arXiv:2605. 14791v2 Announce Type: replace-cross Abstract: Recent advances in artificial intelligence (AI) agents are pushing AI beyond tools toward autonomous scientific discovery.
By Licong Xu, Thomas Borrett
arXiv:2512. 03476v3 Announce Type: replace-cross Abstract: Progress in computational science depends on complex numerical workflows that must faithfully encode physical laws, yet translating conceptual insight into reliable code remains a major bottleneck.
By Juan Diego Toscano, Daniel T. Chen, George Em Karniadakis
The article introduces AgentActionBench, a benchmark designed to evaluate agent-based experiment reproduction across machine learning and AI4Science papers. It employs an MCP-based Action Recorder to capture agents’ behavior during reproduction and assesses the resulting traces against paper-specific rubrics. The benchmark includes 150 papers, with a human-annotated subset and model-assisted augmentation expanding it to over 10,000 rubric items, revealing that current systems face execution bottlenecks but that model-generated rubrics correlate strongly with human judgments.
By Hanhua Hong, Yizhi Li, Luu Gia Huy, Jian Yang, Ming Zhou, Chenghua Lin
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
SAIL is an open model with 35 B total and 3 B active parameters designed for literature research, scientific coding, and multi‑step research workflows. It is developed through a science‑aware improvement loop in which agents built on frontier AI models analyze task failures, create training tasks to address capability gaps, and iterate this process across multiple development cycles. The training pipeline combines supervised fine‑tuning, specialist training, multi‑teacher on‑policy distillation, and agentic reinforcement learning, enabling SAIL to achieve competitive performance on scientific research tasks with fewer parameters than leading open‑weight models.
By SAIL Model Team, Boyuan Sun, Bryan Dai, Che Liu, Chi Liu, Derek Li, Hongming Piao, Mengzhuo Chen, Xidong Wang, Yan Shu, Yinda Chen, Ziyang Zeng
arXiv:2512. 19799v2 Announce Type: replace Abstract: Advances in LLM reasoning and tool use have enabled agentic science, yet frontier theoretical and computational physics remains challenging because research requires deep domain expertise, long-horizon reasoning, and reliable numerical computation.
By Tingjia Miao, Wenkai Jin, Jinxin Tan, Muhua Zhang, Xianghe Pang, Zexi Liu, Yuwen Du, Tian Jin, Tu Guo, Zhengliang Zhang, Jingkun Liu, Yuelin Hu, Jiejun Zhang, Yunjie Huang, Yuhan Wang, Wenbo Li, Yinuo Gao, Shuo Chen, Rui Ye, Yuzhi Zhang, Linfeng Zhang, Kun Chen, Wei Wang, Weinan E, Siheng Chen