Academic citation integrity faces persistent challenges, with research indicating 20% of citations contain errors and manual verification requiring months of expert time. This paper presents a novel AI-powered methodology for systematic, comprehensive reference auditing using agentic AI with tool-use capabilities. We develop a zero-assumption verification protocol that independently validates every reference against multiple academic databases (Semantic Scholar, Google Scholar, CrossRef) without assuming any citation is correct. The methodology was validated across 30 academic documents (2,581 references) spanning undergraduate projects to doctoral theses and peer-reviewed publications. Results demonstrate 91.7% average verification rate on published PLOS papers, with successful detection of fabricated references, retracted articles, orphan citations, and predatory journals. Time efficiency improved dramatically: 90-minute audits for 916-reference doctoral theses versus months of manual review. The system achieved <0.5% false positive rate while identifying critical issues manual review might miss. This work establishes the first validated AI-agent methodology for academic citation integrity, demonstrating practical applicability for supervisors, students, and institutional quality assurance.

翻译：学术引文完整性面临持续挑战，研究表明20%的引文存在错误，且人工验证需要耗费专家数月时间。本文提出了一种新颖的人工智能驱动方法，利用具备工具使用能力的智能体AI，进行系统性、全面的参考文献审计。我们开发了一种零假设验证协议，该协议独立地针对多个学术数据库（Semantic Scholar、Google Scholar、CrossRef）验证每一条参考文献，而不假设任何引文是正确的。该方法在30份学术文档（共2581条参考文献）上进行了验证，涵盖本科项目、博士论文以及同行评议出版物。结果表明，在已发表的PLOS论文上平均验证率达到91.7%，并成功检测出伪造参考文献、已撤稿文章、孤立引文以及掠夺性期刊。时间效率显著提升：对包含916条参考文献的博士论文进行审计仅需90分钟，而人工审查则需要数月。该系统实现了低于0.5%的误报率，同时识别出人工审查可能遗漏的关键问题。本研究确立了首个经过验证的用于学术引文完整性的人工智能体方法，证明了其在导师、学生及机构质量保证方面的实际适用性。