AI RESEARCH
Stability-Weighted Decoding for Diffusion Language Models
arXiv CS.LG
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ArXi:2604.17068v1 Announce Type: cross Diffusion large language models (dLLMs) enable parallel text generation by iteratively denoising a fully masked sequence, unmasking a subset of masked tokens at each step. Existing decoding strategies rely on static confidence metrics computed at a single denoising step, ignoring temporal history and often leading to premature unmasking of unstable tokens.