@mengyer: New research: AdaJEPA perceives, plans, and adapts in a closed loop. It is always learning!
Summary
Introduces AdaJEPA, an adaptive world model that continuously learns from perception, planning, and action in a closed loop.
View Cached Full Text
Cached at: 07/06/26, 02:00 AM
New research: AdaJEPA perceives, plans, and adapts in a closed loop. It is always learning!
Ying Wang✈️ ICML (@yingwww_): Warm take: Your world model should never stop learning
Introducing AdaJEPA, an adaptive WM that plans, acts, and adapts in a closed loop. Every action leads to a new observation, and every transition refines the latent representation and prediction.
📝:
Similar Articles
@yingwww_: Warm take: Your world model should never stop learning Introducing AdaJEPA, an adaptive WM that plans, acts, and adapts…
AdaJEPA introduces an adaptive latent world model that continuously updates during test-time via closed-loop model predictive control, significantly improving planning success under distribution shift.
@LiorOnAI: Most models behind agents don't learn while they're running. You train them, freeze the weights, and deploy them. Every…
Introduces AdaJEPA, an adaptive world model that continuously learns and updates its latent representation during deployment, enabling agents to adjust their plans based on real-world observations without retraining or memory tricks.
Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
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.
@LeRobotHF: VLA-JEPA just dropped in LeRobot What makes this model special is that it does not just learn what action to take from …
VLA-JEPA, a new model integrating a JEPA world model for action-relevant dynamics, has been released in LeRobot. It enables pretraining on human videos and achieves strong performance with minimal fine-tuning, running real-time on NVIDIA DGX Spark.
I built Micro-JEPA: A lightweight JEPA (Joint Embedding Predictive Architecture) in Python
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.