The field of model-based systems engineering is witnessing significant advancements with the integration of artificial intelligence and machine learning techniques. Researchers are exploring innovative approaches to improve the development process, including the use of multi-agent systems, knowledge-guided frameworks, and automated requirements development. The application of DevOps practices in embedded systems and firmware development is also emerging as a key area of research. Furthermore, the development of large language models is driving new opportunities for AI-driven development, with a focus on software engineering approaches and knowledge protocol engineering. Noteworthy papers in this area include SysTemp, which presents a multi-agent system for template-based generation of SysML v2 models, and Knowledge Protocol Engineering, which introduces a new paradigm for AI in domain-specific knowledge work. Additionally, the paper on Efficient Conformance Checking of Rich Data-Aware Declare Specifications presents a novel algorithmic technique for computing data-aware optimal alignments. These advancements have the potential to significantly improve the efficiency and effectiveness of systems engineering and AI-driven development.