@inproceedings{zheng-etal-2026-deeppresenter,
title = "{D}eep{P}resenter: Environment-Grounded Reflection for Agentic Presentation Generation",
author = "Zheng, Hao and
Mo, Guozhao and
Yan, Xinru and
Yuan, Qianhao and
Zhang, Wenkai and
Chen, Xuanang and
Lu, Yaojie and
Lin, Hongyu and
Han, Xianpei and
Sun, Le",
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.1578/",
pages = "31545--31558",
ISBN = "979-8-89176-395-1",
abstract = "Presentation generation requires deep content research, coherent visual design, and iterative refinement based on observation. However, existing presentation agents often rely on predefined workflows and fixed templates. To address this, we present DeepPresenter, an agentic framework that adapts to diverse user intents, enables effective feedback-driven refinement, and generalizes beyond a scripted pipeline. Specifically, DeepPresenter autonomously plans, renders, and revises intermediate slide artifacts to support long-horizon refinement with environmental observations. Furthermore, rather than relying on self-reflection over internal signals (e.g., reasoning traces), our environment-grounded reflection conditions the generation process on perceptual artifact states (e.g., rendered slides), enabling the system to identify and correct presentation-specific issues during execution. Results on the evaluation set covering diverse presentation-generation scenarios show that DeepPresenter achieves state-of-the-art performance, and the fine-tuned DeepPresenter-9B remains highly competitive at substantially lower cost."
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<abstract>Presentation generation requires deep content research, coherent visual design, and iterative refinement based on observation. However, existing presentation agents often rely on predefined workflows and fixed templates. To address this, we present DeepPresenter, an agentic framework that adapts to diverse user intents, enables effective feedback-driven refinement, and generalizes beyond a scripted pipeline. Specifically, DeepPresenter autonomously plans, renders, and revises intermediate slide artifacts to support long-horizon refinement with environmental observations. Furthermore, rather than relying on self-reflection over internal signals (e.g., reasoning traces), our environment-grounded reflection conditions the generation process on perceptual artifact states (e.g., rendered slides), enabling the system to identify and correct presentation-specific issues during execution. Results on the evaluation set covering diverse presentation-generation scenarios show that DeepPresenter achieves state-of-the-art performance, and the fine-tuned DeepPresenter-9B remains highly competitive at substantially lower cost.</abstract>
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%0 Conference Proceedings
%T DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation
%A Zheng, Hao
%A Mo, Guozhao
%A Yan, Xinru
%A Yuan, Qianhao
%A Zhang, Wenkai
%A Chen, Xuanang
%A Lu, Yaojie
%A Lin, Hongyu
%A Han, Xianpei
%A Sun, Le
%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 zheng-etal-2026-deeppresenter
%X Presentation generation requires deep content research, coherent visual design, and iterative refinement based on observation. However, existing presentation agents often rely on predefined workflows and fixed templates. To address this, we present DeepPresenter, an agentic framework that adapts to diverse user intents, enables effective feedback-driven refinement, and generalizes beyond a scripted pipeline. Specifically, DeepPresenter autonomously plans, renders, and revises intermediate slide artifacts to support long-horizon refinement with environmental observations. Furthermore, rather than relying on self-reflection over internal signals (e.g., reasoning traces), our environment-grounded reflection conditions the generation process on perceptual artifact states (e.g., rendered slides), enabling the system to identify and correct presentation-specific issues during execution. Results on the evaluation set covering diverse presentation-generation scenarios show that DeepPresenter achieves state-of-the-art performance, and the fine-tuned DeepPresenter-9B remains highly competitive at substantially lower cost.
%U https://aclanthology.org/2026.findings-acl.1578/
%P 31545-31558
Markdown (Informal)
[DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation](https://aclanthology.org/2026.findings-acl.1578/) (Zheng et al., Findings 2026)
ACL
- Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, and Le Sun. 2026. DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 31545–31558, San Diego, California, United States. Association for Computational Linguistics.