# Cross-Modal Transfer

The Allen Institute for Brain Science and AWS have released Bloom, a single-cell multimodal model that encodes gene expression and chromatin accessibility into one shared cell representation while training on roughly 100-fold fewer cells than comparable pretrained models. On adult mouse whole-brain data, linear classifiers on Bloom embeddings reach 91% cross-validated adjusted balanced accuracy for 315 subclasses (multiome), 94% for 310 subclasses (scRNA-seq) and 94% for 297 subclasses (snRNA-seq), although the model saw only mouse multiome data.

The Allen Institute for Brain Science and AWS have released Bloom, a single-cell multimodal model that encodes gene expression and chromatin accessibility into one shared cell representation while training on roughly 100-fold fewer cells than comparable pretrained models. On adult mouse whole-brain data, linear classifiers on Bloom embeddings reach 91% cross-validated adjusted balanced accuracy for 315 subclasses (multiome), 94% for 310 subclasses (scRNA-seq) and 94% for 297 subclasses (snRNA-seq), although the model saw only mouse multiome data.

EEG foundation models transfer poorly as frozen feature extractors, so SeeEEG takes another route: it renders EEG as images using the montage, filtering and contrast settings of a clinical EEG viewer and trains only those display parameters, leaving the vision backbone frozen. With a frozen DINOv3 ViT-B as encoder, SeeEEG exceeds seven EEG foundation models in balanced accuracy on all eight clinical, sleep, affective and brain-computer interface datasets, reaching a mean rank of 1.3 by Cohen or AUROC against 2.9 for the strongest baseline, REVE.

EEG foundation models transfer poorly as frozen feature extractors, so SeeEEG takes another route: it renders EEG as images using the montage, filtering and contrast settings of a clinical EEG viewer and trains only those display parameters, leaving the vision backbone frozen. With a frozen DINOv3 ViT-B as encoder, SeeEEG exceeds seven EEG foundation models in balanced accuracy on all eight clinical, sleep, affective and brain-computer interface datasets, reaching a mean rank of 1.3 by Cohen or AUROC against 2.9 for the strongest baseline, REVE.

When a target cell line has no drug measurements, GRAFT links that line's genetic perturbation responses to drug responses measured in source cells and predicts the transcriptional changes a drug would induce. In leave-one-line-out evaluation across five LINCS cancer lines, GRAFT-D0 reached a mean Top20 Pearson correlation of 0.734, above the source mean of 0.493 and the strongest comparator PRnet at 0.689; across five external cellular contexts the equally weighted mean Top20 correlation was 0.606 against a source mean of 0.416. The method rests on a conditioning state built from how the target's genetic responses differ from source references, and predictions still improve when target genetic measurements are few, though gains vary with data quality and source coverage.

When a target cell line has no drug measurements, GRAFT links that line's genetic perturbation responses to drug responses measured in source cells and predicts the transcriptional changes a drug would induce. In leave-one-line-out evaluation across five LINCS cancer lines, GRAFT-D0 reached a mean Top20 Pearson correlation of 0.734, above the source mean of 0.493 and the strongest comparator PRnet at 0.689; across five external cellular contexts the equally weighted mean Top20 correlation was 0.606 against a source mean of 0.416. The method rests on a conditioning state built from how the target's genetic responses differ from source references, and predictions still improve when target genetic measurements are few, though gains vary with data quality and source coverage.

A study of text, vision and audio mixture-of-experts pretraining fits a data-mixing scaling law from 268 runs and draws actionable conclusions: audio should spend compute on parameters, repeating vision or audio data beyond two passes buys little, and vision interferes most with audio.

A study of text, vision and audio mixture-of-experts pretraining fits a data-mixing scaling law from 268 runs and draws actionable conclusions: audio should spend compute on parameters, repeating vision or audio data beyond two passes buys little, and vision interferes most with audio.