arXiv (All)
open access
Frozen in a Frame: The Velocity Blind Spot in JEPA World Models
Joint-embedding predictive architectures (JEPAs) for world modeling train an encoder so a predictor maps a current embedding and action to the next frame's embedding, always from a single rendered frame. This has a structural blind spot: a renderer without motion blur draws a scene from configuratio
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
- Video Encoders Built on Image Representations#1215712
- Orthogonal JEPA: Factorized Predictive States for Latent World Models#1052275
- World Models Need a Physical Registry: Learning Internal Predictive States for Physically Plausible World Models#1327955
- PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models#1117543
- JEPA Learns What the Mask Leaves Unrecoverable#1116812
Record · ID 1200236
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