DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation
arXiv:2602.22839v1 Announce Type: new 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 presentati
arXiv:2602.22839v1 Announce Type: new 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: https://github.com/icip-cas/PPTAgent
Executive Summary
The article presents DeepPresenter, an agentic framework for presentation generation that adapts to diverse user intents and enables effective feedback-driven refinement. DeepPresenter autonomously plans, renders, and revises intermediate slide artifacts, leveraging environmental observations to identify and correct presentation-specific issues during execution. Results show state-of-the-art performance and competitive performance at lower cost. The project is available on GitHub, providing a valuable resource for researchers and practitioners. This framework has significant implications for presentation generation, enabling more dynamic and effective communication. Its adaptability and ability to incorporate feedback make it a valuable tool for various applications, including education, business, and public speaking.
Key Points
- ▸ DeepPresenter is an agentic framework for presentation generation that adapts to diverse user intents.
- ▸ The framework enables effective feedback-driven refinement and generalizes beyond a scripted pipeline.
- ▸ DeepPresenter leverages environmental observations to identify and correct presentation-specific issues during execution.
Merits
Innovative Approach
DeepPresenter's environment-grounded reflection and feedback-driven refinement mechanism represent a novel and effective approach to presentation generation, addressing the limitations of existing frameworks.
Adaptability
The framework's adaptability to diverse user intents and ability to incorporate feedback make it a valuable tool for various applications.
Demerits
Limited Evaluation
The article primarily focuses on the framework's performance on a specific evaluation set, which may not fully represent the complexity of real-world presentation generation scenarios.
Scalability
The framework's performance and scalability at lower cost may be limited to specific scenarios, and further evaluation is needed to determine its robustness in diverse settings.
Expert Commentary
DeepPresenter represents a significant advancement in presentation generation, addressing the limitations of existing frameworks and providing a more dynamic and effective approach to communication. Its adaptability and ability to incorporate feedback make it a valuable tool for various applications, and its implications for communication policies and strategies are substantial. However, further evaluation is needed to determine its robustness in diverse settings and to fully understand its potential benefits and limitations.
Recommendations
- ✓ Develop and evaluate the framework in diverse presentation generation scenarios to determine its scalability and robustness.
- ✓ Explore the framework's potential applications in education, business, and public speaking, and investigate its implications for communication policies and strategies.
