Test-time scaling (TTS) has become an effective approach for improving large language model performance by allocating additional computation during inference. However, existing TTS strategies are largely hand-crafted: researchers manually design reasoning patterns and tune heuristics by intuition, leaving much of the computation-allocation space unexplored. We propose an environment-driven framework, AutoTTS, that changes what researchers design: from individual TTS heuristics to environments where TTS strategies can be discovered automatically. The key to AutoTTS lies in environment construction: the discovery environment must make the control space tractable and provide cheap, frequent feedback for TTS search. As a concrete instantiation, we formulate width--depth TTS as controller synthesis over pre-collected reasoning trajectories and probe signals, where controllers decide when to branch, continue, probe, prune, or stop and can be evaluated cheaply without repeated LLM calls. We further introduce beta parameterization to make the search tractable and fine-grained execution trace feedback to improve discovery efficiency by helping the agent diagnose why a TTS program fails. Experiments on mathematical reasoning benchmarks show that the discovered strategies improve the overall accuracy--cost tradeoff over strong manually designed baselines. The discovered strategies generalize to held-out benchmarks and model scales, while the entire discovery costs only $39.9 and 160 minutes. Our data, and code will be open-source at https://github.com/zhengkid/AutoTTS.
System of Agentic AI for the Discovery of Metal-Organic Frameworks
University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（伯克利国家实验室）；Google DeepMind（谷歌DeepMind）；Kavli Energy NanoScience Institute（Kavli能源纳米科学研究所）
CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery
Hong Kong Polytechnic University（香港理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Swiss Federal Technology Institute of Lausanne (EPFL)（洛桑联邦理工学院）；Hong Kong University of Science and Technology (HKUST)（香港科学大学）
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）
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning
State Key Laboratory of Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen University（厦门大学化学化工学院固体表面物理化学国家重点实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）
LLM2TEA: An Agentic AI Designer for Discovery with Generative Evolutionary Multitasking
1 College of Computing \& Data Science (CCDS), Nanyang Technological University (NTU), Singapore 2 Honda Research Institute Europe (HRI-EU), Offenbach am Main, Germany 3 Centre for Frontier AI Research (CFAR), Agency for Science, Technology
LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios
Beijing Jiaotong University（北京交通大学）；Lancaster University（兰卡斯特大学）；Max Planck Institute for Informatics, Saarland Informatics Campus（马克斯·普朗克研究所（信息学）萨尔兰州信息学校区）；University of Electronic Science and Technology of China（电子科技大学）
Self-Improvements in Modern Agentic Systems: A Survey
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；University of Alberta（阿尔伯塔大学）；The Swiss AI Lab IDSIA/USI/SUPSI（瑞士人工智能实验室IDSIA/USI/SUPSI）
RSIPO: Recursive Self-Improving Policy Optimization for Agentic Reinforcement Learning via Skill-Policy Co-Evolution
THU（清华大学）；NUS（新加坡国立大学）；Tongji University（同济大学）；UW-Madison（威斯康星大学麦迪逊分校）；Westlake University（西湖大学）；CUHK（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；PKU（北京大学）
Enhancing Agentic Autonomous Scientific Discovery with Vision-Language Model Capabilities
Department of Physics, University of Cambridge, Cambridge, United Kingdom（剑桥大学物理系）；Kavli Institute for Cosmology, University of Cambridge, Cambridge, United Kingdom（剑桥大学卡弗利天文研究所）；Department of Physics and Astronomy, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041, USA（哈弗福德学院物理与天文学系）；Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA（加州理工学院物理、数学与天文学系）；Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom（剑桥大学天文研究所）
Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence
Laboratory for Atomistic and Molecular Mechanics（原子分子力学实验室）；Department of Biological Engineering（生物工程系）；Massachusetts Institute of Technology（麻省理工学院）；Department of Civil and Environmental Engineering（土木与环境工程系）；Department of Mechanical Engineering（机械工程系）；Center for Computational Science and Engineering（计算科学与工程中心）；Schwarzman College of Computing（施瓦茨曼计算学院）
