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Thinking effort aligns between humans and reasoning models in abductive reasoning
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 abduct
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T21:02:18.000Z
First collected: 2026-09-21T05:51:54.566Z. This is not the publication date.