AutoPR: Let's Automate Your Academic Promotion!

• URL: http://arxiv.org/abs/2510.09558v2
• Date: Wed, 15 Oct 2025 15:32:50 GMT
• Title: AutoPR: Let's Automate Your Academic Promotion!
• Authors: 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,
• Abstract summary: We introduce Automatic Promotion (AutoPR), a novel task that transforms research papers into accurate, engaging, and timely public content.<n>PRAgent is a multi-agent framework that automates AutoPR in three stages: content extraction, collaborative synthesis, and platform-specific adaptation to optimize norms, tone, and tagging for maximum reach.<n>Our results position AutoPR as a tractable, measurable research problem and provide a roadmap for scalable, impactful automated scholarly communication.
• Score: 50.929742814819036
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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