T-Retriever: Tree-based Hierarchical Retrieval Augmented Generation for Textual Graphs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917190598393856 |
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| author | Wei, Chunyu Qin, Huaiyu He, Siyuan Wang, Yunhai Chen, Yueguo |
| author_facet | Wei, Chunyu Qin, Huaiyu He, Siyuan Wang, Yunhai Chen, Yueguo |
| contents | Retrieval-Augmented Generation (RAG) has significantly enhanced Large Language Models' ability to access external knowledge, yet current graph-based RAG approaches face two critical limitations in managing hierarchical information: they impose rigid layer-specific compression quotas that damage local graph structures, and they prioritize topological structure while neglecting semantic content. We introduce T-Retriever, a novel framework that reformulates attributed graph retrieval as tree-based retrieval using a semantic and structure-guided encoding tree. Our approach features two key innovations: (1) Adaptive Compression Encoding, which replaces artificial compression quotas with a global optimization strategy that preserves the graph's natural hierarchical organization, and (2) Semantic-Structural Entropy ($S^2$-Entropy), which jointly optimizes for both structural cohesion and semantic consistency when creating hierarchical partitions. Experiments across diverse graph reasoning benchmarks demonstrate that T-Retriever significantly outperforms state-of-the-art RAG methods, providing more coherent and contextually relevant responses to complex queries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04945 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | T-Retriever: Tree-based Hierarchical Retrieval Augmented Generation for Textual Graphs Wei, Chunyu Qin, Huaiyu He, Siyuan Wang, Yunhai Chen, Yueguo Artificial Intelligence Retrieval-Augmented Generation (RAG) has significantly enhanced Large Language Models' ability to access external knowledge, yet current graph-based RAG approaches face two critical limitations in managing hierarchical information: they impose rigid layer-specific compression quotas that damage local graph structures, and they prioritize topological structure while neglecting semantic content. We introduce T-Retriever, a novel framework that reformulates attributed graph retrieval as tree-based retrieval using a semantic and structure-guided encoding tree. Our approach features two key innovations: (1) Adaptive Compression Encoding, which replaces artificial compression quotas with a global optimization strategy that preserves the graph's natural hierarchical organization, and (2) Semantic-Structural Entropy ($S^2$-Entropy), which jointly optimizes for both structural cohesion and semantic consistency when creating hierarchical partitions. Experiments across diverse graph reasoning benchmarks demonstrate that T-Retriever significantly outperforms state-of-the-art RAG methods, providing more coherent and contextually relevant responses to complex queries. |
| title | T-Retriever: Tree-based Hierarchical Retrieval Augmented Generation for Textual Graphs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.04945 |