The field of vision-language models is moving towards a deeper understanding of how these models integrate cross-modal information and reflect human cognition. Recent studies have highlighted the limitations of current models in capturing complex visual concepts and color perception. Research has also focused on developing new methods for evaluating and improving the performance of vision-language models, including novel testing tasks and fine-tuning strategies. Notably, some studies have introduced innovative approaches to color representation and interpretation, such as fuzzy color models, which have shown promise in bridging the gap between computational color representations and human visual perception. Noteworthy papers include: The paper on COLIBRI Fuzzy Model, which introduces a novel color representation framework that aligns with human perception. The paper on Response Wide Shut, which provides significant insights into the limitations of state-of-the-art vision-language models on fundamental visual tasks.