OpenAI’s framework aims to disclose useful misalignment examples sooner—even before causes or fixes are settled—and sets out criteria, investigation tracks, and report contents.
Why it matters: Earlier disclosure can give researchers and other developers evidence to investigate and use to improve mitigations. But the six cases are not a measure of how often misalignment occurs, and reports may appear while causes or fixes remain unresolved.
Every step can pass its own check while the complete agentic workflow still violates a policy that applies to the execution as a whole.
Why it matters: Improving the accuracy of step-scoped monitors cannot detect a policy property that no individual step determines. The proposed response is to evaluate the whole trace and track guarded quantities through their provenance.