Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency

Autoregressive diffusion models have enabled high-qualityvideo generation, yet their sequential nature inherently suffers from erroraccumulation. In long-horizon video synthesis, minor prediction devia-tions compound over time, inevitably leading to unconstrained genera-tive drift, structural collapse, and severe visual degradation. To addressthis, we propose Cycle-World, a novel framework designed for stable andtemporally consistent long-video generation. Our approach tackles errordrift by enforcing strict temporal reversibility across both the trainingand inference phases. Theoretically, we demonstrate that forward gener-ative drift can be strictly bottlenecked by a cycle-consistency objective.During training, we integrate an efficient reverse-prediction model to im-plicitly embed causal constraints into the forward generator, compellingit to produce reversible sequences that tightly adhere to the natural videomanifold. At inference time, we repurpose this frozen reverse model as aruntime corrector. Through gradient-based cycle guidance, it iterativelyrefines the generated latent representations, actively suppressing accu-mulated errors before they are committed to the historical context. Ex-tensive experiments on the VBench benchmark demonstrate that Cycle-World’s dual-phase synergy significantly mitigates error drift, achievingstate-of-the-art overall generation quality and long-horizon temporal con-sistency in 60-second synthesis.