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Current approaches rely on discrete community detection with manually tuned resolution parameters (e.g., Leiden $\\gamma$), offering no continuous zoom and no formal guarantees. We introduce Semantic Level of Detail (SLoD), a framework that addresses both problems by defining a continuous zoom operator via heat kernel diffusion on a graph Laplacian whose kNN structure is induced by a Poincare-ball embedding. 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