AutoPR：让我们自动化您的学术晋升！
AutoPR: Let's Automate Your Academic Promotion!
AI总结：
本文提出AutoPR任务及PRAgent多智能体框架，通过内容提取、协作合成和平台适配三阶段自动将论文转化为推广内容，在PRBench基准上显著提升参与度，实现可扩展的自动化学术传播。
摘要：
随着同行评审研究数量的激增，学者们越来越依赖社交平台进行发现，而作者则投入大量精力推广其工作以确保可见性和引用。为简化这一过程并减少对人力的依赖，我们引入了自动推广（Automatic Promotion，AutoPR）这一新任务，将研究论文转化为准确、引人入胜且及时发布的公共内容。为支持严格评估，我们发布了PRBench，一个多模态基准，将512篇同行评审文章与高质量推广帖子关联起来，从三个维度评估系统：保真度（准确性和语气）、参与度（受众定位和吸引力）以及一致性（时机和渠道优化）。我们还引入了PRAgent，一个多智能体框架，分三个阶段自动化AutoPR：多模态准备的内容提取、协作合成以生成精炼输出，以及平台特定适配以优化规范、语气和标签，实现最大触达。与PRBench上的直接LLM流水线相比，PRAgent展示了显著改进，包括总观看时长增加604%，点赞数增加438%，整体参与度至少提升2.9倍。消融研究表明，平台建模和定向推广对这些增益贡献最大。我们的结果将AutoPR定位为一个可处理、可测量的研究问题，并为可扩展、有影响力的自动化学术传播提供了路线图。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
