AI agents reshape consensus formation in human groups
As large language model (LLM) agents shift from tools to participants in human groups, a fundamental question for collective behavior is how their growing presence reshapes consensus formation. Here we study mixed human-AI groups in a collaborative description game, in which shared conventions emerge through repeated rounds of random pairwise communication. Varying the proportions of LLM agents, we identify three distinct regimes of consensus formation: low agent proportions facilitate human-led consensus, intermediate proportions disrupt convergence, and high proportions restore strong consensus while shifting it toward agent-led conventions. Crucially, these regimes differ not only in t...
| Authors | Lin Chen, Ziyi Liu, Xia Hu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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