@inproceedings{zhong-etal-2026-parallelism,
title = "Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow",
author = "Zhong, Yangyang and
Gu, Yanmei and
Zang, Zhengqing and
Li, Xiaomeng and
Ding, Yuqi and
Jia, Xibei and
Shen, Yuting and
Lan, Zhenzhong and
Zhu, Liwang and
Liu, Weiping and
Zhou, Junlin and
Liu, Haisheng and
Yu, Zhong Xin and
Luo, Pengxin and
Qi, Donglian and
Yan, Yunfeng and
Zhao, Junbo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.357/",
pages = "7178--7218",
ISBN = "979-8-89176-395-1",
abstract = "Masked Diffusion Language Models (MDLMs) promise parallel token generation and arbitrary-order decoding, yet it remains unclear to what extent current models truly realize these capabilities. We characterize MDLM behavior along two dimensions{---}parallelism strength and generation order{---}using Average Finalization Parallelism (AFP) and Kendall{'}s {\ensuremath{\tau}}. We evaluate eight mainstream MDLMs (up to 100B parameters) on 58 benchmarks spanning knowledge, reasoning, and programming. The results show that MDLMs still lag behind comparably sized autoregressive models, mainly because parallel probabilistic modeling weakens inter-token dependencies. Meanwhile, MDLMs exhibit adaptive decoding behavior: their parallelism and generation order vary significantly with the task domain, the stage of reasoning, and whether the output is correct. On tasks that require ``backward information'' (e.g., Sudoku), MDLMs adopt a solution order that tends to fill easier Sudoku blanks first, highlighting their advantages. Finally, we provide theoretical motivation and design insights supporting a Generate-then-Edit paradigm, which mitigates dependency loss while retaining the efficiency of parallel decoding."
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<abstract>Masked Diffusion Language Models (MDLMs) promise parallel token generation and arbitrary-order decoding, yet it remains unclear to what extent current models truly realize these capabilities. We characterize MDLM behavior along two dimensions—parallelism strength and generation order—using Average Finalization Parallelism (AFP) and Kendall’s \ensuremathτ. We evaluate eight mainstream MDLMs (up to 100B parameters) on 58 benchmarks spanning knowledge, reasoning, and programming. The results show that MDLMs still lag behind comparably sized autoregressive models, mainly because parallel probabilistic modeling weakens inter-token dependencies. Meanwhile, MDLMs exhibit adaptive decoding behavior: their parallelism and generation order vary significantly with the task domain, the stage of reasoning, and whether the output is correct. On tasks that require “backward information” (e.g., Sudoku), MDLMs adopt a solution order that tends to fill easier Sudoku blanks first, highlighting their advantages. Finally, we provide theoretical motivation and design insights supporting a Generate-then-Edit paradigm, which mitigates dependency loss while retaining the efficiency of parallel decoding.</abstract>
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%0 Conference Proceedings
%T Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow
%A Zhong, Yangyang
%A Gu, Yanmei
%A Zang, Zhengqing
%A Li, Xiaomeng
%A Ding, Yuqi
%A Jia, Xibei
%A Shen, Yuting
%A Lan, Zhenzhong
%A Zhu, Liwang
%A Liu, Weiping
%A Zhou, Junlin
%A Liu, Haisheng
%A Yu, Zhong Xin
%A Luo, Pengxin
%A Qi, Donglian
%A Yan, Yunfeng
%A Zhao, Junbo
%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 zhong-etal-2026-parallelism
%X Masked Diffusion Language Models (MDLMs) promise parallel token generation and arbitrary-order decoding, yet it remains unclear to what extent current models truly realize these capabilities. We characterize MDLM behavior along two dimensions—parallelism strength and generation order—using Average Finalization Parallelism (AFP) and Kendall’s \ensuremathτ. We evaluate eight mainstream MDLMs (up to 100B parameters) on 58 benchmarks spanning knowledge, reasoning, and programming. The results show that MDLMs still lag behind comparably sized autoregressive models, mainly because parallel probabilistic modeling weakens inter-token dependencies. Meanwhile, MDLMs exhibit adaptive decoding behavior: their parallelism and generation order vary significantly with the task domain, the stage of reasoning, and whether the output is correct. On tasks that require “backward information” (e.g., Sudoku), MDLMs adopt a solution order that tends to fill easier Sudoku blanks first, highlighting their advantages. Finally, we provide theoretical motivation and design insights supporting a Generate-then-Edit paradigm, which mitigates dependency loss while retaining the efficiency of parallel decoding.
%U https://aclanthology.org/2026.findings-acl.357/
%P 7178-7218
Markdown (Informal)
[Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow](https://aclanthology.org/2026.findings-acl.357/) (Zhong et al., Findings 2026)
ACL
- Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, and Junbo Zhao. 2026. Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow. In Findings of the Association for Computational Linguistics: ACL 2026, pages 7178–7218, San Diego, California, United States. Association for Computational Linguistics.