发现大模型与人类在难题处理上分歧：知难而行，但继续思考的策略不同。

Large reasoning models (LRMs) spend more reasoning tokens on problems that take humans longer, suggesting sensitivity to a similar structure of difficulty. That alignment identifies which problems elicit more deliberation and leaves open how a difficult state is converted into continued computation. We distinguish difficulty registration, the relation between problem difficulty and observable deliberation, from deliberation allocation, the relation between registered difficulty and continued work. We test this distinction in matched human and LRM data from visual abstraction, intuitive physics, and relational reasoning. In visual abstraction, LRM trace length tracks the human ordering of problem difficulty. After problem identity is controlled, successful human attempts are longer than failed human attempts. Failed LRM attempts receive more reasoning tokens than successful model attempts. The same allocation difference appears in intuitive physics. In relational reasoning, the opposite-sign pattern disappears, while the item-controlled human-LRM difference remains, showing that task structure changes the observable form of allocation. Human action counts link longer human trials to continued task engagement, while failed LRM traces show task-dependent signs of hesitation or revisiting after trace length is controlled. A resource-rational account relates these patterns to the expected value of further computation from a difficult state. Humans and LRMs can agree about which problems are difficult while applying different policies to continued deliberation.