Generative artificial intelligence (GenAI) and communication networks are expected to have groundbreaking synergies in 6G. Connecting GenAI agents over a wireless network can potentially unleash the power of collective intelligence and pave the way for artificial general intelligence (AGI). However, current wireless networks are designed as a "data pipe" and are not suited to accommodate and leverage the power of GenAI. In this paper, we propose the GenAINet framework in which distributed GenAI agents communicate knowledge (high-level concepts or abstracts) to accomplish arbitrary tasks. We first provide a network architecture integrating GenAI capabilities to manage both network protocols and applications. Building on this, we investigate effective communication and reasoning problems by proposing a semantic-native GenAINet. Specifically, GenAI agents extract semantic concepts from multi-modal raw data, build a knowledgebase representing their semantic relations, which is retrieved by GenAI models for planning and reasoning. Under this paradigm, an agent can learn fast from other agents' experience for making better decisions with efficient communications. Furthermore, we conduct two case studies where in wireless device query, we show that extracting and transferring knowledge can improve query accuracy with reduced communication; and in wireless power control, we show that distributed agents can improve decisions via collaborative reasoning. Finally, we address that developing a hierarchical semantic level Telecom world model is a key path towards network of collective intelligence.

翻译：生成式人工智能与通信网络有望在6G时代产生突破性协同效应。通过无线网络连接GenAI智能体，可能释放集体智能的力量，为通用人工智能铺平道路。然而，当前无线网络被设计为"数据管道"，无法适应并利用GenAI的能力。本文提出GenAINet框架，使分布式GenAI智能体能够通过知识（高层概念或抽象）通信完成任意任务。我们首先设计集成GenAI能力的网络架构，用于管理网络协议与应用。在此基础上，通过提出语义原生的GenAINet，研究高效通信与推理问题。具体而言，GenAI智能体从多模态原始数据中提取语义概念，构建表征语义关系的知识库，并由GenAI模型检索以进行规划与推理。在该范式下，智能体可快速从其他智能体的经验中学习，通过高效通信做出更优决策。此外，我们开展两项案例研究：在无线设备查询中，知识提取与迁移可在降低通信量的同时提升查询精度；在无线功率控制中，分布式智能体可通过协作推理优化决策。最后，我们指出开发层次化语义级电信世界模型是实现集体智能网络的关键路径。