---
title: "DeepPresenter — paper by Zheng et al. (2026)"
description: "Presenter creates a design plan and builds each slide as HTML from scratch. 32 references, cited by 3 papers here."
authors: ["Hao Zheng", "Guozhao Mo", "Xinru Yan", "Qianhao Yuan", "Wenkai Zhang", "Xuanang Chen", "Yaojie Lu", "Hongyu Lin", "Xianpei Han", "Le Sun"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1578/"
doi: "10.18653/v1/2026.findings-acl.1578"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zheng-2026-deeppresenter-environment-grounded-reflection-agentic/"
---
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# DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation

**Authors:** [Hao Zheng](https://md.research.elamur.ai/free/en/authors/hao-zheng/), [Guozhao Mo](https://md.research.elamur.ai/free/en/authors/guozhao-mo/), Xinru Yan, [Qianhao Yuan](https://md.research.elamur.ai/free/en/authors/qianhao-yuan/), [Wenkai Zhang](https://md.research.elamur.ai/free/en/authors/wenkai-zhang/), [Xuanang Chen](https://md.research.elamur.ai/free/en/authors/xuanang-chen/), [Yaojie Lu](https://md.research.elamur.ai/free/en/authors/yaojie-lu/), [Hongyu Lin](https://md.research.elamur.ai/free/en/authors/hongyu-lin/), [Xianpei Han](https://md.research.elamur.ai/free/en/authors/xianpei-han/) and [Le Sun](https://md.research.elamur.ai/free/en/authors/le-sun/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1578/) · [PDF](https://aclanthology.org/2026.findings-acl.1578.pdf) · [DOI 10.18653/v1/2026.findings-acl.1578](https://doi.org/10.18653/v1/2026.findings-acl.1578) · [OpenAlex](https://openalex.org/W7166855899)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/zheng-2026-deeppresenter-environment-grounded-reflection-agentic/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Building slide decks automatically requires solid research, a visual style that fits the topic, and repeated correction. Existing agents often fall short because they follow fixed pipelines and generic templates. They also check their own code or reasoning, which cannot reveal problems that only appear once a slide is rendered.

DeepPresenter addresses this with two agents that share one backbone model and communicate through a common file system:
- **Researcher** explores sources and writes a structured markdown manuscript.
- **Presenter** creates a design plan and builds each slide as HTML from scratch.

Reflection is tied to what the environment shows. An inspect tool renders slides into images or reports the manuscript's status, and the agent then plans targeted fixes.

The authors also distilled a compact model, DeepPresenter-9B, from synthesized trajectories. During synthesis, an independent critic judged the outputs to reduce self-verification bias.

On 128 held-out tasks, DeepPresenter outperformed open-source systems and the commercial tool Gamma. The 9B model came close to GPT-5 at much lower cost, which makes good automatic presentation generation more affordable.

## Key points
- **Task collection:** 1,152 tasks, of which 1,024 were used for trajectory synthesis and 128 held out for evaluation.
  - Sources: PersonaHub, arXiv and FinePDFs-Edu.
  - Languages: 603 English and 549 Chinese.
  - Each task has checkable constraints (slide count, language, aspect ratio). GLM-4.6 generated the persona-based tasks.
- **Evaluation:** systems were scored on constraint satisfaction, content quality and visual style.
  - DeepPresenter with proprietary backbones averaged 4.44, ahead of Gamma at 4.36.
  - Its scores ranged from 3.82 to 4.44 depending on the backbone, while baseline frameworks stayed roughly flat across backbones.
- **Cost-efficiency:** DeepPresenter-9B scored 4.19, versus GPT-5 at 4.22. At similar cost it clearly beat KCTV + Gemini-3-Pro, which scored 3.83.
- **Failure analysis:**
  - Environment failures (context overflow, infrastructure issues): 32.3%.
  - Constraint violations: 13.5%.
  - Consistency errors: 11.2%.
- **Limitations:** the external critic is used only during training-data synthesis, not at inference time. Multi-step tool rollouts are also costly and fragile. Code is available at github.com/icip-cas/PPTAgent.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 246 works · 24,951 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 188 works · 8,743 citations
- [Institute of Software](https://md.research.elamur.ai/free/en/labs/institute-of-software/) — Research institute, Beijing, China · 27 works · 4,979 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works

## Cite

Cite as: 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. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zheng2026deeppresenter,
  title = {{DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation}},
  author = {Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han and Le Sun},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1578},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1578}
}
```

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32. Yuheng Yang, Wenjia Jiang, Yang Wang, Yiwei Wang, and Chi Zhang. 2025b. Auto-slides: An interactive multi-agent system for creating and customizing research presentations. arXiv preprint arXiv:2509.11062. — [arXiv:2509.11062](https://arxiv.org/abs/2509.11062) [find](https://scholar.google.com/scholar?q=Yuheng+Yang%2C+Wenjia+Jiang%2C+Yang+Wang%2C+Yiwei+Wang%2C+and+Chi+Zhang%2E+2025b%2E+Auto%2Dslides%3A+An+interactive+multi%2Dagent+system+for+creating+and+customizing+research+presentations%2E+arXiv+preprint+arXiv%3A2509%2E11)

*Updated 2 November 2026*