@inproceedings{cui-etal-2026-design,
title = "Design First, Code Later: Aesthetically Pleasing Template-Free Slides Generation",
author = "Cui, Zhiyao and
Wang, Chenxu and
Hu, Shuyue and
Zhang, Yiqun and
Shao, Wenqi and
Zhang, Qiaosheng and
Wang, Zhen",
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.1524/",
pages = "30470--30490",
ISBN = "979-8-89176-395-1",
abstract = "Producing presentation slides automatically entails coordinating narrative structure with page-level graphic design under strict spatial constraints. For such structured multimodal tasks, a well-organized design process is essential to ensure the final quality of slides. Existing approaches rely on fixed templates or directly emit executable code, thereby both limiting the creative layout-design capabilities of LLMs and bypassing the essential slide-page design step. To address these limitations, this paper: (1) proposes a hierarchical slides generation workflow \textbf{DeepSlides} that systematically organizes slide design tasks without any predefined template or style, decoupling slide-page design from implementation; (2) introduces \textbf{SlideDesign}, a dataset tailored specifically for slides generation tasks; (3) presents a multi-agent reinforcement learning training paradigm and trains a couple of models \textbf{SlideQwens} for slide design and implementation. Experimental results demonstrate that our proposed framework outperforms baseline methods on evaluated metrics and achieves superior performance in human preference evaluations. The dataset and code are available at: \url{https://anonymous.4open.science/r/DeepSlides-D14D}"
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<abstract>Producing presentation slides automatically entails coordinating narrative structure with page-level graphic design under strict spatial constraints. For such structured multimodal tasks, a well-organized design process is essential to ensure the final quality of slides. Existing approaches rely on fixed templates or directly emit executable code, thereby both limiting the creative layout-design capabilities of LLMs and bypassing the essential slide-page design step. To address these limitations, this paper: (1) proposes a hierarchical slides generation workflow DeepSlides that systematically organizes slide design tasks without any predefined template or style, decoupling slide-page design from implementation; (2) introduces SlideDesign, a dataset tailored specifically for slides generation tasks; (3) presents a multi-agent reinforcement learning training paradigm and trains a couple of models SlideQwens for slide design and implementation. Experimental results demonstrate that our proposed framework outperforms baseline methods on evaluated metrics and achieves superior performance in human preference evaluations. The dataset and code are available at: https://anonymous.4open.science/r/DeepSlides-D14D</abstract>
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%0 Conference Proceedings
%T Design First, Code Later: Aesthetically Pleasing Template-Free Slides Generation
%A Cui, Zhiyao
%A Wang, Chenxu
%A Hu, Shuyue
%A Zhang, Yiqun
%A Shao, Wenqi
%A Zhang, Qiaosheng
%A Wang, Zhen
%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 cui-etal-2026-design
%X Producing presentation slides automatically entails coordinating narrative structure with page-level graphic design under strict spatial constraints. For such structured multimodal tasks, a well-organized design process is essential to ensure the final quality of slides. Existing approaches rely on fixed templates or directly emit executable code, thereby both limiting the creative layout-design capabilities of LLMs and bypassing the essential slide-page design step. To address these limitations, this paper: (1) proposes a hierarchical slides generation workflow DeepSlides that systematically organizes slide design tasks without any predefined template or style, decoupling slide-page design from implementation; (2) introduces SlideDesign, a dataset tailored specifically for slides generation tasks; (3) presents a multi-agent reinforcement learning training paradigm and trains a couple of models SlideQwens for slide design and implementation. Experimental results demonstrate that our proposed framework outperforms baseline methods on evaluated metrics and achieves superior performance in human preference evaluations. The dataset and code are available at: https://anonymous.4open.science/r/DeepSlides-D14D
%U https://aclanthology.org/2026.findings-acl.1524/
%P 30470-30490
Markdown (Informal)
[Design First, Code Later: Aesthetically Pleasing Template-Free Slides Generation](https://aclanthology.org/2026.findings-acl.1524/) (Cui et al., Findings 2026)
ACL
- Zhiyao Cui, Chenxu Wang, Shuyue Hu, Yiqun Zhang, Wenqi Shao, Qiaosheng Zhang, and Zhen Wang. 2026. Design First, Code Later: Aesthetically Pleasing Template-Free Slides Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 30470–30490, San Diego, California, United States. Association for Computational Linguistics.