Unverified50% confidenceFactExact time
Mamba reduces sequence modeling complexity to linear time through an input-dependent selective mechanism and a hardware-aware parallel scan algorithm, while retaining the ability to model long-range dependencies.
1
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50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
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Related Claims
VerifiedMamba 等基于状态空间模型（SSM）的架构以线性复杂度实现序列建模，更适合实时推理75% similarUnverifiedMamba's key innovation is making the SSM's B and C matrices and the step size Δ functions of the input, thereby gaining content-aware gating capability while retaining linear complexity.74% similarUnverifiedDuring inference, Mamba can run in pure recurrent mode with constant memory usage.74% similarUnverifiedSelective scan is the core operation of the Mamba family of SSMs, and its discretization parameter Δ, input matrix B and output matrix C all change dynamically with each token.73% similarUnverifiedMamba2 maintains linear time complexity and constant state size, and therefore has a theoretical advantage in long-sequence scenarios.73% similar
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