FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning

• URL: http://arxiv.org/abs/2605.01495v1
• Date: Sat, 02 May 2026 15:32:26 GMT
• Title: FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning
• Abstract summary: We introduce FT-RAG, a fine-grained framework that employs knowledge association by decomposing tables into semantic units.<n> FT-RAG surpasses top-performing baselines across all metrics.<n>Our method establishes a new state-of-the-art performance for complex reasoning over mixed-modality documents.
• Score: 1.5612868551214847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding responses in external knowledge during inference. However, conventiona RAG systems under-perform on structured tabular data, largely due to coarse retrieval granularity and insufficient table semantic comprehension. To address these limitations, we introduce FT-RAG, a fine-grained framework that employs knowledge association by decomposing tables into entry-level semantic units to construct a structured graph. FT-RAG employs a structural neighbor expansion mechanism to find semantically connected entities during graph retrieval, followed by multi-modal fusion to consolidate the context of table retrieval results. Further, to address the scarcity of specialized datasets in this domain, we introduce Multi-Table-RAG-Lib, a benchmark comprising 9870 QA pairs with high complexity and difficulty, curated to demand multi-table integration and text-table information fusion for reasoning. FT-RAG surpasses top-performing baselines across all metrics, achieving a 23.5\% and 59.2\% improvement in table-level and cell-level Hit Rates, respectively. Generation performance also sees a remarkable 62.2\% increase in exact value accuracy recall. These metrics verify the framework's effectiveness in factual grounding across both pure tabular and heterogeneous table-text contexts. Therefore, our method establishes a new state-of-the-art performance for complex reasoning over mixed-modality documents.

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