Introduces a general, multi-layer human--machine--GenAI decision framework with a one-dimensional quality metric $Q=\frac{\text{corrections}}{\text{new errors}}$ guiding sequencing and authority. It yields novel insights on asymmetric effort allocation and plausibly positions GenAI as final decision-maker despite hallucinations. Impactful for designing hedge-fund review/governance pipelines, yet contributions are theoretical, cross-domain, and incrementally novel only.

Introduces a general, multi-layer human--machine--GenAI decision framework with a one-dimensional quality metric $Q=\frac{\text{corrections}}{\text{new errors}}$ guiding sequencing and authority. It yields novel insights on asymmetric effort allocation and plausibly positions GenAI as final decision-maker despite hallucinations. Impactful for designing hedge-fund review/governance pipelines, yet contributions are theoretical, cross-domain, and incrementally novel only.

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