AEGIS: An Agent for Extraction and Geographic Identification in Scholarly Proceedings

2025-09-11

AI Summary

The exponential growth of academic literature has severely hampered manual scholarly discovery. To address this, we propose Agent-E—the first end-to-end system integrating task-oriented AI agents with robotic process automation (RPA) to automatically identify geographically relevant research findings from conference proceedings and trigger downstream actions (e.g., award nominations). Methodologically, Agent-E combines named entity recognition, fine-grained geographic coding, and RPA to precisely extract and act upon geographic intelligence. Evaluated on 586 papers across five major conferences, it achieves 100% recall and 99.4% precision for target papers. This work pioneers the deep synergistic integration of AI agents and RPA for automated academic geointelligence, significantly enhancing research administration efficiency. It establishes a reusable technical paradigm for intelligent, domain-aware academic workflows.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

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.

Problem

• Automating geographic identification of academic papers
• Reducing manual effort in scholarly discovery processes
• Enhancing efficiency in academic workflow automation

Innovation

• AI agent for geographic paper identification
• Robotic Process Automation for academic workflows
• Automated system with high recall accuracy