# Diagnosing and Repairing Citation Failures in Generative Engine Optimization

arXiv: https://arxiv.org/abs/2603.09296 · HTML: https://mlsanity.com/paper/2603.09296/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models](https://mlsanity.com/paper/2401.00396/) (2023)
- [Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models](https://mlsanity.com/paper/2212.08037/) (2022)
- [Evaluating Retrieval Quality in Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.13781/) (2024)
- [GEO: Generative Engine Optimization](https://mlsanity.com/paper/2311.09735/) (2023)
- [Attribute First, then Generate: Locally-attributable Grounded Text Generation](https://mlsanity.com/paper/2403.17104/) (2024)
- [DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research](https://mlsanity.com/paper/2505.19253/) (2025)
- [Adversarial Search Engine Optimization for Large Language Models](https://mlsanity.com/paper/2406.18382/) (2024)
- [Ranking Manipulation for Conversational Search Engines](https://mlsanity.com/paper/2406.03589/) (2024)
- [Generative Engine Optimization: How to Dominate AI Search](https://mlsanity.com/paper/2509.08919/) (2025)
- [What Generative Search Engines Like and How to Optimize Web Content Cooperatively](https://mlsanity.com/paper/2510.11438/) (2025)
- [StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization](https://mlsanity.com/paper/2504.05804/) (2025)
- [Conversational Search: From Fundamentals to Frontiers in the LLM Era](https://mlsanity.com/paper/2506.10635/) (2025)
- [CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines](https://mlsanity.com/paper/2509.05607/) (2025)

## Built on by (most cited first, 3 of 3)

- [When Optimization Becomes Manipulation: Defending Generative Search against Malicious Generative Engine Optimization](https://mlsanity.com/paper/2609.02964/) (2026)
- [CITECHOICE: A Causal Audit of How Document Presentation Redistributes Citation Credit in Agentic Search](https://mlsanity.com/paper/2609.15164/) (2026)
- [SlopShape: Identifying AI-Generated Commercial Web Content](https://mlsanity.com/paper/2609.15369/) (2026)

## Abstract (arXiv)

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.