精英同行评审中的复合欺骗：100个2025年NeurIPS伪造引用的失败模式分类
Compound Deception in Elite Peer Review: A Failure Mode Taxonomy of 100 Fabricated Citations at NeurIPS 2025
AI总结：
本研究分析了NeurIPS 2025中100个伪造引用的失败模式，发现所有幻觉均表现出复合失败模式，揭示同行评审在验证引用时的不足，并提出强制自动引用验证的解决方案。
摘要：
大型语言模型（LLMs）越来越多地用于学术写作流程，但它们经常通过生成不存在的引用源而产生幻觉。本研究分析了100个由人工智能生成的幻觉引用，这些引用出现在2025年神经信息处理系统会议（NeurIPS）接受的论文中，NeurIPS是世界上最负盛名的AI会议之一。尽管每篇论文都由3-5位专家研究人员审查，但这些伪造的引用仍然未被检测到，出现在53篇已发表的论文中（约占所有接受论文的1%）。我们开发了一个五类分类法，通过其失败模式对幻觉进行分类：完全伪造（66%）、部分属性破坏（27%）、标识符劫持（4%）、占位符幻觉（2%）和语义幻觉（1%）。我们的分析揭示了一个关键发现：所有幻觉（100%）都表现出复合失败模式。次要特征的分布以语义幻觉（63%）和标识符劫持（29%）为主，这些特征常常与完全伪造同时出现，以创造一种表面的可信度和虚假的可验证性。这些复合结构同时利用了多种验证启发式方法，解释了为什么同行评审无法检测到它们。分布呈现出双峰模式：92%的受污染论文包含1-2个幻觉（最小的AI使用）而8%的论文包含4-13个幻觉（高度依赖）。这些发现表明，当前的同行评审过程没有包含有效的引用验证，且问题不仅限于NeurIPS，还扩展到其他主要会议、政府报告和专业咨询。我们提出在提交时强制进行自动引用验证作为可实施的解决方案，以防止伪造引用在科学文献中变得正常化。
英文摘要：
Large language models (LLMs) are increasingly used in academic writing workflows, yet they frequently hallucinate by generating citations to sources that do not exist. This study analyzes 100 AI-generated hallucinated citations that appeared in papers accepted by the 2025 Conference on Neural Information Processing Systems (NeurIPS), one of the world's most prestigious AI conferences. Despite review by 3-5 expert researchers per paper, these fabricated citations evaded detection, appearing in 53 published papers (approx. 1% of all accepted papers). We develop a five-category taxonomy that classifies hallucinations by their failure mode: Total Fabrication (66%), Partial Attribute Corruption (27%), Identifier Hijacking (4%), Placeholder Hallucination (2%), and Semantic Hallucination (1%). Our analysis reveals a critical finding: every hallucination (100%) exhibited compound failure modes. The distribution of secondary characteristics was dominated by Semantic Hallucination (63%) and Identifier Hijacking (29%), which often appeared alongside Total Fabrication to create a veneer of plausibility and false verifiability. These compound structures exploit multiple verification heuristics simultaneously, explaining why peer review fails to detect them. The distribution exhibits a bimodal pattern: 92% of contaminated papers contain 1-2 hallucinations (minimal AI use) while 8% contain 4-13 hallucinations (heavy reliance). These findings demonstrate that current peer review processes do not include effective citation verification and that the problem extends beyond NeurIPS to other major conferences, government reports, and professional consulting. We propose mandatory automated citation verification at submission as an implementable solution to prevent fabricated citations from becoming normalized in scientific literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
