jepa

Micro-JEPA is a lightweight Python implementation of the Joint Embedding Predictive Architecture (JEPA), enabling an agent to learn environment representations, predict future states in latent space, and plan actions to avoid obstacles.

Yann LeCun criticizes current LLMs as not truly intelligent and describes his new company AMI Labs' development of Joint Embedding Predictive Architecture (JEPA) aimed at creating more flexible AI that can understand the physical world.

Delta-JEPA introduces a reconstruction-free world model that augments latent forward prediction with a Latent Difference Action Decoder to prevent collapse and improve action-sensitivity, achieving better planning performance on visual continuous-control tasks.

Introduces VISReg, a regularization method for JEPA (Joint Embedding Predictive Architecture) training that combines variance, invariance, and sketching constraints.

After 4.5 years at FAIR, a researcher joins AMI Labs to work on JEPA and World Models.

DVD-JEPA is an open-source, minimal JEPA world model that learns representations from video by predicting future embeddings rather than pixels. It uses a bouncing DVD logo to demonstrate position recovery, dreaming, and anomaly detection, all running in a browser.

A robotics researcher compares current robotics approaches to the language model landscape of 2023, arguing that representation prediction (JEPA) is the most scalable method as it can leverage action-free video data like YouTube, unlike other methods that require action-labeled data.

This blog post explains the connection between JEPA (Joint Embedding Predictive Architecture) models and Canonical Correlation Analysis (CCA), a statistical method from 1936, arguing that CCA is the conceptual precursor to JEPA and that the idea of maximizing correlation in embedding space dates back to Hotelling.

A curated list of papers, models, code, datasets, and learning resources for Joint Embedding Predictive Architectures (JEPA), the self-supervised approach to world models proposed by Yann LeCun.

The paper proposes a hybrid pre-training objective combining JEPA latent-space prediction with MLM reconstruction for language models, showing improved embedding uniformity and semantic-lexical balance.

The article explains the concept of world models in detail, comparing them to LLMs, introduces two major camps (pixel prediction and meaning prediction) and representative works such as Dreamer v3, GameNGen, Genie, and JEPA, discusses applications in autonomous driving and robotics, and points out that world models are a key component of physical AI.

This thread presents a theoretical result showing that predicting abstract latent representations (as in JEPA and data2vec) instead of raw tokens can exponentially reduce the data gap between AI and human learning.

Basile Terver and colleagues' paper on Joint-Embedding Predictive World Models (JEPA-WM) for robotics has been accepted to TMLR with a reproducibility certification. The updated version includes new data-scaling experiments, a Lipschitz analysis of multistep rollout training, and extended discussions.

Discusses Yann LeCun's 'World Models' and JEPA from a recent arXiv paper, clarifying that it is not a replacement for LLMs but a model optimized for visual processing in robotics, self-driving, and industrial controls.

Crys-JEPA introduces a joint embedding predictive architecture for crystals that learns an energy-aware latent space, achieving significant improvements in stability and novelty for de novo crystal discovery.

The author discusses applying Yann LeCun's JEPA (Joint Embedding Predictive Architecture) to coding agents, proposing that instead of treating code as text, agents should learn compact state representations and predict future states, potentially achieving orders of magnitude efficiency improvements over current LLM-based approaches.

Sub-JEPA improves LeWorldModel by applying Gaussian regularization in frozen random orthogonal subspaces, consistently outperforming the original on benchmarks with up to +10.7 percentage points improvement.

This paper audits Joint-embedding predictive architectures (JEPA) for LLM fine-tuning on a natural-language-to-regex task, testing twenty-two auxiliary objectives. The results show that hidden-state representation improvements are only weakly coupled to decoded-task accuracy, with no auxiliary surviving family-wise correction.

Yann LeCun leaves Meta to found AI company AMI, focusing on world models based on Joint Embedding Predictive Architecture (JEPA). He believes LLMs are not the path to human-level intelligence and criticizes the current paradigm for lacking prediction and planning capabilities.

This paper introduces a fleet of five sensor-specialized Mini-JEPA foundation models for hydrologic intelligence, achieving high reconstruction accuracy (R² up to 0.97) and outperforming the Google AlphaEarth generalist on physics-matched tasks when routed via an LLM agent.