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Adv."],"published-print":{"date-parts":[[2026,8,21]]},"abstract":"<jats:p>Negative selection in the thymus limits autoimmunity by eliminating T cells that react strongly to self. Individual T cells, however, are only exposed to a small fraction of all self-peptides during their \u201ctraining\u201d in the thymus, and how tolerance is generalized to the remaining \u201ctest\u201d self-peptides across peripheral tissues in the body remains an open question. We show that this can be achieved because the immune system satisfies two conditions necessary for generalization in machine learning settings. Consequently, sparse, random sampling of only 10% of self-peptides in the thymus is sufficient to avoid reactivity to 90% of peripheral self. We support this result and validate predictions from our model with diverse experimental data. 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