Researchers have developed a new framework for detecting sarcasm in text and images. The framework uses a combination of techniques to identify inconsistencies between the literal meaning of words and contextual cues. It involves a dynamic fusion gate that balances the importance of different modalities and a contrastive regularization objective that encourages semantic consistency for non-sarcastic samples while suppressing misleading consistency in sarcastic cases. The method was tested on two datasets and showed better performance than existing methods. --- Why it matters: This matters to AI researchers because it addresses a challenging problem in natural language processing: detecting sarcasm in multimodal content. Accurate detection of sarcasm is important for applications such as sentiment analysis, opinion mining, and human-computer interaction. Source: https://arxiv.org/abs/2608.19942