Composition of Memory Experts for Diffusion World Models
The paper discusses a new approach to memory management in diffusion world models. It proposes a framework that utilizes specialized memory experts to enhance performance in reinforcement learning tasks. This method aims to improve temporal consistency and navigation without the limitations of traditional architectures.
- ▪The proposed framework integrates heterogeneous memory models through a contrastive product-of-experts formulation.
- ▪It includes three types of memory experts: short-term, long-term, and spatial long-term memory experts.
- ▪The approach avoids mode collapse and scales efficiently to long contexts.
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Computer Science > Machine Learning arXiv:2605.18813 (cs) [Submitted on 12 May 2026] Title:Composition of Memory Experts for Diffusion World Models Authors:Sebastian Stapf, Pablo Acuaviva Huertos, Aram Davtyan, Paolo Favaro View a PDF of the paper titled Composition of Memory Experts for Diffusion World Models, by Sebastian Stapf and 3 other authors View PDF HTML (experimental) Abstract:World models aim to predict plausible futures consistent with past observations, a capability central to planning and decision-making in reinforcement learning.
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