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Support Before Frequency in Discrete Diffusion
Adrian Müller, Antoine Gonon, Zebang Shen +2
Discrete diffusion models are increasingly competitive for language modeling, yet it remains unclear how their denoising objectives organize learning. Although these objectives tar…
Manifold Generalization Provably Proceeds Memorization in Diffusion Models
Zebang Shen, Ya-Ping Hsieh, Niao He
Diffusion models often generate novel samples even when the learned score is only \emph{coarse} -- a phenomenon not accounted for by the standard view of diffusion training as dens…
Verifier-Constrained Flow Expansion for Discovery Beyond the Data
Riccardo De Santi, Kimon Protopapas, Ya-Ping Hsieh +1
Flow and diffusion models are typically pre-trained on limited available data (e.g., molecular samples), covering only a fraction of the valid design space (e.g., the full molecula…
A Unified Density Operator View of Flow Control and Merging
Riccardo De Santi, Malte Franke, Ya-Ping Hsieh +1
Recent progress in large-scale flow and diffusion models raised two fundamental algorithmic challenges: (i) control-based reward adaptation of pre-trained flows, and (ii) integrati…