Large Vision-Language Models (LVLMs) achieve strong performance on various vision-language tasks, yet Chain-of-Thought (CoT) generation often weakens attention to visual tokens, degrading evidence-grounded reasoning. During the later stages of autoregressive decoding, models tend to increasingly rely on previously generated text rather than visual evidence, leading to plausible but visually inconsistent rationales. We propose DVAR (Dynamic Visual Attention Recalibration), a training-free method that selectively amplifies visually relevant tokens to mitigate visual attention decay during decoding. DVAR operates at three levels: (i) prompt level structure to encourage visually grounded reasoning; (ii) embedding level relevance weights derived from image–text alignment; and (iii) decoding level scheduling that adjusts recalibration by tracking shifts in visual attention. Without sensitive model-specific hyperparameter retuning, DVAR yields consistent performance improvements across six benchmarks on LLaVA and Qwen-VL models, and better preserves visual attention throughout decoding.