Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Re

arXiv:2604.00421v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely

arXiv:2609.14962v1 Announce Type: new Abstract: Recently, researchers have proposed a graph pooling operation, akin to the pooling process in conventional convolutional neural

arXiv:2604.08553v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic

arXiv:2604.10152v1 Announce Type: new Abstract: The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large

arXiv:2604.09780v1 Announce Type: new Abstract: Mixture of Experts (MoEs) are now ubiquitous in large language models, yet the mechanisms behind their "expert specialization"

arXiv:2604.18816v1 Announce Type: cross Abstract: Principal Component Analysis (PCA) is a fundamental tool for representation learning, but its global linear formulation fails to

arXiv:2604.27387v1 Announce Type: new Abstract: Heterogeneous graphs with heterophily have emerged as a powerful abstraction for modeling complex real-world systems, where nodes