Video diffusion models lack scalable serial computation for sequential reasoning tasks—denoising steps don't add computational depth beyond the backbone network, making them structurally limited for physics simulation and causal prediction.
Video diffusion models struggle with tasks requiring sequential reasoning, like predicting multi-ball collisions. Researchers show this 'seriality gap' stems from the models' inability to scale serial computation as causal chains grow longer, even with more denoising steps. Autoregressive and deeper architectures help, but standard bidirectional diffusion hits a fundamental limit.