@inproceedings{han-etal-2026-c2dlm,
title = "{C}$^2${DLM}: Causal Concept-Guided Diffusion Large Language Models",
author = "Han, Kairong and
Shan, Nuanqiao and
Zhao, Ziyu and
Hu, Zijing and
Dong, Xinpeng and
Jian, Ye Jun and
Pan, Lujia and
Wu, Fei and
Kuang, Kun",
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.40/",
pages = "821--838",
ISBN = "979-8-89176-395-1",
abstract = "Autoregressive (AR) language models and Diffusion Language Models (DLMs) constitute the two principal paradigms of large language models. However, both paradigms suffer from insufficient reasoning capabilities. Human reasoning inherently relies on causal knowledge and thought, which are reflected in natural language. But in the AR paradigm, language is modeled as next token prediction (a strictly left-to-right, token-by-token order), whereas natural language itself exhibits more flexible causal structures. In the DLM paradigm, the attention mechanism is fully connected, which entirely disregards causal order. To fill this gap, we propose the Causal Concept-Guided Diffusion Language Model (C$^2$DLM). Starting from DLM{'}s fully connected attention, C$^2$DLM first obtains a concept-level causal graph from the teacher model, and then explicitly guides attention to learn causal relationships between concepts. By focusing on causal relationships and avoiding interference from difficult subgoals involving causal inversion, C$^2$DLM achieves a 12{\%} improvement and a 3.2{\texttimes} training speedup on the COT-OrderPerturb task, along with an average gain of 1.31{\%} across six downstream reasoning tasks. Code and data are available {~}here."
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<abstract>Autoregressive (AR) language models and Diffusion Language Models (DLMs) constitute the two principal paradigms of large language models. However, both paradigms suffer from insufficient reasoning capabilities. Human reasoning inherently relies on causal knowledge and thought, which are reflected in natural language. But in the AR paradigm, language is modeled as next token prediction (a strictly left-to-right, token-by-token order), whereas natural language itself exhibits more flexible causal structures. In the DLM paradigm, the attention mechanism is fully connected, which entirely disregards causal order. To fill this gap, we propose the Causal Concept-Guided Diffusion Language Model (C²DLM). Starting from DLM’s fully connected attention, C²DLM first obtains a concept-level causal graph from the teacher model, and then explicitly guides attention to learn causal relationships between concepts. By focusing on causal relationships and avoiding interference from difficult subgoals involving causal inversion, C²DLM achieves a 12% improvement and a 3.2× training speedup on the COT-OrderPerturb task, along with an average gain of 1.31% across six downstream reasoning tasks. Code and data are available here.</abstract>
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%0 Conference Proceedings
%T C²DLM: Causal Concept-Guided Diffusion Large Language Models
%A Han, Kairong
%A Shan, Nuanqiao
%A Zhao, Ziyu
%A Hu, Zijing
%A Dong, Xinpeng
%A Jian, Ye Jun
%A Pan, Lujia
%A Wu, Fei
%A Kuang, Kun
%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 han-etal-2026-c2dlm
%X Autoregressive (AR) language models and Diffusion Language Models (DLMs) constitute the two principal paradigms of large language models. However, both paradigms suffer from insufficient reasoning capabilities. Human reasoning inherently relies on causal knowledge and thought, which are reflected in natural language. But in the AR paradigm, language is modeled as next token prediction (a strictly left-to-right, token-by-token order), whereas natural language itself exhibits more flexible causal structures. In the DLM paradigm, the attention mechanism is fully connected, which entirely disregards causal order. To fill this gap, we propose the Causal Concept-Guided Diffusion Language Model (C²DLM). Starting from DLM’s fully connected attention, C²DLM first obtains a concept-level causal graph from the teacher model, and then explicitly guides attention to learn causal relationships between concepts. By focusing on causal relationships and avoiding interference from difficult subgoals involving causal inversion, C²DLM achieves a 12% improvement and a 3.2× training speedup on the COT-OrderPerturb task, along with an average gain of 1.31% across six downstream reasoning tasks. Code and data are available here.
%U https://aclanthology.org/2026.findings-acl.40/
%P 821-838
Markdown (Informal)
[C2DLM: Causal Concept-Guided Diffusion Large Language Models](https://aclanthology.org/2026.findings-acl.40/) (Han et al., Findings 2026)
ACL
- Kairong Han, Nuanqiao Shan, Ziyu Zhao, Zijing Hu, Xinpeng Dong, Ye Jun Jian, Lujia Pan, Fei Wu, and Kun Kuang. 2026. C2DLM: Causal Concept-Guided Diffusion Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 821–838, San Diego, California, United States. Association for Computational Linguistics.