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

2026-03-12

AI Summary

This work addresses the limitations of current AI-driven network management, which relies on data-centric discriminative models and fails to meet the demands of 6G mission-critical services for semantic understanding, goal-oriented operation, and cross-domain collaboration. To bridge this gap, the paper proposes a knowledge-native network architecture that pioneers the deep integration of semantic communication, generative AI, and goal-directed optimization, thereby shifting the paradigm from bit-centric to knowledge-centric networking. The architecture features a three-layer multi-agent design—comprising a knowledge plane, a distributed agent plane, and semantic-aware infrastructure—enabling predictive reasoning and policy generation grounded in a shared knowledge base. This framework endows 6G networks with scalable collective intelligence, facilitating adaptive coordination and optimization aligned with dynamic service intents.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Deep Neural Architectures and Foundation Models
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• 6G wireless networks
• goal-oriented network management
• semantic communication
• generative AI
• knowledge-centric coordination

Innovation

• semantic communication
• generative AI
• goal-oriented optimization
• knowledge-centric networking
• multi-agent architecture