2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2026
Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation
Boxun Xu, Yuming Du, Zichang Liu +7
We introduce Sparse Forcing, a training-and-inference paradigm for autoregressive video diffusion models that improves long-horizon generation quality while reducing decoding laten…
cs.LG2026
ScaleBITS: Scalable Bitwidth Search for Hardware-Aligned Mixed-Precision LLMs
Xinlin Li, Timothy Chou, Josh Fromm +3
Post-training weight quantization is crucial for reducing the memory and inference cost of large language models (LLMs), yet pushing the average precision below 4 bits remains chal…
cs.CL2026★ 2 cited
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…