Belief Coevolution in a Social Network of Generalist and Specialist Large Language Models: A Controlled Study

Large language models (LLMs) are increasingly deployed in multi-agent environments. However, the processes by which beliefs form and propagate among interacting LLMs remain poorly understood. We introduce CoevolveSim, a framework for studying belief diffusion within networked LLM populations. CoevolveSim allows us to isolate and study three factors: domain specialization, social-role assignment, and social network structure. Within this framework, generalist and specialist LLM agents exchange and revise beliefs. In each round, an LLM agent observes a summary of its neighbors' beliefs before updating its own. We find that persona-style role assignment reshapes individual belief revision but has little effect on population-level consensus. In contrast, introducing finetuned specialist LLMs produces a large change in consensus and is accompanied by persistent asymmetries in outgoing influence. We further show that an opinion-dynamics surrogate combining individual persistence with population-level belief composition reproduces consensus with high fidelity in every scenario, whereas predicting individual final-round beliefs in heterogeneous LLM populations additionally benefits from agent identity.