The field of 6G networks is experiencing a significant shift with the integration of large language models (LLMs) to enhance network management and optimization. LLMs are being explored for their potential to enable natural language-driven problem formulation, context-aware reasoning, and adaptive solution refinement. This is leading to the development of innovative frameworks and methods that can efficiently operate in resource-constrained network environments. The use of LLMs is transforming the way optimization problems are approached, allowing for real-time adaptability, scalability, and dynamic handling of user intents. Noteworthy papers include:

• LLM Enabled Multi-Agent System for 6G Networks: Framework and Method of Dual-Loop Edge-Terminal Collaboration, which proposes a framework for LLM-enabled multi-agent systems with dual-loop terminal-edge collaborations.
• Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial, which provides a comprehensive survey of LLM-enabled optimization frameworks tailored for wireless networks.
• SCA-LLM: Spectral-Attentive Channel Prediction with Large Language Models in MIMO-OFDM, which proposes a spectral-attentive framework for channel prediction in MIMO-OFDM systems.