The field of motion planning and multi-agent systems is moving towards more efficient and optimal solutions. Researchers are exploring new algorithms and techniques to improve the performance of motion planning in complex environments, such as kinodynamic motion planning and constrained motion planning. Additionally, there is a growing interest in applying concepts from ecology, such as Hamilton's rule, to multi-agent systems to enable altruism and improve collective goal-reaching efficiency. Noteworthy papers include: KRRF, which proposes a novel approximate method for kinodynamic multi-goal motion planning, providing shorter target-to-target trajectories and final multi-goal trajectories with lower costs. cpRRTC, which presents a GPU-based framework for constrained motion planning, achieving superior performance compared to existing approaches. Improving Trajectory Stitching with Flow Models, which addresses the limitation of generative models in planning via stitching and proposes a novel addition to the training and inference procedures to stabilize and enhance these capabilities. AORRTC, which extends the satisficing RRT-Connect planner to optimal planning, finding initial solutions quickly and converging towards the optimal solution in an anytime manner.