AI RESEARCH
6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence
arXiv CS.AI
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ArXi:2605.01546v1 Announce Type: cross Sixth-generation (6G) networks are increasingly envisioned as AI-native infrastructures integrating communication, sensing, and computing into a unified fabric. However, existing approaches remain largely optimization-centric, relying on closed-loop control with limited reasoning capability. In this paper, we argue for a paradigm shift toward Agentic AI-Native 6G, in which Large Language Model (LLM)-based agents operate as bounded, policy-governed reasoning entities within a semantic control plane layered above deterministic 3GPP infrastructure.