Human intelligence emerged through the process of natural selection and evolution on Earth. We investigate what it would take to re-create this process in silico. While past work has often focused on low-level processes (such as simulating physics or chemistry), we instead take a more targeted approach, aiming to evolve agents that can accumulate open-ended culture and technologies across generations. Towards this, we present JaxLife: an artificial life simulator in which embodied agents, parameterized by deep neural networks, must learn to survive in an expressive world containing programmable systems. First, we describe the environment and show that it can facilitate meaningful Turing-complete computation. We then analyze the evolved emergent agents' behavior, such as rudimentary communication protocols, agriculture, and tool use. Finally, we investigate how complexity scales with the amount of compute used. We believe JaxLife takes a step towards studying evolved behavior in more open-ended simulations. Our code is available at https://github.com/luchris429/JaxLife
CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Zoom Video Communications（Zoom视频通信公司）；University of Central Florida（中佛罗里达大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
Pessimistic Verification for Open Ended Math Questions
Qiuzhen College, Tsinghua University, Beijing, China；Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China；Dept. of Comp. Sci \& Tech., Institute for AI, Tsinghua University, Beijing, China
A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications
The Pennsylvania State University（宾夕法尼亚州立大学）；The University of Utah（犹他大学）；Amazon（亚马逊）；Microsoft（微软）；IBM T.J. Watson Research Center（IBM T.J.沃森研究中心）；Michigan State University（密歇根州立大学）
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；DeepWisdom；Singapore University of Technology and Design（新加坡科技设计大学）；Nanyang Technological University（南洋理工大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；Université de Montréal & Mila（蒙特利尔大学及Mila）
APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins
University Côte d’Azur（蔚蓝海岸大学）；Cancer Research Center of Marseille（马赛癌症研究中心）；Institut Paoli-Calmettes（保利-卡尔梅特研究所）；Mines Paris（巴黎矿业学院）；PSL University（巴黎文理研究大学）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；Institut Galien Paris-Saclay（巴黎萨克雷加利安研究所）；University of Cambridge（剑桥大学）
An Evolutionary Agentic Approach for Open-ended Image Quality Perception
New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；The Hong Kong University of Science and Technology（香港科技大学）；Transsion（传音）
Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution
Princeton University（普林斯顿大学）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；University of Michigan（密歇根大学）；Tianqiao and Chrissy Chen Institute（天桥和克里斯·陈研究所）；The Chinese University of Hong Kong（香港中文大学）
OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
Shanghai AI Laboratory（上海人工智能实验室）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Shanghai Jiaotong University（上海交通大学）；Tsinghua University（清华大学）
Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications
Distributed Systems (qCLOUDS) Lab, School of Computing；Information Systems, The University of Melbourne, Australia；2Department of Computer Science；Engineering, School of Electrical；Computer Sciences (SECS), Indian Institute of Technology Bhubaneswar, India；3Department of Computer Science Banaras Hindu University, Varanasi, India
Artificial Intelligence Graduate School, Ulsan National Institute of Science and Engineering（人工智能研究生院，乌山科学与工程研究院）；Department of Computer Science and Engineering, Ulsan National Institute of Science and Engineering（计算机科学与工程系，乌山科学与工程研究院）
An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
Department of Statistics, Virginia Tech（统计学系，弗吉尼亚理工学院）；Department of Statistical Science, Baylor University（统计科学系，贝勒大学）；Advanced Research Computing, Virginia Tech（高级研究计算，弗吉尼亚理工学院）
