Large Language Models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making, drawing upon their extensive world knowledge and proficiency in language-related tasks. LLMs thus hold tremendous potential for natural language interaction within multi-agent systems to foster cooperation. However, LLM agents tend to over-report and comply with any instruction, which may result in information redundancy and confusion in multi-agent cooperation. Inspired by human organizations, this paper introduces a framework that imposes prompt-based organization structures on LLM agents to mitigate these problems. Through a series of experiments with embodied LLM agents and human-agent collaboration, our results highlight the impact of designated leadership on team efficiency, shedding light on the leadership qualities displayed by LLM agents and their spontaneous cooperative behaviors. Further, we harness the potential of LLMs to propose enhanced organizational prompts, via a Criticize-Reflect process, resulting in novel organization structures that reduce communication costs and enhance team efficiency.
Building Cooperative Embodied Agents Modularly with Large Language Models
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；MIT（麻省理工学院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
COMBO: Compositional World Models for Embodied Multi-Agent Cooperation
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；IIIS, Tsinghua University（清华大学信息科学技术学院）；Peking University（北京大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Honda Research Institute USA（美国本田研究所）；MIT（麻省理工学院）
From Perception to Planning: Evolving Ego-Centric Task-Oriented Spatiotemporal Reasoning via Curriculum Learning
Zhejiang University（浙江大学）；Tianjin University（天津大学）；Qingdao University（青岛大学）；Shanghai Jiao Tong University（上海交通大学）；National University of Singapore（新加坡国立大学）
Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；University of California, Merced（加利福尼亚大学默塞德分校）；Adobe Research（奥多比研究院）；Texas A&M University（德克萨斯农工大学）
MemPrism: Task-Conditioned Relational Memory Views for Long-Horizon Agents
Nanyang Technological University（南洋理工大学）；South China University of Technology（华南理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）
SAIL: Spatial Audio Intelligence with Large Language Models via Disentangled Acoustic-Spatial Encoding and Dual-Stream Q-Former
Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；Tencent Hy Frontier Lab（腾讯Hy前沿实验室）；Beijing Institute of Technology（北京理工大学）；University of Surrey（萨里大学）
Inferring the Unspoken: Aligning Embodied Agents with Implicit Preferences
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；School of Computer Science & Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, China（北京理工大学扬子江地区研究院）；National Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）
PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs
Zhejiang University（浙江大学）；ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙江大学杭州国际科技创新中心）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Bosch Corporate Research（博世企业研究）；King Abdullah University of Science and Technology（卡布斯大学）
