Preacher: Paper-to-Video Agentic System
Jingwei Liu1,2111Work done during an internship at DAMO Academy. Ling Yang6222Contributed equally. Ling Yang, Hao Luo2,3 Fan Wang2 Hongyan Li1,4,5 333Corresponding Authors: Hongyan Li, Mengdi Wang Mengdi Wang6 333Corresponding Authors: Hongyan Li, Mengdi Wang 1School of Intelligence Science and Technology, Peking University 2DAMO Academy, Alibaba group, 310023, Hangzhou, China 3Hupan Lab, 310023, Hangzhou, China 4 National Key Laboratory of General Artificial Intelligence, Peking University 5 PKU-Wuhan Institude of Artificial Intelligence 6 Department of Electrical and Computer Engineering, Princeton University
Abstract
The paper-to-video task converts a research paper into a structured video abstract, distilling key concepts, methods, and conclusions into an accessible, well-organized format. While state-of-the-art video generation models demonstrate potential, they are constrained by limited context windows, rigid video duration constraints, limited stylistic diversity, and an inability to represent domain-specific knowledge. To address these limitations, we introduce Preacher, the first paper-to-video agentic system. Preacher employs a top-down approach to decompose, summarize, and reformulate the paper, followed by bottom-up video generation, synthesizing diverse video segments into a coherent abstract. To align cross-modal representations, we define key scenes and introduce a Progressive Chain of Thought (P-CoT) for granular, iterative planning. Preacher successfully generates high-quality video abstracts across five research fields, demonstrating expertise beyond current video generation models. Code will be released at: https://github.com/Gen-Verse/Paper2Video
原文 arXiv:2508.09632;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2508.09632v6