AutoPR: Let's Automate Your Academic Promotion!
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.
68ec771ccd07fb414898ca1f
Qiguang Chen
claimed_verified
2025-10-24T16:14:45.210Z
636f526a6cd69d9a36ff2b53
/avatars/8f2271a193fcac609d9be270552b5afa.svg
Qiguang Chen
user
LightChen2333
68ec771ccd07fb414898ca20
Zheng Yan
claimed_verified
2025-10-14T07:32:04.425Z
679dc81d3a03be42c1fd3c5e
/avatars/b562c0ef1ff0e80dd5a293574726f0fc.svg
zyan
user
yzweak
68ec771ccd07fb414898ca21
Mingda Yang
68ec771ccd07fb414898ca22
Libo Qin
68ec771ccd07fb414898ca23
Yixin Yuan
68ec771ccd07fb414898ca24
Hanjing Li
68ec771ccd07fb414898ca25
Jinhao Liu
68ec771ccd07fb414898ca26
Yiyan Ji
68ec771ccd07fb414898ca27
Dengyun Peng
68ec771ccd07fb414898ca28
Jiannan Guan
68ec771ccd07fb414898ca29
Mengkang Hu
68ec771ccd07fb414898ca2a
Yantao Du
68ec771ccd07fb414898ca2b
Wanxiang Che
AutoPR: Let's Automate Your Academic Promotion!
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.
AutoPR, a multi-agent framework, automates the promotion of research papers by transforming them into engaging public content, significantly improving engagement metrics compared to direct LLM pipelines.
multimodal benchmark content extraction collaborative synthesis platform-specific adaptation LLM pipelines