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SmartRAG enables LLMs on mobile devices with graph-based RAG

By PulseAugur Editorial · [2 sources] ·

Researchers have developed SmartRAG, a novel on-device framework designed to enable large language models (LLMs) to function as personal assistants on mobile devices. This system decomposes intelligence into four modules: Perception, Memory, Focus, and Thinking, with EvoNER for continual learning of new entity types and MRGraph for storing knowledge in a provenance-preserving graph. SmartRAG aims to achieve competitive multi-hop reasoning performance on commodity smartphones using a quantized 1.7B-parameter backbone, outperforming much larger models while operating entirely offline and within strict hardware constraints. AI

IMPACT Enables more capable and private AI assistants on mobile devices by optimizing LLM performance for edge hardware.

RANK_REASON The cluster contains a research paper detailing a new framework for on-device LLMs.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SmartRAG enables LLMs on mobile devices with graph-based RAG

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhihan Jiang, Meng Li, Shenghao Liu, Keran Li, Ruiben Zhou, Xianjun Deng, Shuai Wang, Haipeng Dai ·

    SmartRAG: Native Graph-Based RAG for Mobile Device

    arXiv:2607.14661v1 Announce Type: new Abstract: Deploying large language models (LLMs) as personal assistants on mobile devices demands privacy, low latency, and offline availability, yet the computational cost of giant models clashes with strict edge-hardware budgets. We argue t…

  2. arXiv cs.AI TIER_1 English(EN) · Haipeng Dai ·

    SmartRAG: Native Graph-Based RAG for Mobile Device

    Deploying large language models (LLMs) as personal assistants on mobile devices demands privacy, low latency, and offline availability, yet the computational cost of giant models clashes with strict edge-hardware budgets. We argue that this tension cannot be resolved by model com…

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