Large Language Model Multi-Agent Systems (LLM-MAS) have achieved great progress in solving complex tasks. It performs communication among agents within the system to collaboratively solve tasks, under the premise of shared information. However, when agents' communication is leveraged to enhance human cooperation, a new challenge arises due to information asymmetry, since each agent can only access the information of its human user. Previous MAS struggle to complete tasks under this condition. To address this, we propose a new MAS paradigm termed iAgents, which denotes Informative Multi-Agent Systems. In iAgents, the human social network is mirrored in the agent network, where agents proactively exchange human information necessary for task resolution, thereby overcoming information asymmetry. iAgents employs a novel agent reasoning mechanism, InfoNav, to navigate agents' communication towards effective information exchange. Together with InfoNav, iAgents organizes human information in a mixed memory to provide agents with accurate and comprehensive information for exchange. Additionally, we introduce InformativeBench, the first benchmark tailored for evaluating LLM agents' task-solving ability under information asymmetry. Experimental results show that iAgents can collaborate within a social network of 140 individuals and 588 relationships, autonomously communicate over 30 turns, and retrieve information from nearly 70,000 messages to complete tasks within 3 minutes.

翻译：大语言模型多智能体系统（LLM-MAS）在解决复杂任务方面取得了显著进展。该系统基于信息共享的前提，通过智能体间的通信实现任务协同求解。然而，当利用智能体通信来增强人类协作时，由于每个智能体仅能获取其对应人类用户的信息，信息不对称问题带来了新的挑战。传统多智能体系统在此条件下难以有效完成任务。为此，我们提出了一种称为iAgents（信息增强型多智能体系统）的新型多智能体范式。在iAgents中，人类社交网络被映射到智能体网络，智能体主动交换任务解决所需的人类信息，从而克服信息不对称问题。iAgents采用一种新型的智能体推理机制InfoNav，引导智能体通信实现有效信息交换。结合InfoNav，iAgents通过混合记忆机制组织人类信息，为智能体提供精准全面的信息交换基础。此外，我们推出了首个专门评估信息不对称环境下大语言模型智能体任务解决能力的基准测试InformativeBench。实验结果表明，iAgents能在包含140个节点和588组关系的社交网络中实现协同，自主进行超过30轮通信，并从近70,000条消息中检索信息，在3分钟内完成任务。