I Act Therefore I Am: When Is JEPA's Action-Conditioning Enough to Learn Causal Mechanisms?
cs.LG
Submitted: 2026-09-25
Updated: 2026-09-25
Terminology
Sources
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
- VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
- MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features
- Revisiting Feature Prediction for Learning Visual Representations from Video
- Weakly supervised causal representation learning
- Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
- CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
- Le MuMo JEPA: Multi-Modal Self-Supervised Representation Learning with Learnable Fusion Tokens
- RoboNet: Large-Scale Multi-Robot Learning
- Value-guided action planning with JEPA world models
- Identifiability Without Gaussianity: Symbolic World Models and Near-Infinite Temporal Consistency
- A-JEPA: Joint-Embedding Predictive Architecture Can Listen
- World Models
- Dream to Control: Learning Behaviors by Latent Imagination
- Temporal Difference Learning for Model Predictive Control
- 3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning
- VJEPA: Variational Joint Embedding Predictive Architectures as Probabilistic World Models
- Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis
- When Does LeJEPA Learn a World Model?
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