Representation Learning
2 related stories
Yann LeCun’s Latest JEPA Paper: Where Does End-to-End Planning Go Next?
A new paper from Yann LeCun’s team introduces H-JEPA, a hierarchical world model that improves long-horizon planning success rates from approximately 18% to 73%. The architecture separates temporal scales to enhance stability and reduce computational costs in embodied AI tasks.
INTACT: Search-Free World Model for Robotics Achieves Faster, More Reliable Control
Researchers propose INTACT, a search-free world model framework that aligns physical and goal intentions using a shared operator. Achieving 95.33% success rate with 300x faster inference than LeWM, it upgrades JEPA from prediction to deployable control.