The field of Unmanned Aerial Vehicles (UAVs) is moving towards more agile and robust control systems, with a focus on precise lateral motion modeling and decoupled control. Researchers are exploring novel modeling and control strategies, such as the use of sideslip force models and YXZ Euler rotation formulations, to improve the dynamic behavior of UAVs. Additionally, there is a growing interest in task-specific design optimization of multirotor UAVs, leveraging techniques like reinforcement learning and Bayesian optimization to achieve superior performance. Another area of research is the development of computationally efficient and performant online inertial parameter estimation methods, enabling real-time adjustment to payload changes and environmental interactions. Noteworthy papers include: TAG-K, a lightweight extension of the Kaczmarz method for fast and stable parameter adaptation, and Performance-guided Task-specific Optimization for Multirotor Design, which introduces a methodology for optimizing aerial robot designs guided by their closed-loop performance in a considered task.