arXiv:2606. 29182v1 Announce Type: new Abstract: Open-ended scientific discovery with large language models (LLMs) increasingly operates as a long-horizon loop of hypothesis search and verification, where a reward signal guides which hypotheses to test next.
By Dhruv Agarwal, Reece Adamson, Andrew McCallum, Peter Clark, Ashish Sabharwal, Bodhisattwa Prasad Majumder
arXiv:2602. 06448v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based scientific agents have accelerated scientific discovery, yet they often suffer from significant inefficiencies due to adherence to fixed initial priors.
By Yingming Pu, Tao Lin, Hongyu Chen
arXiv:2607. 03426v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions required in sequential decision-making settings.
By Jakob Hartmann, James Harvey, Jhonathan Navott, Erik Y. Wang, Luckeciano C. Melo, Flaviu Cipcigan, Cheng Zhang, Alessandro Abate
arXiv:2608. 15669v1 Announce Type: new Abstract: Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs.
By Zhongwei Yu, Yan Song, Xue Yan, Anjie Liu, Xingyu Lu, Yihang Chen, Huichi Zhou, Siyuan Guo, Luoyang Sun, Sihan Chen, Xiangning Yu, Jun Wang
arXiv:2608. 07437v1 Announce Type: new Abstract: Reliable hypothesis testing is the foundation of many empirical scientific claims.
By Jiacheng Miao, Jin Mu, Guanhua Chen, James Zou
arXiv:2606. 11851v1 Announce Type: new Abstract: Open-ended scientific discovery asks agents to move beyond executing analyses for predefined questions.
By Jiayao Chen, Shi Liu, Linyi Yang