Abstract: 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 $\textit{iAgents}$, which denotes $\textit{\textbf{I}nformative Multi-\textbf{Agent} \textbf{S}ystems}$. In $\textit{iAgents}$, the human social network is mirrored in the agent network, where agents proactively exchange human information necessary for task resolution, thereby overcoming information asymmetry. $\textit{iAgents}$ employs a novel agent reasoning mechanism, $\textit{InfoNav}$, to navigate agents' communication towards effective information exchange. Together with $\textit{InfoNav}$, $\textit{iAgents}$ organizes human information in a mixed memory to provide agents with accurate and comprehensive information for exchange.Additionally, we introduce $\textit{InformativeBench}$, the first benchmark tailored for evaluating LLM agents' task-solving ability under information asymmetry. Experimental results show that $\textit{iAgents}$ 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.