DISK让世界模型动态跳过冗余推理，提速两倍且不丢质量。
DISK: Dynamic Inference SKipping for World Models
- 用双分支控制器决定视频与轨迹是否跳过推理步骤
- 在1500个场景上实现轨迹推理快2倍、视频推理快1.6倍
- 适合需要长时序预测的自动驾驶系统部署
我们提出DISK，一种无需训练的自回归世界模型动态推理跳过方法。DISK通过双分支控制器协同控制视频与自身轨迹的扩散过程，实现跨模态跳过决策，保持运动-外观一致性且无需重训练。将高阶隐空间差值跳过测试扩展至自回归前向链，并在滚动回放中传播控制器统计信息以保障长时序稳定性。在1500个NuPlan和NuScenes样本的闭环驾驶回放中，使用NVIDIA L40S GPU集成后，DISK使轨迹扩散速度提升2倍，视频扩散速度提升1.6倍，同时保持L2规划误差、视觉质量（FID/FVD）及NAVSIM PDMS得分不变，证明了低成本下高效长时序视频与轨迹预测的可行性。
原文摘要 · Abstract (English)
We present DISK, a training-free adaptive inference method for autoregressive world models. DISK coordinates two coupled diffusion transformers for video and ego-trajectory via dual-branch controllers with cross-modal skip decisions, preserving motion-appearance consistency without retraining. We extend higher-order latent-difference skip testing to the autoregressive chain-of-forward regime and propagate controller statistics through rollout loops for long-horizon stability. When integrated into closed-loop driving rollouts on 1500 NuPlan and NuScenes samples using an NVIDIA L40S GPU, DISK achieves 2x speedup on trajectory diffusion and 1.6x speedup on video diffusion while maintaining L2 planning error, visual quality (FID/FVD), and NAVSIM PDMS scores, demonstrating practical long-horizon video-and-trajectory prediction at substantially reduced cost.
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