The field of multimodal language models is moving towards enhancing the alignment between vision embeddings and large language models, with a focus on improving the understanding of visual content. Researchers are investigating the role of projectors in compressing vision embeddings and aligning them with word embeddings, as well as proposing new methods such as patch-aligned training to improve patch-level alignment. Another area of focus is on improving the accuracy and completeness of image recaptioning, with approaches such as visual reconstruction and iterative refinement. Additionally, there is a growing interest in developing more efficient and effective methods for human annotation of dense image captions, such as sequential annotation and multimodal interfaces. Notable papers include: Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models, which proposes a multi-semantic alignment hypothesis and achieves improved performance on referring expression grounding tasks. RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction, which significantly improves caption accuracy and completeness through iterative refinement. QLIP: A Dynamic Quadtree Vision Prior Enhances MLLM Performance Without Retraining, which proposes a drop-in replacement for the CLIP vision encoder that enhances visual understanding without requiring retraining.