The field of multimodal learning is rapidly advancing, with a focus on improving the alignment and integration of different modalities, such as vision and language. Recent developments have highlighted the importance of addressing the modality gap and improving the interpretability of multimodal models. Researchers are exploring new approaches to mitigate the limitations of existing models, including the use of contrastive learning, knowledge distillation, and modular alignment frameworks. These innovations have the potential to enhance the performance and reliability of multimodal models in various applications, including medical image analysis, text-video retrieval, and clinical decision support. Noteworthy papers in this area include Closing the Modality Gap for Mixed Modality Search, which proposes a lightweight post-hoc calibration method to remove the modality gap in CLIP's embedding space, and LLM-Adapted Interpretation Framework for Machine Learning Models, which presents a novel knowledge distillation architecture for transforming feature attributions into probabilistic formats.