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HodgeCover uses higher-order topological coverage to compress sparse Mixture-of-Experts layers by addressing irreducible mergeability barriers that pairwise signals miss, matching state-of-the-art baselines on expert reduction and leading on aggressive compression.
This paper proposes a topology-preserving neural operator learning method using Hodge decomposition to separate topological and geometric components, improving accuracy and efficiency on geometric meshes.