LRC-JEPA: Disentangling Dynamics and Residual Context for Efficient World Models
cs.LG, cs.AI, cs.RO
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Sources
- Deep Variational Information Bottleneck
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
- MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features
- SAM 3: Segment Anything with Concepts
- Isolating Sources of Disentanglement in Variational Autoencoders
- A Generalization Theory for JEPA-Based World Models
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
- World Models
- When Does LeJEPA Learn a World Model?
- Temporally Centered SIGReg Improves LeWorldModel Representations for Robot Policy Learning
- LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
- Causal-JEPA: Learning World Models through Object-Level Latent Masking
- Cosmos World Foundation Model Platform for Physical AI
- Scalable Diffusion Models with Transformers
- Joint Embedding Predictive Architectures Focus on Slow Features
- A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures
- Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models
- On the Identifiability of Controlled World Models
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