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This paper presents a homotopy-type-theoretic generalization of neurosymbolic inference that preserves symmetry information and proof multiplicity, showing that this framework recovers classical inference when symmetries are trivial and yields shortcut-aware concept posteriors computable in closed form, with practical improvements on reasoning-shortcut benchmarks.