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cs.CL2026
E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models
Arseny Ivanov, Alexander Kolesov, Alexander Korotin +2
Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically factorized over positions, li…
cs.LG2026
TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models
Arseny Ivanov, Sergei Kholkin, Vladislav Gromadskii +3
Log-likelihood is a standard metric for evaluating generative models. Unfortunately, in contrast to autoregressive models (ARMs), discrete diffusion models generally do not admit e…