CellCompass agentic AI expands virtual-cell dynamics with new measurements

[bioRxiv] A preprint presents CellCompass, an agentic AI system that couples a scientific agent with an extensible dynamical engine for modeling state transitions, population change, and fate and molecular readout. According to the paper, the system reuses existing formulations when sufficient and designs, implements and refines new ones when experimental measurements introduce additional constraints. The authors developed DynBench100, a benchmark of 100 simulated single-cell time-course scenarios with hidden dynamical ground truth, and reported that CellCompass achieved the highest aggregate performance among tested general-purpose and biomedical agents across six dynamical tasks. In four real single-cell studies, incorporating metabolic RNA labeling in drug-response data reduced pulse-control identifiability error by 31.1%; incorporating clonal barcodes in hematopoietic lineage tracing improved clone-fate agreement by 54% over the strongest dynamical baseline; and in time-resolved CITE-seq data the system reduced future surface-protein distribution error by 13.2% for RNA-initialized trajectories. The findings are from a preprint and have not been peer reviewed.