Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments

Embodied agents operating in the real world require multi-scale reasoning and knowledge adaptation as conditions change. We iden-tify two challenges in applying Mixture of Experts (MoE) to this setting:routing lacks an explicit notion of scale, preventing targeted updates atspecific scales, and a uniform update policy cannot accommodate thedifferent rates at which knowledge at each scale becomes outdated. Wepresent MuSix, a framework that addresses both challenges throughscale-aware world model mixture and evolution. A two-stage routingmechanism first maps experiential distance, a measure of situational nov-elty inspired by Construal Level Theory, to a weight over continuous scalespace via a meta-router, then selects world models within the identifiedscale. For adaptation, scale-dependent forgetting rates allow low-scaleknowledge to refresh rapidly while high-scale abstractions persist, andgated inter-scale transfer maintains coherence across the hierarchy. Ex-periments on EmbodiedBench and HAZARD show that MuSix improvesover state-of-the-art baselines.