1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026★ 1 cited
From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training
Jinwen Wang, Youfang Lin, Xiaobo Hu +4
Unsupervised pre-training on large-scale datasets has demonstrated significant potential for improving the sample efficiency and performance of Reinforcement Learning (RL). Given t…
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.CV2026
The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics
Xiangbo Gao, Mingyang Wu, Siyuan Yang +4
While recent generative video models have achieved remarkable visual realism and are being explored as world models, true physical simulation requires mastering both space and time…