Unverified50% confidenceFactExact time
Mamba-2, Gated DeltaNet and KDA are three new state update mechanisms used to replace some attention layers, achieving efficient state memory through a recursive structure with linear complexity.
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Long-term
Relevance
10/16/2026
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VerifiedMamba's key innovation is the introduction of a selective mechanism that allows matrices B and C to change dynamically depending on the input content, enabling the model to selectively memorize or forget historical information based on the current input.73% similarUnverifiedMamba-2 builds on selective state space models by introducing a more structured state transition matrix, expressing the state update in matrix multiplication form, thereby achieving higher throughput during training.71% similarUnverifiedSince 2024, Mamba2 has further unified the theoretical framework of state space models and attention mechanisms, and hybrid architectures such as Jamba interleave Mamba layers with attention layers70% 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.69% similarUnverifiedMost SSM and linear attention models use a linear attention mechanism at the bottom layer, with Mamba-2 implemented through the SSD structure and xLSTM implemented through covariance updates.69% similar
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