{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:56:28Z","timestamp":1770756988154,"version":"3.50.0"},"reference-count":65,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Financial document understanding remains a critical challenge for Large Language Models, primarily due to the complex interplay between narrative text and structured numerical tables. Existing Retrieval-Augmented Generation (RAG) systems often treat these modalities in isolation, leading to significant failures in tasks requiring joint reasoning. This study introduces HierFinRAG, a novel hierarchical multimodal framework designed to unify tabular and textual data processing. Our approach employs a Table-Text Graph Neural Network (TTGNN) to explicitly model semantic and structural dependencies between table cells and corresponding text, coupled with a Symbolic\u2013Neural Fusion module that routes queries between a neural generator and a symbolic calculator for precise arithmetic operations. We evaluate the system on the FinQA and FinanceBench datasets, comparing performance against strong baselines including Vanilla RAG and GPT-4o with Code Interpreter. Results demonstrate that HierFinRAG achieves an Exact Match score of 82.5% on FinQA, surpassing the best baseline by 6.5 percentage points, while maintaining a 3.5\u00d7 faster inference latency than agentic approaches. These findings indicate that integrating hierarchical structural awareness with hybrid reasoning significantly enhances the accuracy and interpretability of financial artificial intelligence systems.<\/jats:p>","DOI":"10.3390\/informatics13020030","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T15:23:14Z","timestamp":1770736994000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HierFinRAG\u2014Hierarchical Multimodal RAG for Financial Document Understanding"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3877-8024","authenticated-orcid":false,"given":"Quang-Vinh","family":"Dang","sequence":"first","affiliation":[{"name":"School of Innovation and Computing Technology, British University Vietnam, Hung Yen 16000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8384-3110","authenticated-orcid":false,"given":"Ngoc-Son-An","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City 70000, Vietnam"}]},{"given":"Thi-Bich-Diem","family":"Vo","sequence":"additional","affiliation":[{"name":"GiaoHangNhanh, Ho Chi Minh City 70000, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., Moussa, R., Beane, M., Huang, T.H., and Routledge, B.R. 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