用AI自动核查论文引用，91.7%准确率，省时90%以上。
AI-Powered Citation Auditing: A Zero-Assumption Protocol for Systematic Reference Verification in Academic Research
- AI自主验证每条引用，不预设任何引用正确。
- 30份文献2581条引用中91.7%被成功验证，误报率<0.5%。
- 适合导师、学生及学术机构做论文质量把关。
学术引用完整性面临持续挑战，研究显示20%的引用存在错误，而人工核查需数月专家时间。本文提出一种基于具备工具使用能力的代理型AI的全新方法，实现系统性、全面性的参考文献审计。我们构建了零假设验证协议，独立比对每条引用至多个学术数据库（Semantic Scholar、Google Scholar、CrossRef），不假设任何引用正确。该方法在30篇学术文档（共2581条引用）上验证，涵盖本科项目至博士论文及同行评审论文。结果显示，在已发表的PLOS论文中平均验证率达91.7%，成功检测出伪造引用、撤稿文章、孤立引用及掠夺性期刊。效率大幅提升：916条引用的博士论文审计仅需90分钟，相较人工审查节省数月。系统误报率低于0.5%，并识别出人工审核易遗漏的关键问题。本工作首次验证了基于AI代理的学术引用完整性方法，展示了其在导师、学生和机构质量保障中的实际应用价值。
原文摘要 · Abstract (English)
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.
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