DLAIJan 30

PaperX: A Unified Framework for Multimodal Academic Presentation Generation with Scholar DAG

arXiv:2602.03866v3h-index: 4
Originality Highly original
AI Analysis

This addresses the problem of automated research dissemination for scholars, offering a more efficient and consistent solution compared to existing isolated methods.

The paper tackles the labor-intensive task of generating multimodal presentations from scientific papers by introducing PaperX, a unified framework that uses a Scholar DAG representation to decouple structure from syntax, achieving state-of-the-art performance in content fidelity and aesthetic quality with improved cost efficiency.

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.

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