arXiv cs.AI
· Papers
The data geometry of masking diffusion: Certified-optimal schedules via unmasking growth complexity
arXiv:2608.13520v1 Announce Type: cross Abstract: We study masking diffusion for discrete sampling and introduce a path-resolved measure of data geometry called the emph{unmasking growth complexity} ({textsf{UGC}xspace}). Its local increments directly control Kullback--Leibler (KL) discretization error, yielding a u