mlsanityData to 14 Oct 2026

SpecMind: Enabling Spectrum Intelligence via Multi-Agent Hybrid Retrieval-Augmented Generation

arXiv 2609.00427 ↗First posted 31 Aug 2026cs.AIcited by 0

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 16 references that are in the map. First posted on arXiv 31 Aug 2026.

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11 of 16 references.

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  1. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG15 Jan

Other references · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  6. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 312
  7. 2019Do NLP Models Know Numbers? Probing Numeracy in Embeddingscited by 159
  8. 2023Understanding Telecom Language Through Large Language Modelscited by 32
  9. 2024Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunicationscited by 26
  10. 2025Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulationscited by 1

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  3. 2026MiRAGE: A Multiagent Framework for Generating Multimodal Multihop Question-Answer Dataset for RAG Evaluation
  4. 2025MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generation
  5. 2024SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering
  6. 2025RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
  7. 2025HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation
  8. 2026SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions