The Content Optimizer initializes its loop by prompting a Seeder to generate testable hypotheses with Beta distribution priors, which a Classifier maps across a structured Rank Factor Class taxonomy. In each round, a two-stage acquisition policy applies freshness weighting across factor classes before using Thompson sampling or UCB to select an individual hypothesis. An Ideator then generates an isolated revision—either an extractive snippet rewrite or an XML-based patch edit on line-numbered page text—conditioned on the previous round's competitive rationale. Finally, a multi-sample Ranker evaluates the modified candidate against competitor content to determine a median rank, awarding a binary reward that updates the hypothesis posterior distribution until reaching convergence.