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

arXiv: https://arxiv.org/abs/2602.13229 · HTML: https://mlsanity.com/paper/2602.13229/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2026.

## Built on by (most cited first, 2 of 2)

- [A Unified Model and Document Representation for On-Device Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.14403/) (2026)
- [Lightweight Chunk Selection for Mobile Retrieval-Augmented Generation](https://mlsanity.com/paper/2608.03148/) (2026)

## Abstract (arXiv)

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.