Author

Ruo-Yu Zhao

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#machine learning Preprint Oct 2026

Rare Gate Disagreements Can Limit Plasticity: When Gradient Flow Mispredicts Finite-Batch SGD

Population gradient flow is a common tool for reasoning about how neural networks adapt, including after pretraining. We show that it can mispredict finite-batch stochastic gradient descent (SGD) qualitatively, and we trace the discrepancy to a specific mechanism. In a two-unit ReLU regression, a source task drives the...

Ruo-Yu Zhao, Ming-Xuan Zhang, Jian-Bo Dai et al. · 0 citations

Preprint Sep 2026

Large Language Model-Guided Evolutionary Discovery of Native Neural Architectures for Spiking Sequence Modeling

Spiking neural networks (SNNs) offer low-energy sequence modeling through sparse, event-driven computation. However, interactions among spike encoding, neuronal dynamics, and information propagation complicate architecture design. Existing SNN sequence models often adapt artificial neural network (ANN) architectures de...

Ruo-Yu Zhao, Jia-Qi Wu, Chen-Yu Zhu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Neural Spectral Capacity: Measuring and Designing Architectures from Network Specification Alone

This work proposes Neural Spectral Capacity (NSC), a closed-form scalar grounded in the singular-value spectrum of each weight matrix, which outperforms #Params, #FLOPs, and representative training-free proxies in ranking across seven Transformer and CNN families.

Chen-Yu Zhu, Ruo-Yu Zhao, Zhi-Chao Lu · 0 citations

Preprint Aug 2026

Mamba with Hierarchical Memory: Solving Representation Bottleneck in Long Sequence Modeling

Hierarchical Memory Mamba integrates a lightweight working memory that extracts slow paragraph-level semantics from the fast sensory memory embedded in the backbone's hidden states and endows HMM cross-task generalization through parametric learning, which is not observed in other long-context enhanced Mamba variants.

Qinwen Wang, Jieping Luo, Ao-Xiang Qin et al. · 0 citations