The paper introduces TRACK, a training‑free trajectory routing method that accelerates video diffusion by selectively switching between large and small models during denoising steps. A calibration process generates a disagreement score map, guiding the selection of the appropriate model at each step to maintain quality while reducing computational cost. Experiments on Wan 2.1, Cosmos 3, TurboDiffusion, and FastVideo show speedups ranging from 1.95× to 2.73× with comparable quality and diversity.
By Mustafa Munir, Huy Vu, Shreyas Misra, Rohit Jena, Sajad Norouzi, Ali Taghibakhshi, Anis Ahmad, Anjul Patney, Pavlo Molchanov, Nima Tajbakhsh
Accelerating Video Diffusion via Training-Free Trajectory Routing (TRACK) introduces a heterogeneous denoising strategy that switches between large and small diffusion models at selected steps, determined by a calibration process that measures disagreement between model predictions. By routing quality-sensitive steps to the large model and low-disagreement steps to the small model, TRACK achieves significant speedups—up to 2.73×—across several video diffusion benchmarks while maintaining comparable quality and diversity. The method requires no retraining, architectural changes, or online dual-model evaluation, making it a practical acceleration paradigm for video diffusion.
Double‑Diffusion is a probabilistic forecasting framework for urban sensor networks that blends a closed‑form graph‑heat prior with a denoising diffusion model. The prior provides a low‑pass, parameter‑free forecast that both serves as the residual target and conditions the denoiser, allowing the reverse diffusion process to start near the prior and refine it over a short chain. Experiments on four real‑world air‑quality and traffic datasets show that Double‑Diffusion achieves the lowest CRPS among probabilistic methods while remaining competitive in point accuracy and requiring far fewer sampling steps than traditional from‑noise diffusion models.
By Hanlin Dong, Arian Prabowo, Hao Xue, Shuang Ao, Tianyi Zhou, Yuxuan Liang, Flora D. Salim
arXiv:2606. 05688v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models scale foundation models efficiently by activating only a subset of experts for each token, but their large number of expert parameters still makes quantization essential for practical deployment.
By Hancheol Park, Geonho Lee, Tairen Piao, Tae-Ho Kim
arXiv:2607. 26618v1 Announce Type: new Abstract: Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples.
By Donghang Duan, Xu Zheng, Lizong Zhang, Chong Mu, Meng Han
The paper examines how Denoising Diffusion Probabilistic Models (DDPMs) perform on globally constrained discrete tasks such as Sudoku and N-queens. It shows that standard diffusion sampling, which keeps updates close to the noisy state, often preserves early mistakes, whereas sampling directly from the model’s clean predictions dramatically improves validity (e.g., Sudoku from 31% to 95%). The authors further introduce self‑correction training, exposing the model to its own predictions to reduce inference errors, which enhances the performance of standard samplers across tasks.
By Mariia Drozdova, St\'ephane Liem Nguyen, Fran\c{c}ois Fleuret