The article "When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment" by Minrui Xu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shiwen Mao, Zhu Han, Dong In Kim, and Khaled B. Letaief explores the integration of large language models (LLMs) into 6G networks to enhance human-computer interaction and personalized services. The authors propose a split learning system that leverages collaboration between mobile devices and edge servers to address the limitations of mobile devices in running complex LLMs. This system divides LLM agents into perception, grounding, and alignment modules, enabling inter-module communication to meet extended user requirements such as integrated sensing, communication, digital twins, and task-oriented communications. The paper also introduces a novel model caching algorithm, *age of thought* (AoT), to optimize model utilization and reduce network costs. The authors discuss the challenges and benefits of this approach, including improved adaptability, long-horizon collaboration, and enhanced performance in dynamic environments. A case study on generating accident reports using mobile and edge LLM agents is presented to illustrate the practical application of the proposed system. The article concludes with future directions, emphasizing the need for further integration of 6G networks and AI agents, as well as addressing model privacy concerns.The article "When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment" by Minrui Xu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shiwen Mao, Zhu Han, Dong In Kim, and Khaled B. Letaief explores the integration of large language models (LLMs) into 6G networks to enhance human-computer interaction and personalized services. The authors propose a split learning system that leverages collaboration between mobile devices and edge servers to address the limitations of mobile devices in running complex LLMs. This system divides LLM agents into perception, grounding, and alignment modules, enabling inter-module communication to meet extended user requirements such as integrated sensing, communication, digital twins, and task-oriented communications. The paper also introduces a novel model caching algorithm, *age of thought* (AoT), to optimize model utilization and reduce network costs. The authors discuss the challenges and benefits of this approach, including improved adaptability, long-horizon collaboration, and enhanced performance in dynamic environments. A case study on generating accident reports using mobile and edge LLM agents is presented to illustrate the practical application of the proposed system. The article concludes with future directions, emphasizing the need for further integration of 6G networks and AI agents, as well as addressing model privacy concerns.