Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm
On the map: Retrieval-augmented generation → RAG pipeline design and evaluation
Abstract (arXiv)
As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval accuracy and computational efficiency. This paper presents a novel RAG framework called Bridge-RAG. To overcome the accuracy challenge, we introduce the concept of abstract to bridge query entities and document chunks, providing robust semantic understanding. We organize the abstracts into a tree structure and design a multi-level retrieval strategy to ensure the inclusion of sufficient contextual information. While this hierarchical organization substantially improves answer quality, traversing the tree to locate the abstracts that contain a query entity inevitably introduces additional retrieval overhead. To restore retrieval efficiency, we further integrate the Cuckoo Filter in CFT-RAG, which provides O(1) entity lookup and naturally fits the entity-to-abstract pathway of our framework. Extensive experiments show that Bridge-RAG achieves consistent accuracy improvements across all metrics and up to 1.9× faster retrieval compared to structured RAG baselines.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 15 of its 27 references that are in the map. First posted on arXiv 12 Jan 2026.
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- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
- 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
- 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,324
- 2021Improving language models by retrieving from trillions of tokenscited by 1,278
- 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
- 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
- 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
- 2025HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representationcited by 68
- 2024Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphscited by 38
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- 2024T-RAG: Lessons from the LLM Trenchescited by 23
- 2019Role of Bloom Filter in Big Data Research: A Surveycited by 3
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- 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
- 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
- 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
- 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
- 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
- 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
- 2024CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity
- 2024Evaluation of Retrieval-Augmented Generation: A Survey