AdaCell: Population-Level Steering for Single-Cell Perturbation Prediction

Single-cell perturbation prediction aims to estimate how biological interventions affect cellular states, which is essential for prioritizing targets and compounds in drug discovery and functional genomics. Existing diffusion-based virtual cell models can capture complex and heterogeneous response distributions, but fitting the overall perturbed distribution does not necessarily ensure accurate recovery of the perturbation response relative to matched controls. A prediction may reproduce broad expression patterns while misestimating which genes respond, the strength and direction of their responses, or the fraction of responding cells. To address these limitations, we propose **AdaCell**, a population-level test-time steering framework for diffusion-based perturbation prediction. Specifically, AdaCell represents each sampling particle as an entire candidate cell population and jointly evaluates its change relative to the corresponding control using criteria that capture differential expression programs, as well as the direction and geometry of population shifts. To handle unseen perturbations, AdaCell estimates their response priors from observed training perturbations and perturbation descriptors available before the experiment, without accessing any ground-truth responses from the test split. Finally, population-level reweighting and resampling steer reverse diffusion toward candidate populations that better match these priors, while the frozen diffusion model anchors the search to its learned distribution. Extensive experiments on representative single-cell perturbation benchmarks demonstrate that AdaCell improves the recovery of perturbation-specific population changes over frozen diffusion backbones and competitive test-time baselines while preserving cellular realism and population diversity. Our code is available at [our website](https://anonymous.4open.science/r/AdaCell-submit).