Author

Ze-Chen Hu

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#machine learning Preprint Jan 2026

Elastic Spectral State Space Models for Train-Once Budgeted Inference

Elastic Spectral State Space Models is proposed, a train-once, export-many sequence modeling framework that gains elasticity through spectral approximation of the SSM sequence operator that can be truncated to competitive compact models compared with modern Transformer and SSM baselines at similar parameter scales.

Da-Chuan Song, Ju Yin, Ze-Chen Hu et al. · 1 citation

Jul 2026

Mergeable Model-Side Aggregation States for Long-Context Language Models

A model-side aggregation interface is introduced that maintains compact Hash-based HyperLogLog sketch states alongside a frozen language model that improves over direct full-context reasoning over chain-of-thought reasoning.

Da-Chuan Song, Ju Yin, Ze-Chen Hu et al. · arXiv.org · 1 citation