Testing the Platonic Representation Hypothesis with Life-Event Models

Do models trained on life histories and language learn similar relationships between events? We test the Platonic Representation Hypothesis by comparing (a) models trained on nationwide registry histories with (b) independently trained language models given descriptions of the same events. Across life models of increasing size, we measure local alignment, whether the two representation spaces agree on which events are neighbours, and global alignment, whether they agree on the similarities between all pairs of events. Following the Aristotelian critique, we calibrate these comparisons for chance agreement and layer selection. Local alignment increases at every life-model size step for every language model in the original comparison. Better language performance is also associated with greater local alignment, both in this cohort and in a broader comparison of language models. These local-alignment findings persist after controls for shared wording and coding categories. Global alignment also exceeds chance for every pairing, but it stops increasing with life-model size beyond a few million parameters. The results support the local form of the Platonic Representation Hypothesis in a modality that shares no training data with language.