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该研究提出"加性分歧(AD)"指标，评估扰动预测模型在未见过的基因组合上的外推可靠性，发现专门的神经预测模型并未稳定优于简单加性基线，且偏离基线越大的预测误差越大，该信号可指导自动扰动筛选工作流。
Arc Institute argues that virtual cell models can only predict how cell types respond to perturbations if they are trained on high-quality causal data across diverse contexts, so technical noise is not mistaken for real biology.
This paper analyzes the Virtual Cell Challenge benchmark for held-out CRISPRi perturbation prediction, finding that simple magnitude-based scalar features outperform deep MLP encoders, and that magnitude-only predictors transfer better across cell types.
PerturbCellRL introduces a reinforcement learning framework that post-trains a pretrained single-cell transcriptomic generator using cell-level verifiers as rewards, improving biological consistency of perturbation predictions beyond distributional matching.