mlsanityData to 14 Oct 2026

Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots

arXiv 2606.12439 ↗First posted 18 May 2026cs.CY cs.AIcited by 0

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Abstract (arXiv)

Large language model (LLM) answer engines are increasingly used for information seeking, shifting visibility from ranked lists to synthesized answers. This enables Generative Engine Optimization (GEO), which targets LLM answer engines' evidence pool and generation. We analyze the search engine optimization (SEO) to GEO transition to identify two risks: (i) concentrated influence from low contestability and system sensitivity, and (ii) undisclosed commercial influence embedded in evidence and reasoning. We then formalize a general GEO pipeline to locate where optimization acts and compare academic and industry practices, revealing a third risk: (iii) academic-industry blind spots driven by visibility and evaluation asymmetries between offline setups and deployed systems. This position argues the need for answer-level governance and measurement: stronger contestability, high-precision disclosure, black-box auditing of material influence, and deployment-aligned metrics for exposure persistence.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 66 references that are in the map. First posted on arXiv 18 May 2026.

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13 of 66 references.

References · 13

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  3. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  4. 2018Model Cards for Model Reportingcited by 1,080
  5. 2023Tree of Attacks: Jailbreaking Black-Box LLMs Automaticallycited by 672
  6. 2023Auditing large language models: a three-layered approachcited by 137
  7. 2017How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utilitycited by 120
  8. 2023GEO: Generative Engine Optimizationcited by 56
  9. 2024Adversarial Search Engine Optimization for Large Language Modelscited by 40
  10. 2024Manipulating Large Language Models to Increase Product Visibilitycited by 28
  11. 2023Widespread Flaws in Offline Evaluation of Recommender Systemscited by 22
  12. 2025Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systemscited by 8
1 more
  1. 2025Source Coverage and Citation Bias in LLM-based vs. Traditional Search Enginescited by 6

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