Emergent Coordination in Multi-Agent Language Models

• URL: http://arxiv.org/abs/2510.05174v1
• Date: Sun, 05 Oct 2025 11:26:41 GMT
• Title: Emergent Coordination in Multi-Agent Language Models
• Authors: Christoph Riedl,
• Abstract summary: We introduce an information-theoretic framework to test whether multi-agent systems show signs of higher-order structure.<n>This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems.<n>We apply our framework to experiments using a simple guessing game without direct agent communication.
• Score: 2.504366738288215
• License: http://creativecommons.org/licenses/by/4.0/
• 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 performance-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 emergence measures and entropy estimators, and not explained by coordination-free baselines or temporal dynamics alone. 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.

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