Title: Spectrum Intelligence via Multi-Agent Hybrid Retrieval-Augmented Generation
Primary area: generative models
Keywords: Multi-Agent Systems; Hybrid Retrieval-Augmented Generation (Hybrid RAG); Spectrum Intelligence; Question Answering; Structured and Unstructured Data; Domain-Specific Applications

Abstract: Spectrum policy making requires reviewing large volumes of information from different sources, including technical reports, stakeholder filings and incumbent licenses, which makes the process slow and resource-intensive. To speed this process, we propose a Multi-Agent Hybrid Retrieval-Augmented Generation (RAG) system for analyzing spectrum proceeding documents and license data. Through customized prompts and tailored tools, the system demonstrates the ability to handle complex queries and perform multi-step retrieval and analysis. For evaluation, we built a benchmark dataset in the form of question-answer pairs and used it to compare our system with others. Extensive experimental results show that our Multi-Agent Hybrid RAG system significantly improves the understanding of spectrum data, providing more reliable and verifiable support for spectrum policy analysis. Our approach provides new insights into how agentic RAG can leverage heterogeneous structured and unstructured data sources to accomplish complex policy analysis tasks, highlighting its potential for broader applications in domain-specific decision support.