: A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding
Dawei Zhu, Rui Meng, Jie-Feng Chen et al.
· 0 citations
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Few-Step Generation via Data-Space Iteration
Flow matching has emerged as a scalable paradigm for training high-quality generative models, but sampling from the learned probability flow requires many network evaluations. Distillation can reduce this cost to one or a few evaluations; however, one-step generation often sacrifices quality, making few-step generation...
Shanchuan Lin, Yan-Song Peng, Fu-Yun Wang et al.
· 0 citations
Jul 2026
Scaling Properties of Text Conditioning in Visual Generation
Surprisingly, it is found that the converged diffusion loss scales with the amount of structured language in the prompt, and two complementary measures are adapted: a white-box likelihood metric (GPG) and a black-box attribute metric (ED).
Zilong Chen, Chao-Rui Deng, Kunchang Li et al.
· arXiv.org · 2 citations