The field of multimodal large language models (MLLMs) is moving rapidly towards improving perception and reasoning capabilities, particularly in visual mathematical problem-solving and chart understanding. Researchers are addressing the limitations of current MLLMs through the development of modular problem-solving pipelines, contrastive learning frameworks, and perception-oriented datasets. Notable papers include MathFlow, Benchmarking Visual Language Models on Standardized Visualization Literacy Tests, Unmasking Deceptive Visuals, and On the Perception Bottleneck of VLMs for Chart Understanding. Meanwhile, the field of Composed Image Retrieval (CIR) is advancing with a focus on improving accuracy and efficiency. Innovative approaches, such as generative models and fine-grained textual inversion networks, are being explored to enhance CIR system performance. Noteworthy papers include Generative Compositor and FineCIR. Vision-language models are also being improved to enhance the alignment between visual and language modalities. Researchers are developing novel distillation techniques, patch generation-to-selection approaches, and global-local object alignment learning. Notable papers include Seeing What Matters and GenHancer. Additionally, assistive technologies for visually impaired individuals are being developed, including wearable devices that utilize haptic feedback, object detection, and generative AI. Multimodal language models are being evaluated as visual assistants, with a focus on understanding contextual information and recognizing objects. Noteworthy papers include LLM-Glasses and VocalEyes. The field of multimodal large language models is moving towards improving the alignment and integration of vision and language representations. Researchers are exploring new training strategies, architectures, and evaluation datasets to enhance visual understanding capabilities. Notable papers include Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models and LangBridge. The field of vision-language models is moving towards a deeper understanding of the underlying structures and relationships between visual and linguistic representations. Recent research has focused on exploring compositionality, spatial awareness, and geometry-aware architectures. Noteworthy papers include Galaxy Walker and Beyond Semantics. Lastly, the field of sequence modeling is witnessing a significant shift towards State Space Models (SSMs) due to their ability to efficiently capture long-range dependencies. Notable papers include Gene42, GLADMamba, vGamba, and Q-MambaIR. Overall, these advances have the potential to significantly improve the performance of MLLMs, CIR systems, vision-language models, and assistive technologies, enabling more accurate and efficient image retrieval, visual understanding, and language processing.