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

# Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments
Source: [https://arxiv.org/abs/2607.00457](https://arxiv.org/abs/2607.00457)
[View PDF](https://arxiv.org/pdf/2607.00457)

> Abstract:Embodied agents operating in the real world require multi\-scale reasoning and knowledge adaptation as conditions change\. We identify two challenges in applying Mixture of Experts \(MoE\) to this setting: routing lacks an explicit notion of scale, preventing targeted updates at specific scales, and a uniform update policy cannot accommodate the different rates at which knowledge at each scale becomes outdated\. We present MuSix, a framework that addresses both challenges through scale\-aware world model mixture and evolution\. A two\-stage routing mechanism grounds scale selection in experiential distance, a measure of situational novelty inspired by Construal Level Theory: a meta\-router first maps this quantity to a weight over continuous scale space, then per\-scale base routers select world models within the identified scale\. For adaptation, scale\-dependent forgetting rates allow low\-scale knowledge to refresh rapidly while high\-scale abstractions persist, and gated inter\-scale transfer maintains coherence across the hierarchy\. Experiments on EmbodiedBench and HAZARD show that MuSix improves over state\-of\-the\-art baselines on multi\-scale reasoning and dynamic adaptation\.

## Submission history

From: Jinwoo Jang \[[view email](https://arxiv.org/show-email/3b4d85fb/2607.00457)\] **\[v1\]**Wed, 1 Jul 2026 05:23:56 UTC \(9,191 KB\)