## 2 Related Work
### LLMs in SEC Networks  
The integration of LLM services into SEC networks can substantially enhance the intelligent management and optimization of these complex systems. By enabling the SEC to effectively adapt to dynamic and heterogeneous environments, AI contributes to the improvement of critical performance metrics, including latency, energy efficiency, bandwidth utilization, and real-time adaptability. This enhancement is particularly valuable given AI's capacity to process extensive datasets and deliver instantaneous decisions, which is essential for addressing the intricate challenges inherent in the operation of the SEC. [PERSON] et al. [24] investigated how LLMs, which are advanced AI systems capable of processing and generating human-like text, can be applied to optimize and intelligently manage space-air-ground integrated networks (SAGINs) that combine satellite, aerial, and terrestrial networks to ensure seamless connectivity and coverage. [PERSON] et al. [10] jointly optimized model caching and inference to deliver LLM agents across SAGINs, minimizing bandwidth and computational and storage costs through an age-of-thought metric and a DRL-based auction mechanism for resource allocation. In [25], the authors developed a diffusion-model-driven digital-twin synchronization framework that orchestrates UAV, LEO satellite, and ground edge resources within space-air-ground integrated networks to deliver timely low-latency AI-generated content (AIGC) services. [PERSON] et al. [26] introduced generative-AI agents driven by LLM that auto-construct satellite-network optimization problems and solve them with a mixture-of-experts PPO transmission strategy, mitigating interference and boosting 6G satellite link performance. The authors in [27] devised a parameter-training-efficiency aware resource allocation (PARA) framework that co-optimizes user association, data offloading, and communication-compute resources in space-air-ground integrated networks to maximize parameter-efficient finetuning throughput for AIGC tasks under wireless constraints. Despite the advancements in leveraging LLMs for SAGIN and SEC networks, several critical research gaps persist. While existing research has made significant strides in deploying LLMs within MEC and SEC networks, critical gaps remain. The current work predominantly focuses on text-based LLMs and optimization strategies, overlooking the unique challenges posed by multimodal large language models (MLLMs) that process diverse data types, including text, images, and sensor data. This limitation is particularly acute in SEC environments where MLLMs could enable transformative applications such as real-time disaster response and multi-sensor remote monitoring.  
Furthermore, existing approaches fail to adequately address the joint optimization of task offloading, resource allocation, and mobility management in highly dynamic SEC environments characterized by LEO satellite movement and heterogeneous computing resources. The hybrid discrete-continuous nature of this optimization problem, combining discrete offloading decisions with continuous power and trajectory variables, remains unexplored. To address these gaps, we propose a novel MLLM inference framework for UAV-assisted SEC networks that jointly optimizes offloading decisions, power allocation, and UAV trajectories. Our approach uniquely considers the computational demands of MLLMs while adapting to dynamic satellite coverage, filling a critical void in the current literature.