  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.
