Looped Virtual Cell

Virtual cell perturbation models often collapse a dynamic cellular response into a static endpoint expression profile. We introduce Looped Virtual Cell, instantiated as Looped X-Cell, which turns endpoint prediction into a learned recurrent rollout over genes. Rather than predict all genes in one pass, Looped X-Cell repeatedly ranks unresolved genes, commits selected expression values, carries the partial endpoint state forward, and uses lookahead supervision so stages preview genes that will be committed shortly afterward. A scorer-latent outer loop can then revisit the staged rollout through recurrent latent state without directly feeding predicted expression values back into the input. Across four Replogle-Nadig cellular contexts, Looped X-Cell improves endpoint prediction over a fine-tuned X-Cell model, scGPT, and a linear baseline under matched held-out perturbation splits. The learned ranks also expose biological structure that is never used as supervision: they recover Reactome pathways across Th17, Replogle-Nadig, and Marson, recover CORUM protein-complex relationships in rank-feature perturbation clusters, and reveal GO-enriched RNA/protein rank gaps in a multimodal Th17 model. Component ablations identify staged global ranking and lookahead supervision as the main drivers of HepG2 endpoint gains, while compute sweeps show that inner-loop resolution improves prediction more consistently than outer-loop recurrence. Together, these results show that learned gene rollout is a structured compute axis for virtual-cell endpoint models and an auditable internal trace for perturbation biology.