发表机构
IQRush（IQRush）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人工智能可见性测量中缺乏确定数据是否足够的原则方法这一问题，引入基于排名稳定性和结构充分性两个互补标准的顺序收敛框架，应用于多种平台 - 主题组合，为测量何时支持比较分析提供实际基础。
AI 中文摘要
人工智能可见性测量具有比较性，从业者想了解生成式搜索引擎最常引用哪些领域以及差异是否足以支持决策。但行业缺乏确定是否收集到足够数据的原则方法。研究和平台的收集预算差异大，结论常基于稳定性和精度未知的排名得出。我们引入基于两个互补标准的顺序收敛框架：排名稳定性评估排名相关轨迹是否达到结构平稳期，结构充分性评估既定领域（其置信区间不包括零）中引用份额的分布是否超过估计的不确定性。这两个标准区分了仅稳定的排名和足以支持推断的排名。框架保留少量结构常数，无需外部指定查询计数、相关目标或置信区间宽度目标；停止由观察到的测量不确定性驱动，在一系列充分性阈值范围内保持稳健。在跨越Gemini、SearchGPT和Perplexity的30个平台 - 主题组合上应用，该框架适应特定平台和主题的引用分布。结果表明，无法在各种情况下证明固定收集预算的合理性，相反，可以从观察到的分布结构评估收敛情况。该框架为确定人工智能可见性测量何时准备好支持比较分析提供了实际基础。
英文摘要
AI visibility measurement is comparative: practitioners want to know which domains generative search engines cite most often and whether observed differences are large enough to support decisions. Yet the industry lacks a principled way to determine whether enough data has been collected. Collection budgets vary widely across studies and platforms, and conclusions are often drawn from rankings whose stability and precision are unknown. We introduce a sequential convergence framework based on two complementary criteria: rank stability evaluates whether the rank-correlation trajectory has reached a structural plateau, while structural sufficiency evaluates whether the spread of citation shares among established domains -- those whose confidence intervals exclude zero -- exceeds the uncertainty of those estimates. Together, these criteria distinguish rankings that have merely stabilized from those sufficiently resolved to support inference. Both are derived from regularities in the observed citation distribution, including its rank structure, uncertainty profile, and the boundary between observed and established domains. The framework retains a small number of structural constants but requires no externally specified query count, correlation target, or confidence-interval width target; stopping is driven by observed measurement uncertainty and remains robust across a range of sufficiency thresholds. Applied across 30 platform-topic combinations spanning Gemini, SearchGPT, and Perplexity, the framework adapts to platform- and topic-specific citation distributions. Results show that no fixed collection budget can be justified across contexts and that convergence can instead be evaluated from the structure of the observed distribution. The framework provides a practical basis for determining when AI visibility measurements are ready to support comparative analysis.
Comments31 pages, 11 figures. See https://iqrush.ai/articles
