Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots
Yizhu Wen, Nan Zhang, Haohan Yuan, Xun Chen, Haopeng Zhang, Hanqing Guo
Abstract
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 the 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.** Companion demonstration website: https://anonymous.4open.science/w/Position-GEO-AE91/
BibTeX
@inproceedings{icml2026_positiongenerati,
title = {Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots},
author = {Yizhu Wen and Nan Zhang and Haohan Yuan and Xun Chen and Haopeng Zhang and Hanqing Guo},
booktitle = {ICML 2026},
year = {2026}
}