SpecMind Boosts Spectrum Intelligence with Multi-Agent RAG

The escalating demand for wireless devices necessitates increasingly granular spectrum management decisions, which in turn requires policymakers and engineers to process vast amounts of data from varied sources and formats. This information, often disaggregated and human-centric, poses significant challenges for integration, search, and automated interpretation. To tackle these issues, a new system named SpecMind has been developed. SpecMind is a Multi-Agent Retrieval-Augmented Generation (RAG) system specifically engineered for spectrum intelligence. It enables autonomous agents to orchestrate specialized sub-agents that retrieve and synthesize knowledge from disparate sources, including policy documents, legal regulations, and license databases. Accompanying SpecMind is SpecBench, a new question and answer (Q&A) dataset derived from real-world license records and policy proceedings, created to fill a critical gap in evaluation resources for RAG systems in the spectrum domain. Experimental results demonstrate that SpecMind significantly outperforms conventional, general-purpose RAG systems, achieving over an 80% win rate on spectrum-related tasks due to its agent-based design, which facilitates more accurate retrieval, superior contextual reasoning, and improved task completion across various query types.