Abstract
World models allow agents to simulate the consequences of actions in imagined environments for planning, control, and long-horizon decision-making. However, existing autoregressive world models struggle with visually coherent predictions due to disrupted spatial structure, inefficient decoding, and inadequate motion modeling. In response, we propose \textbf{S}cale-wise \textbf{A}utoregression with \textbf{M}otion \textbf{P}r\textbf{O}mpt (\textbf{SAMPO}), a hybrid framework that combines visual autoregressive modeling for intra-frame generation with causal modeling for next-frame generation. Specifically, SAMPO integrates temporal causal decoding with bidirectional spatial attention, which preserves spatial locality and supports parallel decoding within each scale. This design significantly enhances both temporal consistency and rollout efficiency. To further improve dynamic scene understanding, we devise an asymmetric multi-scale tokenizer that preserves spatial details in observed frames and extracts compact dynamic representations for future frames, optimizing both memory usage and model performance. Additionally, we introduce a trajectory-aware motion prompt module that injects spatiotemporal cues about object and robot trajectories, focusing attention on dynamic regions and improving temporal consistency and physical realism. Extensive experiments show that SAMPO achieves competitive performance in action-conditioned video prediction and model-based control, improving generation quality with 4.4$\times$ faster inference. We also evaluate SAMPO's zero-shot generalization and scaling behavior, demonstrating its ability to generalize to unseen tasks and benefit from larger model sizes.
Abstract (translated)
世界模型允许代理在想象的环境中模拟行动后果，从而进行规划、控制和长期决策。然而，现有的自回归世界模型由于空间结构中断、解码效率低以及运动建模不足等问题，在视觉连贯预测方面存在困难。为此，我们提出了**SAMPO**（Scale-wise Autoregression with Motion Prompt）框架，这是一种结合了视觉自回归建模用于帧内生成和因果建模用于下一帧生成的混合架构。具体来说，SAMPO通过整合时序因果解码与双向空间注意力机制，保持了空间局部性，并支持各尺度内的并行解码，从而大大提升了时间一致性和展开效率。 为了进一步提高对动态场景的理解，我们设计了一种不对称多尺度编码器，该编码器在观察帧中保留空间细节，在未来帧中提取紧凑的运动表示。这种策略优化了内存使用和模型性能。此外，我们引入了一个轨迹感知运动提示模块，该模块注入关于对象及机器人轨迹的空间时间线索，使注意力集中在动态区域上，从而提高了时间和物理现实性的一致性。 大量的实验表明，SAMPO在动作条件下的视频预测和基于模型的控制中实现了竞争性的表现，并且比现有方法快4.4倍地提升了生成质量。我们还评估了SAMPO零样本泛化能力及规模效应，证明其能够将学习到的知识迁移到未见过的任务上，并从更大的模型尺寸中获益。
URL
https://arxiv.org/abs/2509.15536
