The convergence of generative large language models (LLMs), edge networks, and multi-agent systems represents a groundbreaking synergy that holds immense promise for future wireless generations, harnessing the power of collective intelligence and paving the way for self-governed networks where intelligent decision-making happens right at the edge. This article puts the stepping-stone for incorporating multi-agent generative artificial intelligence (AI) in wireless networks, and sets the scene for realizing on-device LLMs, where multi-agent LLMs are collaboratively planning and solving tasks to achieve a number of network goals. We further investigate the profound limitations of cloud-based LLMs, and explore multi-agent LLMs from a game theoretic perspective, where agents collaboratively solve tasks in competitive environments. Moreover, we establish the underpinnings for the architecture design of wireless multi-agent generative AI systems at the network level and the agent level, and we identify the wireless technologies that are envisioned to play a key role in enabling on-device LLM. To demonstrate the promising potentials of wireless multi-agent generative AI networks, we highlight the benefits that can be achieved when implementing wireless generative agents in intent-based networking, and we provide a case study to showcase how on-device LLMs can contribute to solving network intents in a collaborative fashion. We finally shed lights on potential challenges and sketch a research roadmap towards realizing the vision of wireless collective intelligence.

翻译：大型生成式语言模型（LLM）、边缘网络与多智能体系统的融合，代表着一种具有突破性的协同效应，为未来无线通信代际演进带来了巨大潜力——它能够利用集体智能的力量，为边缘侧自主决策的自治网络铺平道路。本文奠定了将多智能体生成式人工智能（AI）融入无线网络的基础，并描绘了实现设备端LLM的蓝图：通过多智能体LLM协作规划与任务求解，达成多项网络目标。我们进一步揭示了基于云端的LLM存在的根本局限，并从博弈论视角探索多智能体LLM，研究智能体在竞争环境下如何协作解决问题。此外，我们在网络层面与智能体层面建立了无线多智能体生成式AI系统架构设计的理论基础，并识别出有望在实现设备端LLM中发挥关键作用的无线技术。为展示无线多智能体生成式AI网络的巨大潜力，我们着重阐述了在意图驱动网络中部署无线生成式智能体可获得的优势，并提供了案例研究，展示设备端LLM如何以协作方式助力网络意图求解。最后，我们揭示了潜在挑战，并勾勒出实现无线集体智能愿景的研究路线图。