The ranker model evaluates a lineup of candidate pages or snippets against a target query across multiple repeated passes to counteract stochastic output variance. During each evaluation pass, it generates an ordered ranking alongside a per-item rationale detailing why items placed higher or lower, from which the system calculates a stable median rank to reliably measure whether a draft outperformed the baseline.