Ke-Mu Xu

This work studies organizational margins on externally verified software-engineering tasks using controlled workflow experiments on externally verified software-engineering tasks to find out how capacity and task information are distributed across stages of AI production.

Which component matters more depends on the loss assigned to erroneous acceptance: at low liability the planning gain dominates; at high liability the verifier's avoided false passes dominate; and a standalone verifier captures nearly all the false-pass benefit of the full planning-plus-verification stack at a fraction of its cost.