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cs.LG2026
PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction
Dongxia Wu, Mingyu Li, Yuhui Zhang +4
Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions. While recent generative models improve popul…
cs.LG2026
CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
Dongxia Wu, Shiye Su, Yuhui Zhang +4
Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-base…
cs.LG2026
Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging
Dongxia Wu, Yuhui Zhang, Serena Yeung-Levy +2
Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating medical images across condition…