AEGIS: An Agent for Extraction and Geographic Identification in Scholarly Proceedings
This addresses the time-consuming manual effort in academic workflows for researchers and institutions, though it appears incremental as it applies existing AI and RPA techniques to a specific domain.
The paper tackled the problem of automating scholarly discovery by developing a specialized AI agent that identifies papers from specific geographic regions in conference proceedings and executes actions like form submissions, achieving 100% recall and 99.4% accuracy on 586 papers from five conferences.
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.