The Bayesian optimization loop operates by seeding distinct claims against competitor content, selecting top candidates via a Bayesian acquisition strategy, generating line-level edits, and updating parameter posteriors through multi-sample re-ranking across rounds.
sequenceDiagram
autonumber
participant S as Seeder
participant B as Bayesian State
participant A as Acquisition Loop
participant I as Ideator
participant R as Ranker / Judge
S->>B: Seed initial claims & Beta priors
loop Each Round
B->>A: Sample candidate claims
A->>I: Pick winning claim
I->>I: Generate patch edit on page
I->>R: Submit modified page & competitor set
R->>R: N-sample median re-ranking
R->>B: Reward: 1 if improved, 0 otherwise
B->>B: Update Beta(alpha, beta) posterior
endReferenced by