arXiv (All)
open access
Frozen Flows Forget: Diagnosing and Restoring Lost Motion in a Latent-flow World Model
Latent world models that integrate a flow in a frozen self supervised latent space train stably and cheaply, yet silently lose the property manipulation depends on most: motion. The pretrained flow never moves the manipulated object; retraining it with latent-only losses only trades stillness for te
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
- What Can Latent World Models Know? Physical Information in Multimodal Predictive Representations#1116388
- Object-Aware Background-Controlled Editing via Weighted Velocity Guidance#986931
- Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization#997343
- Experiencing timelessness and the phenomenology of temporal flow#76745
- GeoTTER: Leveraging Local Geometry of Optimal Transport for Zero-Shot Classification#1071302
Record · ID 1065017
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