Attention-Discounted Adaptive Sampler Proposed for Masked Diffusion Language Models

A new arXiv paper introduces an adaptive sampling method for masked diffusion language models that decides which tokens to commit during each denoising step. The approach targets a known failure mode where individually confident positions become unsafe when decoded in parallel, aiming to preserve accuracy while still reducing the number of inference iterations. The work is a revision of an earlier preprint and has not been peer reviewed.