AdaJEPA

AdaJEPA: Adaptive Latent World Model with Test-Time Self-Supervised Recalibration

AdaJEPA is a latent world model that performs test-time adaptation within model predictive control (MPC) loops, addressing the common failure mode of frozen world models under distribution shift. After training, the model uses observed state transitions as self-supervised adaptation signals to continuously recalibrate predictions during planning, requiring as few as one gradient step per replanning step. Evaluated on goal-reaching tasks, AdaJEPA substantially improves planning success rates compared to static world models.