ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks

• URL: http://arxiv.org/abs/2601.19607v1
• Date: Tue, 27 Jan 2026 13:43:59 GMT
• Title: ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks
• Authors: Haoyun Li, Ming Xiao, Kezhi Wang, Robert Schober, Dong In Kim, Yong Liang Guan,
• Abstract summary: 6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
• Score: 62.031889234230725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emerging 6G networks rely on complex cross-layer optimization, yet manually translating high-level intents into mathematical formulations remains a bottleneck. While Large Language Models (LLMs) offer promise, monolithic approaches often lack sufficient domain grounding, constraint awareness, and verification capabilities. To address this, we present ComAgent, a multi-LLM agentic AI framework. ComAgent employs a closed-loop Perception-Planning-Action-Reflection cycle, coordinating specialized agents for literature search, coding, and scoring to autonomously generate solver-ready formulations and reproducible simulations. By iteratively decomposing problems and self-correcting errors, the framework effectively bridges the gap between user intent and execution. Evaluations demonstrate that ComAgent achieves expert-comparable performance in complex beamforming optimization and outperforms monolithic LLMs across diverse wireless tasks, highlighting its potential for automating design in emerging wireless networks.

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