An immune world model for multiscale forecasting and therapeutic hypothesis generation

Abstract:Immune therapies act across cell-intrinsic programs, tissue ecosystems, and patient-specific immune states, yet most predictors address these scales separately. We used a governed evolutionary AI Scientist to construct the Immune World Model, an action-conditioned model that learns how interventions move immune states across cellular, tissue, and individual levels. The Immune World Model--building Scientist searched candidate architectures and workflows, and the resulting world model was frozen before independent confirmation. The frozen model generalized to unseen interventions and biological contexts, recovered intervention-specific cellular programs, integrated cell and tissue information to improve ecosystem and patient-response prediction, and forecast unseen perturbation combinations. Immune World Model--guided analysis then combined measured perturbations with cross-axis inference to nominate IL-36$\gamma$ plus SIRP$\alpha$ inhibition as a complementary-axis therapeutic hypothesis, whereas a governed self-correction audit rejected every screened cytokine pair. The Immune World Model provides a framework for multiscale immune simulation that connects AI Scientist-driven model construction, intervention forecasting, and the generation of prospectively testable therapeutic hypotheses.

Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)

arXiv:2609.14709 [cs.LG]

(or arXiv:2609.14709v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.14709 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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