AutoPR: Let's Automate Your Academic Promotion!
Authors: name: Qiguang Chen, name: Zheng Yan, name: Mingda Yang, name: Libo Qin, name: Yixin Yuan, name: Hanjing Li, name: Jinhao Liu, name: Yiyan Ji, name: Dengyun Peng, name: Jiannan Guan, name: Mengkang Hu, name: Yantao Du, name: Wanxiang Che

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.