As Large Language Models (LLMs) serve a global audience, alignment must transition from enforcing universal consensus to respecting cultural pluralism. 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. Extensive evaluations on WorldValuesBench, Community Alignment, and PRISM demonstrate that \textsc{ CuMA} achieves state-of-the-art performance, significantly outperforming both dense baselines and semantic-only MoEs. Crucially, our analysis confirms that \textsc{ CuMA} effectively mitigates mean collapse, preserving cultural diversity. Our code is available at https://github.com/Throll/CuMA.
Categories: cs.CL cs.AI cs.LG