Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?

• URL: http://arxiv.org/abs/2605.25929v1
• Date: Mon, 25 May 2026 15:10:48 GMT
• Title: Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?
• Abstract summary: We show that Friedkin-Johnsen (FJ) parameters are input-dependent, turning multi-agent deliberation into a mixture of experts.<n>We analyze how influence is established through observable proxies: agents' self-assessed confidence, their perceived confidence, and initial alignment with other agents' views.
• Score: 31.047683581225087
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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