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This paper addresses critical bottlenecks in latent world models by integrating a content-addressable memory bank to mitigate representational collapse and extend long-horizon planning capabilities. By leveraging JEPA-style prediction with adaptive layer normalization, the method achieves state-of-the-art results on embodied tasks like PushT and Tworoom while maintaining a lightweight architecture. The work represents a significant advancement in scalable embodied AI through improved memory management and temporal scale handling.
Enhancing Latent World Models with Associative Memory
arXiv:2610.19683v1 Announce Type: new Abstract: World models trained via latent dynamics offer a scalable path for embodied planning, yet existing methods suffer from representational collapse, computational overhead, and limited long-horizon capability. We introduce a memory-enhanced latent world model that augments JEPA-style prediction with a content-addressable memory bank. Our approach learns high-level representations directly from observations, operating at multiple temporal scales within a shared latent space, and conditions predictions via adaptive layer normalization. To prevent collapse and ensure effective memory usage, we design two complementary losses that encourage slot diversity and balanced temporal utilization. Experiments on PushT, Tworoom, Reacher, and OGBCube demonstrate that our method achieves state-of-the-art performance with a lightweight architecture, outperforming conventional latent world models on long-horizon planning tasks.
Abstract:World models trained via latent dynamics offer a scalable path for embodied planning, yet existing methods suffer from representational collapse, computational overhead, and limited long-horizon capability. We introduce a memory-enhanced latent world model that augments JEPA-style prediction with a content-addressable memory bank. Our approach learns high-level representations directly from observations, operating at multiple temporal scales within a shared latent space, and conditions predictions via adaptive layer normalization. To prevent collapse and ensure effective memory usage, we design two complementary losses that encourage slot diversity and balanced temporal utilization. Experiments on PushT, Tworoom, Reacher, and OGBCube demonstrate that our method achieves state-of-the-art performance with a lightweight architecture, outperforming conventional latent world models on long-horizon planning tasks.
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2610.19683 [cs.LG]
(or arXiv:2610.19683v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.19683 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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