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Hyperbolic geometry retrieval system enables RAG on edge devices

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel hybrid retrieval system that leverages hyperbolic geometry for retrieval-augmented generation (RAG) on edge devices. This system projects word embeddings into hyperbolic space, allowing for efficient indexing and querying of documents. By combining BM25 lexical scoring with Lorentzian inner-product similarity, the system achieves competitive NDCG@10 scores across several benchmark datasets. The design prioritizes real-time indexing and resource-efficient querying, making it suitable for deployment on devices with limited computational power. AI

IMPACT Enables more efficient and real-time AI applications on resource-constrained edge devices.

RANK_REASON The cluster contains a research paper detailing a novel technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Hyperbolic geometry retrieval system enables RAG on edge devices

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The cluster contains a research paper detailing a novel technical approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Aradhya Chakrabarti ·

    Real-Time Hybrid Retrieval in Hyperbolic Space for Retrieval-Augmented Generation on Edge Devices

    This paper presents a hybrid document retrieval system designed for retrieval-augmented generation (RAG) that operates entirely within the Lorentz model of hyperbolic geometry. Unlike conventional dense retrievers confined to Euclidean space, this system projects pretrained word …

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