在生成引擎优化中诊断和修复引用失败
Diagnosing and Repairing Citation Failures in Generative Engine Optimization
AI总结：
本文提出AgentGEO系统，通过诊断引用失败模式并针对性修复，提升生成引擎优化中的引用率，同时揭示通用优化对长尾内容的负面影响。
摘要：
生成引擎优化（GEO）旨在提高AI生成响应中的内容可见性。然而，现有方法测量的是贡献——文档对响应的影响程度——而非引用，这是真正驱动流量返回创作者的机制。此外，这些方法应用通用的重写规则，无法诊断为何某些文档未被引用。本文介绍了一种GEO的诊断方法，旨在询问为何文档未能被引用，并相应地进行干预。我们开发了一个统一框架，包括：（1）涵盖引用流程不同阶段的首个引用失败模式分类法；（2）AgentGEO，一个使用此分类法诊断失败、从相应工具库中选择针对性修复并迭代直至实现引用的代理系统；以及（3）以文档为中心的基准测试，评估优化是否能跨留出查询进行泛化。AgentGEO在仅修改5%内容的情况下，实现了超过40%的引用率提升，相比基线方法的25%。我们的分析揭示了通用优化可能损害长尾内容，且某些文档面临的挑战无法仅靠优化解决——这些发现对AI介导的信息访问中的公平可见性具有启示意义。
英文摘要：
Generative Engine Optimization (GEO) aims to improve content visibility in AI-generated responses. However, existing methods measure contribution-how much a document influences a response-rather than citation, the mechanism that actually drives traffic back to creators. Also, these methods apply generic rewriting rules uniformly, failing to diagnose why individual document are not cited. This paper introduces a diagnostic approach to GEO that asks why a document fails to be cited and intervenes accordingly. We develop a unified framework comprising: (1) the first taxonomy of citation failure modes spanning different stages of a citation pipeline; (2) AgentGEO, an agentic system that diagnoses failures using this taxonomy, selects targeted repairs from a corresponding tool library, and iterates until citation is achieved; and (3) a document-centric benchmark evaluating whether optimizations generalize across held-out queries. AgentGEO achieves over 40% relative improvement in citation rates while modifying only 5% of content, compared to 25% for baselines. Our analysis reveals that generic optimization can harm long-tail content and some documents face challenges that optimization alone cannot fully address-findings with implications for equitable visibility in AI-mediated information access.
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。
