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This paper introduces a categorical framework for transfer learning using Kan extensions, defining a transfer discrepancy that compares target invariants against those forced by a prescribed task transformation. It proves finite cokernel formulas for chain complexes and persistence modules and validates the approach on neural latent point clouds.