KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence

• URL: http://arxiv.org/abs/2507.08164v1
• Date: Thu, 10 Jul 2025 20:54:36 GMT
• Title: KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence
• Authors: Yun Tang, Mengbang Zou, Zeinab Nezami, Syed Ali Raza Zaidi, Weisi Guo,
• Abstract summary: Large language models (LLMs) and agentic systems are enabling autonomous 6G networks with advanced intelligence.<n>We propose KP-A: a unified Network Knowledge Plane specifically designed for Agentic network intelligence.<n>We demonstrate KP-A in two representative intelligence tasks: live network knowledge Q&A and edge AI service orchestration.
• Score: 8.933721953167115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of large language models (LLMs) and agentic systems is enabling autonomous 6G networks with advanced intelligence, including self-configuration, self-optimization, and self-healing. However, the current implementation of individual intelligence tasks necessitates isolated knowledge retrieval pipelines, resulting in redundant data flows and inconsistent interpretations. Inspired by the service model unification effort in Open-RAN (to support interoperability and vendor diversity), we propose KP-A: a unified Network Knowledge Plane specifically designed for Agentic network intelligence. By decoupling network knowledge acquisition and management from intelligence logic, KP-A streamlines development and reduces maintenance complexity for intelligence engineers. By offering an intuitive and consistent knowledge interface, KP-A also enhances interoperability for the network intelligence agents. We demonstrate KP-A in two representative intelligence tasks: live network knowledge Q&A and edge AI service orchestration. All implementation artifacts have been open-sourced to support reproducibility and future standardization efforts.

Related papers

• Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
  The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
  arXiv  Detail & Related papers  (2025-07-28T17:58:12Z)
• AI Flow: Perspectives, Scenarios, and Approaches [51.38621621775711]
  We introduce AI Flow, a framework that integrates cutting-edge IT and CT advancements.<n>First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters.<n>Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features.<n>Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow.
  arXiv  Detail & Related papers  (2025-06-14T12:43:07Z)
• INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization [43.37351326629751]
  In-network AI is a transformative approach to addressing the escalating demands of Artificial Intelligence (AI) on network infrastructure.<n>This paper provides a comprehensive analysis of optimizing in-network computation for AI.<n>It examines methodologies for mapping AI models onto resource-constrained network devices, addressing challenges like limited memory and computational capabilities.
  arXiv  Detail & Related papers  (2025-05-30T06:47:55Z)
• From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications [57.38526350775472]
  This tutorial provides a systematic introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies.<n>We outline the background of 6G communications, review the technological evolution from LAMs to Agentic AI, and clarify the tutorial's motivation and main contributions.
  arXiv  Detail & Related papers  (2025-05-28T12:54:07Z)
• Internet of Agents: Fundamentals, Applications, and Challenges [66.44234034282421]
  We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
  arXiv  Detail & Related papers  (2025-05-12T02:04:37Z)
• Distillation-Enabled Knowledge Alignment Protocol for Semantic Communication in AI Agent Networks [38.5438416972178]
  We propose a distillation-enabled knowledge alignment protocol (DeKAP) for massive artificial intelligence (AI) agents.<n>The DeKAP distills the expert knowledge of each agent into parameter-efficient low-rank matrices, allocates them across the network, and allows agents to simultaneously maintain aligned knowledge for multiple tasks.<n>We formulate the joint minimization of alignment loss, communication overhead, and storage cost as a large-scale integer linear programming problem.
  arXiv  Detail & Related papers  (2025-05-07T14:45:02Z)
• Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach [35.05793485239977]
  We propose AgentNet, a novel framework for supporting interaction, collaborative learning, and knowledge transfer among AI agents.<n>We consider two application scenarios, digital-twin-based industrial automation and metaverse-based infotainment system, to describe how to apply AgentNet.
  arXiv  Detail & Related papers  (2025-03-20T00:48:44Z)
• Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking [87.82985288731489]
  Agentic AI has emerged as a key paradigm for intelligent communications and networking.<n>This article emphasizes the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems.
  arXiv  Detail & Related papers  (2025-02-24T06:02:25Z)
• Large Language Models Empowered Autonomous Edge AI for Connected Intelligence [51.269276328087855]
  Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
  arXiv  Detail & Related papers  (2023-07-06T05:16:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.