The field of metamaterials and electromagnetic structure design is rapidly advancing, with a focus on developing innovative solutions to complex problems. Researchers are exploring new approaches to design and optimize metamaterials with unique properties, such as ultra-stiffness and negative material indices. Machine learning models are being developed to capture the complex relationships between 3D topology, density condition, and mechanical properties, enabling the creation of more efficient and effective metamaterials. Additionally, significant improvements are being made in power handling and beam squint mitigation, particularly in wideband hybrid beamformers. New methods, such as progressive search algorithms and consistency-driven sample selection mechanisms, are being proposed to address the challenges of high-dimensional design spaces and limited evaluation budgets in electromagnetic structure design. These advancements have the potential to enable the development of more efficient and effective devices, such as high-power Ku-band filters and ultra-wideband antennas. Noteworthy papers include: UniMate, which proposes a unified model for mechanical metamaterial generation, property prediction, and condition confirmation, achieving significant improvements over baseline models. Another notable paper is Deep Electromagnetic Structure Design Under Limited Evaluation Budgets, which introduces a novel method called Progressive Quadtree-based Search, enabling satisfactory designs under limited computational budgets and reducing evaluation costs by 75-85%.