The evolution of 6G networking toward agentic AI networking (AgentNet) systems requires a shift from traditional data pipelines to task-aware, agentic AI-native communication solutions. Emergent communication, a novel communication paradigm in which autonomous agents learn their own signaling protocols through interaction, is increasingly viewed as a promising solution to address the challenges posed by existing rigid, predefined protocol-based networking architecture. However, most existing emergent communication frameworks fail to account for physical networking constraints, such as bandwidth and computational complexity, and often lack a rigorous information-theoretical foundation. To address these challenges, this paper introduces a novel emergent communication framework that facilitates collaborative task-solving among heterogeneous agents through an information-theoretic lens. We propose a novel joint loss function that unifies the optimization of decision-making functions and the learning of communication signaling. Our proposed solution is grounded on the multi-agent and multi-task distributed information bottleneck (DIB) theory, which allows the quantification of the fundamental trade-off between task-relevant information representation and computational complexity. We further provide theoretical generalization bounds of the emergent communication protocol during decentralized inference across unseen environmental states. Experimental validation on a real-world hardware prototype confirms that our proposed framework significantly improves generalization performance, compared to the state-of-the-art solutions.
Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；University of Technology and Design, Singapore（新加坡科技设计大学）；Institute for Infocomm Research, Agency for Science, Technology and Research（新加坡科技研究局信息通信研究院）；Auburn University（奥本大学）；University of Houston（休斯顿大学）；Kyung Hee University（庆熙大学）
SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking
1 School of Elect. Inform. \& Commun., Huazhong Univ. of Science \& Technology, China；2 Peng Cheng Laboratory, Shenzhen, China；3 Pazhou Laboratory (Huangpu), Guangzhou, China；4 Department of Electrical；Computer Engineering, the University of Arizona
Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach
School of Electronic Information and Communications, the Huazhong University of Science and Technology, Wuhan, China（电子信息与通信学院，华中科技大学，武汉）；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室，深圳，中国）；Pazhou Laboratory (Huangpu), Guangzhou, China（琶洲实验室（黄埔），广州，中国）；School of Artificial Intelligence, Xidian University, Xi’an, Shaanxi 710071, China（人工智能学院，西安电子科技大学，西安，陕西）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China（网络与交换技术国家重点实验室，北京邮电大学，北京，中国）
Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agentic AI Networking
School of Electronic Information and Communications, the Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））；School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan)（中国地质大学（武汉）机械工程与电子信息学院）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）
SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G
the School of Electronic Information and Communications, the Huazhong University of Science and Technology, Wuhan, China（电子信息学院，华中科技大学，武汉，中国）；the Peng Cheng Laboratory, Shenzhen, China（鹏城实验室，深圳，中国）；the School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), China（机械工程与电子信息学院，中国地质大学（武汉），中国）；the State Key Laboratory of Networking and Switching（网络与交换技术国家重点实验室）
BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks
School of Electrical Engineering and Computer Science, TU Berlin（技术大学柏林电气工程与计算机科学学院）；Optical Zeitgeist Laboratory, INRS（光感知实验室，INRS）；Federal Ministry of Research, Technology and Space (BMFTR, Germany)（德国联邦研究、科技与航天部）
A Sober Look at Agentic Misalignment in Automated Workflows
University of Virginia（弗吉尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Utah（犹他大学）；University of Notre Dame（圣约翰大学）；George Washington University（乔治华盛顿大学）
Department of Computer and Systems Sciences, Stockholm University（斯德哥尔摩大学计算机与系统科学系）；Center for Ubiquitous Computing, University of Oulu（奥卢大学无处不在计算中心）；College of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Zhejiang A\&F University, Hangzhou（浙江工业大学之江学院）；School of Computer Science, Peking University（北京大学计算机科学学院）；Department of Mechatronics, University of Prishtina（普里什蒂纳大学机电系）；Department of Computer Science, IISER Berhampur（伯尔哈普尔IISER计算机科学系）；Systems Research Institute Polish Academy of Sciences（波兰科学院系统研究所）；Department of Computer Science, University of Helsinki（赫尔辛基大学计算机科学系）
Agentic Peer-to-Peer Networks: From Content Distribution to Capability and Action Sharing
College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；School of Informatics, Xiamen University（厦门大学信息学院）；School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）
Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm
College of Computer Science, University of Exeter, Exeter, U.K.（埃克塞特大学计算机科学学院）；Sony China Research Laboratory, Beijing, China（索尼中国研究实验室）；School of Information Science and Engineering, Southeast University, Nanjing, China（东南大学信息科学与工程学院）；Purple Mountain Laboratories, Nanjing, China（紫金山实验室）；Information System Technology and Design Pillar, Singapore University of Technology and Design, Singapore（新加坡科技设计大学信息系统技术与设计支柱）
FastOMOP: A Foundational Architecture for Reliable Agentic Real-World Evidence Generation on OMOP CDM data
Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院国家健康服务信托基金会）；Lancaster University（兰卡斯特大学）；EPSRC DRIVE-Health, Department of Biostatistics & Health Informatics（EPSRC DRIVE-Health，生物统计学与健康信息学部门）；King’s College London（伦敦国王学院）；Institute for Health Informatics（健康信息学研究所）；University College London（伦敦大学学院）；NIHR Biomedical Research Centre（英国国家健康服务研究院生物医学研究中心）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院国家健康服务信托基金会）；Health Data Research UK London（英国健康数据研究伦敦）；South London and Maudsley NHS Foundation Trust and King’s College London（伦敦南部及莫德利国家健康服务信托基金会和伦敦国王学院）；Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience（生物统计学与健康信息学部门，精神病学、心理学与神经科学研究所）
Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis
School of Electronic and Computer Engineering, Peking University（电子与计算机工程学院，北京大学）；Shenzhen Graduate School, Shenzhen, 518055, China（深圳研究生院，深圳，518055，中国）；International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；Peng Cheng Laboratory, Shenzhen, 518000, China（鹏城实验室，深圳，518000，中国）
CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios
Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology（工程科学系，创新工程学院，澳门科学技术大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University（应用信息与应用数学博士学院，奥布达大学）；School of Artificial Intelligence, Anhui University（人工智能学院，安徽大学）；State Key Laboratory for Management and Control of Complex Systems（复杂系统管理与控制国家重点实验室）
AOP-Wiki EMOD 3.0: Data Model Expansions and Content Evaluation Framework for Using Agentic AI to Improve Integration between AOPs and New Approach Methodologies (NAMs)
Open BioData Modeling（开放生物数据建模）；Environmental Genomics and Systems Biology（环境基因组学与系统生物学）；Lawrence Berkeley National Laboratory（伯克利国家实验室）；National Wildlife Research Centre（国家野生动物研究中心）；UL Research Institutes - Chemical Insights（UL研究机构-化学洞察）
The Misattribution Gap: When Memory Poisoning Looks Like Model Failure in Agentic AI Systems
Department of Computer Science, University of Texas at El Paso（德克萨斯大学埃尔帕索分校计算机科学系）；School of Computing, Southern Illinois University Carbondale（南方伊利诺伊大学卡本代尔分校计算机学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Towards Adaptive, Scalable, and Robust Coordination of LLM Agents: A Dynamic Ad-Hoc Networking Perspective
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学北京校区人工智能学院）；Huawei Technologies Ltd.（华为技术有限公司）；Beijing University of Posts and Telecommunications（北京邮电大学）
