Theoretical Constraints on the Expressive Power of RoPE-based Tensor Attention Transformers. - Xiaoyu Li 001; Yingyu Liang; Zhenmei Shi; Zhao Song 002; Mingda Wan (CoRR, 2024)

NRFlow: Towards Noise-Robust Generative Modeling via High-Order Mechanism. - Bo Chen 029; Chengyue Gong; Xiaoyu Li 001; Yingyu Liang; Zhizhou Sha; Zhenmei Shi; Zhao Song 002; Mingda Wan; Xugang Ye (UAI, 2025)

High-Order Matching for One-Step Shortcut Diffusion Models. - Bo Chen 029; Chengyue Gong; Xiaoyu Li 001; Yingyu Liang; Zhizhou Sha; Zhenmei Shi; Zhao Song 002; Mingda Wan (CoRR, 2025)

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling. - Yang Cao 020; Bo Chen 029; Xiaoyu Li 001; Yingyu Liang; Zhizhou Sha; Zhenmei Shi; Zhao Song 002; Mingda Wan (CoRR, 2025)

HOFAR: High-Order Augmentation of Flow Autoregressive Transformers. - Yingyu Liang; Zhizhou Sha; Zhenmei Shi; Zhao Song 002; Mingda Wan (CoRR, 2025)