这篇论文用多球碰撞实验揭示了视频扩散模型做因果推理时的短板，增加去噪步数没用，但自回归和加深网络有效。对做视频生成和物理模拟的人很有启发。
论文通过多球硬球动力学实验发现，标准双向视频扩散模型在因果链长度增加时性能显著下降，即使增加去噪步数也无法缓解。长度匹配的单球控制实验排除了视频长度的影响，证明依赖事件结构才是主因。自回归/逐块生成和增加架构深度等方法能有效提升序列推理性能，但去噪步数本身不增加序列计算。作者将此模式定义为序列性差距，并证明确定性视频预测中，去噪步数无法提供可扩展的序列计算能力。
The Seriality Gap in Video Diffusion Models
When one ball strikes another, then another, video models should predict the consequences of each bounce. In controlled experiments on multi-ball hard-sphere dynamics, we find that the performance of standard bidirectional video diffusion degrades as the causal chain lengthens, even when provided more denoising steps. In a length-matched single-ball control, where ball-ball interactions are absent, the degradation largely disappears, isolating dependent-event structure rather than video length as the cause. Across intervention studies, methods that increase effective serial computation improve performance disproportionately, including autoregressive/blockwise generation and architectural depth. We identify this pattern as the seriality gap: a mismatch between tasks requiring growing serial computation and video diffusion models whose denoising loop does not provide scalable serial compute. We then prove that, for deterministic video prediction, denoising steps do not add serial computation beyond the backbone, indicating a structural obstacle for video diffusion on serial reasoning and simulation tasks.