## 3 System Framework of LLM-Assisted Navigation for MASSs  
For this study, we innovatively constructed a framework that uniquely integrates the power of LLMs, to enhance the performance of MASSs navigation systems. To the best of our knowledge, this is the first attempt to apply LLMs to assist in ship navigation, pioneering this field. This framework allows MASSs to intelligently interact with LLMs located in remote clouds or onshore bases via satellite links or advanced 5G mobile networks. It also supports MASSs in consulting LLMs deployed on the ship directly for immediate navigation strategies. For example, MASSs navigating crowded inland waterways or port areas can communicate with land-based LLMs through nearby cellular network access points. In contrast, ships traversing vast open seas can seek complex navigation decisions via satellite communications with LLM servers deployed in remote clouds. Furthermore, large freight or luxury cruise ships can deploy LLMs directly on board, to achieve real-time, efficient navigation recommendations. The maneuvering inertia of ships is substantial, and collision avoidance actions typically begin when two ships are several kilometers or even tens of kilometers apart. This provides sufficient time for communication and course adjustments within the radar monitoring range; thus, autonomous ship navigation does not require extremely high LLM response speeds. Standard cellular or satellite communication speeds are entirely sufficient to support remote LLM response rates.  
To meet the needs of ship navigation, we will meticulously customize and fine-tune existing top-tier LLM models, such as GPT-4o, Meta-Llama-3-70B, and Qwen-turbo. These models were originally built upon vast and diverse datasets, with a deep knowledge base and excellent generalization capabilities. After specialized tuning, these LLMs deeply understand maritime domain knowledge and can be precisely applied in practice, providing reliable and flexible auxiliary decision-making services for MASSs.  
The integration of LLM technology in MASSs systems heralds a new phase for autonomous ship navigation applications, encompassing but not limited to real-time exchange of vessel status, environmental perception, and navigation intent information within maritime areas through Automatic Identification Systems (AISs), Very High Frequency (VHF), and cellular network technologies. This promotes collaborative environmental perception, multi-ship cooperative navigation, automatic fleet formation navigation, and other advanced functions. This advancement not only significantly enhances navigation efficiency and safety but also lays a solid foundation for future intelligent and networked maritime traffic management.  
Figure 1 illustrates a vivid example of how an LLM can be utilized to assist navigation. In the scenario, a sudden collision accident occurs between a passenger ship and another vessel in a specific water area, causing the passenger ship to capsize. At this moment, a MASS equipped with an LLM-assisted navigation system is passing by. Its advanced sensing system immediately detects the abnormal situation and automatically initiates a navigation assistance request to the LLM deployed on a remote cloud server. Upon receiving the signal, the LLM server rapidly analyzes the situation and guides the MASS to take action, such that it maintains a safe navigation distance while urgently deploying lifeboats and related rescue equipment, to quickly participate in the rescue of people in the water.