DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation
#DeepPresenter #Agentic framework #Presentation generation #Environment-grounded reflection #AI research #arXiv #Machine learning
📌 Key Takeaways
- DeepPresenter is an agentic framework that overcomes limitations of rigid presentation systems
- The framework uses environment-grounded reflection rather than self-reflection for better presentation refinement
- DeepPresenter autonomously plans, renders, and revises slide artifacts for long-horizon refinement
- The system achieves state-of-the-art performance while maintaining cost efficiency with a 9B model
📖 Full Retelling
Researchers led by Hao Zheng and 9 other authors introduced DeepPresenter, an innovative agentic framework for presentation generation, on arXiv on February 26, 2026, aiming to overcome limitations in current presentation systems that depend on rigid workflows and fixed templates. The new system represents a significant advancement in AI-powered content creation, addressing the complex requirements of presentation generation which necessitates deep content research, coherent visual design, and iterative refinement based on observation. Unlike existing presentation agents that operate within constrained parameters, DeepPresenter demonstrates remarkable adaptability to diverse user intentions while maintaining effective feedback-driven refinement capabilities. The framework autonomously plans, renders, and revises intermediate slide artifacts to support long-horizon refinement with environmental observations, enabling more natural and contextually appropriate presentation development. What distinguishes DeepPresenter is its environment-grounded reflection mechanism, which conditions the generation process on perceptual artifact states rather than relying solely on internal signals like reasoning traces. This approach allows the system to identify and correct presentation-specific issues during execution, resulting in more polished and effective final products. Evaluation results across diverse presentation-generation scenarios confirm that DeepPresenter achieves state-of-the-art performance, with the fine-tuned 9B model remaining highly competitive while operating at substantially lower computational costs, making advanced presentation generation more accessible to researchers and practitioners.
🏷️ Themes
Artificial Intelligence, Presentation Generation, Agent Frameworks, Machine Learning
📚 Related People & Topics
Artificial intelligence
Intelligence of machines
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Machine learning
Study of algorithms that improve automatically through experience
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22839 [Submitted on 26 Feb 2026] Title: DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation Authors: Hao Zheng , Guozhao Mo , Xinru Yan , Qianhao Yuan , Wenkai Zhang , Xuanang Chen , Yaojie Lu , Hongyu Lin , Xianpei Han , Le Sun View a PDF of the paper titled DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation, by Hao Zheng and 9 other authors View PDF HTML 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 9B model remains highly competitive at substantially lower cost. Our project is available at: this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22839 [cs.AI] (or arXiv:2602.22839v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22839 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hao Zheng [ view email ] [v1] Thu, 26 Feb 2026 10:26:48 UTC (3,412 KB) Full-tex...
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