ResearcharXiv cs.AI
Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments
#ai#embodied agents#world models#multi-scale#adaptation
English
The paper presents MuSix, a framework designed for embodied agents that addresses challenges in multi-scale reasoning and knowledge adaptation in evolving environments. It introduces a two-stage routing mechanism for scale selection and employs scale-dependent forgetting rates to enhance dynamic adaptation and coherence across knowledge hierarchies.
中文
本文提出了MuSix框架，旨在解决在不断变化的环境中，具身代理的多尺度推理和知识适应中的挑战。它引入了一个两阶段路由机制用于尺度选择，并采用尺度依赖的遗忘率来增强动态适应性和知识层次之间的一致性。