Cimrag: Cim-aware Domain-adaptive And Noise-resilient Retrieval-augmented Generation For Edge-based Llms

Abstract

Personalized virtual assistants powered by large language models (LLMs) on edge devices are attracting growing attention, with Retrieval-Augmented Generation (RAG) emerging as a key method for personalization by retrieving relevant profile data and generating tailored responses. However, deploying RAG on edge devices faces efficiency hurdles due to the rapid growth of profile data, such as user-LLM interactions and recent updates. While Computing-in-Memory (CiM) architectures mitigate this bottleneck by eliminating data movement between memory and processing units via in-situ operations, they are susceptible to environmental noise that can degrade retrieval precision. This poses a critical issue in dynamic, multi-domain edge-based scenarios (e.g., travel, medicine, and law) where both accuracy and adaptability are paramount. To address these challenges, we propose Task-Oriented Noise-resilient Embedding Learning (TONEL), a framework that improves noise robustness and domain adaptabilit

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keychiu2026cimrag

Related papers

• Ragdb: A Zero-dependency, Embeddable Architecture For Multimodal Retrieval-augmented Generation On The Edge (2025)0.00
• MLLM Is A Strong Reranker: Advancing Multimodal Retrieval-augmented Generation Via Knowledge-enhanced Reranking And Noise-injected Training (2024)9.18
• LMAR: Language Model Augmented Retriever For Domain-specific Knowledge Indexing (2025)1.57
• Re-ranking The Context For Multimodal Retrieval Augmented Generation (2025)0.00
• Advancing Retrieval-augmented Generation For Structured Enterprise And Internal Data (2025)1.20
• Domain-aware RAG: Mol-enhanced RL For Efficient Training And Scalable Retrieval (2025)0.00
• Edgerag: Online-indexed RAG For Edge Devices (2024)0.00
• LUMA-RAG: Lifelong Multimodal Agents With Provably Stable Streaming Alignment (2025)0.00