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Deploying a language model under different computing and latency budgets calls for compact models with different quality-cost trade-offs. Towards this end, elastic spectral state space models provide ordered and temporally decomposed channels that can be truncated, but they are associated with linear time-invariant fil...
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.
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.
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