Unverified50% confidenceFactExact time
The mathematical roots of SSMs come from linear time-invariant systems in control theory, using matrices A, B, and C to describe how inputs update the hidden state and how the hidden state maps to outputs.
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50%
Confidence
Long-term
Relevance
10/6/2026
First Seen
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Related Claims
UnverifiedState Space Models (SSMs) pull the direction back toward fixed-size recurrent memory, but use a different state structure and update rules.71% similarUnverifiedEarly SSMs such as S4 efficiently capture long-range dependencies by applying HiPPO initialization to matrix A, while maintaining the inference efficiency of linear recurrence.70% similarUnverified传统线性时不变SSM（如S4模型）系统矩阵A、B、C在序列处理中保持不变，递推关系可展开为卷积形式，利用FFT在O(N log N)时间内并行计算70% similarUnverifiedState space models (SSMs) have O(1) memory footprint and computation during inference, whereas the KV cache of Transformers grows linearly with sequence length.69% similarVerifiedState space models (SSMs) such as Mamba abandon the attention mechanism and use recurrent structures to achieve linear time complexity sequence modeling.69% similar
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