Researchers have proposed a method to supervise AI models using governance records generated by machine-verifiable workflows. These records link various components of the workflow and can be used to train AI models to produce reliable outputs. The authors tested their approach on a benchmark dataset and found that it improved the performance of a planning model, increasing accepted plans from 1 to 57 and reducing latency. They also compared their method with other alternatives, including self-selection and stronger-teacher approaches, and found that verifier-selected supervision outperformed them. --- Why it matters: This research matters because it provides a new approach to supervising AI models, which is essential for ensuring the reliability and trustworthiness of AI systems. The proposed method can be used in various applications where machine-checkable capabilities are required, such as planning and decision-making tasks. Source: https://arxiv.org/abs/2608.18324