MemoReason: Evaluating the Effect of Parametric Memory on Contextual Reasoning in LLMs

Large Language Models (LLMs) perform well on reasoning benchmarks, but it remains unclear whether this reflects genuine contextual reasoning or reliance on facts memorized in their parameters. We investigate this by distinguishing two possibilities: a broad $\text{memory bias}$, where familiar content improves reasoning performance, and the $\text{Strong Parametric Shortcut Hypothesis}$, where models skip reasoning entirely and recall stored answers. To test these effects, we introduce $\text{MemoReason}$, a human-curated benchmark that pairs factual reasoning tasks with structurally identical fictitious versions where real entities like people, companies, or dates are systematically replaced by fictitious ones of the same type. This preserves task structure and specified reasoning operations while varying the familiarity of the context, allowing controlled measurement of how the parametric memory affects reasoning. Our evaluation of recent LLMs reveals consistent and statistically significant performance drops of up to 15.7% in the fictitious setting, demonstrating a clear memory bias. However, a targeted analysis of questions failed in the fictitious setting shows that models rarely respond with the corresponding factual answer, indicating that direct parametric shortcuts are not the dominant failure mode. These findings suggest that parametric memory influences reasoning through mechanisms more complex than simple factual recall. $\text{MemoReason}$ provides a controlled framework for studying these mechanisms and for extending paired factual-fictitious evaluation to broader reasoning settings.