The field of multimodal large language models (MLLMs) is rapidly evolving, with a focus on improving their capabilities in web development. Recent research has highlighted the importance of evaluating MLLMs on tasks that require reasoning, robustness, and safety, such as end-to-end web application generation. Noteworthy papers in this area include Benchmarking MLLM-based Web Understanding: Reasoning, Robustness and Safety, which introduces a comprehensive benchmark for evaluating MLLMs on web understanding tasks, and WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning, which proposes a novel agent-based approach for generating websites. Additionally, papers like IWR-Bench: Can LVLMs reconstruct interactive webpage from a user interaction video? and Automatically Generating Web Applications from Requirements Via Multi-Agent Test-Driven Development demonstrate the potential of MLLMs in reconstructing interactive webpages and generating full-stack web applications. Other notable papers include 90% Faster, 100% Code-Free: MLLM-Driven Zero-Code 3D Game Development, which introduces a framework for automated 3D game development, and GenIA-E2ETest: A Generative AI-Based Approach for End-to-End Test Automation, which leverages generative AI for end-to-end test automation. These papers collectively demonstrate the significant progress being made in the field of MLLMs for web development, with a focus on improving their capabilities in reasoning, robustness, and safety.