# From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAV

arXiv: https://arxiv.org/abs/2506.02649 · HTML: https://mlsanity.com/paper/2506.02649/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agents](https://mlsanity.com/paper/2309.09919/) (2023)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networks](https://mlsanity.com/paper/2504.14556/) (2025)
- [Diffusion Models for Smarter UAVs: Decision-Making and Modeling](https://mlsanity.com/paper/2501.05819/) (2025)
- [Deep Reinforcement Learning for Joint Cruise Control and Intelligent Data Acquisition in UAVs-Assisted Sensor Networks](https://mlsanity.com/paper/2312.09953/) (2023)

## Built on by (most cited first, 10 of 10)

- [FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring](https://mlsanity.com/paper/2507.10134/) (2025)
- [Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial](https://mlsanity.com/paper/2602.19534/) (2026)
- [Adaptive Multilevel Twisted Sequential Monte Carlo for Rare Events Estimation in Language Models](https://mlsanity.com/paper/2608.21736/) (2026)
- [AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoring](https://mlsanity.com/paper/2510.05698/) (2025)
- [DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments](https://mlsanity.com/paper/2606.06217/) (2026)
- [Risk-Conditioned Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2609.08064/) (2026)
- [Cognition Envelopes for Bounded Decision Making in Autonomous UAS Operations](https://mlsanity.com/paper/2510.26905/) (2025)
- [Advancing Autonomous Emergency Response Systems: A Generative AI Perspective](https://mlsanity.com/paper/2511.09044/) (2025)
- [LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problems](https://mlsanity.com/paper/2607.09756/) (2026)
- [LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Dataset](https://mlsanity.com/paper/2608.15175/) (2026)

## Abstract (arXiv)

A public safety Uncrewed Aerial Vehicle (UAV) enhances situational awareness during emergency response. Its agility, mobility optimization, and ability to establish Line-of-Sight (LoS) communication make it increasingly important for managing emergencies such as disaster response, search and rescue, and wildfire monitoring. Although Deep Reinforcement Learning (DRL) has been used to optimize UAV navigation and control, its high training complexity, low sample efficiency, and the simulation-to-reality gap limit its practicality in public safety applications. Recent advances in Large Language Models (LLMs) present a promising alternative. With strong reasoning and generalization abilities, LLMs can adapt to new tasks through In-Context Learning (ICL), enabling task adaptation via natural language prompts and example-based guidance without retraining. Deploying LLMs at the network edge, rather than in the cloud, further reduces latency and preserves data privacy, making them suitable for real-time, mission-critical public safety UAVs. This paper proposes integrating LLM-assisted ICL with public safety UAVs to address key functions such as path planning and velocity control in emergency response. We present a case study on data collection scheduling, demonstrating that the LLM-assisted ICL framework can significantly reduce packet loss compared to conventional approaches while also mitigating potential jailbreaking vulnerabilities. Finally, we discuss LLM optimizers and outline future research directions. The ICL framework enables adaptive, context-aware decision-making for public safety UAVs, offering a lightweight and efficient solution to enhance UAV autonomy and responsiveness in emergencies.