The recent developments in the research area of Vision-Language Models (VLMs) and Large Language Models (LLMs) have shown significant advancements in handling complex and long-context tasks, as well as improving the integration of multimodal data. Key innovations include the introduction of hybrid encoders that enhance fine-grained recognition and the ability to process high-resolution images without semantic breaks. Additionally, novel single-stage pretraining methods have been proposed to simplify the training process for long-context modeling in LLMs, demonstrating competitive performance against multi-stage methods. Another notable trend is the development of demonstration retrievers for multimodal models, which optimize the selection of in-context learning demonstrations, thereby improving task performance. Furthermore, advancements in tokenization techniques for Vision Transformers, such as superpixel-based methods, have shown to enhance the semantic integrity of visual tokens, leading to improved accuracy and robustness in downstream tasks. These innovations collectively push the boundaries of what VLMs and LLMs can achieve, making them more versatile and effective for a wide range of real-world applications.