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This paper introduces Symbolic Augmentation, a training-time framework that uses symbolic verifiers to generate augmented data, closing a blind spot in neural fact-checkers where canonical-equivalent quantity rewrites cause accuracy collapse. The method achieves 98.2% robustness on such rewrites, improving from 36.5%, and slightly boosts overall performance.