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This paper provides a unified theoretical framework for pseudo observation batch Bayesian optimization, proving that Gaussian processes produce distinct batch points and that common methods like Constant Liar and Kriging Believer are instances of a single conditioning mechanism. It introduces the Structural Diversity Diagnostic (SDD) for testing surrogate compatibility and validates predictions across multiple benchmark functions and hyperparameter tuning.