Economic decision-making depends not only on structured signals such as prices and taxes, but also on unstructured language, including peer dialogue and media narratives. While multi-agent reinforcement learning (MARL) has shown promise in optimizing economic decisions, it struggles with the semantic ambiguity and contextual richness of language. We propose LAMP (Language-Augmented Multi-Agent Policy), a framework that integrates language into economic decision-making and narrows the gap to real-world settings. LAMP follows a Think-Speak-Decide pipeline: (1) Think interprets numerical observations to extract short-term shocks and long-term trends, caching high-value reasoning trajectories; (2) Speak crafts and exchanges strategic messages based on reasoning, updating beliefs by parsing peer communications; and (3) Decide fuses numerical data, reasoning, and reflections into a MARL policy to optimize language-augmented decision-making. Experiments in economic simulation show that LAMP outperforms both MARL and LLM-only baselines in cumulative return (+63.5%, +34.0%), robustness (+18.8%, +59.4%), and interpretability. These results demonstrate the potential of language-augmented policies to deliver more effective and robust economic strategies.
Stephanie2: Thinking, Waiting, and Making Decisions Like Humans in Step-by-Step AI Social Chat
Yiwu Research Institute of Fudan University（复旦大学义乌研究院）；FaceMind Corporation（FaceMind公司）；Fudan University（复旦大学）；Duke Kunshan University（昆山杜克大学）；The Chinese University of Hong Kong（香港中文大学）；Tongji University（同济大学）
Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing
Nanjing University（南京大学）；Shanghai Jiao Tong University（上海交通大学）；University of California, San Diego（加州大学圣地亚哥分校）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Information Engineering, Beijing Institute of Graphic Communication（北京印刷学院信息工程学院）；Ant International, Ant Group（蚂蚁集团国际部）；Independent Researcher（独立研究者）
Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Pengcheng Laboratory（鹏城实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Think Again or Think Longer? Selective Verification for Budget-Aware Reasoning
Department of Computer Science, Virginia Tech（弗吉尼亚理工大学计算机科学系）；Fralin Biomedical Research Institute, Virginia Tech（弗吉尼亚理工大学弗拉林生物医学研究所）；FBRI Cancer Research Center（FBRI癌症研究中心）
Explore Briefly, Then Decide: Mitigating LLM Overthinking via Cumulative Entropy Regulation
Tongji University（同济大学）；Hong Kong Polytechnic University（香港理工大学）；Shanghai University of Electric and Power（上海电力大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy（广东省人工智能与数字经济实验室）；University of Electronic Science and Technology of China（电子科技大学）
