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- Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety76
This survey provides a systematic review of current safety research on large models, covering Vision Foundation Models, Large Language Models, Vision-Language Pre-training models, Vision-Language Models, Diffusion Models, and large-model-powered Agents, and presents a comprehensive taxonomy of safety threats to these models.
- Survey on Graph Neural Network Acceleration: An Algorithmic Perspective57
A new taxonomy to classify existing acceleration methods for GNNs into five categories is presented and some promising prospects for future research are suggested.
- Ultrahyperbolic Knowledge Graph Embeddings48
Experimental results show that UltraE outperforms previous Euclidean, hyperbolic, and mixed-curvature KG embedding approaches, and model each relation as a pseudo-orthogonal transformation that preserves the pseudo-Riemannian bilinear form.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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