The recent advancements in Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs) have significantly pushed the boundaries of what is possible in various applications, from video-to-text reasoning to user interface design automation. A notable trend is the focus on addressing biases and improving the robustness of VLMs through novel calibration techniques and benchmarks. For instance, the introduction of post-processing calibration methods like BOLD aims to mitigate selection biases in multiple-choice question answering, enhancing both debiasing metrics and overall model performance. Similarly, benchmarks such as NaturalBench and Sketch2Code are challenging models with natural adversarial samples and rudimentary sketches, respectively, to better evaluate their real-world applicability and robustness. Another emerging area is the exploration of order sensitivity in MLLMs, where the sequence of multimodal inputs can drastically affect model performance, prompting the development of new evaluation metrics like Position-Invariant Accuracy (PIA). Additionally, the integration of deep learning with interface generation algorithms is revolutionizing UI design, making it more efficient and accessible without compromising on quality. These developments collectively indicate a shift towards more nuanced and context-aware models that can handle complex, real-world scenarios more effectively.