The field of geometric design and reconstruction is witnessing significant advancements with the development of innovative methods and algorithms. Researchers are focusing on improving the accuracy and efficiency of design generation, reconstruction, and analysis of complex objects and structures. Notably, optimization-based post-processing techniques are being explored to refine auto-generated designs and reduce flaws. Additionally, novel approaches are being proposed for reconstructing deformable objects, designing programmable structures, and predicting mechanical properties of materials. These advancements have the potential to impact various applications, including graphic design, robotics, medical imaging, and aerospace engineering. Noteworthy papers include: LayoutRectifier, which proposes an optimization-based method for refining auto-generated graphic design layouts. PROD, which introduces a novel method for reconstructing deformable objects using elastostatic signed distance functions. Denoising diffusion models for inverse design of inflatable structures, which presents a generative design framework for programmable structures. A novel geometric predictive algorithm for assessing Compressive Elastic Modulus, which develops a predictive algorithm for obtaining the elastic modulus of plastic materials manufactured with MEX. Eliminating Rasterization, which proposes a novel deep learning framework for direct vector floor plan generation.