The field of large language models is rapidly advancing, with a growing focus on multimodal capabilities. Recent developments have seen the integration of diverse modalities, such as text, images, tables, and other sensors, to enable more robust and adaptable models. A key trend is the development of methods to improve the stability and effectiveness of multimodal in-context learning, including the use of task mapping, context-aware modulated attention, and contrastive learning. Another area of research is the application of large language models to real-world problems, such as economic dispatch and tool selection, demonstrating their potential for practical impact. Notably, papers such as 'CAMA: Enhancing Multimodal In-Context Learning with Context-Aware Modulated Attention' and 'HoloLLM: Multisensory Foundation Model for Language-Grounded Human Sensing and Reasoning' have made significant contributions to the field, introducing innovative approaches to multimodal learning and human sensing. Overall, the field is moving towards more generalizable, efficient, and adaptable models that can effectively integrate multiple modalities and apply to a wide range of tasks.