Techniques for verifying AI-generated code at scale, focusing on 'critic' models and low-safety-tax review processes.
Recent research highlights a strategy for "low safety tax" code review.
The reviewer model should only flag issues it is confident about. False positives (flagging good code as bad) destroy developer trust and slow down the process.
Instead of saying "this looks wrong," the critic must point to the specific line and explain why (e.g., "This variable x might be null here").
The review happens automatically in the CI/CD pipeline or IDE, acting as an advanced linter.