VAST: Verified Asymmetric Supervision Transfer for Histology-Based Spatial Transcriptomics

Predicting spatial gene expression from routine histology offers a scalable comple- ment to spatial transcriptomics, yet most models optimize only a small evaluation panel and discard measurements for thousands of other genes. Although recent work uses these genes as auxiliary supervision, it remains unclear whether en- larging the supervised panel consistently benefits the target genes. We study this question as gene-supervision scaling. Our analysis separates average improvement into gene-level positive and negative transfer and indicates that scaling behavior varies across architectures: adding supervision can improve the aggregate score while degrading a nontrivial subset of target genes. Existing auxiliary-supervision methods give auxiliary labels direct optimization access to the target predictor. Our results challenge the premise behind this design: more supervision does not neces- sarily provide more useful information for every target. We argue that the missing component is a mechanism that controls whether newly added supervision is al- lowed to modify the final target predictor. We therefore formulate auxiliary-gene learning as asymmetric supervision transfer and introduce VAST. For each target gene, VAST first establishes and freezes a target-only predictor as a panel-invariant reference, lets expanded supervision propose a correction to that reference, and ap- plies the correction only to the extent supported by pre-update, slide-level evidence computed exclusively from the training set. This two-stage protect–propose–verify interface implements a simple principle: auxiliary supervision should modify target predictions only through a rejectable transfer interface