Author

Chao Chen

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#artificial intelligence Preprint Sep 2026

JIVE: Jacobian-Informed Volume Expansion for Diverse Generative Sampling

JIVE is introduced, a training-free framework that enhances generative diversity by injecting velocity perturbations aligned with the leading right singular subspace of the generator's endpoint Jacobian, which provably maximizes endpoint diversity while preserving sample quality.

Guang-Xun Zhang, Brian Cai, Boxuan Zhang et al. · 0 citations

Preprint Aug 2026

Model the Edit, Not the Image: Visual Autoregressive Editing from a Source-Centric Perspective

This work takes a source-centric perspective on VAR editing, in which the encoded source image tokens serve as the primary visual state and the editing process focuses on condition-induced changes, and proposes EditMod, which compares source- and target-conditioned predictions under a shared autoregressive context.

Hongyi Fang, Chu-Wen Xie, Ben-Jia Zhou et al. · 0 citations