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News/Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots
arxiv
PublishedJune 12, 2026 at 4:00 AM
—neutral

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

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Publisher summary· verbatim

arXiv:2606.12439v1 Announce Type: cross 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

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arxiv
Read original ↗All from arxiv →
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#optimization#governance#artificial-intelligence#search-engine

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arxivAdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science2harxivScoped Semantic Invariance as a Foundation for LLM Reliability2harxivGEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation2harxivEstimation of Propagated Risk in LLM-based Multi-Agent Systems2h
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