Author

Jia-Yi Luo

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#artificial intelligence Feb 2026

Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts

This work proposes GAD-MoRE, a novel framework for zero-shot Generalizable Graph Anomaly Detection with a Mixture of Riemannian Experts architecture, which significantly outperforms state-of-the-art generalist GAD baselines in the zero-shot setting.

Xinyu Zhao, Qingyun Sun, Jiayi Luo et al. · arXiv.org · 0 citations

Preprint Aug 2026

Token Radius Attention for Efficient Video Generation

This work proposes Token Radius Attention (TRA), a training-free framework that maps query entropy to an analytic token budget and converts it into a temporally decayed radius without explicit key ranking and achieves 1.05x speedup with competitive generation quality.

Jia-Yu Chen, Zhi-Kun Jiang, Mao-Liang Li et al. · 0 citations