Differentiable Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation
PT-RAG introduces a two-stage retrieval module for generative modeling of cellular responses to genetic perturbations. It augments existing models by learning access to related perturbation contexts, improving distributional similarity and predictive quality. This framework learns context selection based on cell state and query perturbation, outperforming naive retrieval methods.