AutoPR: Let's Automate Your Academic Promotion!

TL;DR

AutoPR introduces a formal task to transform academic papers into platform-optimized promotional content. It provides PRBench, a 512-pair multimodal benchmark with fidelity, alignment, and engagement evaluation, and PRAgent, a three-stage multi-agent pipeline for content extraction, synthesis, and platform adaptation. Across PRBench and real-world RedNote deployments, PRAgent substantially outperforms direct prompting baselines, achieving large gains in watch time and likes, with ablations underscoring the necessity of platform-specific modeling. The work demonstrates that automated scholarly promotion is tractable and measurable, offering a scalable pathway to broaden research visibility while reducing manual effort.

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.

Figures (31)

• Figure 1: Overview of our study: Automatic Promotion (AutoPR) task, its benchmark PRBench, and the associated method PRAgent. The details of citation trend analysis are shown in Appendix \ref{['append:citation']}.
• Figure 2: The definition and overview of Automatic Promotion (AutoPR) Task.
• Figure 3: overview of PRAgent, including: (1) Content extraction for preparing multimodal research material; (2) Multi-agent synthesis to transform structured data from Stage 1 into refined drafts; (3) Platform-specific adaptation to finalize the draft for publication.
• Figure 4: AI-generated academic promotion analysis with three primary limitations. The analysis is based on 512 posts generated by the Qwen-2.5-VL-32B-Ins.
• Figure 5: PRAgent significantly outperforms a direct-prompt baseline in a 10-day real-world study on the social media platform RedNote, with both methods using GPT-5 as the backbone model.