The field of vision-language models is rapidly advancing, with a focus on developing more intelligent and adaptive solutions for interactive systems. Recent research has explored the application of vision-language models to various domains, including automotive UI, autonomous driving, and mobile UI testing. A key trend in this area is the integration of language models with visual understanding, enabling more effective interaction and scene understanding. Another notable direction is the development of scalable and efficient methods for deploying vision-language models on edge devices, such as robots and cameras. Overall, the field is moving towards more comprehensive and human-like understanding of visual scenes, with a focus on improving performance, efficiency, and usability. Noteworthy papers in this area include: Leveraging Vision-Language Models for Visual Grounding and Analysis of Automotive UI, which achieves strong performance on automotive UI understanding and interaction. Camera Control at the Edge with Language Models for Scene Understanding, which presents a framework for controlling PTZ cameras using language models, achieving a 35% improvement over traditional techniques. SLAG: Scalable Language-Augmented Gaussian Splatting, which introduces a multi-GPU framework for scalable language-augmented Gaussian splatting, achieving an 18 times speedup in embedding computation. Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving, which proposes a framework for integrating LVLMs with a spatial processor, achieving a 9.86% improvement on the 3D visual grounding task. Seeing Beyond the Scene: Enhancing Vision-Language Models with Interactional Reasoning, which proposes a framework for enhancing VLMs' interactional reasoning, significantly outperforming baseline methods on interaction-heavy reasoning benchmarks.