[论文] Are Large Reasoning Models Good Everyday Reasoners? A Comparison wi...

论文概要

研究领域: NLP
作者: Kentaro Ozeki, Risako Ando, Takanobu Morishita, Hirohiko Abe, Koji Mineshima, Mitsuhiro Okada
发布时间: 2026-10-19
arXiv: 2610.22538

中文摘要

利用测试时计算的大推理模型（LRM）在数学与科学基准上表现强劲，并日益部署到真实应用，但其执行「日常逻辑推理」的能力——常与教科书式形式逻辑相悖——尚未被充分探索。本文构建基于语言与语用推理现象的基准，在受控提示条件下比较推理模型与非推理模型。发现有三：其一，当日常推理偏离形式逻辑规则时，推理模型表现反而退化；其二，一些配合思维链提示的非推理模型在日常推理上超过可比的推理模型；其三，附加指令对推理行为的控制有限。这些发现表明当前推理模型并不能可靠地把握日常推理，指向后训练阶段的局限，并呼吁改进训练目标、覆盖更多样的推理形式评估。

原文摘要

Large reasoning models (LRMs) that leverage test-time computation achieve strong performance on mathematical and scientific benchmarks and are increasingly deployed in real-world applications. However, their ability to perform everyday logical reasoning, which often departs from textbook formal logic, remains underexplored. In this work, we systematically evaluate how well reasoning models align with everyday reasoning patterns by introducing a benchmark based on linguistic and pragmatic reasoning phenomena. We also compare reasoning models with non-reasoning models under controlled prompting conditions. We find that, in many cases, (1) reasoning models show degraded performance in everyday reasoning when such reasoning deviates from formal logical rules, (2) some non-reasoning models with chain-of-thought prompting outperform comparable reasoning models in everyday reasoning, and (3) prompting with additional instructions provides limited control over reasoning behavior. These findings suggest that current reasoning models do not reliably capture everyday reasoning and point to limitations in post-training, motivating improved training objectives and evaluation across diverse forms of reasoning.

自动采集于 2026-10-19

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