eval-fix

An adversarial fix loop improves quality assurance by strictly separating fix generation from evaluation, forcing an independent evaluator to validate proposed fixes against criteria and evidence, iterating until convergence to prevent self-assessment bias.

To start an iterative fix and evaluate cycle, first generate an evaluation report for the feature, then launch the fix loop to automatically propose concrete fixes, validate them independently, and log iteration progress and convergence metrics.

Yes, you can use an automated fix loop to converge feature implementations without manual intervention by setting a target score; the loop repeats generation and evaluation cycles automatically until that score is reached or you manually stop it.

The best way to prevent self-assessment bias during automated feature repair is enforcing strict role separation where an independent evaluator validates generator fixes against evaluation reports and screenshots, ensuring objective convergence tracking.

An automated fix loop requires evidence from evaluation reports to ensure the generator proposes concrete fixes based on actual feature gaps and the evaluator can objectively validate improvements, enabling systematic iterative convergence.

You should manually intervene in an automated fix and evaluate workflow when the iteration fails to converge to the target score, leaving remaining gaps that require human guidance to progress beyond the automated loop.