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.

翻译：多智能体LLM协商的有效性不仅取决于智能体的个体预测，还取决于它们如何沟通与协作。我们通过Friedkin-Johnsen（FJ）观点动力学（一种可处理的模型，用于分析多智能体系统中的固执性、影响力和观点变化，并能捕捉经验观察到的协商模式）来研究这一机制。我们证明，FJ参数具有输入依赖性，从而将多智能体协商转化为专家混合。这一视角表明，当路由反映智能体能力时，多智能体系统可以优于单个智能体和静态集成。由于实际中能力是潜在的，我们通过可观察的代理指标（智能体自我评估的置信度、他人感知的置信度，以及与其他智能体观点的初始对齐程度）分析影响力是如何建立的。