The field of geometric network optimization and robot motion planning is experiencing significant developments, with a focus on improving the efficiency and scalability of algorithms for various problems. Researchers are exploring new techniques for constructing sparse spanners, tree covers, and other geometric structures that can be used to approximate complex networks and facilitate motion planning. Notably, the development of simple yet effective reduction tools, such as constant-stretch tree covers, is enabling the solution of problems that concern approximate distances in the Euclidean plane. Additionally, the study of constrained reconfiguration and motion planning is leading to a deeper understanding of the complexity of these problems and the development of polynomial-time algorithms for special cases. Some papers are particularly noteworthy, including: A paper that resolves the question of whether a low stretch can be achieved using two trees in the Euclidean plane, presenting a constant-stretch cover with a pair of trees. A paper that presents a tight lower bound for doubling spanners, showing that the superior Euclidean bounds cannot be achieved in doubling metrics. A paper that improves the upper bound on the diameter of the compatible flip graph in plane spanning trees, matching the upper bound for unrestricted flips up to an additive constant.