MoRAG: Bringing Graph-Based Retrieval-Augmented Generation to Real Smartphones

Graph-based RAG organizes fragmented text into explicit knowledge structures for retrieval through entities, relations, and article summaries. However, directly bringing graph-based RAG to smartphones creates a mismatch between cloud-oriented graph methods and mobile constraints: compact on-device models are more likely to miss entities, predict incorrect relations, or merge distinct entity mentions, while multi-level graph summaries require frequent, large-context LLM invocations. We build MoRAG, to the best of our knowledge, the first graph-based RAG framework end-to-end deployed on commercial smartphones. MoRAG combines MRGraph, a two-layer mobile graph that preserves entities, relations, and source paragraphs; EvoNER, which incrementally learns unseen entity types and locally updates the graph; and ATPlanner, which uses one LLM call to guide hybrid retrieval under a fixed budget. On real smartphones, MoRAG outperforms prior mobile RAG baselines by up to 22.4 points in correctness, constructs knowledge graphs 13.77× faster, and consumes only 1.36% of the energy compared with the latest lightweight methods. **Code & Demo**: https://anonymous.4open.science/r/MoRAG-31B4/.