Medical VLMs frequently hallucinate clinically unsupported findings. We identify a stability gap: visually grounded tokens maintain relatively stable logits under semantics-preserving image perturbations, whereas visually misgrounded tokens exhibit substantially larger shifts. We introduce LENS (Logit-ENhanced Stability), which exploits this signal at both training and inference. During training, Semantics-Preserving Preference Optimization (SPPO) scores preference pairs using clinical faithfulness and cross-view stability; at inference, stability-aware contrastive decoding penalizes tokens whose logits diverge across original and counterfactual views. Constrained counterfactuals use attribution-guided masking and feature-space proximity guards to reduce disruption of lesion- and device-level evidence. Across radiology VQA, chest X-ray reporting, and gastrointestinal endoscopy, LENS consistently outperforms strong decoding-time and preference-optimization baselines. On MIMIC-CXR, entity-level RadGraph recall improves from 21.0 to 22.8 over PCCD, while expert evaluation shows a hallucination-rate reduction from 17.0% to 8.5%.