PROVE: Training-Free Reference-Guided Pareto-Consensus Velocity Editing for Cross-Context Single-Cell Perturbation Prediction

Flow-matching models learn condition-dependent velocity fields from observed context-perturbation pairs. In cross-context covariate transfer, however, the target pair is unobserved, and the training objective provides no direct supervision for its response prediction. The resulting conditional extrapolation may collapse toward the control state, align with another perturbation, or drift beyond the cross-context variation supported by available data. Although responses from related contexts do not uniquely determine the missing target distribution, they provide useful reference constraints for detecting these failures. Motivated by this, we introduce PaReto-COnsensus Velocity Editing (PROVE), a training-free inference-time method for correcting reference-inconsistent predictions from frozen flow-matching models. At each sampling step, PROVE obtains a final-state estimate from the backbone velocity and diagnoses three patterns including effect collapse, perturbation confusion, and context drift. It constructs a local correction for each active pattern, combines the frozen and corrective directions using an MGDA-inspired minimum-norm consensus, and redirects the velocity without changing its instantaneous norm. Experiments across cross-cell-line and cross-state transfer settings show that PROVE improves the original samplers across multiple back-bones and strengthens the recovery of perturbation-specific differential-expression and pathway-level effects.