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WaveGSSM is introduced, a second-order graph state-space model that maintains two coupled latent states at each node, one for the current pattern and one for its temporal rate of change, and achieves the best mean performance across the temporal-graph benchmarks and global weather forecasting.
In deep graph neural networks, increasing depth enlarges the receptive field but often leads to over-smoothing, where node representations tend to align. We develop a unified, mode-wise stability framework for deep GNN propagation that provides a principled characterization of over-smoothing. By interpreting layer dept...
This work presents CRAFTER (Corrective Residual Agent with Feature-based Temporal Exploration and Reasoning), which keeps the backbone frozen and mines its residual with two complementary generators: a compositional search over the raw input channels, and a large language model that proposes named feature combinations,...
This survey investigates the current research landscape of multimodality modeling from three perspectives: the first group of multimodal models adopts a heterogeneous architecture to bridge different modality data, the second leverages LLM for multimodality modeling via a unified language modeling objective, and the th...
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