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        <title>DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models</title>
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        <namePart type="given">Xueyu</namePart>
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    <abstract>Masked diffusion language models (MDLMs) have recently emerged as a new paradigm in language modeling, offering flexible generation dynamics and enabling efficient parallel decoding. However, existing decoding strategies for pre-trained MDLMs predominantly rely on token-level uncertainty criteria, while largely overlooking sequence-level information and inter-token dependencies. To address this limitation, we propose Dependency-Oriented Sampler (DOS), a training-free decoding strategy that leverages inter-token dependencies to inform token updates during generation. Specifically, DOS exploits attention matrices from transformer blocks to approximate inter-token dependencies, emphasizing information from unmasked tokens when updating masked positions. Empirical results demonstrate that DOS consistently achieves superior performance on both code generation and mathematical reasoning tasks. Moreover, DOS can be seamlessly integrated with existing parallel sampling methods, leading to improved generation efficiency without sacrificing generation quality.</abstract>
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%0 Conference Proceedings
%T DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models
%A Zhou, Xueyu
%A Hu, Yangrong
%A Huang, Jian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhou-etal-2026-dos
%X Masked diffusion language models (MDLMs) have recently emerged as a new paradigm in language modeling, offering flexible generation dynamics and enabling efficient parallel decoding. However, existing decoding strategies for pre-trained MDLMs predominantly rely on token-level uncertainty criteria, while largely overlooking sequence-level information and inter-token dependencies. To address this limitation, we propose Dependency-Oriented Sampler (DOS), a training-free decoding strategy that leverages inter-token dependencies to inform token updates during generation. Specifically, DOS exploits attention matrices from transformer blocks to approximate inter-token dependencies, emphasizing information from unmasked tokens when updating masked positions. Empirical results demonstrate that DOS consistently achieves superior performance on both code generation and mathematical reasoning tasks. Moreover, DOS can be seamlessly integrated with existing parallel sampling methods, leading to improved generation efficiency without sacrificing generation quality.
%R 10.18653/v1/2026.findings-acl.861
%U https://aclanthology.org/2026.findings-acl.861/
%U https://doi.org/10.18653/v1/2026.findings-acl.861
%P 17404-17419

Markdown (Informal)

[DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models](https://aclanthology.org/2026.findings-acl.861/) (Zhou et al., Findings 2026)

• DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models (Zhou et al., Findings 2026)

ACL

• Xueyu Zhou, Yangrong Hu, and Jian Huang. 2026. DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17404–17419, San Diego, California, United States. Association for Computational Linguistics.