The field of research is moving towards leveraging AI and machine learning to automate and optimize design processes, particularly in areas such as chemical manufacturing, computer system architecture, and electronic design automation. Innovative approaches, including the use of large language models, reinforcement learning, and discrete flow matching, are being explored to improve efficiency, accuracy, and feasibility in these domains. Notable papers in this area have demonstrated significant advancements, including the development of frameworks for automated chemical process and instrumentation diagram generation, analog circuit topology synthesis, and simulation-guided neural network accelerator design. Noteworthy papers include: AutoChemSchematic AI, which presents a closed-loop, physics-aware agentic framework for auto-generating chemical process and instrumentation diagrams. QiMeng, a novel system for fully automated hardware and software design of processor chips, comprising three hierarchical layers and leveraging domain-specific Large Processor Chip Models.