Sixth-generation (6G) wireless networks are expected to support autonomous, immersive, and mission-critical services that require not only extreme data rates and ultra-low latency but also adaptive reasoning, cross-domain coordination, and objective-driven control across distributed edge-cloud infrastructures. Current AI-enabled network management remains largely data-centric, relying on discriminative models that optimize intermediate quality-of-service metrics without explicitly reasoning about long-term service objectives. This article advocates a transition from bit-centric communication toward knowledge-centric coordination in 6G systems. Semantic communication prioritizes task-relevant information and contextual meaning over raw data delivery, while generative artificial intelligence enables predictive reasoning and adaptive policy synthesis aligned with dynamic service intents. Network optimization is therefore reframed around goal-oriented performance metrics capturing application-level outcomes rather than solely protocol-level indicators. To operationalize this vision, we introduce Kraken, a multi-agent architecture composed of a Knowledge Plane, a distributed Agent Plane, and a semantic-aware Infrastructure Plane. By integrating semantic communication, generative reasoning, and goal-oriented optimization over a shared knowledge substrate, Kraken enables scalable collective intelligence and outlines an evolutionary path from current 5G infrastructures toward knowledge-native 6G systems.

翻译：第六代（6G）无线网络预计将支持自主化、沉浸式及任务关键型服务，这些服务不仅需要极高的数据速率与超低时延，还要求具备跨分布式边缘-云基础设施的自适应推理、跨域协调及目标驱动控制能力。当前基于人工智能的网络管理仍以数据为中心，主要依赖判别式模型优化中间服务质量指标，而未对长期服务目标进行显式推理。本文主张在6G系统中实现从以比特为中心向以知识为中心的协同范式转变。语义通信优先传递任务相关信息与上下文语义而非原始数据，而生成式人工智能则支持与动态服务意图对齐的预测性推理与自适应策略合成。因此，网络优化被重新构建为围绕目标导向的性能度量，其关注应用层结果而非仅协议层指标。为实现这一愿景，我们提出Kraken——一种由知识平面、分布式代理平面及语义感知基础设施平面构成的多智能体架构。通过在共享知识基座上整合语义通信、生成式推理与目标导向优化，Kraken实现了可扩展的群体智能，并为从当前5G基础设施向知识原生型6G系统的演进指明了路径。