Thinking to Recall: How Reasoning Unlocks Parametric Knowledge in LLMs
While reasoning in LLMs plays a natural role in math, code generation, and multi-hop factual questions, its effect on simple, single-hop factual questions remains unclear. Such questions do not require step-by-step logical decomposition, making the utility of reasoning highly counterintuitive. Nevertheless, we find that enabling reasoning substantially expands the capability boundary of the model's parametric knowledge recall, unlocking correct answers that are otherwise effectively unreachable. Why does reasoning aid parametric knowledge recall when there are no complex reasoning steps to be done? To answer this, we design a series of hypothesis-driven controlled experiments, and identif...
| Authors | Zorik Gekhman, Roee Aharoni, Eran Ofek et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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