AdaJEPA: An Adaptive Latent World Model
AdaJEPA introduces closed-loop test-time adaptation for latent world models, enabling them to recalibrate continuously during planning without retraining. Rather than freezing learned representations at deployment, the system uses observed transitions as self-supervised signals to update the model mid-execution within model predictive control loops. This addresses a critical failure mode in embodied AI: distribution shift between training and deployment environments. The approach matters because it decouples adaptation from expert data collection, potentially making learned world models more robust in real-world robotics and control tasks where conditions inevitably drift from training conditions.62
