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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.19607 |
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| _version_ | 1866914283697209344 |
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| author | Li, Haoyun Xiao, Ming Wang, Kezhi Schober, Robert Kim, Dong In Guan, Yong Liang |
| author_facet | Li, Haoyun Xiao, Ming Wang, Kezhi Schober, Robert Kim, Dong In Guan, Yong Liang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19607 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks Li, Haoyun Xiao, Ming Wang, Kezhi Schober, Robert Kim, Dong In Guan, Yong Liang Artificial Intelligence 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. |
| title | ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.19607 |