Reproducible Social Structure Emerges From Free-Form Interaction Among LLM Agents

Multi-agent LLM simulations are increasingly used as models of social systems, but most published findings are anecdotal, i.e., drawn from a single run. It is therefore unclear which observed phenomena are robust findings of the simulated system itself, and which were random events in one particular run. Here, we introduce a repeatable, goal-free, proactive multi-agent framework in which fifty persona-conditioned agents with persistent memory choose whether, with whom, and how to communicate across three social platforms. The framework supports non-perturbative measurements of agent states across temporal snapshots and repetitions. Running the same world ten times from identical initial conditions for a total of 700 hours, we find that most measured network- and agent-level parameters are stable across repetitions. Throughout a run, initially broad communication contracts into sparse sustained dyads in all ten worlds. Importantly, we find that a substantial number of identical dyads re-form across worlds significantly more probably than expected by chance. Static personality similarity largely fails to predict these dyads. Instead, alignment and extremity in the emotions and traits expressed during first contact predict whether a pair later forms a sustained dyad. Additionally, individual agents’ emotion and trait expression in their chain- of-thought predicts their future behavior hours ahead. Finally, swapping either persona prompts or accumulated memories between agents causes recipients to exhibit behavior more similar to donors. Interestingly, persona and memories affect agent behavior to a similar degree. Overall, we show that repeated worlds enable robust conclusions about emergent behavior in agent societies.