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.

翻译：生成式引擎优化旨在提升内容在AI生成回答中的可见性。然而，现有方法主要衡量贡献度——即文档对回答的影响程度——而非实际驱动流量回创作者端的引证机制。此外，这些方法统一应用通用改写规则，未能诊断个别文档未被引证的具体原因。本文提出一种诊断式生成式引擎优化方法，旨在探究文档引证失败的原因并实施针对性干预。我们开发了一个统一框架，包含：（1）首个涵盖引证流程不同阶段的引证失效模式分类体系；（2）AgentGEO——一个基于该分类体系的智能诊断系统，可从对应工具库中选择针对性修复方案，并迭代执行直至实现引证；（3）以文档为中心的基准测试，用于评估优化策略在预留查询中的泛化能力。实验表明，AgentGEO仅需修改5%的内容即可实现超过40%的相对引证率提升，而基线方法仅提升25%。我们的分析表明，通用优化可能损害长尾内容的可见性，且部分文档面临的挑战无法仅通过优化完全解决——这些发现对AI中介信息获取中的公平可见性具有重要启示。