A platform for reproducible world model research and evaluation
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Updated
Jun 11, 2026 - Python
A platform for reproducible world model research and evaluation
implementing minimal versions of joint-embedding predictive architecture (JEPA)
The first robot-native JEPA physical-world model.
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"
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.
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.
This VL-JEPA implimentation takes direct insperation from the original VL-JEPA paper
Seeing Beyond Words: Self-Supervised Visual Learning for Multimodal Large Language Models
Run existing World Models from ROS 2 — runtime, adapters, benchmark, visualization.
A Video Joint Embedding Predictive Architecture (JEPA) that runs on a personal computer.
Ten-ABC cognitive architecture for autonomous AI with forward compatibility . Route any architecture current or future without.
A commit-and-audit proof system for deterministic, quantized inference of a JEPA-style world model (LeWorldModel)
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