GenAINet：通过知识传递与推理实现无线集体智能
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning
AI总结：
本文提出GenAINet框架，使分布式GenAI智能体通过知识传递与语义原生通信实现协作推理，从而在6G网络中支持集体智能，并通过查询和功率控制案例验证其有效性。
摘要：
生成式人工智能（GenAI）与通信网络预计将在6G中产生突破性的协同效应。通过无线网络连接GenAI智能体，有望释放集体智能（CI）的力量，并为通用人工智能（AGI）铺平道路。然而，当前的无线网络被设计为“数据管道”，并不适合容纳和利用GenAI的能力。在本文中，我们提出了GenAINet框架，其中分布式GenAI智能体通过传递知识（事实、经验和方法）来完成任意任务。我们首先提出单个GenAI智能体的架构，然后给出一种集成GenAI能力的网络架构，以同时管理网络协议和应用。在此基础上，我们通过提出语义原生的GenAINet来研究有效的通信与推理问题。具体而言，GenAI智能体从异构原始数据中提取语义，构建并维护一个表示知识片段之间语义关系的知识模型，该模型由GenAI模型检索以用于规划和推理。在这一范式下，可以根据目标任务的复杂程度灵活实现不同层次的协作。此外，我们进行了两个案例研究：在无线设备查询中，我们证明提取、压缩和传递公共知识可以在降低通信成本的同时提高查询准确率；在无线功率控制问题中，我们展示了分布式智能体可以在没有预定义通信协议的情况下，通过协作推理独立完成一般任务。最后，我们讨论了在6G网络中应用大语言模型（LLMs）所面临的挑战和未来研究方向。
英文摘要：
Generative Artificial Intelligence (GenAI) and communication networks are expected to have groundbreaking synergies for 6G. Connecting GenAI agents via a wireless network can potentially unleash the power of Collective Intelligence (CI) 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 (facts, experiences, and methods) to accomplish arbitrary tasks. We first propose an architecture for a single GenAI agent and then 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 semantics from heterogeneous raw data, build and maintain a knowledge model representing the semantic relationships among pieces of knowledge, which is retrieved by GenAI models for planning and reasoning. Under this paradigm, different levels of collaboration can be achieved flexibly depending on the complexity of targeted tasks. Furthermore, we conduct two case studies in which, through wireless device queries, we demonstrate that extracting, compressing and transferring common knowledge can improve query accuracy while reducing communication costs; and in the wireless power control problem, we show that distributed agents can complete general tasks independently through collaborative reasoning without predefined communication protocols. Finally, we discuss challenges and future research directions in applying Large Language Models (LLMs) in 6G networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
