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
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:172555-172570, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-wen26m, title = {Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots}, author = {Wen, Yizhu and Zhang, Nan and Yuan, Haohan and Chen, Xun and Zhang, Haopeng and Guo, Hanqing}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {172555--172570}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/wen26m/wen26m.pdf}, url = {https://proceedings.mlr.press/v306/wen26m.html}, 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.} }
Endnote
%0 Conference Paper %T Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots %A Yizhu Wen %A Nan Zhang %A Haohan Yuan %A Xun Chen %A Haopeng Zhang %A Hanqing Guo %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-wen26m %I PMLR %P 172555--172570 %U https://proceedings.mlr.press/v306/wen26m.html %V 306 %X 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.
APA
Wen, Y., Zhang, N., Yuan, H., Chen, X., Zhang, H. & Guo, H.. (2026). Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:172555-172570 Available from https://proceedings.mlr.press/v306/wen26m.html.

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