Our framework for reporting model misalignment

OpenAI introduced a framework for tracking, investigating, and disclosing cases of model misalignment, and published six reports describing unexpected or concerning behavior found through that process. The framework sets criteria and timelines for disclosure that apply even before OpenAI fully understands or fixes the underlying cause, a departure from waiting until a fix ships. The six initial reports cover findings from the last six months of training and evaluation work, giving outside researchers and engineers building on OpenAI's models a public record of what kinds of failures the company has actually found, rather than a general statement of intent. For teams deploying agents built on these models, the reports function as a watch list, a documented trail of known failure modes worth checking for in production systems, even when OpenAI has not yet explained the mechanism behind them. The approach mirrors calls from safety researchers for more transparency earlier in the development cycle, rather than only at model release. It does not commit OpenAI to disclosing every internal finding, only those that meet the framework's criteria, so the practical scope depends on how the company applies its own thresholds going forward.