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

• URL: http://arxiv.org/abs/2608.01450v1
• Date: Sun, 02 Aug 2026 19:14:59 GMT
• Title: Real-Time Hybrid Retrieval in Hyperbolic Space for Retrieval-Augmented Generation on Edge Devices
• Abstract summary: This paper presents a hybrid document retrieval system that operates entirely within the Lorentz model of hyperbolic geometry.<n>Unlike conventional dense retrievers confined to Euclidean space, this system projects pretrained word embeddings into hyperbolic space.<n>The system supports real-time indexing of user-supplied documents and resource-efficient querying over tens of thousands of moderately sized documents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 embeddings into hyperbolic space through a learned HyTE-H transformation, whose exponential volume growth suits the hierarchical organization of natural language. Documents are segmented into overlapping chunks, indexed by their Lorentz embeddings, and retrieved through a two-stage pipeline that first applies BM25 lexical scoring, then re-ranks candidates using Lorentzian inner-product similarity. A tunable parameter $α$ blends the BM25 score with the hyperbolic similarity score. The system was evaluated on five datasets from the BEIR benchmark suite, SciFact, NFCorpus, ArguAna, SciDocs, and FiQA, achieving NDCG@10 scores of 0.654, 0.304, 0.342, 0.150, and 0.217 respectively with word embeddings alone, without fine-tuned neural encoders or cross-attention rerankers. The system supports real-time indexing of user-supplied documents and resource-efficient querying over tens of thousands of moderately sized documents, so hyperbolic retrieval can run on edge devices at interactive latencies.

Related papers

• REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling [0.0]
  REIGN is a bi-encoder that operates on sequences of contextualised chunk embeddings from a frozen Guidance Network (GN) rather than on raw tokens.<n>We release a synthetic long-document retrieval benchmark for contrastive training and evaluation at long context lengths.<n>A paired significance test puts it on par with models 1.6-4.3x larger on the patent task, and it stays within 0.65 nDCG@10 of a 20x-larger model on LoCo.
  arXiv  Detail & Related papers  (2026-08-30T16:50:38Z)
• eMEM: A Hybrid Spatio-Temporal Memory System For Embodied Agents [1.8047694351309203]
  We present eMEM, a hybrid graph-based memory system for embodied agents operating in physical environments.<n>eMEM fills this gap with a multi-index architecture (ITE for structured storage, hlib for approximate nearest neighbour semantic search, and an R-tree for spatial queries)<n>We also introduce eMEM-Bench v1, a benchmark we construct over ProcTHOR-10K scenes for embodied memory evaluation.
  arXiv  Detail & Related papers  (2026-06-02T09:22:12Z)
• Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search [30.80883548177376]
  We present V-SPLADE, an inference-free sparse retriever for visual-document retrieval.<n>V-SPLADE improves average NDCG@5 across six visual-document retrieval benchmarks by +13.8pp over the same-scale dense baseline.<n>On an 18.7M-document corpus, it more than doubles R@5 over the same-scale dense baseline.
  arXiv  Detail & Related papers  (2026-05-29T07:01:45Z)
• No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval [51.43543998583709]
  Single-stage Sparse Retrieval (SSR) is a paradigm shift that replaces expensive clustering with efficient sparse coding.<n>It reduces indexing time by 15x compared to ColBERTv2, halves retrieval latency, and simultaneously improves retrieval performance.
  arXiv  Detail & Related papers  (2026-05-28T15:53:34Z)
• Semantic Entanglement in Vector-Based Retrieval: A Formal Framework and Context-Conditioned Disentanglement Pipeline for Agentic RAG Systems [0.0]
  We formalize semantic entanglement as a model-relative measure of cross-topic overlap in embedding space.<n>We introduce the Semantic Disentanglement Pipeline (SDP), a four-stage preprocessing framework that restructures documents prior to embedding.<n>We evaluate SDP on a real-world enterprise healthcare knowledge base comprising over 2,000 documents across approximately 25 sub-domains.
  arXiv  Detail & Related papers  (2026-04-20T00:24:34Z)
• Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage [89.58253972744531]
  Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation.<n>We investigate whether upstream retrieval metrics can serve as reliable early indicators of the final generated response's information coverage.<n>Our findings demonstrate strong correlations between coverage-based retrieval metrics and nugget coverage in generated responses at both topic and system levels.
  arXiv  Detail & Related papers  (2026-03-09T18:20:20Z)
• Multi-Vector Index Compression in Any Modality [73.7330345057813]
  Late interaction has emerged as a dominant paradigm for information retrieval in text, images, visual documents, and videos.<n>We introduce four approaches for index compression: sequence resizing, memory tokens, hierarchical pooling, and a novel attention-guided clustering (AGC)<n>AGC uses an attention-guided mechanism to identify the most semantically salient regions of a document as cluster centroids and to weight token aggregation.
  arXiv  Detail & Related papers  (2026-02-24T18:57:33Z)
• HyEm: Query-Adaptive Hyperbolic Retrieval for Biomedical Ontologies via Euclidean Vector Indexing [3.362526994670267]
  We present HyEm, a lightweight retrieval layer integrating hyperbolic embeddings into existing Euclidean ANN infrastructure.<n>Experiments on biomedical subsets demonstrate HyEm 94-98% of Euclidean baseline performance on entity-centric queries.
  arXiv  Detail & Related papers  (2026-01-26T07:04:46Z)
• AIRwaves at CheckThat! 2025: Retrieving Scientific Sources for Implicit Claims on Social Media with Dual Encoders and Neural Re-Ranking [0.0]
  Team AIRwaves ranked second in Subtask 4b of the CLEF-2025 CheckThat! Lab with an evidence-retrieval approach that markedly outperforms the competition baseline.<n>To surpass this baseline, a two-stage retrieval pipeline is introduced: (i) a first stage that uses a dual encoder based on E5-large, fine-tuned using in-batch and mined hard negatives and enhanced through chunked tokenization and rich document metadata; and (ii) a neural re-ranking stage using a SciBERT cross-encoder.
  arXiv  Detail & Related papers  (2025-09-23T19:26:31Z)
• How Does Generative Retrieval Scale to Millions of Passages? [68.98628807288972]
  We conduct the first empirical study of generative retrieval techniques across various corpus scales. We scale generative retrieval to millions of passages with a corpus of 8.8M passages and evaluating model sizes up to 11B parameters. While generative retrieval is competitive with state-of-the-art dual encoders on small corpora, scaling to millions of passages remains an important and unsolved challenge.
  arXiv  Detail & Related papers  (2023-05-19T17:33:38Z)
• Precise Zero-Shot Dense Retrieval without Relevance Labels [60.457378374671656]
  Hypothetical Document Embeddings(HyDE) is a zero-shot dense retrieval system. We show that HyDE significantly outperforms the state-of-the-art unsupervised dense retriever Contriever.
  arXiv  Detail & Related papers  (2022-12-20T18:09:52Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.