Large language models (LLMs) are widely used for claim verification, yet remain brittle for numerical reasoning: even small changes in value can sharply degrade accuracy.
We show that this brittleness persists in frontier LLMs, but can be mitigated through adversarial fine-tuning on numerically perturbed examples.
Using parameter-efficient fine-tuning, small Qwen3 models (0.6B$\unicode{x2013}$8B) reach 98.7% accuracy on label-flipping perturbations, outperforming larger zero-shot models and frontier systems (GPT-5.4 Pro (74.0%) and Gemini 2.5 Flash (73.9%)).
The gains generalise to unseen perturbation types, indicating robust numerical decision boundaries rather than memorised edits.
Robustness also transfers without target-domain data, significantly improving cross-lingual performance in Spanish.
We further show that the same fine-tuning recipe confers robustness to evidence-side perturbations, using the VitaminC dataset.