The Seriality Gap in Video Diffusion Models
CatAstro_Piyush · x · 2026-07-17
This thread discusses whether video diffusion models can truly predict long-chain causal events, focusing on a phenomenon known as the seriality gap: standard video diffusion models degrade significantly as dependency chains grow longer, and the author argues that simply adding more compute won't fix this.
The thread also proposes a stronger theoretical claim: diffusion models might not be "true sequence models" like RNNs, functioning more like a fixed-depth network. If true, they shouldn't be expected to reliably handle video tasks requiring long sequences of step-by-step reasoning.
Related event: Video Diffusion Models Expose Seriality Gap(2 posts)→
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