Controllable Sequence Editing for Biological and Clinical Trajectories

ICLR 2026 Conference Submission13156 Authors

Keywords: conditional generation, sequence editing, time series forecasting, counterfactual prediction, multivariate sequences, concept-based learning, longitudinal modeling

TL;DR: CLEF is a controllable sequence editing model for conditional generation of immediate and delayed effects. We evaluate CLEF on cellular, patient, and sales trajectories, including a real-world case study on patients with type 1 diabetes.

Abstract: 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. In scientific and clinical settings, interventions instead begin at a specific moment, such as the time of drug administration or surgery, and influence only a subset of measurements while the rest of the trajectory remains unchanged. CLEF learns temporal concepts that encode how and when a condition alters future sequence evolution. These concepts allow CLEF to apply targeted edits to the affected time steps and variables while preserving the rest of the sequence. We evaluate CLEF on 8 datasets spanning cellular reprogramming, patient health, and sales, comparing against 9 state-of-the-art baselines. CLEF improves immediate sequence editing accuracy by up to 36.74% (MAE). Unlike prior models, CLEF enables one-step conditional generation at arbitrary future times, outperforming them in delayed sequence editing by up to 65.71% (MAE). We test CLEF under counterfactual inference assumptions and show up to 63.19% (MAE) improvement on zero-shot conditional generation of counterfactual trajectories. In a case study of patients with type 1 diabetes mellitus, CLEF identifies clinical interventions that generate realistic counterfactual trajectories shifted toward healthier outcomes.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 13156