The fields of optimization, autonomous systems, edge computing, and robotics are experiencing significant developments, with a common theme of improving efficiency, adaptability, and safety. Recent advancements in multi-objective optimization and machine learning have introduced new algorithms and frameworks, such as Global-Order GFlowNets and Contrastive Learning-based Constraint Reordering, which have shown promise in various applications, including robotics, materials science, and smart manufacturing. The use of GPU acceleration, autonomous experimentation, and Bayesian optimization are also trending, with notable papers including Global-Order GFlowNets, CLCR, and MOUR-QD. In autonomous systems, motion planning and control are being improved through innovative approaches, such as curvature-constrained vector fields and dynamic objective MPC, with applications in autonomous vehicles, robotics, and logistics. Edge computing is advancing with optimizing architectures, leveraging machine learning, and designing effective control systems, with notable advancements including cooperative inference methods, self-learning-based optimization, and benchmark suites for evaluating DNN models. State estimation is also experiencing significant developments, with a focus on safe planning and control in unknown environments, including geometric approaches and nonlinear observer design. Finally, autonomous systems and robotics are rapidly advancing, with a focus on developing efficient, scalable, and robust solutions, including optimized density-based lane keeping systems, visual simultaneous localization and mapping methods, and comprehensive frameworks and tools. Overall, these advancements have the potential to impact various applications and improve the performance and robustness of autonomous systems and robotics.