Title: Emergent Coordination in Multi-Agent Language Models
Primary area: generative models
Keywords: multi-agent systems, LLMs, information decomposition, emergence, collective intelligence

Abstract: When are multi-agent LLM systems merely a collection of individual agents versus an integrated collective with higher-order structure? We introduce an information-theoretic framework to test---in a purely data-driven way---whether multi-agent systems show signs of higher-order structure. This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems, localize it, and distinguish spurious temporal coupling from task-relevant cross-agent synergy. We implement both a practical criterion and an emergence capacity criterion  operationalized as partial information decomposition of time-delayed mutual information (TDMI). We apply our framework to experiments using a simple guessing game without direct agent communication and only minimal group-level feedback with three randomized interventions. Groups in the control condition exhibit strong temporal synergy but only little coordinated alignment across agents. Assigning a persona to each agent introduces stable identity-linked differentiation. Combining personas with an instruction to ``think about what other agents might do'' shows identity-linked differentiation and goal-directed complementarity across agents. Taken together, our framework establishes that multi-agent LLM systems can be steered with prompt design from mere aggregates to higher-order collectives. Our results are robust across different indicators of emergence, alternative approaches to estimating entropy, and residualizing data of time trends. Without attributing human-like cognition to the agents, the patterns of interaction we observe mirror well-established principles of collective intelligence in human groups: effective performance requires both alignment on shared objectives and complementary contributions across members.

TL;DR: Information-theoretic framework reveals how prompt design steers multi-agent LLMs from simple aggregates to emergent collectives with higher-order structure