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SL(n) Representation Learning in Intrinsic Mixed-Curvature Space
Researchers propose a representation learning framework built on SL(n) that operates in an intrinsic mixed-curvature space rather than relying on manually composed product manifolds. The approach aims to provide higher curvature capacity and deeper order-aware composition for capturing complex geometric structure. It is presented as an alternative to existing product-manifold methods that require hand-specified curvature combinations.