新闻
Breaking the Uniformity Trap: Scaling Video Diffusion Model via SplitMoE
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- SplitMoE, a new sparse architecture for video diffusion models, splits experts into semantic and generic roles to improve scaling beyond traditional mixture-of-experts approaches.
- 为何重要
- Matters for engineers building or scaling video generation systems who want better quality and efficiency than conventional load-balanced expert routing methods.
- 注意
- Paper is recent arXiv submission accepted to NeurIPS 2026; practical availability and reproducibility of code or models not yet confirmed from this source.
- language model
- rag
- token
- mixture-of-experts
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