arXiv:2604. 24196v4 Announce Type: replace-cross Abstract: A drifting model is a one-step generator trained by moving each sample along a field of kernel-weighted attraction toward data samples and repulsion between model samples; training halts once this field vanishes.
By HakGeun Lee, Hyonho Chun
arXiv:2608. 01547v1 Announce Type: cross Abstract: Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations.
By Sam Andersson, Ricky Mol\'en
arXiv:2605.22795v3 Announce Type: replace-cross
Abstract: We analyze finite-particle drifting models for one-step generative modeling. For a conservative velocity given by the difference of the kerne...
By Krishnakumar Balasubramanian
arXiv:2608. 01268v1 Announce Type: cross Abstract: Detecting that a stream of high-dimensional embeddings has changed is usually framed as a choice of statistic.
By Adel Kaleche
arXiv:2603. 15384v2 Announce Type: replace-cross Abstract: We improve and extend persistence spheres, introduced in~\cite{pegoraro2025persistence}.
By Matteo Pegoraro
arXiv:2607. 24662v1 Announce Type: new Abstract: Generative models of temporal graphs are trained on one stretch of an evolving network and deployed on the next, and they degrade badly in the gap.
By Tianpeng Li, Xuan Guo, Wenjun Wang, Wang Zhang, Pengfei Jiao