Virtual-cell models compress unseen intervention geometry through a target-specific generalization bottleneck

Abstract

Predictive models of cellular perturbation are often judged by how closely they reconstruct molecular states after unseen interventions. We show that high state-level similarity can coexist with loss of the relationships that distinguish perturbations, a failure we term Intervention Geometry Compression (IGC). Across established models and perturbation settings, unseen interventions show weakened global and local geometry, reduced between-intervention variance and spectral collapse. The failure is not primarily explained by response-space capacity. Instead, diagnostic projections localize much of the missing geometry to a small number of residual response directions learned from seen interventions; these directions outperform complexity-matched random subspaces and replicate in an independent Jiang perturbation resource. Polarity captures part, but not all, of this continuous orientation signal. Time-resolved analyses further show that correct trajectory entry markedly improves downstream propagation, while a held target's own early empirical response rapidly reveals endpoint orientation. Finally, same-target empirical anchoring transfers intervention identity across contexts far more effectively than increasing exposure to other interventions. These results identify intervention-coordinate assignment as an information bottleneck in virtual-cell generalization and support a design principle: empirically anchor intervention identity, then use models to generalize anchored effects across cellular contexts.