CellCanon links perturbation prediction, sourced explanation and observation-driven updating in one world state, letting a single new measurement revise unmeasured cell lines
Single-cell perturbation screens still leave many compounds and cell contexts unmeasured, and CellCanon puts expression prediction, source-grounded explanation and observation-driven updating into one shared world state. A language model compiles drug and target evidence, and numerical readouts then guide single-cell generation; on 339 held-out SciPlex3 conditions it reaches differential-expression AUPRC 0.8067 and Macro-F1 0.8164, against 0.4938 and 0.6961 for the best baseline. In simulated experimental feedback, one measured source context per drug lifts differential-expression AUPRC to 0.9077 for unmeasured cell lines with a fixed-weight updater.