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Life sciences · Preprint
arXiv · August 17, 2026
Early or partial results. Treat as a signal, not a conclusion.
PertMind is a novel machine learning approach that combines supervised learning and reinforcement learning on cellular perturbation data to improve biological reasoning in large language models. The method shows transfer capability across multiple reasoning tasks without task-specific retraining, but the work is computational, uncontrolled, and has not undergone peer review.
Computational method development study; uncontrolled. Cellular perturbation atlases used as reinforcement-learning environments; no human subjects enrolled.. Intervention: PertMind: reinforcement learning combining trusted-trajectory supervised initialization with gene-, pathway-, and format-level reinforcement signals applied to cellular perturbation data.
PertMind improved response inference in unseen cellular contexts while retaining general language capabilities Transfer without task-specific post-training was demonstrated across reverse perturbation identification, double-perturbation reasoning, phenotypic-screen prioritization, and biological-process interpretation Generated biological profiles supported competitive gene, cell, and donor representations across multiscale downstream tasks
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An uncontrolled, single computational method development study using reinforcement learning on cellular data; no clinical outcomes, no comparator group, and no peer review yet.
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Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces. Here we show that cellular perturbation atlases can instead become reinforcement-learning environments, where measured gene responses provide computable rewards for biological reasoning. We introduce PertMind, which combines trusted-trajectory supervised initialization with gene-, pathway-, and format-level reinforcement signals. Trained only on forward perturbation-response prediction, PertMind improved response inference in unseen cellular contexts while retaining general language capabilities. It also transferred without task-specific post-training to reverse perturbation identification, double-perturbation reasoning, phenotypic-screen prioritization, and biological-process interpretation. PertMind further generated biological profiles that supported competitive gene, cell, and donor representations across multiscale downstream tasks. These results support the hypothesis that reinforcement on experimental endpoints can concentrate reusable biological strategies already accessible to pretrained models. More broadly, perturbation-derived reinforcement learning offers a scalable route for transforming expanding experimental atlases into training environments for general-purpose biological reasoning.
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