AutoPR:讓您的學術晉升自動化!
AutoPR: Let's Automate Your Academic Promotion!
October 10, 2025
作者: Qiguang Chen, Zheng Yan, Mingda Yang, Libo Qin, Yixin Yuan, Hanjing Li, Jinhao Liu, Yiyan Ji, Dengyun Peng, Jiannan Guan, Mengkang Hu, Yantao Du, Wanxiang Che
cs.AI
摘要
隨著同行評審研究數量的激增,學者們日益依賴社交平台進行學術發現,而作者們則投入大量精力推廣其研究成果,以確保其可見性和引用率。為簡化這一過程並減少對人力的依賴,我們引入了自動推廣(AutoPR)這一新任務,它將研究論文轉化為精確、引人入勝且及時的公開內容。為實現嚴格的評估,我們發布了PRBench,這是一個多模態基準測試,將512篇同行評審文章與高質量的推廣帖子相連接,從三個維度評估系統:忠實度(準確性和語氣)、參與度(受眾定位和吸引力)以及一致性(時機和渠道優化)。我們還介紹了PRAgent,這是一個多代理框架,分三個階段自動化AutoPR:通過多模態準備進行內容提取,協作合成以產出精煉的成果,以及針對特定平台的適應性調整,以優化規範、語氣和標籤,實現最大覆蓋。與PRBench上的直接LLM管道相比,PRAgent展現了顯著的改進,包括總觀看時間增加了604%,點贊數上升了438%,整體參與度至少提升了2.9倍。消融研究表明,平台建模和定向推廣對這些增益貢獻最大。我們的成果將AutoPR定位為一個可處理、可測量的研究問題,並為可擴展、有影響力的自動化學術交流提供了路線圖。
English
As the volume of peer-reviewed research surges, scholars increasingly rely on
social platforms for discovery, while authors invest considerable effort in
promoting their work to ensure visibility and citations. To streamline this
process and reduce the reliance on human effort, we introduce Automatic
Promotion (AutoPR), a novel task that transforms research papers into accurate,
engaging, and timely public content. To enable rigorous evaluation, we release
PRBench, a multimodal benchmark that links 512 peer-reviewed articles to
high-quality promotional posts, assessing systems along three axes: Fidelity
(accuracy and tone), Engagement (audience targeting and appeal), and Alignment
(timing and channel optimization). We also introduce PRAgent, a multi-agent
framework that automates AutoPR in three stages: content extraction with
multimodal preparation, collaborative synthesis for polished outputs, and
platform-specific adaptation to optimize norms, tone, and tagging for maximum
reach. When compared to direct LLM pipelines on PRBench, PRAgent demonstrates
substantial improvements, including a 604% increase in total watch time, a 438%
rise in likes, and at least a 2.9x boost in overall engagement. Ablation
studies show that platform modeling and targeted promotion contribute the most
to these gains. Our results position AutoPR as a tractable, measurable research
problem and provide a roadmap for scalable, impactful automated scholarly
communication.