Controllable Sequence Editing for Biological and Clinical Trajectories

ArXi:2502.03569v3 Announce Type: replace Conditional generation models for longitudinal sequences can produce new or modified trajectories given a conditioning input. However, they often lack control over when the condition should take effect (timing) and which variables it should influence (scope). Most methods either operate only on univariate sequences or assume that the condition alters all variables and time steps.