@rohanpaul_ai: This paper shows a strange weakness in AI reasoning: models can solve math, yet fail to judge reasoning. The unsettling…

This paper shows a strange weakness in AI reasoning: models can solve math, yet fail to judge reasoning. The unsettling part is not that frontier models make arithmetic mistakes. It is that they can reach the right answer, see the right answer in someone else’s solution, and then forgive broken logic that should have been easy to catch. The authors call this the production-evaluation gap: the gap between generating a solution and evaluating whether a given solution actually earns its conclusion. Their Valid-Answer-Invalid-Reasoning (VAIR) benchmark makes the trap clean. The final answer is correct, but the reasoning is damaged by missing steps, shuffled steps, missing premises, or circular explanation. A careful evaluator should say, “Yes, the answer is right, but the argument does not justify it.” Many reasoning models instead appear to do something lazier and more dangerous: they solve the problem themselves, confirm the final answer, and then rationalize the path as acceptable. That is not reasoning vigilance. It is answer confirmation bias wearing the costume of mathematical judgment. The mechanism matters because modern AI training often rewards outcomes more than valid intermediate thought. A model trained to get the answer may learn to treat the answer as the evidence, especially when grading another chain of reasoning. Humans were not perfect here, but the contrast is revealing: people showed only a small drop from solving to grading, while models collapsed much more sharply on the same kind of task. This is where the result becomes larger than math. If AI systems can mass-produce plausible arguments but cannot reliably police the logic inside them, they become engines of confidence rather than engines of understanding. ---- Link – arxiv. org/abs/2606.01462 Title: "An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models"