A platform for reproducible world model research and evaluation
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Updated
Aug 25, 2026 - Python
A platform for reproducible world model research and evaluation
The first robot-native JEPA physical-world model.
implementing minimal versions of joint-embedding predictive architecture (JEPA)
INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models.
Official code for AdaJEPA: An Adaptive Latent World Model
Free interactive course on world models in AI. Nine visual chapters on prediction, latent dynamics, planning, JEPA, video models, and failure modes.
Explorations into some of the approaches advocated by Yann LeCun, and just a more wholistic architecture (JEPA) in general
Harness framework to build world model based workflows for physical AI systems.
[ICLR 2026] The implementation of the paper Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors
Experiments in Joint Embedding Predictive Architectures (JEPAs).
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
GenBio-PathFM is a histopathology foundation model from GenBio AI.
👆PyTorch Implementation of JEDi Metric described in "Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality"
An auditable and reproducible evaluation suite for LeWM-compatible latent world models, with official-compatible and difficulty-controlled protocols.
A tiny, fully-reproducible JEPA world model that learns the physics of a bouncing DVD logo in representation space, dreams its future, and detects anomalies. Trains on a CPU in ~10s. Interactive browser demo. CA: 0x42bef487C250dd054035d5E8d69C12549d1F7Ba3
Joint Embedding Predictive Architecture for World Models, written in Rust.
An open-source attempt at training a variant of LeCun's energy-based models (EBM) to reason in latent space and solve Sudoku.
Official Code for LpWM: A Case for Sparse Representations in World Models
This VL-JEPA implimentation takes direct insperation from the original VL-JEPA paper
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