发表机构
Center for Mind/Brain Sciences, University of Trento（特伦托大学心智/脑科学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对溯因推理任务，证实大型推理模型（LRM）与人类的思考努力及错误模式存在对齐，且多路径解码方法可提升二者的推理成本对齐程度。
AI 中文摘要
认知建模中的一个核心问题是，大型语言模型（LLM）在语言及非语言任务上的行为是否与人类对齐。与标准LLM不同，大型推理模型（LRM）通过可验证奖励的强化学习进行优化，旨在生成推理任务的正确解，而非偏好对齐的响应。近期de Varda等人（2025）的研究通过对比人类反应时与模型在各类推理任务中的推理轨迹，探究人类与LRM的思考成本。本研究转向溯因推理以分离这种对齐：与演绎任务不同，溯因推理的难度无法从形式结构推断，且模型无法利用捷径模仿努力而无需真正搜索，为共享努力的实证主张提供更坚实基础。我们发现LRM与人类推理努力对齐的进一步证据，以及模型与人类易犯相似错误的证据。最后，我们表明，允许模型探索多条推理路径的解码方法，可提升所测试的三个模型中人类与LRM在推理成本上的对齐程度。
英文摘要
A major question in cognitive modeling concerns the behavioral alignment between large language models and humans across linguistic and non-linguistic tasks. Unlike standard LLMs, large reasoning models (LRMs) are optimized with reinforcement learning from verifiable rewards, encouraging correct solutions to reasoning tasks rather than preference-aligned responses. Recent work (de Varda et al., 2025) investigates the cost of thinking in humans and LRMs by comparing human reaction times with model reasoning traces across a range of reasoning tasks. We isolate this alignment by turning to abductive reasoning: unlike deductive tasks, its difficulty cannot be inferred from formal structure and offers no shortcuts a model could exploit to mimic effort without genuine search, providing firmer ground for empirical claims of shared effort. We find further evidence of alignment between LRM and human reasoning effort, as well as evidence that models and humans tend to make similar errors. Finally, we show that decoding methods that let models explore multiple reasoning paths increase alignment in reasoning cost between humans and LRMs across the three models tested.
Comments14 pages, 5 figures. To appear in Findings of EMNLP 2026
