Cascades in LLM agent networks become predictable, but only if each agent is measured with its follower count
On social networks of large-language-model agents, whether a post spreads depends on how many of each agent's contacts have shared it. Earlier predictions measured a single response curve independent of contact count, or one response in a large fully connected group. Measuring the response separately at each contact count and feeding it into mean-field cascade theory reproduces the cascade sizes and tipping seed fractions of full LLM simulations, while responses that ignore contact count mispredict cascades increasingly on heterogeneous networks.