Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?
Abstract
The effectiveness of multi-agent LLM deliberation depends not only on the agents' individual predictions, but also on how they communicate and collaborate. We study this mechanism through the lens of Friedkin-Johnsen (FJ) opinion dynamics, a tractable model for analyzing stubbornness, influence, and opinion change in multi-agent systems that captures empirically observed deliberation patterns. We show that the FJ parameters are input-dependent, turning multi-agent deliberation into a mixture of experts. This perspective implies that multi-agent systems can outperform single agents and static ensembles when routing reflects agent competence. Since competence is latent in practice, we analyze how influence is established through observable proxies: agents' self-assessed confidence, their perceived confidence, and initial alignment with other agents' views.
Cite
@article{arxiv.2605.25929,
title = {Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?},
author = {Franka Bause and Jonas Niederle and Martin Pawelczyk and Rebekka Burkholz},
journal= {arXiv preprint arXiv:2605.25929},
year = {2026}
}