Retrying vs Resampling in AI Control

ArXi:2605.26047v1 Announce Type: new AI coding scaffolds like Claude Code and Codex use \textit{retrying}: blocking actions flagged as risky and continuing the trajectory. We study retrying from an AI control perspective, which treats the model as potentially adversarial. We find that while retrying reduces honest suspicion scores, the untrusted model can exploit monitor rationale to construct sneakier attacks, negating safety gains. We also study \textit{resampling}: drawing multiple samples from the same context, which does not leak exploitable information.