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This paper introduces the Valid-Answer-Invalid-Reasoning (VAIR) benchmark to expose the production-evaluation gap in AI reasoning models, where models can generate correct answers but fail to detect flawed reasoning, revealing answer confirmation bias.
This paper investigates the production-evaluation gap in large reasoning models (LRMs), finding that they fail to robustly evaluate reasoning despite near-perfect solution production, due to an answer confirmation bias.