@inproceedings{he-etal-2026-fouriersampler,
title = "{F}ourier{S}ampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation",
author = "He, Siyang and
Wang, Qiqi and
Liu, Xiaoran and
Ma, Hongnan and
Shi, Yiwei and
Song, Yuerong and
Zhu, Ying and
Liang, Tianyi and
Huang, Zengfeng and
He, Ziwei and
Qiu, Xipeng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1851/",
pages = "39525--39542",
ISBN = "979-8-89176-462-0",
abstract = "Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies exhibit positional bias, failing to fully unlock the potential of arbitrary-order generation. In this work, we delve into the inherent spectral characteristics of dLLMs and present the first frequency-domain analysis of dLLMs, showing that low-frequency components in hidden states primarily encode global structural information and long-range dependencies, while high-frequency components capture local details. Based on this observation, we propose FourierSampler, which leverages a frequency-domain sliding window mechanism to dynamically guide the model to achieve ``structure-to-detail'' generation. FourierSampler outperforms other inference enhancement strategies on LLaDA and SDAR, achieving relative task-specific gains up to 20.4{\%} on LLaDA-1.5 and 16.0{\%} on LLaDA-8B-Instruct, as well as average gains up to 8.6{\%} across tasks."
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%0 Conference Proceedings
%T FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation
%A He, Siyang
%A Wang, Qiqi
%A Liu, Xiaoran
%A Ma, Hongnan
%A Shi, Yiwei
%A Song, Yuerong
%A Zhu, Ying
%A Liang, Tianyi
%A Huang, Zengfeng
%A He, Ziwei
%A Qiu, Xipeng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F he-etal-2026-fouriersampler
%X Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies exhibit positional bias, failing to fully unlock the potential of arbitrary-order generation. In this work, we delve into the inherent spectral characteristics of dLLMs and present the first frequency-domain analysis of dLLMs, showing that low-frequency components in hidden states primarily encode global structural information and long-range dependencies, while high-frequency components capture local details. Based on this observation, we propose FourierSampler, which leverages a frequency-domain sliding window mechanism to dynamically guide the model to achieve “structure-to-detail” generation. FourierSampler outperforms other inference enhancement strategies on LLaDA and SDAR, achieving relative task-specific gains up to 20.4% on LLaDA-1.5 and 16.0% on LLaDA-8B-Instruct, as well as average gains up to 8.6% across tasks.
%U https://aclanthology.org/2026.findings-emnlp.1851/
%P 39525-39542
Markdown (Informal)
[FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation](https://aclanthology.org/2026.findings-emnlp.1851/) (He et al., Findings 2026)
ACL
- Siyang He, Qiqi Wang, Xiaoran Liu, Hongnan Ma, Yiwei Shi, Yuerong Song, Ying Zhu, Tianyi Liang, Zengfeng Huang, Ziwei He, and Xipeng Qiu. 2026. FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 39525–39542, Budapest, Hungary. Association for Computational Linguistics.