Hypothesis-Evidence Feedback Reasoning for Cellular Perturbation Prediction

Predicting cellular responses to unseen perturbations requires generalizing from sparse experimental knowledge across interventions and cellular contexts. Which experimental evidence is most useful can depend not only on the perturbation query but also on the current response prediction, motivating feedback between prediction and evidence selection. We therefore introduce Hypothesis-Evidence Feedback Reasoning (**HEFR**), a unified *Propose–Reason–Realize* framework that turns response prediction into an adaptive process of hypothesis initialization, hypothesis-evidence interaction, and cell-population realization. Starting from an initial latent hypothesis of the perturbation response, HEFR iteratively uses the current hypothesis to retrieve evidence that grounds its revision into the next hypothesis. This loop terminates once further reasoning is no longer expected to yield substantive refinement. The final hypothesis is decoded into an aggregate response and realized as a heterogeneous population of perturbed cells consistent with that response. Across chemical and genetic perturbation benchmarks, HEFR substantially outperforms existing methods in key metrics of aggregate accuracy and distributional fidelity. Theoretical and biological interpretability analyses further demonstrate the validity of HEFR. Ultimately, HEFR shifts virtual-cell modeling from passive pattern mapping toward adaptive, feedback-driven scientific reasoning. Code will be released publicly upon publication.