Author

Weili Nie

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#machine learning Feb 2026

Mode Seeking meets Mean Seeking for Fast Long Video Generation

This paper proposes a training paradigm where Mode Seeking meets Mean Seeking, decoupling local fidelity from long-term coherence based on a unified representation via a Decoupled Diffusion Transformer, and closes the fidelity-horizon gap by jointly improving local sharpness, motion and long-range consistency.

Shengqu Cai, Weili Nie, Chao Liu et al. · arXiv.org · 12 citations · ⚡2

Jul 2026

Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model

Flex-Forcing is introduced, a unified training and inference framework that enables a video diffusion model to seamlessly operate under both bidirectional and autoregressive generation regimes, and achieves consistently better video quality, long-video stability than strong baselines with a rigid inference schedule.

Xinyin Ma, Julius Berner, Chao Liu et al. · arXiv.org · 1 citation