English(EN) Thinking effort aligns between humans and reasoning models in abductive reasoning

研究发现人类和人工智能在溯因任务中的推理努力保持一致

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

一项新研究发布在arXiv上，探讨了在溯因推理任务中，人类与大型推理模型（LRMs）之间思维努力的一致性。该研究建立在先前比较人类反应时间和模型推理轨迹的工作基础上，特别关注溯因推理，因为其难度不仅由形式结构决定，这为更可靠地比较真实的认知努力提供了基础。研究结果表明，人类和LRMs在推理努力和错误模式上存在显著的一致性，而允许模型探索多条推理路径的方法进一步增强了这种一致性。 AI

影响表明大型推理模型可能正在发展出与人类在复杂问题解决中的思维模式相呼应的认知过程。

排序理由发表在arXiv上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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arXiv cs.AI TIER_1 English(EN) · Henry Arthur · 2026-09-03 04:00

人类与推理模型在溯因推理中的思维努力保持一致

arXiv:2609.01867v1 Announce Type: cross Abstract: 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 r…

本文如何被排名

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人类与推理模型在溯因推理中的思维努力保持一致

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