arXiv:2607. 12616v1 Announce Type: new Abstract: Continuous-time generative frameworks construct probability paths between base and target domains by optimizing time-dependent velocity fields.
By Shuchan Wang
arXiv:2607. 21427v1 Announce Type: new Abstract: Discrete flow matching provides a flexible framework for generative modeling on discrete structures.
By Daniil Cherniavskii, Daniel Severo, Karen Ullrich
High-fidelity image generation faces a trade-off between speed and quality. Diffusion models produce strong visuals but require costly iterative sampling.
arXiv:2511. 10841v3 Announce Type: replace-cross Abstract: Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge.
By YongKyung Oh, Dong-Young Lim, Sungil Kim
arXiv:2606. 07481v1 Announce Type: new Abstract: While Computational Fluid Dynamics (CFD) provides high-fidelity flow fields for optimizing indoor environments, its computational cost limits rapid exploration.
By Chris R. Jung, Markus D\"orr, Natalie J\"ungling, Jennifer Niessner, Adam T. M\"uller, Nicolaj C. Stache
arXiv:2606. 00140v1 Announce Type: cross Abstract: While the rapid adoption of multimodal generative models offers immense potential, it has also increased the risks of harmful content synthesis, deepfakes, and copyright infringements.
By Jonas Henry Grebe, Tobias Braun, Anna Rohrbach, Marcus Rohrbach