Enhancing Latent World Models with Associative Memory
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. Exper...
| Authors | Yuwei Sun, Siyu Zhu |
|---|---|
| Primary category | cs.LG |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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