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.

Methods for mitigating toxic content through masking and infilling often overlook the decision-making process, leading to either insufficient or excessive modifications of toxic tokens. To address this challenge, we propose XDetox, a novel method that integrates token-level toxicity explanations with the masking and infilling detoxification process. We utilized this approach with two strategies to enhance the performance of detoxification. First, identifying toxic tokens to improve the quality of masking. Second, selecting the regenerated sentence by re-ranking the least toxic sentence among candidates. Our experimental results show state-of-the-art performance across four datasets compared to existing detoxification methods. Furthermore, human evaluations indicate that our method outperforms baselines in both fluency and toxicity reduction. These results demonstrate the effectiveness of our method in text detoxification.