DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation

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 DEEPPRESEN-TER, an agentic framework that adapts to diverse user intents, enables effective feedbackdriven refinement, and generalizes beyond a scripted pipeline. Specifically, DEEPPRESEN-TER autonomously plans, renders, and revises intermediate slide artifacts to support longhorizon 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. Our project is available at: https: //github.com/icip-cas/PPTAgent