Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments
Quick Answer
MuSix is a new framework for embodied agents that enhances multi-scale reasoning and adaptation in evolving environments.
Quick Take
MuSix is a new framework for embodied agents that enhances multi-scale reasoning and adaptation in evolving environments. It introduces a two-stage routing mechanism and scale-dependent forgetting rates, outperforming state-of-the-art methods on benchmarks like EmbodiedBench and HAZARD.
Key Points
- MuSix addresses routing challenges in Mixture of Experts for embodied agents.
- Experiential distance informs scale selection in the two-stage routing mechanism.
- Scale-dependent forgetting rates enable rapid updates for low-scale knowledge.
- Gated inter-scale transfer ensures coherence across different knowledge scales.
- Experiments show significant improvements over existing methods on key benchmarks.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2607. 00457v1 Announce Type: new 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scales, and a uniform update policy cannot accommodate the different rates at which knowledge at each scale becomes outdated.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.
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