AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
--> [Submitted on 11 Aug 2026] Title:Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning View a PDF of the paper titled Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning, by Hansen Jin Lillemark and Alex Rojas and Zachary Novack and Runqian Wang and Yilun Du and Yian Ma and Taylor Berg-Kirkpatrick and Rose Yu View PDF HTML (experimental) Abstract:Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedules, limiting inference procedures from adapting to the data. We introduce Equilibrium Forcing (EqF), a simplified framework for video denoising generative models without noise level conditioning. EqF pioneers modular training- and inference-time designs for noise-unconditional generation that decouple learning the denoising field from sampling. This flexibility allows for inference-time algorithms that operate in a closed loop by adapting to feedback from the sample, improving video quality and consistency on challenging autoregressive video generation benchmarks. Extensive analysis elucidates exactly how removing the noise level conditioning enables EqF's data-dependent inference properties to surpass the performance of standard noise level-conditional denoising video methods. Comments: Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.14706 [cs.CV] (or arXiv:2608.14706v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14706 arXiv-issued DOI via DataCite (pending registration) Submission history From: Hansen Lillemark [view email] [v1] Tue, 11 Aug 2026 00:02:32 UTC (9,033 KB) Full-text links: Access Paper: View a PDF of the paper titled Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning, by Hansen Jin Lillemark and Alex Rojas and Zachary Novack and Runqian Wang and Yilun Du and Yian Ma and Taylor Berg-Kirkpatrick and Rose Yu View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-08 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)