Jul 10, 2026, 4:00 AM

SAGA: Stable Acceleration Guidance for Autoregressive Video Generation

TickrWire Editorial Desk·Jul 10, 2026, 4:00 AM·1 min read AI-assisted, human-reviewed

Reported by arXiv cs.CV: SAGA: Stable Acceleration Guidance for Autoregressive Video Generation. Analysis and context written by TickrWire.

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arXiv:2607.08020v1 Announce Type: new Abstract: Autoregressive video diffusion enables efficient streaming and long-horizon video generation, but repeatedly reusing generated latents as causal context can amplify temporal errors, resulting in flickering, motion jitter, and structural drift. In this paper, we investigate this failure mode from a spectral kinematic perspective and identify discrete latent acceleration as an effective signal for revealing unstable high-frequency temporal perturbations. To this end, we propose SAGA, a training-free \textbf{\textit{s}}table \textbf{\textit{a}}ccel

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arXiv:2607.08020v1 Announce Type: new

Abstract: Autoregressive video diffusion enables efficient streaming and long-horizon video generation, but repeatedly reusing generated latents as causal context can amplify temporal errors, resulting in flickering, motion jitter, and structural drift. In this paper, we investigate this failure mode from a spectral kinematic perspective and identify discrete latent acceleration as an effective signal for revealing unstable high-frequency temporal perturbations. To this end, we propose SAGA, a training-free \textbf{\textit{s}}table \textbf{\textit{a}}ccel

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