mlsanityData to 14 Oct 2026

From Intent to Infrastructure: LLM-Driven Agent Compilers for ISAC Networks

arXiv 2607.16269 ↗First posted 7 Jul 2026eess.SP cs.AIcited by 0

On the map: LLM agents and tool use → Agentic AI for wireless networks

Abstract (arXiv)

Integrated sensing and communications (ISAC) is moving from proof-of-concept demonstrations to system-level deployment in sixth-generation (6G) networks. Because sensing and communication share hardware, spectrum, and waveform resources, ISAC design now involves many tightly coupled choices, including waveform selection, sensing algorithm setup, resource scheduling, and deployment planning. This design space is already too large to manage well through manual tuning or isolated optimizers. This article introduces the textitAgent Compiler, a large language model (LLM)-enabled compilation layer that translates high-level engineering intent into complete and executable ISAC system configurations. The Agent Compiler works in four stages: intent parsing, task decomposition, policy graph synthesis, and infrastructure mapping. It produces a verifiable intermediate representation called the ISAC Policy Graph (IPG). A runtime engine then deploys the compiled configuration and supports closed-loop adaptation at three levels: fast parameter updates, partial recompilation of affected subgraphs, and full workflow recompilation. The core design principle is strict time-scale separation: the LLM handles slow-loop strategic decisions, while proven algorithms retain real-time control in the fast loop. A UAV-assisted disaster rescue example illustrates the full compilation process. We also discuss open issues, including compilation latency, output reliability, constraint verification, and pipeline security, to guide future research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. Builds on 10 of its 15 references that are in the map. First posted on arXiv 7 Jul 2026.

Builds on

10 of 15 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025LLM Enabled Multi-Agent System for 6G Networks: Framework and Method of Dual-Loop Edge-Terminal Collaboration5 Sep

Other references · 9

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2024Understanding the planning of LLM agents: A surveycited by 421
  4. 2021Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyondcited by 91
  5. 2020MLIR: A Compiler Infrastructure for the End of Moore's Lawcited by 65
  6. 2023Large Language Models for Telecom: Forthcoming Impact on the Industrycited by 44
  7. 2021A Survey on Fundamental Limits of Integrated Sensing and Communicationcited by 19
  8. 2023Integrated Sensing and Communications: Recent Advances and Ten Open Challengescited by 14
  9. 2025LLM-Enhanced Space-Air-Ground-Sea Integrated Networkscited by 1

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Not cited yet.

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Close in wording, no citation either way

  1. 2026AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC
  2. 2025Agentic AI for ISAC: Analysis, Framework, and Case Study
  3. 2026Scaling LLM-Driven Multi-Agent Systems: Design Principles and Architectural Scalability Analysis
  4. 2026When Agentic AI Meets Integrated Sensing and Communication
  5. 2026Agent-Native Metamorphic Communication Fabric ↗
  6. 2025AI-Native Integrated Sensing and Communications for Self-Organizing Wireless Networks: Architectures, Learning Paradigms, and System-Level Design
  7. 2026Architectural Design, Not Only Model Intelligence, Governs Multi-Agent LLM Performance
  8. 2023Formally Specifying the High-Level Behavior of LLM-Based Agents