Pathology foundation models are typically trained with narrow objectives on limited data scales, fragmenting complementary strengths across separate models. This paper presents a unified pathology foundation model built through staged distillation, combining knowledge from eight vision-only, vision-language, and slide-level teacher models into one backbone, trained on nearly 25 million pathology images. Evaluated across 96 downstream tasks and 48 data sources, it achieves top average performance across tissue-level, multimodal, and whole-slide clinical tasks. Applications include a single, versatile AI backbone for computational pathology supporting cancer diagnosis, tissue analysis, and clinical decision support across diverse tasks and institutions.
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