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| Natura: | Preprint |
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2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.24328 |
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| _version_ | 1866917361583390720 |
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| author | Zhang, Ping Meng, Rui Xu, Xiaodong Wang, Yaheng Huang, Zixuan Liu, Yiming Zhang, Ruichen Liu, Yinqiu Tong, Haonan Song, Huishi Wu, Gang Lu, Zhaoming Kang, Jiawen Sun, Geng Du, Qinghe Yang, Zhaohui Zhang, Jingxuan Meng, Han Xu, Lexi Zhao, Haitao Fei, Zesong Zhou, Yiqing Xiao, Pei Tao, Meixia Zhang, Qinyu Cui, Shuguang Tafazolli, Rahim |
| author_facet | Zhang, Ping Meng, Rui Xu, Xiaodong Wang, Yaheng Huang, Zixuan Liu, Yiming Zhang, Ruichen Liu, Yinqiu Tong, Haonan Song, Huishi Wu, Gang Lu, Zhaoming Kang, Jiawen Sun, Geng Du, Qinghe Yang, Zhaohui Zhang, Jingxuan Meng, Han Xu, Lexi Zhao, Haitao Fei, Zesong Zhou, Yiqing Xiao, Pei Tao, Meixia Zhang, Qinyu Cui, Shuguang Tafazolli, Rahim |
| contents | The International Telecommunication Union (ITU) identifies "Artificial Intelligence (AI) and Communication" as one of six key usage scenarios for 6G. Agentic AI, characterized by its ca-pabilities in multi-modal environmental sensing, complex task coordination, and continuous self-optimization, is anticipated to drive the evolution toward agent-based communication net-works. Semantic communication (SemCom), in turn, has emerged as a transformative paradigm that offers task-oriented efficiency, enhanced reliability in complex environments, and dynamic adaptation in resource allocation. However, comprehensive reviews that trace their technologi-cal evolution in the contexts of agent communications remain scarce. Addressing this gap, this paper systematically explores the role of semantics in agent communication networks. We first propose a novel architecture for semantic-based agent communication networks, structured into three layers, four entities, and four stages. Three wireless agent network layers define the logical structure and organization of entity interactions: the intention extraction and understanding layer, the semantic encoding and processing layer, and the distributed autonomy and collabora-tion layer. Across these layers, four AI agent entities, namely embodied agents, communication agents, network agents, and application agents, coexist and perform distinct tasks. Furthermore, four operational stages of semantic-enhanced agentic AI systems, namely perception, memory, reasoning, and action, form a cognitive cycle guiding agent behavior. Based on the proposed architecture, we provide a comprehensive review of the state-of-the-art on how semantics en-hance agent communication networks. Finally, we identify key challenges and present potential solutions to offer directional guidance for future research in this emerging field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24328 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Semantic-based Agent Communication Networks: Vision, Technologies, and Challenges Zhang, Ping Meng, Rui Xu, Xiaodong Wang, Yaheng Huang, Zixuan Liu, Yiming Zhang, Ruichen Liu, Yinqiu Tong, Haonan Song, Huishi Wu, Gang Lu, Zhaoming Kang, Jiawen Sun, Geng Du, Qinghe Yang, Zhaohui Zhang, Jingxuan Meng, Han Xu, Lexi Zhao, Haitao Fei, Zesong Zhou, Yiqing Xiao, Pei Tao, Meixia Zhang, Qinyu Cui, Shuguang Tafazolli, Rahim Signal Processing The International Telecommunication Union (ITU) identifies "Artificial Intelligence (AI) and Communication" as one of six key usage scenarios for 6G. Agentic AI, characterized by its ca-pabilities in multi-modal environmental sensing, complex task coordination, and continuous self-optimization, is anticipated to drive the evolution toward agent-based communication net-works. Semantic communication (SemCom), in turn, has emerged as a transformative paradigm that offers task-oriented efficiency, enhanced reliability in complex environments, and dynamic adaptation in resource allocation. However, comprehensive reviews that trace their technologi-cal evolution in the contexts of agent communications remain scarce. Addressing this gap, this paper systematically explores the role of semantics in agent communication networks. We first propose a novel architecture for semantic-based agent communication networks, structured into three layers, four entities, and four stages. Three wireless agent network layers define the logical structure and organization of entity interactions: the intention extraction and understanding layer, the semantic encoding and processing layer, and the distributed autonomy and collabora-tion layer. Across these layers, four AI agent entities, namely embodied agents, communication agents, network agents, and application agents, coexist and perform distinct tasks. Furthermore, four operational stages of semantic-enhanced agentic AI systems, namely perception, memory, reasoning, and action, form a cognitive cycle guiding agent behavior. Based on the proposed architecture, we provide a comprehensive review of the state-of-the-art on how semantics en-hance agent communication networks. Finally, we identify key challenges and present potential solutions to offer directional guidance for future research in this emerging field. |
| title | Towards Semantic-based Agent Communication Networks: Vision, Technologies, and Challenges |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2603.24328 |