When New Experiments Arrive: An Evidence-adjudicated Self-correction Framework for Virtual Cells

Virtual-cell models increasingly need to support both accurate perturbation prediction and interpretable biological reasoning. However, numerical models provide precise continuous responses but only implicit biological knowledge, whereas LLM/knowledge-graph-based models offer explicit mechanistic reasoning but coarse categorical predictions. Jointly maintaining these capabilities raises a further challenge: as new experimental evidence arrives, which states should be corrected and which should be preserved? To address these challenges, we propose **VCrectify**, an evidence-adjudicated self-correction framework for virtual cells. It maintains a numerical predictive state for continuous perturbation prediction and an explicit knowledge state for knowledge-grounded mechanistic reasoning. Cross-state disagreement between the two states guides the active evidence acquisition of new perturbation experiments. Once outcomes are revealed, an evidence adjudicator determines which states receive correction. The updated states then guide subsequent experiment selection, forming a continual loop of evidence acquisition and self-correction. Experiments across multiple virtual-cell backbones and perturbation datasets show that VCrectify improves numerical prediction and knowledge-based directional reasoning, generalizes better to unseen cellular contexts, and produces higher-quality mechanistic rationales.