Learning When Cellular Responses Transfer: A Factorized Memory-Guided Agent for Perturbation Prediction

One main goal of virtual cell modeling is to predict cellular responses under unseen conditions, since the combinatorial space of perturbations and cellular contexts is too large to assay exhaustively. Existing perturbation prediction agents use gene-expression responses and knowledge graphs, but gene-level response tendencies can obscure perturbation-specific effects in their predictions. Retrieved evidence can also be unhelpful or misleading when incorporated without assessing its predictive value for the target query. We introduce the Multi-route Memory-guided Cellular Agent with Calibrated Transfer (MMCA-CT), which extracts perturbation-specific information and learns to assess the predictive value of response estimates to guide evidence selection and model reasoning. Its factorized response memory separates stable gene tendencies from perturbation-specific effects to estimate responses under unseen conditions. The agent learns to weight predictions by their estimated improvement over a gene-prior reference and to select response estimates expected to further improve predictions based on observed responses. Across five cancer cell lines, MMCA-CT improves response detection and direction prediction in both cross-perturbation and cross-cell-line settings. With QWEN3.5-9B, it yields relative gains of 15.3% in macro-per-gene AUPRC over VCWORLD for cross-perturbation response detection and 14.0% in macro-per-gene AUROC over CRISP for cross-cell-line direction prediction. Ablation studies further show that factorized response memory yields the largest accuracy gains, with additional improvements from conditional transfer estimation and utility-guided evidence selection.