AutoPR: Let's Automate Your Academic Promotion!

2025-10-10

AI Summary

To address the growing disparity between the exponential growth of academic outputs and the inefficiency of manual science communication, this paper introduces AutoPR—a novel task of automated academic promotion—aiming to transform scholarly papers into platform-optimized public-facing content. We propose PRAgent, a multi-agent framework integrating multimodal paper understanding, collaborative agent-based generation, platform-specific modeling, and channel-aware optimization to achieve end-to-end automation from paper comprehension to cross-platform adaptation. Evaluated on the PRBench benchmark, PRAgent achieves substantial improvements: +604% in total view duration, +438% in likes, and at least a 2.9× increase in overall engagement rate. This work is the first to formally define and systematically tackle the problem of automated, personalized, and platform-aware public dissemination of academic research, establishing a scalable technical paradigm for enhancing scholarly impact.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

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.

Problem

• Automating promotional content creation from research papers
• Reducing human effort in academic work promotion
• Optimizing scholarly communication for visibility and engagement

Innovation

• Automated promotion transforms papers into public content
• Multi-agent framework automates promotion in three stages
• Platform-specific adaptation optimizes reach and engagement