AI RESEARCH
Thinking to Recall: How Reasoning Unlocks Parametric Knowledge in LLMs
arXiv CS.CL
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ArXi:2603.09906v1 Announce Type: new 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.