SpecMind：通过多智能体混合检索增强生成实现频谱智能
SpecMind: Enabling Spectrum Intelligence via Multi-Agent Hybrid Retrieval-Augmented Generation
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中文总结 AI 辅助
针对无线设备增长带来的频谱管理数据分散等挑战，提出多智能体混合RAG系统SpecMind，其在频谱任务上胜率超80%，优于传统通用RAG，还构建了频谱领域RAG评估数据集SpecBench。
中文摘要 AI 辅助
无线设备的指数级增长正推动着前所未有的频谱需求，促使频谱管理朝着跨空间、时间和设备约束的更细粒度决策方向发展。因此，频谱政策制定者和工程师必须处理来自不同来源、形式多样的大量数据，例如文本和表格。这些数据源通常是分散的，需要大量时间和精力进行整合、搜索和解读。此外，大部分此类信息是为人类理解而格式化的，无法被自动化系统轻松访问。为应对这一挑战，我们提出SpecMind，一种用于频谱智能的新型多智能体检索增强生成（RAG）系统，可对异构数据源进行推理。该系统使自主智能体能够协调专门的子智能体，在政策程序、法律法规和许可数据库中检索并综合知识。我们开发了SpecBench，一个基于真实许可记录和政策程序的问答（Q&A）数据集，以解决频谱领域RAG系统缺乏评估资源的问题。实验结果表明，SpecMind在频谱相关任务上的表现优于传统通用RAG系统，与强大基线相比，胜率超过80%。基于智能体的设计在不同查询类型中实现了更准确的检索、更好的上下文推理和更出色的任务完成效果。
英文摘要
The exponential growth of wireless devices is driving unprecedented spectrum demand, pushing spectrum management toward more fine-grained decisions across space, time, and device constraints. As a result, spectrum policymakers and engineers must process large volumes of data that come from diverse sources and take many different forms, such as text and tables. These data sources are often disaggregated and require significant time and effort to integrate, search, and interpret. Furthermore, most of this information is formatted for human understanding and is not readily accessible to automated systems. To address this challenge, we propose SpecMind, a novel Multi-Agent Retrieval-Augmented Generation (RAG) system for spectrum intelligence that performs reasoning over heterogeneous data sources. This system enables autonomous agents to coordinate specialized sub-agents that retrieve and synthesize knowledge across policy proceedings, legal regulations, and license databases. We develop SpecBench, a question and answer (Q&A) dataset based on real-world license records and policy proceedings, addressing the lack of evaluation resources for RAG systems in the spectrum domain. Experimental results demonstrate that SpecMind outperforms traditional, general-purpose RAG systems across spectrum-related tasks, achieving over 80% win rate against strong baselines. The agent-based design enables more accurate retrieval, better contextual reasoning, and improved task completion across diverse query types.
发表机构
- University of Virginia（弗吉尼亚大学）
- University of Notre Dame（圣母大学）
- University of California, Santa Cruz（加州大学圣克鲁兹分校）
机构由 AI 辅助整理，请以论文原文为准。
