AI agents can coordinate via majority-following beyond human scale.

Authors

Giordano De Marzo, Claudio Castellano, David Garcia

Published in

Science advances. Volume 12. Issue 33. Pages eaea6091. Aug 14, 2026. Epub Aug 14, 2026.

Abstract

Large language models (LLMs) are increasingly deployed in collaborative tasks forming "AI agent societies" where agents interact and influence one another. Whether such groups can spontaneously coordinate without external influence, a hallmark of self-organized regulation in human societies, remains an open question. Here, we use principles from complexity and behavioral science to investigate coordination in AI agent groups through majority-following, a fundamental mechanism for spontaneous consensus formation. Using binary opinion dynamics experiments across multiple LLM architectures and group sizes, we find that agents exhibit majority-following characterized by a universal functional form with a single parameter, the "majority force." This majority force diminishes as group size increases, leading to a critical size beyond which coordination becomes unattainable. The critical group size grows rapidly with model capabilities and, for advanced LLMs, exceeds 1000 agents, larger than typical human informal groups. Our findings have implications for designing collaborative AI systems where coordination could be beneficial or pose safety threats.

PMID:
42600034
Bibliographic data and abstract were imported from PubMed on 15 Aug 2026.

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