CellWright: Mechanism-Conditioned Transport for Verifiable Virtual Cells

Virtual cell models promise to forecast cellular responses to unseen perturbations in silico, yet recent audits show that most deep models barely beat mean or linear baselines, and even strong systems answer a narrower question than biologists ask: they regress a control-referenced expression shift, under-model real population heterogeneity, and offer no account of *why* the response occurs. We argue that the right target is the full conditional distribution of perturbed cells *together with* the mechanism that generates it. The obstacle is supervision: mechanisms carry no ground-truth labels at scale, so mechanistic reasoning cannot be trained by imitation and readily hallucinates. CellWright recasts this as a verification problem. First, it replaces the latent delta with a *mechanism-conditioned latent Schrödinger bridge*, trained by conditional flow matching over an optimal-transport coupling, that transports an entire control population to the perturbed population and composes velocity fields with an explicit epistatic residual for unseen combinations. Second, an LLM agent emits machine-readable mechanism graphs over the TF$\to$DNA$\to$RNA$\to$protein axis and is optimized by reinforcement learning with verifiable rewards: an energy-based verifier trained by noise-contrastive estimation against hallucinated negatives supplies edge-decomposable rewards, calibrated confidence and a test-time search signal. On held-out perturbations from Norman and Replogle-K562, CellWright reduces energy distance by $13.2\%$ and $13.0\%$ over the strongest prior method and improves Top-20 DEG $\Delta$-correlation, while cutting mechanism hallucination from $0.52$ to $0.17$ and expected calibration error from $0.34$ to $0.061$ relative to a free-text LLM reasoner. We also report where it does not win: a neural optimal-transport baseline retains the best energy distance on the chemical corpus, the mean baseline retains the best MSE, and the gain on the Systema-corrected score is not significant. Ablations show that distributional transport drives accuracy, while the verifier drives honesty. Our code is available at [https://anonymous.4open.science/r/CellWright-D4B2/](https://anonymous.4open.science/r/CellWright-D4B2/).