arXiv:2606. 26769v1 Announce Type: new Abstract: The adoption of powerful diffusion models is hindered by their significant inference latency.
By Qicheng Zhao, Yu Li, Qi Sun, Zheyu Yan
arXiv:2608. 07572v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation.
By Jinlong Yang, Jinke Wu, Lizilin, Yao Zhou
arXiv:2607. 22599v1 Announce Type: new Abstract: Diffusion models have become a widely used framework for probabilistic time series forecasting, modeling the distribution of future values given an observed history.
By Chen Su, Yuanhe Tian, Yan Song
arXiv:2506. 13058v2 Announce Type: replace-cross Abstract: Diffusion probabilistic models (DPMs) have demonstrated remarkable success in visual generation.
By Hu Yu, Hao Luo, Xueyang Fu, Jie Huang, Fan Wang, Feng Zhao
arXiv:2512. 19643v2 Announce Type: replace Abstract: Numerical simulation of time-dependent partial differential equations (PDEs) is central to scientific and engineering applications, but high-fidelity solvers are often prohibitively expensive for long-horizon or time-critical settings.
By Rajyasri Roy, Dibyajyoti Nayak, Somdatta Goswami
STITCH-OPE is a model‑based generative framework that uses denoising diffusion to perform off‑policy evaluation (OPE) in high‑dimensional, long‑horizon settings. It generates synthetic trajectories for a target policy by guiding a diffusion model trained on behavior data, subtracting the behavior policy’s score to avoid over‑regularization and stitching partial trajectories to extend horizon length. The authors provide theoretical variance‑reduction guarantees and demonstrate improved mean squared error, correlation, and regret on D4RL and OpenAI Gym benchmarks.
By Hossein Goli, Michael Gimelfarb, Nathan Samuel de Lara, Haruki Nishimura, Masha Itkina, Florian Shkurti