From Stochastic to Stable: Rank Stability and Structural Sufficiency in AI Visibility Measurement
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 sha...
| Authors | Ronald Sielinski |
|---|---|
| Primary category | stat.AP |
| Published | 11 July 2026 |
| Last updated at source | 26 August 2026 |
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