Unverified50% confidenceFactExact time
State Space Models (SSMs) pull the direction back toward fixed-size recurrent memory, but use a different state structure and update rules.
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50%
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Long-term
Relevance
10/6/2026
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UnverifiedState space models (SSMs) have O(1) memory footprint and computation during inference, whereas the KV cache of Transformers grows linearly with sequence length.74% similarVerifiedState space models (SSMs) such as Mamba abandon the attention mechanism and use recurrent structures to achieve linear time complexity sequence modeling.74% similarUnverifiedThe 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.71% similarUnverifiedMemory机制在大语言模型工程中对应系统提示词工程与外部记忆存储的结合，用于模拟无状态模型的持久化状态67% similarUnverifiedCartesia's core technology is based on State Space Models (SSM), which differs from the Transformer architecture widely adopted by mainstream TTS systems67% similar
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