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This paper introduces reasoning consistency scanning, a method to audit whether chain-of-thought reasoning is logically consistent with the final answer in AI safety evaluations, distinguishing it from faithfulness. The authors formalize inconsistency subtypes, build a benchmark, implement a scanner, and report findings across models and tasks.