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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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