Loop LeWorldModel: Reliable Recursion for Depth Reuse and Temporal Rollout in Latent World Models

Visual world models based on joint embedding predictive architectures (JEPAs) support planning by predicting action consequences before execution. In real-world deployment, compact and reliable end-to-end world models are increasingly needed under limited computational and memory budgets. Recursive layer sharing offers a natural way to reduce model size. However, naively looping pretrained layers across depth erases depth-specific computations, and recursively feeding predicted states back often accumulates errors and destabilize imagined trajectories. To address these two issues, we propose Loop LeWorldModel, a loop-based adaptation of pretrained visual world models. Specifically, we utilize depth-specific low-rank adapters to preserve distinct transformations at each loop. Additionally, we design a training-free self-correction gate to suppress abnormal prediction steps using recent motion statistics. Extensive experiments on various visual-control tasks demonstrate that Loop LeWorldModel can significantly improve planning success with fewer parameters and less accumulated errors than the original model. All source codes will be released online.