World Models for Policy Refinement in StarCraft II
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（认知与决策智能复杂系统重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）
Large Language Models (LLMs) have recently shown strong reasoning capabilities, motivating their use in complex decision-making environments. StarCraft II (SC2), with its massive state-action space and partial observability, is a challenging testbed. However, existing LLM-based SC2 agents primarily focus on improving the policy itself, leaving the integration of a learnable, action-conditioned dynamics model into the decision loop largely unexplored. In this work, we propose StarWM and StarWM-Agent, and conduct the first systematic study of the learnability and decision utility of player-view, action-conditioned textual world models for SC2. StarWM predicts short-horizon future observations under partial observability. StarWM-Agent integrates StarWM into a lightweight Generate-Simulate-Refine loop for foresight-driven policy refinement. Extensive experiments show that StarWM substantially outperforms zero-shot baselines across multiple dimensions, while StarWM-Agent achieves consistent win-rate gains of 30%, 15%, and 30% against the SC2 built-in AI at Hard (LV5), Harder (LV6), and VeryHard (LV7), respectively. Additional analyses show that our method is complementary to existing history-summarization SC2-agent approaches, with their combination achieving the strongest online performance in our evaluation.
Adaptive Command: Real-Time Policy Adjustment via Language Models in StarCraft II
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；AI Centre, Department of Computer Science, UCL（UCL计算机科学系人工智能中心）；Nanjing Artificial Intelligence Research of IA, China（南京人工智能研究院）
TacticCraft: Natural Language-Driven Tactical Adaptation for StarCraft II
Institute of Automation, Chinese Academy of Sciences, China（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences, China（中国科学院大学人工智能学院）；Tencent AI Lab, Shenzhen, China（腾讯AI实验室）；Nanjing Artificial Intelligence Research of IA, China（南京人工智能研究院）
Direct Alignment of Language Models via Quality-Aware Self-Refinement
Hong Kong University of Science and Technology（香港科技大学）；LightSpeed Studios, Tencent（腾讯光速工作室）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, CAS（中国科学院香港创新研究院人工智能与机器人中心）
Options, Not Clicks: Lattice Refinement for Consent-Driven MCP Authorization
University of California, Los Angeles（加州大学洛杉矶分校）；University of California, San Diego（加州大学圣地亚哥分校）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of California, Santa Barbara（加州大学圣芭芭拉分校）
OPDSearch+: On-Policy Distillation with RL Refinement for Search-Augmented Reasoning
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Macau（澳门大学）；Wuhan University（武汉大学）；Georgia Institute of Technology（佐治亚理工学院）；Northwestern Polytechnical University（西北工业大学）；University of Hong Kong（香港大学）
$K$-Level Policy Gradients for Multi-Agent Reinforcement Learning
Department of Computer Science, TU Darmstadt（图恩-达姆施塔特技术大学计算机科学系）；Hessian Center for Artificial Intelligence (Hessian.ai)（黑森人工智能中心）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Center for Cognitive Science, TU Darmstadt（图恩-达姆施塔特技术大学认知科学中心）；Center for Artificial Intelligence and Data Science, University of Würzburg（弗赖堡大学人工智能与数据科学中心）
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
University of Oxford（牛津大学）；Huawei R&D UK（华为英国研发中心）；ShanghaiTech University（上海科技大学）；Shanghai Jiao Tong University（上海交通大学）；University College London（伦敦大学学院）；Institute for AI, Peking University（北京大学人工智能研究院）；BIGAI（北京通用人工智能研究院）
FP3O: Enabling Proximal Policy Optimization in Multi-Agent Cooperation with Parameter-Sharing Versatility
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；The State Key Lab of Brain-Machine Intelligence, Zhejiang University（浙江大学脑机智能国家重点实验室）
Settling the Variance of Multi-Agent Policy Gradients
Imperial College London（帝国理工学院）；Huawei R&D UK（华为英国研发中心）；Shanghai Jiao Tong University（上海交通大学）；Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）；University College London（伦敦大学学院）；Institute for AI, Peking University（北京大学人工智能研究院）
World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulation
The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Zhongguancun Academy（中关村学院）；Beijing Zhongke Huiling Robot Technology Co（北京中科创联机器人技术有限公司）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）
