Paper Detail
Ziwen Zhao, Menglin Yang
Retrieval-augmented generation (RAG) enhances large language models with external knowledge, and tree-based RAG organizes documents into hierarchical indexes to support queries at multiple granularities. However, existing Tree-RAG methods designed for single-document retrieval face critical challenges in scaling to cross-document multi-hop questions: (1) poor distribution adaptability, where k-means clustering introduces noise due to rigid distribution assumptions; (2) structural isolation, as tree indexes lack explicit cross-document connections; and (3) coarse abstraction, which obscures fine-grained details. To address these limitations, we propose Ψ-RAG, a tree-RAG framework with two key components. First, a hierarchical abstract tree index built through an iterative "merging and collapse" process that adapts to data distributions without a priori assumption. Second, a multi-granular retrieval agent that intelligently interacts with the knowledge base with reorganized queries and an agent-powered hybrid retriever. Ψ-RAG supports diverse tasks from token-level question answering to document-level summarization. On cross-document multi-hop QA benchmarks, it outperforms RAPTOR by 25.9% and HippoRAG 2 by 7.4% in average F1 score. Code is available at https://github.com/Newiz430/Psi-RAG.
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@misc{zhao2026hierarchical,
title = {Hierarchical Abstract Tree for Cross-Document Retrieval-Augmented Generation},
author = {Ziwen Zhao and Menglin Yang},
year = {2026},
abstract = {Retrieval-augmented generation (RAG) enhances large language models with external knowledge, and tree-based RAG organizes documents into hierarchical indexes to support queries at multiple granularities. However, existing Tree-RAG methods designed for single-document retrieval face critical challenges in scaling to cross-document multi-hop questions: (1) poor distribution adaptability, where k-means clustering introduces noise due to rigid distribution assumptions; (2) structural isolation, as t},
url = {https://huggingface.co/papers/2605.00529},
keywords = {retrieval-augmented generation, tree-based RAG, k-means clustering, hierarchical abstract tree index, multi-granular retrieval, cross-document multi-hop questions, document-level summarization, token-level question answering, code available, huggingface daily},
eprint = {2605.00529},
archiveprefix = {arXiv},
}
{}