Unverified50% confidenceFactExact time
State space models (SSMs) such as Mamba completely abandon the attention matrix and use recurrent hidden states to achieve O(N) complexity, but still fall short on tasks requiring precise key-value retrieval.
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50%
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Long-term
Relevance
10/10/2026
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UnverifiedState space models (SSMs) such as Mamba are compromise attempts between temporal recurrence and self-attention, but their reasoning capabilities still lag behind Transformers.79% similarUnverifiedLinear attention, sparse attention, and State Space Models (such as Mamba) can reduce computational complexity to linear levels, opening the door to million-token context windows.79% similarUnverifiedMamba 等状态空间模型试图从架构层面解决长上下文的效率瓶颈73% similarUnverifiedSSM用固定维度d的隐状态压缩长度为N的历史序列时，根据率失真理论存在信息保留能力的理论上限，当N远大于d时必然产生信息丢失69% similarUnverifiedUnlike the global attention mechanism of Transformers, Mamba's computational complexity is linear with respect to sequence length.68% similar
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