Corrigible Judgment Execution: Governance of Entropy in Generative AI

Contemporary generative AI systems are frequently evaluated as one-shot producers of text or images. However, many real-world uses follow a different pattern: iterative refinement under ongoing human oversight. This paper formalizes that pattern as Corrigible Judgment Execution (CJE)—an interaction architecture in which stochastic generative systems propose candidate states while human agents iteratively regulate uncertainty through framing, evaluation, and constraint articulation. Using entropy as a descriptive lens, we characterize generative outputs as samples from conditioned probability distributions whose dispersion reflects underspecification. Early iterations exhibit high entropy under weak constraints. Through successive updates, probability mass becomes progressively concentrated within bounded regions of possibility space. Convergence emerges not through parameter modification or deterministic optimization, but through regulated entropy contraction under retained human authority. Creative writing serves as a high-resolution case in which this dynamic is particularly visible: stylistic “voice” corresponds to path-dependent concentration within a constrained expressive basin shaped by iterative guidance. From this analysis follow two structural implications. First, authorship in generative contexts is best understood as governance over uncertainty reduction rather than exclusive control over token production. Second, alignment can arise at interaction time through corrigible regulatory loops without retraining the underlying model. By situating generative AI use within a feedback-regulated systems framework, this paper extends alignment discourse beyond training-time optimization and clarifies the architectural role of human judgment in governing stochastic generative systems.