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arXiv · 2602.13229

Pocket RAG: On-Device RAG for First Aid Guidance in Offline Mobile Environment

Dong Ho Kang·Hyunjoon Lee·Hyeonjeong Cha·Minkyu Choi·Sungsoo Lim
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Abstract

In disaster scenarios or remote areas, first responders often lose network connectivity when providing first aid. In such situations, server-based AI systems fail to provide critical guidance. To address this issue, we present a lightweight, mobile-based retrieval-augmented generation system for small language models (SLMs) that can run directly on Android devices. Our system integrates a mobile-friendly optimized pipeline featuring Hybrid RAG, selective compression, batched prompt decoding, and quantization caching. Despite the model's small size, our RAG-based system achieves 94.5\% accuracy for physical first aid and 97.0\% for psychological first aid. Additionally, we reduce response time from 14.2s to 3.7s, achieving a nearly 4x speedup. These results prove that our system is practical and can deliver reliable first aid guidance even without internet connectivity.

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Dong Ho Kang, Hyunjoon Lee, Hyeonjeong Cha, Minkyu Choi, Sungsoo Lim. 2026-01-28. Pocket RAG: On-Device RAG for First Aid Guidance in Offline Mobile Environment. https://arxiv.org/abs/2602.13229

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