arXiv (All)
open access
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Anisotropic Representations Improve Planning in JEPA World Models#1141763
- H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning#1215889
- H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning#1194423
- What Can Latent World Models Know? Physical Information in Multimodal Predictive Representations#1116388
- DWM: Separating World Effects from Actions in Latent World Models#1117525
Record · ID 1354372
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive