Temporal Knowledge Graph (TKG) reasoning remains challenging to characterize with conventional flat representations due to its intrinsic heterogeneous structure.Existing multigeometry approaches face two key bottlenecks: 1) the Riemannian depth barrier driven by numerical instability, which restricts models to shallow architectures; and 2) gate collapse, where adaptive fusion mechanisms suffer from gradient starvation and degenerate into singlegeometry solutions.To this end, we propose MAGIC (Multi-geometry Annealing Graph Interaction with Consensus).Our framework introduces a Tangent-Residual Engine in multigeometric spaces, which enables the first stable 8-layer geometric evolution and reveals a phenomenon termed Geometric Annealing, where manifold curvature spontaneously evolves from semantic flatness in shallow layers to structural complexity in deeper layers.We further design an explicit reasoning module with structural consensus, leveraging geometric invariants and structural priors to regulate gradient flow, prevent collapse, and ensure robust synergy across Hyperbolic, Spherical, and Euclidean spaces.Experiments show that MAGIC achieves stateof-the-art performance in TKG reasoning, improving MRR by up to 2.9 points.

Depth information provides valuable insights into the 3D structure, especially the outline of objects, which can be utilized to enhance semantic segmentation. However, a naive fusion of depth information can disrupt features and compromise accuracy due to the gap between depth and RGB modalities. In this work, we introduce a depth-guided texture diffusion approach that effectively tackles the outlined challenge. Our method extracts low-level features from edges and textures to create a texture image. This image is then selectively diffused across the depth map, enhancing structural information vital for precisely extracting object outlines. By integrating this enhanced depth map with the original RGB image into a joint feature embedding, our method effectively bridges the disparity between depth and RGB modalities, enabling more accurate semantic segmentation. We conduct comprehensive experiments on diverse, widely-used datasets covering various semantic segmentation tasks, including Camouflaged Object Detection (COD), Salient Object Detection (SOD), and indoor semantic segmentation. With source-free estimated depth or depth captured by depth cameras, our method consistently outperforms existing baselines and achieves new state-of-the-art results, demonstrating the effectiveness of our depth-guided texture diffusion for image semantic segmentation. The source code and datasets are publicly available athttps://github.com/Wistzz/Texture-Diffusion.git.