Large language models (LLMs) have shown promise in constructing mechanistic models from data. However, existing evaluations largely focus on simplified settings and fail to capture the complexity of real-world scientific modeling. In practice, such modeling often involves neural-integrated formulations, where a mechanistic model component and a neural network component are jointly constructed, leading to a significantly more complex search space. Motivated by this gap, we introduce the Neural-Integrated Mechanistic Modeling (NIMM) benchmark, which evaluates LLM-generated neural-integrated mechanistic models across three scientific domains. Experiments on NIMM reveal that existing LLM-based approaches struggle to effectively explore this complex space, resulting in limited search stability and solution quality. To address this challenge, we propose NIMMGen, a tree-guided agentic framework that enables diversified exploration via branch-level search and improves solutions through atomic model refinement. Extensive experiments demonstrate that NIMMGen achieves state-of-the-art performance on NIMM, significantly improving search stability and solution quality.
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
Weizmann Institute of Science（魏茨曼科学研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Haifa（海法大学）；Jonkoping University（延雪平大学）
Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs
Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；Microsoft Research（微软研究院）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Shanghai AI Laboratory（上海人工智能实验室）；Xiaohongshu Inc.（小红书公司）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Alibaba Group（阿里巴巴集团）
Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance
Aalto University（阿尔托大学）；Tencent（腾讯）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Hong Kong Polytechnic University（香港理工大学）；Aarhus University（奥胡斯大学）；Technical University of Munich（慕尼黑工业大学）
CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations
College of Intelligence and Computing, Tianjin University（天津大学智能计算学院）；Tianjin Key Laboratory of Healthy Habitat and Smart Technology（天津健康人居环境与智能技术重点实验室）；Laboratory of Computation and Analytics of Complex Management Systems, Tianjin University（复杂管理系统计算与分析实验室）
Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Duke-NUS Medical School, National University of Singapore, Singapore（新加坡国立大学杜克-新加坡医学部）
How Do Agentic LLMs Decide to Call Tools? A Tool-Call Vector Shaped by Suppression
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Electronic Science and Technology of China（电子科技大学）；Nanjing University（南京大学）；Griffith University（格里菲斯大学）；University of New South Wales（新南威尔士大学）
Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models
Ant Group（蚂蚁集团）；Alibaba Group（阿里巴巴集团）；JD Group（京东集团）；Meituan（美团）；Southwestern University of Finance and Economics（西南财经大学）；Simon Fraser University（西蒙菲莎大学）
A Probabilistic Framework for LLM-Based Model Discovery
Machine Learning in Science, University of Tübingen, Tübingen, Germany（图宾根大学机器学习科学系，图宾根，德国）；Tübingen AI Center, Tübingen, Germany（图宾根人工智能中心，图宾根，德国）；Department Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany（经验推断部门，智能系统马克斯·普朗克研究所，图宾根，德国）
