Each optimization claim starts with a Beta prior distribution derived from an honest seeder prior, which is iteratively updated by incrementing posterior parameters based on the binary Bernoulli trial outcome of whether the edited variant improved search rank.
stateDiagram-v2
[*] --> Initialized: Seeder assigns Prior P(H)
Initialized --> Selection: Beta(alpha, beta) defined
state "Testing Loop" as Loop {
Selection --> Evaluation: Thompson Sampling
Evaluation --> Reward: LLM Ranker (Before vs After)
Reward --> PosteriorUpdate: Binary Reward (r=1 or r=0)
PosteriorUpdate --> Selection: Update alpha += r, beta += (1-r)
}
PosteriorUpdate --> Classification: Convergence / Threshold Check
state Classification {
[*] --> Untested
Untested --> Testing
Testing --> Likely: High Win Rate
Testing --> Weak: Low Win Rate
Likely --> Proven: Strong Significance
Weak --> Disproven: Strong Rejection
}
Classification --> [*]Referenced by