DEMASK adds a lightweight pairwise-dependency predictor to dLLMs and uses greedy selection to enable parallel unmasking whose total-variation error is provably bounded under sub-additivity.
arXiv preprint arXiv:2509.22738 , year=
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Fast-dLLM++ generalizes Fast-dLLM decoding to heterogeneous confidence profiles via Fréchet profile selection, delivering up to 37% throughput gains on GSM8K, MATH, HumanEval, and MBPP with LLaDA-8B.
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Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models
DEMASK adds a lightweight pairwise-dependency predictor to dLLMs and uses greedy selection to enable parallel unmasking whose total-variation error is provably bounded under sub-additivity.