The field of geometric deep learning and non-Euclidean data analysis is rapidly advancing, with a focus on developing new methods and frameworks for analyzing and processing complex data on manifolds and other non-Euclidean spaces. Researchers are exploring new approaches to autoencoding dynamics, soil sensing, and geometric field theory, as well as developing innovative techniques for learning topology-driven multi-subspace fusion and physics-informed neural networks. Notable papers in this area include: SoilX, which introduces a calibration-free soil sensing system that jointly measures six key components, reducing estimation errors by 23.8% to 31.5% over baselines. Learning Topology-Driven Multi-Subspace Fusion for Grassmannian Deep Network, which proposes a topology-driven multi-subspace fusion framework that enables adaptive subspace collaboration on the Grassmannian, achieving state-of-the-art performance on several tasks.