, et al. Delocalized, asynchronous, closed-loop discovery of organic laser emitters. Science, 384(6697):eadk9227, 2024. find
  305. Haoyang Su, Renqi Chen, Shixiang Tang, Zhenfei Yin, Xinzhe Zheng, Jinzhe Li, Biqing Qi, Qi Wu, Hui Li, Wanli Ouyang, Philip Torr, Bowen Zhou, and Nanqing Dong. Many heads are better than one: Improved scientific idea generation by a LLM-based multi-agent system. In Wanxiang Che, Joyce Nabende, Ekate…
  306. Houcheng Su, Weicai Long, and Yanlin Zhang. Biomaster: Multi-agent system for automated bioinformatics analysis workflow. bioRxiv, pages 2025–01, 2025. find
  307. Yanzhou Su, Tianbin Li, Jiyao Liu, Chenglong Ma, Junzhi Ning, Cheng Tang, Sibo Ju, Jin Ye, Pengcheng Chen, Ming Hu, et al. Gmai-vl-r1: Harnessing reinforcement learning for multimodal medical reasoning. arXiv preprint arXiv:2504.01886, 2025. — [arXiv:2504.01886](https://arxiv.org/abs/2504.01886) find
  308. Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang. Adaplanner: Adaptive planning from feedback with language models. 36:58202–58245, 2023.
  309. Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu, Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, et al. A survey of reasoning with foundation models: Concepts, methodologies, and outlook. ACM Computing Surveys, 57(11):1–43, 2025. find
  310. Liangtai Sun, Danyu Luo, Da Ma, Zihan Zhao, Baocai Chen, Zhennan Shen, Su Zhu, Lu Chen, Xin Chen, and Kai Yu. Scidfm: A large language model with mixture-of-experts for science. arXiv preprint arXiv:2409.18412, 2024. — [arXiv:2409.18412](https://arxiv.org/abs/2409.18412) find
  311. Zechang Sun, Yuan-Sen Ting, Yaobo Liang, Nan Duan, Song Huang, and Zheng Cai. Interpreting multi-band galaxy observations with large language model-based agents. arXiv preprint arXiv:2409.14807, 2024.
  312. Mirac Suzgun and Adam Tauman Kalai. Meta-prompting: Enhancing language models with task-agnostic scaffolding. arXiv preprint arXiv:2401.12954, 2024.
  313. Kyle Swanson, Wesley Wu, Nash L Bulaong, John E Pak, and James Zou. The virtual lab: Ai agents design new sars-cov-2 nanobodies with experimental validation. bioRxiv, pages 2024–11, 2024. find
  314. Nathan J Szymanski, Bernardus Rendy, Yuxing Fei, Rishi E Kumar, Tanjin He, David Milsted, Matthew J McDermott, Max Gallant, Ekin Dogus Cubuk, Amil Merchant, et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature, 624(7990):86–91, 2023. find
  315. Pratiksha Tadas and Sudhir Agarmore. Redefining Work in the Age of AI: Challenges and Pathways to Opportunities. In SPICES, pages 1–5. IEEE, 2024. find
  316. Shiro Takagi, Ryutaro Yamauchi, and Wataru Kumagai. Towards autonomous hypothesis verification via language models with minimal guidance, 2023. URL https://arxiv.org/abs/2311.09706. — [arXiv:2311.09706](https://arxiv.org/abs/2311.09706) ↗
  317. Qian Tan, Dongzhan Zhou, Peng Xia, Wanhao Liu, Wanli Ouyang, Lei Bai, Yuqiang Li, and Tianfan Fu. Chemmllm: Chemical multimodal large language model. arXiv preprint arXiv:2505.16326, 2025. — [arXiv:2505.16326](https://arxiv.org/abs/2505.16326) find
  318. Xiangru Tang, Tianyu Hu, Muyang Ye, Yanjun Shao, Xunjian Yin, Siru Ouyang, Wangchunshu Zhou, Pan Lu, Zhuosheng Zhang, Yilun Zhao, et al. Chemagent: Self-updating library in large language models improves chemical reasoning. arXiv preprint arXiv:2501.06590, 2025.
  319. Xiangru Tang et al. MedAgents: Large language models as collaborators for zero-shot medical reasoning. In Findings of the Association for Computational Linguistics, Aug. 2024.
  320. Mingxu Tao, Dongyan Zhao, and Yansong Feng. Chain-of-discussion: A multi-model framework for complex evidence-based question answering. arXiv preprint arXiv:2402.16313, 2024. find
  321. Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085, 2022.
  322. Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al. Kimi k1. 5: Scaling reinforcement learning with llms. arXiv preprint arXiv:2501.12599, 2025.
  323. NovelSeek Team, Bo Zhang, Shiyang Feng, Xiangchao Yan, Jiakang Yuan, Zhiyin Yu, Xiaohan He, Songtao Huang, Shaowei Hou, Zheng Nie, et al. Novelseek: When agent becomes the scientist–building closed-loop system from hypothesis to verification. arXiv preprint arXiv:2505.16938, 2025.
  324. David Thulke, Yingbo Gao, Petrus Pelser, Rein Brune, Rricha Jalota, Floris Fok, Michael Ramos, Ian van Wyk, Abdallah Nasir, Hayden Goldstein, et al. Climategpt: Towards ai synthesizing interdisciplinary research on climate change. arXiv preprint arXiv:2401.09646, 2024.
  325. Chuan Tian et al. Optimizing collaboration of large language model based agents for autonomous finite element analysis. 2025. find
  326. Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, et al. A multi-agent framework integrating large language models and generative ai for accelerated metamaterial design. arXiv preprint arXiv:2503.19889,… — [arXiv:2503.19889](https://arxiv.org/abs/2503.19889) find
  327. Minyang Tian, Luyu Gao, Shizhuo Dylan Zhang, Xinan Chen, Cunwei Fan, Xuefei Guo, Roland Haas, Pan Ji, Kittithat Krongchon, Yao Li, Shengyan Liu, Di Luo, Yutao Ma, Hao Tong, Kha Trinh, Chenyu Tian, Zihan Wang, Bohao Wu, Yanyu Xiong, Shengzhu Yin, Minhui Zhu, Kilian Lieret, Yanxin Lu, Genglin Liu, Yuf… — SciCode: A Research Coding Benchmark Curated by Scientists
  328. Yuanhe Tian, Ruyi Gan, Yan Song, Jiaxing Zhang, and Yongdong Zhang. Chimed-gpt: A chinese medical large language model with full training regime and better alignment to human preferences. arXiv preprint arXiv:2311.06025, 2023. find
  329. Emanuel Todorov, Tom Erez, and Yuval Tassa. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5026–5033. IEEE, 2012. doi: 10.1109/IROS.2012.6386109. — [doi:10.1109/iros.2012.6386109](https://doi.org/10.1109/iros.2012.6386109) find
  330. Augustin Toma, Patrick R Lawler, Jimmy Ba, Rahul G Krishnan, Barry B Rubin, and Bo Wang. Clinical camel: An open expert-level medical language model with dialogue-based knowledge encoding. arXiv preprint arXiv:2305.12031, 2023. find
  331. Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O’Sullivan, and Hoang D Nguyen. Multi-agent collaboration mechanisms: A survey of llms. arXiv preprint arXiv:2501.06322, 2025.
  332. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212.10509, 2022.
  333. Laura van Weesep, Samuel Genheden, Ola Engkvist, and Jens Sjölund. Exploring modularity of agentic systems for drug discovery. arXiv preprint arXiv:2506.22189, 2025. — [arXiv:2506.22189](https://arxiv.org/abs/2506.22189) find
  334. Guangya Wan, Yuqi Wu, Jie Chen, and Sheng Li. Dynamic self-consistency: Leveraging reasoning paths for efficient llm sampling. arXiv preprint arXiv:2408.17017, 2024. — [arXiv:2408.17017](https://arxiv.org/abs/2408.17017) find
  335. Bingning Wang, Haizhou Zhao, Huozhi Zhou, Liang Song, Mingyu Xu, Wei Cheng, Xiangrong Zeng, Yupeng Zhang, Yuqi Huo, Zecheng Wang, et al. Baichuan-m1: Pushing the medical capability of large language models. arXiv preprint arXiv:2502.12671, 2025.
  336. Chao Wang, Hehe Fan, Ruijie Quan, and Yi Yang. Protchatgpt: Towards understanding proteins with large language models. arXiv preprint, 2024. doi: 10.48550/arXiv.2402.09649. v2, 2025. — [doi:10.48550/arxiv.2402.09649](https://doi.org/10.48550/arxiv.2402.09649) find
  337. Cunshi Wang, Xinjie Hu, Yu Zhang, Xunhao Chen, Pengliang Du, Yiming Mao, Rui Wang, Yuyang Li, Ying Wu, Hang Yang, et al. Starwhisper telescope: Agent-based observation assistant system to approach ai astrophysicist. arXiv preprint arXiv:2412.06412, 2024.
  338. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. 2023. find
  339. Hanchen Wang, Yichun He, Paula P Coelho, Matthew Bucci, Abbas Nazir, Bob Chen, Linh Trinh, Serena Zhang, Kexin Huang, Vineethkrishna Chandrasekar, et al. Spatialagent: An autonomous ai agent for spatial biology. bioRxiv, pages 2025–04, 2025. find
  340. Haoran Wang, Pingzhi Li, Min Chen, Jinglei Cheng, Junyu Liu, and Tianlong Chen. Grovergpt: A large language model with 8 billion parameters for quantum searching. arXiv preprint arXiv:2501.00135, 2024. — [arXiv:2501.00135](https://arxiv.org/abs/2501.00135) find
  341. Kun Wang, Guibin Zhang, Zhenhong Zhou, Jiahao Wu, Miao Yu, Shiqian Zhao, Chenlong Yin, Jinhu Fu, Yibo Yan, Hanjun Luo, et al. A comprehensive survey in llm (-agent) full stack safety: Data, training and deployment. arXiv preprint arXiv:2504.15585, 2025. — [arXiv:2504.15585](https://arxiv.org/abs/2504.15585) find
  342. Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. arXiv preprint arXiv:2305.04091, 2023. find
  343. Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong, and Yangqiu Song. Rethinking the bounds of llm reasoning: Are multi-agent discussions the key? arXiv preprint arXiv:2402.18272, 2024.
  344. Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang. Smiles-bert: Large scale unsupervised pre-training for molecular property prediction. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB ’19, page 429–436, Ne… — [doi:10.1145/3307339.3342186](https://doi.org/10.1145/3307339.3342186) find
  345. Wenxuan Wang, Zizhan Ma, Zheng Wang, Chenghan Wu, Jiaming Ji, Wenting Chen, Xiang Li, and Yixuan Yuan. A survey of llm-based agents in medicine: How far are we from baymax? arXiv preprint arXiv:2502.11211, 2025. find
  346. Xidong Wang, Nuo Chen, Junyin Chen, Yan Hu, Yidong Wang, Xiangbo Wu, Anningzhe Gao, Xiang Wan, Haizhou Li, and Benyou Wang. Apollo: Lightweight multilingual medical llms towards democratizing medical ai to 6b people, 2024. find
  347. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022.
  348. Yi Wang, Yuejie Hou, Lin Yang, Shisen Li, Weiting Tang, Hui Tang, Qiushun He, Siyuan Lin, Yanyan Zhang, Xingyu Li, et al. Accelerating primer design for amplicon sequencing using large language model-powered agents. Nature Biomedical Engineering, pages 1–16, 2025. find
  349. Zhizheng Wang, Qiao Jin, Chih-Hsuan Wei, Shubo Tian, Po-Ting Lai, Qingqing Zhu, Chi-Ping Day, Christina Ross, and Zhiyong Lu. Geneagent: Self-verification language agent for gene set knowledge discovery using domain databases. arXiv preprint arXiv:2405.16205, 2024. — [arXiv:2405.16205](https://arxiv.org/abs/2405.16205) find
  350. Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, and Tomas Pfister. Chain-of-table: Evolving tables in the reasoning chain for table understanding, 2024. URL https://arxiv.org/abs/2401.04398. ↗
  351. Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, Alexis Chevalier, Sanjeev Arora, and Danqi Chen. Charxiv: Charting gaps in realistic chart understanding in multimodal llms, 2024. URL https://arxiv.org/abs/2406.18521. ↗
  352. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. 35:24824–24837, 2022.
  353. Jiaqi Wei, Bin Jiang, and Yanxia Zhang. Identification of blue horizontal branch stars with multimodal fusion. Publications of the Astronomical Society of the Pacific, 135(1050):084501, 2023. find
  354. Jiaqi Wei, Hao Zhou, Xiang Zhang, Di Zhang, Zijie Qiu, Wei Wei, Jinzhe Li, Wanli Ouyang, and Siqi Sun. Alignrag: Leveraging critique learning for evidence-sensitive retrieval-augmented reasoning. arXiv preprint arXiv:2504.14858, 2025. — [arXiv:2504.14858](https://arxiv.org/abs/2504.14858) find
  355. Bo Wen, Wen-Feng Zeng, Yuxing Liao, Zhiao Shi, Sara R Savage, Wen Jiang, and Bing Zhang. Deep learning in proteomics. Proteomics, 20(21-22):1900335, 2020. find
  356. Yixuan Weng, Minjun Zhu, Guangsheng Bao, Hongbo Zhang, Jindong Wang, Yue Zhang, and Linyi Yang. Cycleresearcher: Improving automated research via automated review. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=bjcsVLoHYs. ↗
  357. Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. Pmc-llama: Towards building open-source language models for medicine, 2023. URL https://arxiv.org/abs/2304.14454. ↗
  358. Mengsong Wu, YaFei Wang, Yidong Ming, Yuqi An, Yuwei Wan, Wenliang Chen, Binbin Lin, Yuqiang Li, Tong Xie, and Dongzhan Zhou. Chemagent: Enhancing llms for chemistry and materials science through tree-search based tool learning. arXiv preprint arXiv:2506.07551, 2025. find
  359. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. Autogen: Enabling next-gen llm applications via multi-agent conversation, 2023.
  360. Qingyun Wu et al. Autogen: Enabling next-gen LLM applications via multi-agent conversation, 2024.
  361. Shengguang Wu, Keming Lu, Benfeng Xu, Junyang Lin, Qi Su, and Chang Zhou. Self-evolved diverse data sampling for efficient instruction tuning. arXiv preprint arXiv:2311.08182, 2023. — [arXiv:2311.08182](https://arxiv.org/abs/2311.08182) find
  362. xAI. Grok 4, 2025. URL https://x.ai/news/grok-4. ↗
  363. Yingce Xia, Peiran Jin, Shufang Xie, Liang He, Chuan Cao, Renqian Luo, Guoqing Liu, Yue Wang, Zequn Liu, Yuan-Jyue Chen, et al. Nature language model: Deciphering the language of nature for scientific discovery. arXiv preprint arXiv:2502.07527, 2025. — [arXiv:2502.07527](https://arxiv.org/abs/2502.07527) find
  364. Yanzheng Xiang, Hanqi Yan, Shuyin Ouyang, Lin Gui, and Yulan He. Scireplicate-bench: Benchmarking llms in agent-driven algorithmic reproduction from research papers. arXiv preprint arXiv:2504.00255, 2025.
  365. Yanzheng Xiang, Hanqi Yan, Shuyin Ouyang, Lin Gui, and Yulan He. Scireplicate-bench: Benchmarking llms in agent-driven algorithmic reproduction from research papers, 2025. URL https://arxiv.org/abs/2504.00255. ↗
  366. Meng Xiao, Xunxin Cai, Qingqing Long, Chengrui Wang, Yuanchun Zhou, and Hengshu Zhu. m-kailin: Knowledge-driven agentic scientific corpus distillation framework for biomedical large language models training. arXiv preprint arXiv:2504.19565, 2025. — [arXiv:2504.19565](https://arxiv.org/abs/2504.19565) find
  367. Yihang Xiao, Jinyi Liu, Yan Zheng, Xiaohan Xie, Jianye Hao, Mingzhi Li, Ruitao Wang, Fei Ni, Yuxiao Li, Jintian Luo, et al. Cellagent: An llm-driven multi-agent framework for automated single-cell data analysis. bioRxiv, pages 2024–05, 2024. find
  368. Yijia Xiao, Edward Sun, Yiqiao Jin, Qifan Wang, and Wei Wang. Proteingpt: Multimodal llm for protein property prediction and structure understanding. arXiv preprint, 2024. doi: 10.48550/arXiv.2408.11363. v2, 2025. — [doi:10.48550/arxiv.2408.11363](https://doi.org/10.48550/arxiv.2408.11363) find
  369. Junlin Xie, Zhihong Chen, Ruifei Zhang, Xiang Wan, and Guanbin Li. Large multimodal agents: A survey. arXiv preprint arXiv:2402.15116, 2024. — [arXiv:2402.15116](https://arxiv.org/abs/2402.15116) find
  370. Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, Xinyu Zhou, Lingfei Qian, Huan He, Dennis Shung, Lucila Ohno-Machado, Yonghui Wu, Hua Xu, and Jiang Bian. Me llama: Foundation large language models for medical application… — Me LLaMA: Foundation Large Language Models for Medical Applications
  371. Qiujie Xie, Yixuan Weng, Minjun Zhu, Fuchen Shen, Shulin Huang, Zhen Lin, Jiahui Zhou, Zilan Mao, Zijie Yang, Linyi Yang, et al. How far are ai scientists from changing the world? arXiv preprint arXiv:2507.23276, 2025. find
  372. Tong Xie, Yuwei Wan, Wei Huang, Zhenyu Yin, Yixuan Liu, Shaozhou Wang, Qingyuan Linghu, Chunyu Kit, Clara Grazian, Wenjie Zhang, et al. Darwin series: Domain specific large language models for natural science. arXiv preprint arXiv:2308.13565, 2023.
  373. Qi Xin, Quyu Kong, Hongyi Ji, Yue Shen, Yuqi Liu, Yan Sun, Zhilin Zhang, Zhaorong Li, Xunlong Xia, Bing Deng, et al. Bioinformatics agent (bia): Unleashing the power of large language models to reshape bioinformatics workflow. bioRxiv, pages 2024–05, 2024. find
  374. Kai Xiong et al. Examining inter-consistency of large language models collaboration: An in-depth analysis via debate. In Findings of the Association for Computational Linguistics: EMNLP 2023, Dec. 2023.
  375. Hanwen Xu and Sheng Wang. Protranslator: Zero-shot protein function prediction using textual description. arXiv preprint, 2022. doi: 10.48550/arXiv.2204.10286. — [doi:10.48550/arxiv.2204.10286](https://doi.org/10.48550/arxiv.2204.10286) find
  376. Hanwen Xu, Addie Woicik, Russ B. Altman, Hoifung Poon, and Sheng Wang. Multilingual translation for zero-shot biomedical classification using biotranslator. Nature Communications, 14, 2023. doi: 10.1038/s41467-023-36476-2. URL https://www.nature.com/articles/s41467-023-36476-2. ↗
  377. Huihui Xu, Yuanpeng Nie, Hualiang Wang, Ying Chen, Wei Li, Junzhi Ning, Lihao Liu, Hongqiu Wang, Lei Zhu, Jiyao Liu, et al. Medground-r1: Advancing medical image grounding via spatial-semantic rewarded group relative policy optimization. arXiv preprint arXiv:2507.02994, 2025. — [arXiv:2507.02994](https://arxiv.org/abs/2507.02994) find
  378. Wujiang Xu, Kai Mei, Hang Gao, Juntao Tan, Zujie Liang, and Yongfeng Zhang. A-mem: Agentic memory for llm agents. arXiv preprint arXiv:2502.12110, 2025.
  379. Yinggan Xu, Hana Kimlee, Yijia Xiao, and Di Luo. Advancing ai-scientist understanding: Making llm think like a physicist with interpretable reasoning, 2025. URL https://arxiv.org/abs/2504.01911. — [arXiv:2504.01911](https://arxiv.org/abs/2504.01911) ↗
  380. Zhaoqian Xue, Beichen Wang, Suiyuan Zhu, Kai Mei, Hua Tang, Wenyue Hua, Mengnan Du, and Yongfeng Zhang. What if llms have different world views: Simulating alien civilizations with llm-based agents. arXiv preprint arXiv:2402.13184, 2024. — [arXiv:2402.13184](https://arxiv.org/abs/2402.13184) find
  381. Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search. arXiv preprint arXiv:2504.08066, 2025.
  382. Keqiang Yan, Yi Liu, Yuchao Lin, and Shuiwang Ji. Periodic graph transformers for crystal material property prediction. Advances in Neural Information Processing Systems, 35:15066–15080, 2022. find
  383. Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, and Shuiwang Ji. Complete and efficient graph transformers for crystal material property prediction. arXiv preprint arXiv:2403.11857, 2024. — [arXiv:2403.11857](https://arxiv.org/abs/2403.11857) find
  384. Siyuan Yan, Ming Hu, Yiwen Jiang, Xieji Li, Hao Fei, Philipp Tschandl, Harald Kittler, and Zongyuan Ge. Derm1m: A million-scale vision-language dataset aligned with clinical ontology knowledge for dermatology. arXiv preprint arXiv:2503.14911, 2025. — [arXiv:2503.14911](https://arxiv.org/abs/2503.14911) find
  385. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025.
  386. Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li, Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, et al. Mattersim: A deep learning atomistic model across elements, temperatures and pressures. arXiv preprint arXiv:2405.04967, 2024. — [arXiv:2405.04967](https://arxiv.org/abs/2405.04967) find
  387. Junwei Yang, Hanwen Xu, Srbuhi Mirzoyan, Tong Chen, Zixuan Liu, Zequn Liu, Wei Ju, Luchen Liu, Zhiping Xiao, Ming Zhang, et al. Poisoning medical knowledge using large language models. Nature Machine Intelligence, 6(10):1156–1168, 2024. find
  388. Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, and Yuandong Tian. Rlcd: Reinforcement learning from contrastive distillation for lm alignment. 2024. find
  389. Rui Yang, Lin Song, Yanwei Li, Sijie Zhao, Yixiao Ge, Xiu Li, and Ying Shan. Gpt4tools: Teaching large language model to use tools via self-instruction. 36:71995–72007, 2023.
  390. Songhua Yang, Hanjie Zhao, Senbin Zhu, Guangyu Zhou, Hongfei Xu, Yuxiang Jia, and Hongying Zan. Zhongjing: Enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue. In Proceedings of the AAAI Conference on Artificial Intelligence,… find
  391. Yaotian Yang, Yiwen Tang, Yizhe Chen, Xiao Chen, Jiangjie Qiu, Hao Xiong, Haoyu Yin, Zhiyao Luo, Yifei Zhang, Sijia Tao, et al. Automat: Enabling automated crystal structure reconstruction from microscopy via agentic tool use. arXiv preprint arXiv:2505.12650, 2025. — [arXiv:2505.12650](https://arxiv.org/abs/2505.12650) find
  392. Zhenyu Yang, Xiaoxi Zeng, Yi Zhao, and Runsheng Chen. Alphafold2 and its applications in the fields of biology and medicine. Signal Transduction and Targeted Therapy, 8(1):115, 2023. find
  393. Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, and Erik Cambria. Large language models for automated open-domain scientific hypotheses discovery. arXiv preprint arXiv:2309.02726, 2023.
  394. Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, and Erik Cambria. Large language models for automated open-domain scientific hypotheses discovery. In Findings of the Association for Computational Linguistics ACL 2024, pages 13545–13565, 2024.
  395. Zonglin Yang, Wanhao Liu, Ben Gao, Tong Xie, Yuqiang Li, Wanli Ouyang, Soujanya Poria, Erik Cambria, and Dongzhan Zhou. Moose-chem: Large language models for rediscovering unseen chemistry scientific hypotheses. arXiv preprint arXiv:2410.07076, 2024.
  396. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. 2023.
  397. Nicolas Yax, Hernán Anlló, and Stefano Palminteri. Studying and improving reasoning in humans and machines. Communications Psychology, 2(1):51, 2024. find
  398. Asaf Yehudai, Lilach Eden, Alan Li, Guy Uziel, Yilun Zhao, Roy Bar-Haim, Arman Cohan, and Michal Shmueli-Scheuer. Survey on evaluation of llm-based agents. arXiv preprint arXiv:2503.16416, 2025. — [arXiv:2503.16416](https://arxiv.org/abs/2503.16416) find
  399. Zhangyue Yin et al. Exchange-of-thought: Enhancing large language model capabilities through cross-model communication. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15135–15153, Singapore, Decembe… find
  400. Naruki Yoshikawa, Marta Skreta, Kourosh Darvish, Sebastian Arellano-Rubach, Zhi Ji, Lasse Bjørn Kristensen, Andrew Zou Li, Yuchi Zhao, Haoping Xu, Artur Kuramshin, Alán Aspuru-Guzik, Florian Shkurti, and Animesh Garg. Large language models for chemistry robotics. Autonomous Robots, 47: 1057–1086, 20… — [doi:10.1007/s10514-023-10136-2](https://doi.org/10.1007/s10514-023-10136-2) find
  401. Botao Yu, Frazier N Baker, Ziru Chen, Garrett Herb, Boyu Gou, Daniel Adu-Ampratwum, Xia Ning, and Huan Sun. Chemtoolagent: The impact of tools on language agents for chemistry problem solving. arXiv preprint arXiv:2411.07228, 2024. — [arXiv:2411.07228](https://arxiv.org/abs/2411.07228) find
  402. Miao Yu, Fanci Meng, Xinyun Zhou, Shilong Wang, Junyuan Mao, Linsey Pan, Tianlong Chen, Kun Wang, Xinfeng Li, Yongfeng Zhang, et al. A survey on trustworthy llm agents: Threats and countermeasures. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2, pages 62… find
  403. Chaohao Yuan, Songyou Li, Geyan Ye, Yikun Zhang, Long-Kai Huang, Wenbing Huang, Wei Liu, Jianhua Yao, and Yu Rong. Annotation-guided protein design with multi-level domain alignment. arXiv preprint, 2024. find
  404. Jiakang Yuan, Xiangchao Yan, Botian Shi, Tao Chen, Wanli Ouyang, Bo Zhang, Lei Bai, Yu Qiao, and Bowen Zhou. Dolphin: Closed-loop open-ended auto-research through thinking, practice, and feedback. arXiv e-prints, pages arXiv–2501, 2025. find
  405. Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Ren Kan, Dongsheng Li, and Deqing Yang. Easytool: Enhancing llm-based agents with concise tool instruction. arXiv preprint arXiv:2401.06201, 2024. find
  406. Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. Self-rewarding language models, 2024. find
  407. Ling Yue, Nithin Somasekharan, Yadi Cao, and Shaowu Pan. Foam-agent: Towards automated intelligent cfd workflows. arXiv preprint arXiv:2505.04997, 2025. — [arXiv:2505.04997](https://arxiv.org/abs/2505.04997) find
  408. Fatemeh Pesaran Zadeh, Juyeon Kim, Jin-Hwa Kim, and Gunhee Kim. Text2chart31: Instruction tuning for chart generation with automatic feedback, 2025. URL https://arxiv.org/abs/2410.04064. ↗
  409. Sharaf Zaman, Michael J Smith, Pranav Khetarpal, Rishabh Chakrabarty, Michele Ginolfi, Marc Huertas-Company, Maja Jabłońska, Sandor Kruk, Matthieu Le Lain, Sergio José Rodríguez Méndez, et al. Astrollava: towards the unification of astronomical data and natural language. arXiv preprint arXiv:2504.08… — [arXiv:2504.08583](https://arxiv.org/abs/2504.08583) find
  410. Eric Zelikman, YH Wu, Jesse Mu, and Noah D Goodman. Star: Self-taught reasoner bootstrapping reasoning with reasoning. volume 1126, 2024. find
  411. Shenglai Zeng, Jiankun Zhang, Pengfei He, Yue Xing, Yiding Liu, Han Xu, Jie Ren, Shuaiqiang Wang, Dawei Yin, Yi Chang, et al. The good and the bad: Exploring privacy issues in retrieval-augmented generation (rag). arXiv preprint arXiv:2402.16893, 2024. find
  412. Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, et al. A generative model for inorganic materials design. Nature, 639(8055):624–632, 2025. find
  413. Baohua Zhang, Xin Li, Huangchao Xu, Zhong Jin, Quansheng Wu, and Ce Li. Topomas: Large language model driven topological materials multiagent system. arXiv preprint arXiv:2507.04053, 2025. — [arXiv:2507.04053](https://arxiv.org/abs/2507.04053) find
  414. Ceyao Zhang, Kaijie Yang, Siyi Hu, Zihao Wang, Guanghe Li, Yihang Sun, Cheng Zhang, Zhaowei Zhang, Anji Liu, Song-Chun Zhu, et al. Proagent: building proactive cooperative agents with large language models. volume 38, pages 17591–17599, 2024. find
  415. Dan Zhang, Ziniu Hu, Sining Zhoubian, Zhengxiao Du, Kaiyu Yang, Zihan Wang, Yisong Yue, Yuxiao Dong, and Jie Tang. Sciglm: Training scientific language models with self-reflective instruction annotation and tuning. arXiv preprint arXiv:2401.07950, 2024.
  416. Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, and Jie Tang. Rest-mcts*: Llm self-training via process reward guided tree search. 37:64735–64772, 2024.
  417. Di Zhang, Wei Liu, Qian Tan, Jingdan Chen, Hang Yan, Yuliang Yan, Jiatong Li, Weiran Huang, Xiangyu Yue, Wanli Ouyang, et al. Chemllm: A chemical large language model. arXiv preprint arXiv:2402.06852, 2024.
  418. Guorui Zhang, Chao Song, Liyuan Liu, Qiuyu Wang, and Chunquan Li. Transagent: Dynamizing transcriptional regulation analysis via multi-omics-aware ai agent. bioRxiv, pages 2025–04, 2025. find
  419. Haotian Zhang, Yu H Sun, Wenxing Hu, Xu Cui, Zhengyu Ouyang, Derrick Cheng, Xinmin Zhang, and Baohong Zhang. Compbioagent: An llm-powered agent for single-cell rna-seq data exploration. bioRxiv, pages 2025–03, 2025. find
  420. Haoxuan Zhang, Ruochi Li, Yang Zhang, Ting Xiao, Jiangping Chen, Junhua Ding, and Haihua Chen. The evolving role of large language models in scientific innovation: Evaluator, collaborator, and scientist. arXiv preprint arXiv:2507.11810, 2025. — [arXiv:2507.11810](https://arxiv.org/abs/2507.11810) find
  421. Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Jianquan Li, Guiming Chen, Xiangbo Wu, Zhiyi Zhang, Qingying Xiao, Xiang Wan, Benyou Wang, and Haizhou Li. Huatuogpt, towards taming language models to be a doctor. arXiv preprint arXiv:2305.15075, 2023. — [arXiv:2305.15075](https://arxiv.org/abs/2305.15075) find
  422. Huan Zhang, Yu Song, Ziyu Hou, Santiago Miret, and Bang Liu. HoneyComb: A flexible LLM-based agent system for materials science. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3369–3382, Miami, Florida, USA,… find
  423. Huan Zhang, Yu Song, Ziyu Hou, Santiago Miret, and Bang Liu. Honeycomb: A flexible llm-based agent system for materials science. arXiv preprint arXiv:2409.00135, 2024. find
  424. Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Xinyu Zhang, Fangzhi Xu, Qika Lin, Rui Mao, Erik Cambria, and Jun Liu. Maps: A multi-agent framework based on big seven personality and socratic guidance for multimodal scientific problem solving. arXiv preprint arXiv:2503.16905, 2025. — [arXiv:2503.16905](https://arxiv.org/abs/2503.16905) find
  425. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xiong-Hui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bang Liu, Yuyu Luo, and Chenglin Wu. AFlow: Automating agentic workflow generation. 2025.
  426. Jintian Zhang et al. Exploring collaboration mechanisms for LLM agents: A social psychology view. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, Aug. 2024.
  427. Qiang Zhang, Keyan Ding, Tianwen Lv, Xinda Wang, Qingyu Yin, Yiwen Zhang, Jing Yu, Yuhao Wang, Xiaotong Li, Zhuoyi Xiang, et al. Scientific large language models: A survey on biological & chemical domains. ACM Computing Surveys, 57(6):1–38, 2025.
  428. Xiang Zhang, Juntai Cao, Jiaqi Wei, Chenyu You, and Dujian Ding. Why prompt design matters and works: A complexity analysis of prompt search space in llms. arXiv preprint arXiv:2503.10084, 2025. find
  429. Xiang Zhang, Tianze Ling, Zhi Jin, Sheng Xu, Zhiqiang Gao, Boyan Sun, Zijie Qiu, Jiaqi Wei, Nanqing Dong, Guangshuai Wang, et al. π-primenovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing. Nature Communications, 16(1):267, 2025. find
  430. Xiang Zhang, Jiaqi Wei, Zijie Qiu, Sheng Xu, Nanqing Dong, Zhiqiang Gao, and Siqi Sun. Curriculum learning for biological sequence prediction: The case of de novo peptide sequencing. arXiv preprint arXiv:2506.13485, 2025. — [arXiv:2506.13485](https://arxiv.org/abs/2506.13485) find
  431. Xiang Zhang, Jiaqi Wei, Zijie Qiu, Sheng Xu, Zhi Jin, ZhiQiang Gao, Nanqing Dong, and Siqi Sun. Bidirectional representations augmented autoregressive biological sequence generation: Application in de novo peptide sequencing. arXiv preprint arXiv:2510.08169, 2025. — [arXiv:2510.08169](https://arxiv.org/abs/2510.08169) find
  432. Xiang Zhang, Jiaqi Wei, Zijie Qiu, Sheng Xu, Zhi Jin, ZhiQiang Gao, Nanqing Dong, and Siqi Sun. Bidirectional representations augmented autoregressive biological sequence generation:application in de novo peptide sequencing, 2025. URL https://arxiv.org/abs/2510.08169. — [arXiv:2510.08169](https://arxiv.org/abs/2510.08169) ↗
  433. Xiaowen Zhang, Zhenyu Bi, Xuan Wang, Tiziana Di Matteo, and Rupert AC Croft. Bridging literature and the universe via a multi-agent large language model system. arXiv preprint arXiv:2507.08958, 2025. — [arXiv:2507.08958](https://arxiv.org/abs/2507.08958) find
  434. Yuan-Hang Zhang and Massimiliano Di Ventra. Transformer quantum state: A multipurpose model for quantum many-body problems. Physical Review B, 107(7):075147, 2023. find
  435. Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. Siren’s song in the ai ocean: A survey on hallucination in large language models. Computational Linguistics, pages 1–45, 2025. — Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
  436. Zhengde Zhang, Yiyu Zhang, Haodong Yao, Jianwen Luo, Rui Zhao, Bo Huang, Jiameng Zhao, Yipu Liao, Ke Li, Lina Zhao, et al. Xiwu: A basis flexible and learnable llm for high energy physics. arXiv preprint arXiv:2404.08001, 2024. — [arXiv:2404.08001](https://arxiv.org/abs/2404.08001) find
  437. Zhongyue Zhang, Zijie Qiu, Yingcheng Wu, Shuya Li, Dingyan Wang, Zhuomin Zhou, Duo An, Yuhan Chen, Yu Li, Yongbo Wang, et al. Origene: A self-evolving virtual disease biologist automating therapeutic target discovery. bioRxiv, pages 2025–06, 2025. find
  438. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Llm agents are experiential learners. pages 19632–19642, 2024. find
  439. Fuyong Zhao, Yuyang Li, Yanhao Wang, Hui Li, Mei Chen, Panfeng Chen, Ningchen Sun, Cunshi Wang, and Jifeng Liu. Pulsar candidate classification with multimodal large language models. In Neurips 2024 Workshop Foundation Models for Science: Progress, Opportunities, and Challenges, 2024. URL https://op… find
  440. Zihan Zhao, Da Ma, Lu Chen, Liangtai Sun, Zihao Li, Yi Xia, Bo Chen, Hongshen Xu, Zichen Zhu, Su Zhu, et al. Chemdfm: a large language foundation model for chemistry. arXiv preprint arXiv:2401.14818, 2024. — [arXiv:2401.14818](https://arxiv.org/abs/2401.14818) find
  441. Tianshi Zheng, Zheye Deng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Zihao Wang, and Yangqiu Song. From automation to autonomy: A survey on large language models in scientific discovery. arXiv preprint arXiv:2505.13259, 2025.
  442. Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. Self-evolution learning for discriminative language model pretraining. pages 4130–4145, 2023. find
  443. Tianyang Zhong, Zhengliang Liu, Yi Pan, Yutong Zhang, Yifan Zhou, Shizhe Liang, Zihao Wu, Yanjun Lyu, Peng Shu, Xiaowei Yu, et al. Evaluation of openai o1: Opportunities and challenges of agi. arXiv preprint arXiv:2409.18486, 2024.
  444. Lianhao Zhou, Hongyi Ling, Keqiang Yan, Kaiji Zhao, Xiaoning Qian, Raymundo Arróyave, Xiaofeng Qian, and Shuiwang Ji. Toward greater autonomy in materials discovery agents: Unifying planning, physics, and scientists. arXiv preprint arXiv:2506.05616, 2025. — [arXiv:2506.05616](https://arxiv.org/abs/2506.05616) find
  445. Wangchunshu Zhou, Yixin Ou, Shengwei Ding, Long Li, Jialong Wu, Tiannan Wang, Jiamin Chen, Shuai Wang, Xiaohua Xu, Ningyu Zhang, et al. Symbolic learning enables self-evolving agents. arXiv preprint arXiv:2406.18532, 2024.
  446. Xibin Zhou, Chenchen Han, Yingqi Zhang, Jin Su, Kai Zhuang, Shiyu Jiang, Zichen Yuan, Wei Zheng, Fengyuan Dai, Yuyang Zhou, et al. Decoding the molecular language of proteins with evolla. bioRxiv, pages 2025–01, 2025. find
  447. Yuhao Zhou, Yiheng Wang, Xuming He, Ruoyao Xiao, Zhiwei Li, Qiantai Feng, Zijie Guo, Yuejin Yang, Hao Wu, Wenxuan Huang, et al. Scientists’ first exam: Probing cognitive abilities of mllm via perception, understanding, and reasoning. arXiv preprint arXiv:2506.10521, 2025. — [arXiv:2506.10521](https://arxiv.org/abs/2506.10521) find
  448. Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, et al. From hypothesis to publication: A comprehensive survey of ai-driven research support systems. arXiv preprint arXiv:2503.01424, 2025. find
  449. Zhenhong Zhou, Zherui Li, Jie Zhang, Yuanhe Zhang, Kun Wang, Yang Liu, and Qing Guo. Corba: Contagious recursive blocking attacks on multi-agent systems based on large language models. arXiv preprint arXiv:2502.14529, 2025.
  450. Max Zhu, Adrián Bazaga, and Pietro Liò. Fluid-llm: Learning computational fluid dynamics with spatiotemporal-aware large language models. arXiv preprint arXiv:2406.04501, 2024. — [arXiv:2406.04501](https://arxiv.org/abs/2406.04501) find
  451. Pengyu Zhu, Zhenhong Zhou, Yuanhe Zhang, Shilinlu Yan, Kun Wang, and Sen Su. Demonagent: Dynamically encrypted multi-backdoor implantation attack on llm-based agent. arXiv preprint arXiv:2502.12575, 2025. find
  452. Yinghao Zhu, Yifan Qi, Zixiang Wang, Lei Gu, Dehao Sui, Haoran Hu, Xichen Zhang, Ziyi He, Liantao Ma, and Lequan Yu. Healthflow: A self-evolving ai agent with meta planning for autonomous healthcare research. arXiv preprint arXiv:2508.02621, 2025. — [arXiv:2508.02621](https://arxiv.org/abs/2508.02621) find
  453. Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Ningyu Zhang, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, and Huajun Chen. Knowagent: Knowledge-augmented planning for llm-based agents. arXiv preprint arXiv:2403.03101, 2024. find
  454. Xiang Zhuang, Keyan Ding, Tianwen Lyu, Yinuo Jiang, Xiaotong Li, Zhuoyi Xiang, Zeyuan Wang, Ming Qin, Kehua Feng, Jike Wang, et al. Advancing biomolecular understanding and design following human instructions. Nature Machine Intelligence, pages 1–14, 2025. find
  455. Yunheng Zou, Austin H. Cheng, Abdulrahman Aldossary, Jiaru Bai, Shi Xuan Leong, Jorge Arturo Campos-Gonzalez-Angulo, Changhyeok Choi, Cher Tian Ser, Gary Tom, Andrew Wang, Zijian Zhang, Ilya Yakavets, Han Hao, Chris Crebolder, Varinia Bernales, and Alán Aspuru-Guzik. El Agente: An Autonomous Agent f… — [doi:10.48550/arxiv.2505.02484](https://doi.org/10.48550/arxiv.2505.02484) find

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