Recall Is Not the Same as Reusable Knowledge

A model may recall a fact without representing it in a form that supports new learning. The authors create arbitrary relabelings of known relations—such as mapping continents to animal words—fine-tune on a subset of subjects and test whether the new labels transfer to held-out subjects sharing the original answer. Across eleven language models and seventy relations, the linear readability of the original relation before fine-tuning predicted later generalization, with within-model correlations from 0.87 to 0.92. The association survived controls for recall accuracy, subject frequency and training-set structure. The study uses constructed relations, but it cleanly separates stored facts from representations that can be repurposed.