Transforming scientific papers into multimodal presentation content is essential for research dissemination but remains labor intensive. Existing automated solutions typically treat each format as an isolated downstream task, leading to redundant processing and semantic inconsistency. We introduce PaperX, a unified framework that models academic presentation generation as a structural transformation and rendering process. Central to our approach is the Scholar DAG, an intermediate representation that decouples the paper's logical structure from its final presentation syntax. By applying adaptive graph traversal strategies, PaperX generates diverse, high quality outputs from a single source. Comprehensive evaluations demonstrate that our framework achieves the state of the art performance in content fidelity and aesthetic quality while significantly improving cost efficiency compared to specialized single task agents.

翻译：将科学论文转化为多模态演示内容对于研究传播至关重要，但目前仍是一项劳动密集型工作。现有的自动化解决方案通常将每种格式视为独立的下游任务，导致冗余处理和语义不一致。我们提出了PaperX，一个将学术演示生成建模为结构转换与渲染过程的统一框架。我们方法的核心是学者DAG，这是一种中间表示，它将论文的逻辑结构与最终演示语法解耦。通过应用自适应图遍历策略，PaperX能够从单一源生成多样化、高质量的输出。综合评估表明，我们的框架在内容保真度和美学质量方面达到了最先进的性能，同时与专用的单任务代理相比，显著提高了成本效益。