From Black-Box Predictors to Scientific Instruments: Biological Rule Faithfulness in Genomic Foundation Models

Genomic foundation models are becoming essential tools for AI-driven genomics research. Although their downstream performance has been demonstrated across benchmarks, their black-box nature obscures the biological basis of predictions, limiting assessments of scientific reliability. Here we present a biological rule–based framework for evaluating the scientific reliability of genomic foundation models. Using RNA splicing as a test system, we identify biological component subspaces in frozen genomic foundation models and assess their interactions against experimentally established biological rules. Across six models with distinct architectures and pretraining strategies, we find that similar predictive performance can mask differences in component use and biological rule faithfulness (BRF). To assess whether BRF provides information beyond conventional benchmarks, we examined model pairs with conflicting rankings using measurements reported in independent published experimental studies. Across the evaluated settings, BRF favored the better-performing model in 57.1–88.9% of these comparisons. Grounded in biological rules, our framework reveals differences in underlying representations and task generalization beyond conventional benchmarks. These findings motivate evaluation and training paradigms for developing scientifically reliable foundation models for biological discovery.