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arXiv · 2611.01126

Do GEO Gains Survive the Live Web? Evaluating In-the-Wild Visibility Gains of Generative Engine Optimization via LIVE-GEO

Runze Kong·Haibo Jin·Xucheng Yu·Xiaoqi Wang·Huimin Zeng·Haohan Wang
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Abstract

Generative engine optimization (GEO) rewrites web content so that LLM-based search systems, also termed generative engines (GEs), rank it higher in their answers and recommendations. However, existing GEO evaluations typically rely on directly supplied text or controlled retrieval pipelines, and whether GEO gains survive the live Web remains unclear, leaving a gap in understanding how GEO strategies affect the visibility of live webpages when accessed and ranked by GEs. To address this gap, we introduce LIVE-GEO, a benchmark for Live, In-the-wild Visibility Evaluation for GEO. LIVE-GEO publishes GEO-rewritten products as live webpages. We use LIVE-GEO to evaluate five existing GEO strategies (CORE, C-SEO, E-GEO, AgenticGEO, and Statistics Addition), applying them to the product descriptions and reviews of 80 fictional brands across 18 product families. We run experiments on three GEs (ChatGPT with GPT-6, Gemini 3.6 Thinking, and Grok in Expert mode), four direct-text API evaluators (GPT-4o, DeepSeek, Kimi, and Claude), and a direct-text local evaluator (Qwen2.5-3B-Instruct). With frozen $\pm0.2\%$ prices, most direct-text evaluators and GEs receiving product URLs give CORE the highest Top-1 rate, reaching 66.3%-76.3% on the GEs, yet other strategies vary in relative performance, and the GEs select the same top product for only 48.8%-66.3% of brands. We also find that ChatGPT is sensitive to the order of candidate URLs: CORE's Top-1 rate is 73.8% and 93.8% under two opposite fixed orders, and the randomized order used by LIVE-GEO yields 82.5%, between the two. In the numerical-faithfulness check, the JEV verifier agrees with the reference labels on 90.7% of 194 numerical benchmark cases and with human judgments on all 50 sampled descriptions, and it flags 86 of the 400 rewritten descriptions, including all 80 Statistics Addition descriptions.

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Runze Kong, Haibo Jin, Xucheng Yu, Xiaoqi Wang, Huimin Zeng, Haohan Wang. 2026-10-31. Do GEO Gains Survive the Live Web? Evaluating In-the-Wild Visibility Gains of Generative Engine Optimization via LIVE-GEO. https://arxiv.org/abs/2611.01126

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