From Belief To Action: A Bayesian Co-Evolution Framework For LLM Agent System

LLM-centric agent systems have shown remarkable potential, yet their self-evolution from experience remains limited. Existing approaches either optimize context alone while keeping the agent model fixed, or perform joint adaptation for them across heterogeneous spaces. In both cases, nevertheless, context and model are treated as decoupled entities, lacking a common ground for representing and propagating their mutual uncertainties. We propose a unified Bayesian inference framework that treats context and agent model as coupled variables within a single probability measure space, directly updated by online trajectory likelihoods without parameter retraining. This enables few-shot trial-and-error adaptation, where belief updates for the variables are derived directly from empirical returns. Experiments demonstrate consistent outperformance over both existing paradigms, substantiating the viability of formulating self-evolution as principled inference under coherent probabilistic belief dynamics.