The feature augmentations based on adaptive and sum readout low-fidelity models are compared to baseline non-augmented models and the raw low-fidelity labels, with test set metrics reported in Figure 6A. On AZ-DR-R2, we notice a large uplift just by the simple inclusion of low-fidelity labels (from 0.049 to 0.331 in R 2 for the 1K split). In contrast, the best performance of non-augmented models is achieved for the 5K training set size (R 2 of 0.220), which is more than 50% lower than the performance on the 1K set with low-fidelity labels. Adaptive embeddings produced in a transductive setting improve