HugRAG: Hierarchical Causal Knowledge Graph Design for RAG

• URL: http://arxiv.org/abs/2602.05143v1
• Date: Wed, 04 Feb 2026 23:59:02 GMT
• Title: HugRAG: Hierarchical Causal Knowledge Graph Design for RAG
• Authors: Nengbo Wang, Tuo Liang, Vikash Singh, Chaoda Song, Van Yang, Yu Yin, Jing Ma, Jagdip Singh, Vipin Chaudhary,
• Abstract summary: HugRAG is a framework that rethinks knowledge organization for graph-based RAG.<n>Our work establishes a principled foundation for structured, scalable, and causally grounded RAG systems.
• Score: 13.71290541205767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval augmented generation (RAG) has enhanced large language models by enabling access to external knowledge, with graph-based RAG emerging as a powerful paradigm for structured retrieval and reasoning. However, existing graph-based methods often over-rely on surface-level node matching and lack explicit causal modeling, leading to unfaithful or spurious answers. Prior attempts to incorporate causality are typically limited to local or single-document contexts and also suffer from information isolation that arises from modular graph structures, which hinders scalability and cross-module causal reasoning. To address these challenges, we propose HugRAG, a framework that rethinks knowledge organization for graph-based RAG through causal gating across hierarchical modules. HugRAG explicitly models causal relationships to suppress spurious correlations while enabling scalable reasoning over large-scale knowledge graphs. Extensive experiments demonstrate that HugRAG consistently outperforms competitive graph-based RAG baselines across multiple datasets and evaluation metrics. Our work establishes a principled foundation for structured, scalable, and causally grounded RAG systems.

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