The paper investigates how AI agents can discover and validate scientific formulas, focusing on the hydrotope—a geometric formula that unifies polynomial pieces of nonlinear surface-wave scattering. Through controlled experiments, the authors compare human–agent collaboration, single-prompt rediscovery, and various guided approaches, finding that only a PI$+$two-student workflow reliably reconstructs the full hydrotope and even uncovers a new analytic expression for the six-point amplitude $A_6$. The study highlights the challenges of combining exponentially many terms and the importance of coordinated analytic and numerical tasks for successful formula discovery.
By Zihan Zhou, Digvijay Wadekar, Matias Zaldarriaga
EPOCH is an evidence‑governed architecture for AI research agents that improves how candidate discoveries are evaluated and validated. By combining explicit task contracts, typed memory, active falsification, admission checks, and independent replay, EPOCH ensures that each candidate is judged against the strength and scope of its claim. The system achieves state‑of‑the‑art performance on several benchmarks, including AlgoTune, Math14, and AgentHPO, and demonstrates significant advances in executable constructions, algorithm optimization, counterexamples, and proof‑supported results across ten discovery problems.
By Binjie Guo, Aisheng Mo, Ruitong Li, Xinle Deng
arXiv:2607. 06820v1 Announce Type: new Abstract: Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS) in agentic LLM workflows underexplored.
By Pavel Snopov, German Magai
arXiv:2608. 04872v1 Announce Type: cross Abstract: Symbolic regression aims to discover closed-form equations from data, but existing LLM-guided methods often rely on a unified proposal loop that compresses heterogeneous search failures into a scalar score and a single prompt.
By Wenxiao Zhao, Dong Liu, Kaiyi Xu, Feng Liu, Zhen Zhao, Fei Ben, Shu Wang, Wenhao Li, Yingnian Wu, Fenghua Ling, Haobo Li, Lei Bai
arXiv:2602. 02905v2 Announce Type: replace Abstract: Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery remains a central challenge.
By Zhen Wang, Fan Bai, Zhongyan Luo, Jinyan Su, Kaiser Sun, Xinle Yu, Jieyuan Liu, Kun Zhou, Claire Cardie, Mark Dredze, Zhiting Hu, Eric P. Xing
ASI‑Bench is a new benchmark that evaluates AI systems on their ability to conduct innovative exploration and autonomous scientific research across 11 domains, using 60 project‑level tasks. It progressively removes human methodological guidance to test whether AI can independently select methods, execute research, and produce verifiable results. Results from 18 state‑of‑the‑art agent–model configurations show a sharp performance drop when guidance is reduced, indicating current systems still rely heavily on human input.
By Junwei Zhou, Zhen Sun, Binyu Li, Jiangyu Zhou, Yuexi Pan, Hengyu Wang, Honghe Ren, Xiaohan Jia, Xueyang Zhou, Xiaoyu Cao, Yongchao Chen, Yuanning Feng, Junhao Wu, Cheng Zhang, Sijia Chen, Haoyu Xue, Chengsong You, Huan Wang, Koutian Wu, Peigan Gao, Jiakun Wu, Wenzhe Li, Ergan Shang, Qingyuan Zheng, Jingjing Zhou, Ruixuan Jia, Yan Xu, Hongrui Zhang, Xiao-Han Ma, Zhengxiang Cheng, Yuexing Hao, Liting Mai, Xianglin Ji, Wenjun Zhang, Zhuofan Chen, Yixiao Huang, Chi Wang, Wenyue Hua, Yilun Hao, Yuantao Zhai, Ziyan Zhao, Jingyan Xie
EurekaBench is a cross‑domain benchmark designed to assess AI agents’ capacity to discover new scientific insights. It comprises 26 long‑horizon tasks spanning fields such as neuroscience, chemistry, and astrophysics, with 306 expected insights that mechanisms should support. The evaluation measures adherence to scientific constraints, predictive accuracy, and the ability to generate insights or guide future research, revealing that current AI agents excel at prediction but lag in producing meaningful scientific insights.
By Jiayi Geng, Zhengxuan Wu, Kevin S. Chen, Seungone Kim, Joseph Janssen, Zora Zhiruo Wang, Bhupalee Kalita, Runtian Gao, Aaron Ho, Andrew Oakleigh Nelson, Olexandr Isayev, Francisco Villaescusa-Navarro, Ching-Yao Lai, Howard Chen, Graham Neubig
arXiv:2606. 09276v1 Announce Type: new Abstract: Equation discovery aims to automate the discovery of scientific models in the form of mathematical equations from data.
By Paul Kahlmeyer, Henrik Voigt, Michael Habeck, Joachim Giesen
OpenDiscoveryTrace is a public dataset of 558 complete AI scientific agent trajectories that records the reasoning process—thoughts, tool calls, observations, errors, revision triggers, and confidence—across 124 scientific tasks in drug discovery, materials science, genomics, and literature analysis. The dataset includes seven models (three frontier models and four open‑weight models) and 60 live‑retrieval variants, providing a balanced view of performance and error patterns. Pilot analysis shows that process traces reveal behavioral differences invisible to output‑only evaluation, such as differing error rates and types among frontier models.
By Aayam Bansal, Keertan Balaji
arXiv:2607. 14582v1 Announce Type: new Abstract: Existing LLM-based theorem provers have achieved impressive results on formal mathematics benchmarks, yet they remain confined to acting as autonomous agents that prove a stated proposition.
By Junjie Zhang, Jiayu Liu, Wenbin Liu, Zhenya Huang, Doudou Wang, Yan Jiang, Leiye Xu, Tao Xiong, Wen Huang, Qi Liu, Guoping Hu, Enhong Chen, Mengping Zhang, Xiangdong Ye
The paper introduces a framework for evaluating large language model agents by attempting to end‑to‑end reproduce published astronomy studies, separating execution from verification and distinguishing computational failures from methodological ambiguities. Applying this to fourteen papers—one from The Astrophysical Journal and thirteen from Nature—revealed that eleven contained ambiguities that prevented a uniquely specified reproduction path. In a controlled case study, twelve different analysis paths produced distance estimates ranging from 2.16 to 3.53 kpc, with only one matching the published value of ~2.70 kpc, demonstrating that matching outcomes does not guarantee that the agent has reconstructed the underlying reasoning.
whyItMatters":"The study shows that end‑to‑end reproduction can expose gaps in implicit scientific knowledge within AI systems, highlighting the need for better integration of causal relevance in LLM agents."
By Yuehui Wang, Xinyu Qi, Guirong Xue, Cheng Wang, Yangbin Xie, Xiaoyu Tang, Cong Sun
arXiv:2608. 11341v1 Announce Type: new Abstract: Apollo did not reach the Moon merely because its engineers could solve difficult equations.
By Brian Wang, Bin Feng, Xiaoman Pan, Chenyang An, Felix Liu, Tangqi Fang, Gongbo Sun, Lingfeng Shen, Ning Wang, Handuo Zhang, Feng Chen, Fuchao Yang, Xiang Wang, Jiacheng Lin, Siting Li, Zixuan Liu, Chi Han, Zhenhailong Wang, Kunlun Zhu, Lawrence Zhao, Yueqi Guo, Kailong Wen, Feng Xing, Yiling Guo, Lidong Bing, David Tan, Bo An, Heng Ji, Sheng Wang