Author

Qin-Zhe Hu

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#machine learning Preprint Oct 2026

Universal Test-Time Training

Recent Test-Time Training (TTT) architectures compress context into fast weights that are updated online and queried as memory. Existing TTT designs keep this memory private to each layer: it recurs only over time, and depth merely indexes L separate memories. We argue that memory ownership need not be tied to depth, a...

Ze-Fan Cai, Qin-Zhe Hu, Ziqiao Ma et al. · 1 citation

#artificial intelligence Jun 2026

TF-MoE: Time-Frequency Mixture-of-Experts for Efficient Speech Separation

TF-MoE, a sparse Mixture-of-Experts (MoE) framework that enhances model capacity with almost no increase in inference cost, is proposed and demonstrated that TF-MoE consistently improves separation performance under computation cost constraints.

Qin-Zhe Hu, Chenda Li, Wangyou Zhang et al. · arXiv.org · 3 citations · ⚡1