{"ID":2887764,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00280","arxiv_id":"2508.00280","title":"WMAS: A Multi-Agent System Towards Intelligent and Customized Wireless Networks","abstract":"The fast development of Artificial Intelligence (AI) agents provides a promising way for the realization of intelligent and customized wireless networks. In this paper, we propose a Wireless Multi-Agent System (WMAS), which can provide intelligent and customized services for different user equipment (UEs). Note that orchestrating multiple agents carries the risk of malfunction, and multi-agent conversations may fall into infinite loops. It is thus crucial to design a conversation topology for WMAS that enables agents to complete UE task requests with high accuracy and low conversation overhead. To address this issue, we model the multi-agent conversation topology as a directed acyclic graph and propose a reinforcement learning-based algorithm to optimize the adjacency matrix of this graph. As such, WMAS is capable of generating and self-optimizing multi-agent conversation topologies, enabling agents to effectively and collaboratively handle a variety of task requests from UEs. Simulation results across various task types demonstrate that WMAS can achieve higher task performance and lower conversation overhead compared to existing multi-agent systems. These results validate the potential of WMAS to enhance the intelligence of future wireless networks.","short_abstract":"The fast development of Artificial Intelligence (AI) agents provides a promising way for the realization of intelligent and customized wireless networks. In this paper, we propose a Wireless Multi-Agent System (WMAS), which can provide intelligent and customized services for different user equipment (UEs). Note that or...","url_abs":"https://arxiv.org/abs/2508.00280","url_pdf":"https://arxiv.org/pdf/2508.00280v1","authors":"[\"Jingchen Peng\",\"Dingli Yuan\",\"Boxiang Ren\",\"Jie Fan\",\"Hao Wu\",\"Lu Yang\"]","published":"2025-08-01T02:59:30Z","proceeding":"cs.MA","tasks":"[\"cs.MA\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}