PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models

arXiv:2608.05720 · cs.CV · Submitted 2026-08-06 · Read on arXiv

cs.CV

Submitted: 2026-08-06

Updated: 2026-09-27

Terminology

• World Model — 21× in this paper · explained in 7 episodes
• Alignment — 10× in this paper · explained in 1 episode
• Latent Denoising — 10× in this paper
• Grounding — 9× in this paper · explained in 3 episodes
• Physical State Grounding — 9× in this paper
• Regularization — 9× in this paper · explained in 3 episodes
• Latent Space — 8× in this paper · explained in 7 episodes
• Future Representation Alignment — 6× in this paper
• Representation Learning — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Self-Supervised Learning — 5× in this paper · explained in 7 episodes
• Static Visual Invariance Constraint — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Latent World Models — 2× in this paper · explained in 2 episodes
• Model Predictive Control (MPC) — 2× in this paper · explained in 5 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Action-Conditioned World Model — 1× in this paper · explained in 1 episode
• Angular Error — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
• Revisiting Feature Prediction for Learning Visual Representations from Video
• Sensorimotor World Models: Perception for Action via Inverse Dynamics
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
• Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models
• Representation Learning with Contrastive Predictive Coding
• Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models

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