Social Networks of LLM Agents

Large language model (LLM) agents are increasingly deployed in interacting populations, raising the question of what they come to believe collectively. Whether a population aggregates genuine knowledge or collapses into a false consensus affects how much such systems can be trusted. Classical social-network models assume that the network structure alone determines how beliefs combine. This assumption breaks down for LLM agents, whose belief updates incorporate only part of the information they are exposed to, so these models may mischaracterize information aggregation and cannot distinguish genuine consensus from herding. We introduce SNLA, a framework that models each agent's realized influence on others rather than network connectivity. This influence depends on each agent's position in the network and on how sharply attention focuses. Theoretically, we show in a linear belief model that narrow attention leads to herding, with only a few agents shaping the collective belief as the population grows. Wide attention recovers wisdom-of-crowds behavior only on undirected, degree-regular exposure graphs. Empirically, a controlled testbed validates these predictions, and the herding–wisdom transition is demonstrated on operator-controlled variants of three multi-agent LLM benchmarks. These results identify attention breadth, together with exposure structure, as the key determinant of whether an LLM population aggregates knowledge or herds, offering a principled basis for designing and auditing multi-agent systems that are trustworthy at scale.