AI代理自动识别论文地理来源并自动提交提名，提升学术流程效率
AEGIS: An Agent for Extraction and Geographic Identification in Scholarly Proceedings
- 用AI代理自动从会议论文中识别特定地区论文
- 在586篇论文上实现100%召回率和99.4%准确率
- 适合需要自动化学术提名与数据筛选的研究机构
应对学术文献激增带来的挑战，我们提出一个全自动系统，将文献发现直接转化为行动。该系统通过专用AI代理'Agent-E'，从会议论文中识别特定地理区域的论文，并利用机器人流程自动化（RPA）执行预设操作，如提交提名表单。我们在五个不同会议的586篇论文上验证了该系统，成功识别出所有目标论文，召回率达100%，准确率为99.4%。这表明任务导向型AI代理不仅能筛选信息，还能主动参与并加速学术流程。
原文摘要 · Abstract (English)
Keeping pace with the rapid growth of academia literature presents a significant challenge for researchers, funding bodies, and academic societies. To address the time-consuming manual effort required for scholarly discovery, we present a novel, fully automated system that transitions from data discovery to direct action. Our pipeline demonstrates how a specialized AI agent, 'Agent-E', can be tasked with identifying papers from specific geographic regions within conference proceedings and then executing a Robotic Process Automation (RPA) to complete a predefined action, such as submitting a nomination form. We validated our system on 586 papers from five different conferences, where it successfully identified every target paper with a recall of 100% and a near perfect accuracy of 99.4%. This demonstration highlights the potential of task-oriented AI agents to not only filter information but also to actively participate in and accelerate the workflows of the academic community.
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