PerturbCellRL: תיקון תפוצות והתחברות לביולוגיה דרך יוצרי פרטורבציות אחרי-אימון

תקציר מקורי באנגליתarXiv:2606.27752v2 Announce Type: replace Abstract: Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions. Recent advances in flow-matching have enabled population-level prediction of cellular responses. However, flow-matching training can fail to recover certain target distributions even within the model family, limiting its ability to capture cellular heterogeneity. We first prove that post-training can recover these distributions, then introduce PerturbCellRL, a reinforcement learning framework that post-trains single-cell perturbation generators using per-cell rewards. The central component is a gene-expression energy witness that translates population-level discrepancies into per-cell feedback. We further