MuSix introduces scale-aware world model mixtures with experiential-distance routing and adaptive forgetting to improve multi-scale reasoning and dynamic adaptation in embodied agents.
arXiv preprint arXiv:2401.09870 (2024)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments
MuSix introduces scale-aware world model mixtures with experiential-distance routing and adaptive forgetting to improve multi-scale reasoning and dynamic adaptation in embodied agents.