Social-JEPA: Emergent Geometric Isomorphism

arXiv:2603.02263 · cs.CV, cs.AI · Submitted 2026-02-28 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-02-28

Updated: 2026-08-29

Terminology

• Alignment — 70× in this paper · explained in 1 episode
• World Model — 21× in this paper · explained in 7 episodes
• Latent Space — 19× in this paper · explained in 7 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Distillation — 7× in this paper · explained in 2 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Self-Supervised Learning — 5× in this paper · explained in 7 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Representation Learning — 3× in this paper · explained in 2 episodes
• Self-distillation — 3× in this paper · explained in 6 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Anisotropy — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 2× in this paper
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Linear probe — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Joint-Embedding Predictive Architectures — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Model Merging — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes

Sources

• Understanding intermediate layers using linear classifier probes
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
• Revisiting Feature Prediction for Learning Visual Representations from Video
• Distilling the Knowledge in a Neural Network
• The Kinetic Fokker-Planck equation in a domain: Ultracontractivity, hypocoercivity and long-time asymptotic behavior
• World Models
• The information bottleneck method

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models