Anisotropic Representations Improve Planning in JEPA World Models
cs.RO
Submitted: 2026-09-29
Updated: 2026-09-29
Project page: https://rkdrn79.github.io/AnisoWM-page
Terminology
Sources
- Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
- Fast LeWorldModel
- SCALE: State-Calibrated Latent Embeddings for JEPA Planning in the Right Geometry
- When Does LeJEPA Learn a World Model?
- Radial-VCReg: More Informative Representation Learning Through Radial Gaussianization
- Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations
- World Model Control by Trajectory Reachability Metrics
- Predictive but Not Plannable: RC-aux for Latent World Models
- Temporally Centered SIGReg Improves LeWorldModel Representations for Robot Policy Learning
- LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
- KerJEPA: Kernel Discrepancies for Euclidean Self-Supervised Learning
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