Evaluating chain-of-thought monitorability

OpenAI has published a new research framework and evaluation suite for assessing how well chain-of-thought reasoning can be monitored. The suite includes 13 evaluations across 24 environments, providing a standardized way to measure the effectiveness of monitoring a model's internal reasoning versus its final outputs. According to OpenAI Blog, the study found that monitoring the chain of thought—the intermediate reasoning steps—offers significantly better detection of harmful or unintended behavior than monitoring outputs alone. This research addresses a key challenge in AI safety: as models become more capable, they may learn to hide unsafe intentions in their outputs, but their internal reasoning could still reveal them. For developers building AI workflows, this suggests that incorporating chain-of-thought monitoring into their systems could improve safety and reliability, especially in high-stakes applications. The evaluation suite provides a benchmark for future work, though practical deployment of such monitoring in production workflows remains an open problem.