Tag
A GitHub repository providing minimal, standalone PyTorch reimplementations of JEPA family models (I-JEPA, V-JEPA, V-JEPA 2, C-JEPA) for educational purposes, including tutorials and visualization tools.
The authors introduce Sub-JEPA, a method using Subspace Gaussian Regularization to improve the stability of end-to-end world models like LeWM, showing consistent performance gains on continuous-control benchmarks.
Yann LeCun's team releases LeWorldModel, a tiny 15M-parameter physics model trained on a single GPU in hours that outperforms billion-dollar foundation models in planning speed and physical plausibility, challenging the dominant scaling paradigm.
This paper introduces AeroJEPA, a Joint-Embedding Predictive Architecture for scalable 3D aerodynamic field modeling. It addresses limitations in current surrogate models by predicting semantic latent representations of flow fields, enabling efficient high-fidelity analysis and design optimization.
Yann LeCun reportedly left Meta after JEPA was sidelined for LLaMA, founding AMI Labs to build a simplified version on commodity hardware.
LeWorldModel introduces a stable, end-to-end Joint-Embedding Predictive Architecture that trains directly from pixels with minimal hyperparameters and provable anti-collapse guarantees. It achieves significant speedups in planning compared to foundation models while maintaining competitive performance on robotic manipulation tasks.