Kraken*: Architecting Generative, Semantic, and Goal-Oriented Network Management for 6G Wireless Systems

Abstract

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.

Figures (9)

• Figure 1: The transition from data-centric to knowledge-centric networks relies on three complementary pillars: semantic communication (meaning-aware representation), generative reasoning (world-model-based adaptation), and goal-oriented optimization (intent alignment). Their structured integration enables distributed collective intelligence through coordinated semantic, predictive, and intent-consistent control mechanisms.
• Figure 2: Evolution of networking from a data-centric approach toward distributed collective intelligence in 6G systems through semantic abstraction, generative reasoning, and goal-oriented coordination.
• Figure 3: The KRAKEN three-plane architecture showing the Infrastructure Plane (PHY/MAC/NET layers with semantic awareness), Agent Plane (distributed Generative Network Agents with perception-memory-planning-action cycles), and Knowledge Plane (semantic substrate, foundation model priors, and goal governance). Arrows indicate bidirectional information flow and closed-loop control.
• Figure 4: Infrastructure Plane of the Kraken architecture with semantic-aware Physical, Data Link, and Network layers.
• Figure 5: Knowledge types in 6G. Knowledge objects include facts (with confidence and temporal validity), experiences (historical patterns), models (parametric representations), intentions (future objectives), and reasoning traces (explanations). Each type enables different forms of reasoning under uncertainty.