Unverified50% confidenceFactExact time
In recent years, state space models and linear RNNs such as Mamba and RWKV have emerged, attempting to circumvent the quadratic complexity bottleneck of Transformers on long contexts.
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50%
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Long-term
Relevance
9/11/2026
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Related Claims
UnverifiedMamba 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76% similarUnverifiedState space models (SSMs) such as Mamba are compromise attempts between temporal recurrence and self-attention, but their reasoning capabilities still lag behind Transformers.72% similarVerifiedTransformer overcomes the long-range dependency attenuation caused by sequential computation in RNN/LSTM architectures, while natively supporting parallel computation.66% similarUnverifiedSubstack作者分析指出，Mamba若要在长序列上维持表达能力，可能必须让表示空间以二次方式增长，从而在参数维度上重新遭遇二次需求65% similarUnverified业界持续探索线性注意力、稀疏注意力及状态空间模型（如Mamba）等替代架构以应对长上下文瓶颈65% similar
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