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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">TACS</journal-id><journal-title-group><journal-title>Technology and Application of Computer Science</journal-title></journal-title-group><issn>2998-8926</issn><eissn>2998-8934</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/TACS.2026050008</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于RAG 与工具调用的税务多智能体系统</title><url>https://artdesignp.com/journal/TACS/3/5/10.61369/TACS.2026050008</url><author>文颖,黎栋梁,徐丰磊,张颖</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>3</volume><issue>5</issue><history><date date-type="pub"><published-time>2026-03-14</published-time></date></history><abstract>针对税务政策具备繁杂内容、多样规则，以及问答系统常会出现知识缺口、推理偏差的现状，设计一套融合检索增强生成与工具调用能力的税务多智能体架构；搭建起用户、智能体、工具和数据四层完整体系，依托多智能体协同运作的方式，完成知识检索、规则推理和税额计算的一体化流程，进而拉高税务问答具备的精准度、可解释程度与自动化层级。</abstract><keywords>多智能体系统,检索增强生成,工具调用,税务智能服务,大语言模型</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]Aslett J, Hamilton S, Gonzalez I, et al. Understanding artificial intelligence in tax and customs administration[M]. Washington, DC: International Monetary Fund, 2024.[2]Nay J J, Karamardian D, Lawsky S B, et al. Large language models as tax attorneys: a case study in legal capabilities emergence[J]. Philosophical Transactions of the Royal Society A, 2024, 382(2270): 20230159.[3]Fei Z, Shen X, Zhu D, et al. Lawbench: Benchmarking legal knowledge of large language models[C]//Proceedings of the 2024 conference on empirical methods in natural language processing. 2024: 7933-7962.[4]Pipitone N, Alami G H. Legalbench-rag: A benchmark for retrieval-augmented generation in the legal domain[J]. arXiv preprint arXiv:2408.10343, 2024.[5]Gao Y, Xiong Y, Gao X, et al. Retrieval-augmented generation for large language models: A survey[J]. arXiv preprint arXiv:2312.10997, 2023, 2(1): 32.[6]Huang Y, Huang J. A Survey on Retrieval-Augmented Text Generation for Large Language Models[J]. arXiv e-prints, 2024: arXiv: 2404.10981.[7]Fan W, Ding Y, Ning L, et al. A survey on rag meeting llms: Towards retrieval-augmented large language models[C]//Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining. 2024: 6491-6501.[8]Schick T, Dwivedi-Yu J, Dess&amp;igrave; R, et al. Toolformer: Language models can teach themselves to use tools[J]. Advances in neural information processing systems, 2023, 36: 68539-68551.[9]Yao S, Zhao J, Yu D, et al. React: Synergizing reasoning and acting in language models[C]//The eleventh international conference on learning representations. 2022.[10]Qin Y, Hu S, Lin Y, et al. Tool learning with foundation models[J]. ACM Computing Surveys, 2024, 57(4): 1-40.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
