CORE is a novel framework that orchestrates collaborative Large Language Model (LLM) agents across hierarchical 6G edge networks to enable ubiquitous intelligence. It addresses the challenges of fragmented resources by integrating real-time perception, dynamic role orchestration, and pipeline-parallel execution, significantly enhancing system efficiency and task completion in various 6G applications.

This paper explores the integration of large Artificial Intelligence (AI) models into future wireless communication systems, addressing the increasing complexity and demands of next-generation networks. It proposes an architecture for these models, highlights their benefits in data analysis, resource allocation, and real-time adaptation, and discusses significant challenges alongside potential solutions.

This paper systematically explores how agentic AI, with its perception, memory, reasoning, and action capabilities, enhances semantic communications for 6G networks. It proposes a unified framework and demonstrates its effectiveness through an agentic knowledge base-based joint source-channel coding case study, showing improved information reconstruction quality.