A platform for reproducible world model research and evaluation
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Updated
May 26, 2026 - Python
A platform for reproducible world model research and evaluation
implementing minimal versions of joint-embedding predictive architecture (JEPA)
Testable world-model 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
👆PyTorch Implementation of JEDi Metric described in "Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality"
GenBio-PathFM is a histopathology foundation model from GenBio AI.
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.
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
A Video Joint Embedding Predictive Architecture (JEPA) that runs on a personal computer.
Ten-ABC cognitive architecture for autonomous AI. AbstractDivergenceRouter (V1–V6), Safety-Learning Equivalence, domain isomorphism proven.
Physics-based, AI-driven simulation of human civilization on Planet Earth — JEPA-augmented agents, macro ODE, emergent geopolitics.
"Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning" — hybrid JEPA + MLM pre-training for text encoders with GLUE evaluation : https://doi.org/10.13140/RG.2.2.17818.30404
The first robot-native JEPA physical-world model.
Train a JEPA world model on a set of pre-collected trajectories from an environment involving an agent in two rooms.
Using the JEPA architecture for multimodal language translation
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