Hierarchical Abstract Tree for Cross-Document Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2605.00529v1
• Date: Fri, 01 May 2026 09:06:23 GMT
• Title: Hierarchical Abstract Tree for Cross-Document Retrieval-Augmented Generation
• Abstract summary: $$-RAG is a tree-RAG framework with two key components.<n>It supports diverse tasks from token-level question answering to document-level summarization.<n>It outperforms RAPTOR by 25.9% and HippoRAG 2 by 7.4% in average F1 score.
• Score: 3.338186852016847
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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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