The field of collaborative design and visual analytics is moving towards more efficient and effective processes, with a focus on improving awareness and management of dependencies, integrating different sources of observations, and promoting data-rich acquisition of multimodal observations. Researchers are exploring new frameworks and methodologies to support collaborative design, such as modular and adaptable frameworks for workshop setup and data acquisition, and interactive visual analysis systems. The development of new tools and technologies, such as dataflow-based models and declarative grammars, is also facilitating human-AI collaboration and urban visual analytics. Noteworthy papers include: A Multimodal Framework for Understanding Collaborative Design Processes, which proposes a practical framework for analyzing collaborative design processes. Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics, which leverages a dataflow-based model to enable interactive alignment across the specification, process, and evaluation stages of urban analytics.