Paper 2510.09558
AutoPR: Let's Automate Your Academic Promotion!
- Published
- Oct 2025
- Research lab
- Independent
- Citations
- 4
- GitHub
- 103 stars
01 In brief
Summary
The paper introduces AutoPR, a novel task for automatically generating promotional content from academic papers, along with PRBench, a benchmark of 512 paper-post pairs, and PRAgent, a multi-agent framework.
PRAgent operates in three stages: content extraction, multi-agent synthesis, and platform-specific adaptation.
Evaluations on PRBench show PRAgent outperforms direct LLM pipelines, with real-world tests on RedNote yielding a 604% increase in watch time, 438% increase in likes, and at least 2.9x engagement boost.
Ablations highlight the importance of platform modeling and targeted promotion.
The work addresses the gap in automated scholarly communication, providing a measurable research problem and a roadmap for scalable impact.
02 From the paper
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.