The field of geometric modeling and computer-aided design (CAD) is witnessing significant innovations, driven by advances in machine learning, geometry processing, and topological analysis. Researchers are developing novel frameworks for modeling complex shapes, generating high-quality meshes, and creating multimodal representations of CAD models. These developments have far-reaching implications for various applications, including digital fabrication, education, and industrial design. Notably, the integration of geometry and topology into unified representations is enabling the creation of more accurate and user-friendly CAD systems. Furthermore, the development of large-scale datasets and benchmarks is facilitating the training and evaluation of machine learning models for CAD generation and refinement. Noteworthy papers include: CLR-Wire, which introduces a novel framework for 3D curve-based wireframe generation that integrates geometry and topology into a unified Continuous Latent Representation. CMT, which proposes a cascade MAR with topology predictor for multimodal conditional CAD generation, achieving superior results in both conditional and unconditional CAD generation tasks.