DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation
Summary: arXiv:2602.22839v2 Announce Type: replace
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.
Introduction
In the evolving landscape of artificial intelligence, the ability to create compelling and effective presentations has become increasingly important. Traditional presentation tools and agents have limitations, predominantly due to their reliance on rigid templates and workflows. DeepPresenter emerges as a groundbreaking solution aimed at enhancing the presentation generation process by incorporating environmental feedback and user-specific requirements.
Key Features of DeepPresenter
- Autonomous Planning: DeepPresenter is designed to autonomously plan the presentation structure, ensuring that the flow of content is logical and engaging.
- Dynamic Rendering: The framework renders slides in real-time, adapting visual elements based on the content being presented and user preferences.
- Iterative Refinement: Feedback-driven refinement allows the system to revise intermediate artifacts, improving the overall quality of the presentation.
- Environment-Grounded Reflection: Unlike existing systems that rely on internal signals for self-reflection, DeepPresenter utilizes perceptual artifact states, ensuring that the generation process is closely aligned with the current presentation context.
Methodology
DeepPresenter employs cutting-edge techniques in artificial intelligence to ensure that each presentation generated is not only coherent but also tailored to the user’s intent. By leveraging an environment-grounded approach, it continuously monitors the state of rendered slides, allowing for real-time adjustments and corrections. This methodology represents a significant shift from traditional models that often struggle with flexibility and context-awareness.
Evaluation and Results
Extensive evaluations have been conducted to assess DeepPresenter’s performance across diverse presentation-generation scenarios. The results demonstrate that DeepPresenter achieves state-of-the-art performance metrics, surpassing traditional agents in both quality and adaptability. Notably, the fine-tuned 9B model of DeepPresenter remains competitive while operating at a substantially lower cost, making it an attractive option for users seeking effective presentation solutions.
Conclusion
DeepPresenter represents a significant advancement in the field of presentation generation, combining agentic capabilities with a user-centered approach. By integrating environmental feedback and adaptive processes, it not only enhances the quality of presentations but also empowers users to engage more deeply with their content. This project is available for further exploration and implementation at GitHub: PPTAgent.