The recent advancements in vision-language models (VLMs) and multi-modal large language models (MLLMs) have shown significant progress in enhancing contextual understanding and visual perception across various domains. These models are increasingly being applied to specialized tasks, such as medical diagnostics, engineering education, and cultural heritage preservation, demonstrating their versatility and potential for innovation. Notably, there is a growing emphasis on developing datasets that can rigorously evaluate and improve the visual perception capabilities of these models, particularly in areas requiring fine-grained understanding of geometric and color information. Additionally, the integration of large language models (LLMs) into traditional computational tasks, such as digital circuit design and arithmetic operations, is opening new avenues for optimization and efficiency in hardware design. The field is moving towards more specialized and domain-specific applications, with a focus on improving the reliability and accuracy of VLMs and MLLMs through targeted datasets and iterative model enhancements.

Noteworthy papers include:

• The introduction of a novel approach to evaluate uncertainty in VLMs' responses using a convex hull method, particularly relevant for critical applications like healthcare.
• The creation of a specialized dataset for evaluating MLLMs' performance on digital electronic circuit problems, aimed at enhancing engineering education.
• The development of a dataset designed to directly evaluate the visual perception capabilities of LVLMs on geometric and numerical information, highlighting the need for improved training data and model architectures.