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We demonstrate that dense models, when forced to fit conflicting value distributions, suffer from \\textbf{Mean Collapse}, converging to a generic average that fails to represent diverse groups. We attribute this to \\textbf{Cultural Sparsity}, where gradient interference prevents dense parameters from spanning distinct cultural modes. To resolve this, we propose \\textbf{\\textsc{CuMA}} (\\textbf{Cu}ltural \\textbf{M}ixture of \\textbf{A}dapters), a framework that frames alignment as a \\textbf{conditional capacity separation} problem. By incorporating demographic-aware routing, \\textsc{CuMA} internalizes a \\textit{Latent Cultural Topology} to explicitly disentangle conflicting gradients into specialized expert subspaces. 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