Impact of Communication Masking on Problem Solving in LLM Agent Networks

In this study, we simulate a collective of Large Language Model (LLM) agents communicating across a network using natural language. Motivated by the concept of information bottlenecks, we investigate the impact of limiting information exchange between agents through the masking of words during communication. By examining how different network structures affect the accuracy and information processing capabilities of agents under these constrained communication conditions, we aim to understand how information restrictions influence collective intelligence. Different network structures respond differently to communication masking, with some maintaining effective collaboration while others experience performance drops. These results underscore the critical role of network architecture in resilient communication systems and have significant implications for distributed systems and artificial intelligence applications, especially in scenarios where information flow is limited or disrupted. Additionally, our findings lay the groundwork for future information-theoretic studies in collective intelligence, providing insight into the mechanisms of information aggregation within such systems. The code for experiments is available in: https://github.com/NeoGendaijin/LLM_Agents_Network