Learning Heterogeneous Preferences (arXiv:2609.17847v1)
This preprint introduces "individuated utility" models that condition utility on both the individual and decision context and proposes a multi-stage architecture to estimate them from multi-modal data. The authors evaluate on a new dataset of 575,000 pairwise aesthetic judgments from 2,398 participants comparing automotive wheel designs and report that individuated utility models substantially outperform universal utility and foundation-model baselines, arguing that annotator disagreement reflects systematic preference heterogeneity rather than noise.