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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. We disentangle design choices that previous work on resampling had bundled together. In BashArena, with Claude Opus 4.6 as the untrusted model and MiMo-V2-Flash as the trusted monitor, drawing five samples per step and auditing on the maximum suspicion score raises safety from 61\\% to 71\\% at a 0.3\\% audit budget, at no cost to usefulness. Selectively resampling only the steps that look suspicious on the first draw rec","title":"Retrying vs Resampling in AI Control","url":"https://arxiv.org/abs/2605.26047","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.26047v1 Announce Type: new \nAbstract: 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. We disentangle design choices that previous work on resampling had bundled together. 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