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This theoretical paper studies atomic concept learning through the geometry of hypercubes and hyperplanes, showing that complexity collapses uniformly on most hyperplanes except the full diagonal, where it grows without bound. It provides a taxonomy of hyperplane behavior and a worked binary and ternary case analysis.