# CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter

arXiv: https://arxiv.org/abs/2501.15098 · HTML: https://mlsanity.com/paper/2501.15098/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 25 Jan 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions](https://mlsanity.com/paper/2410.12837/) (2024)
- [T-RAG: Lessons from the LLM Trenches](https://mlsanity.com/paper/2402.07483/) (2024)
- [Role of Bloom Filter in Big Data Research: A Survey](https://mlsanity.com/paper/1903.06565/) (2019)

## Built on by (most cited first, 4 of 4)

- [Harmonia: End-to-End RAG Serving Optimization](https://mlsanity.com/paper/2505.07833/) (2025)
- [RAG without Forgetting: Continual Query-Infused Key Memory](https://mlsanity.com/paper/2602.05152/) (2026)
- [Rethinking Retrieval-Augmentation as Synthesis: A Query-Aware Context Merging Approach](https://mlsanity.com/paper/2603.20286/) (2026)
- [Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm](https://mlsanity.com/paper/2603.26668/) (2026)

## Abstract (arXiv)

Although retrieval-augmented generation(RAG) significantly improves generation quality by retrieving external knowledge bases and integrating generated content, it faces computational efficiency bottlenecks, particularly in knowledge retrieval tasks involving hierarchical structures for Tree-RAG. This paper proposes a Tree-RAG acceleration method based on the improved Cuckoo Filter, which optimizes entity localization during the retrieval process to achieve significant performance improvements. Tree-RAG effectively organizes entities through the introduction of a hierarchical tree structure, while the Cuckoo Filter serves as an efficient data structure that supports rapid membership queries and dynamic updates. The experiment results demonstrate that our method is much faster than naive Tree-RAG while maintaining high levels of generative quality. When the number of trees is large, our method is hundreds of times faster than naive Tree-RAG. Our work is available at https://github.com/TUPYP7180/CFT-RAG-2025.