An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models
Summary
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.
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Paper page - An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models
Source: https://huggingface.co/papers/2606.01462
Abstract
Large reasoning models exhibit a significant gap between their ability to produce and evaluate reasoning, with models showing answer confirmation bias that prevents accurate reasoning evaluation.
Studies of human reasoning have shown that people are typically stronger at evaluating reasoning than producing it from scratch. In contrast,large reasoning models(LRMs) are trained to excel at producing long chains of reasoning to solve complex problems. How then do LRMs perform at evaluating reasons? We investigate this with the Valid-Answer-Invalid-Reasoning (VAIR) dataset: math problems and solutions with trivial reasoning flaws but valid answers, designed to isolatereasoning evaluationfrom the confound ofreasoning production. Unlike humans, who we find are only 6% worse at grading than solving such problems, we find a substantial production-evaluation gap in LRMs: frontier models score as low as 48% when evaluating VAIR solutions, despite near-perfect solution production. Why this enigma? Throughchain-of-thought(CoT) analysis, we find evidence of ananswer confirmation bias: LRMs often produce then check for the correct answer instead of carefully verifying each step, fabricating rationalizations even when noticing anomalous reasoning.Linear probescorroborate this, showing that while LRM activations encode some representation of valid reasoning, they fail to robustly represent VAIR solutions as invalid.Causal patchingof the final answer’s representations causes LRM verdicts and activations to flip, demonstrating thatanswer validityis responsible for models’ confirmation biases. These findings indicate an outstanding limitation in dominant approaches to reasoning training, which incentivize LRMs to produce and confirm reasoning towards correct answers, but not to robustly evaluate the underlying reasons.
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