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The Hidden Attention of Mamba Models
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The Mamba layer offers an efficient selective state space model (SSM) that is highly effective in modeling multiple domains, including NLP, long-range sequence processing, and computer vision. Selective SSMs are viewed as dual models, in which one trains in parallel on the entire sequence via an IO-aware parallel scan, and deploys in an autoregressive manner. We add a third view and show that such models can be viewed as attention-driven models. This new perspective enables us to empirically and theoretically compare the underlying mechanisms to that of the self-attention layers in transformers and allows us to peer inside the inner workings of the Mamba model with explainability methods. Our code is publicly available.
Forward citations
Cited by 3 Pith papers
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Beyond BEV: Optimizing Point-Level Tokens for Collaborative Perception
CoPLOT replaces BEV features with semantically ordered, frequency-enhanced point-level tokens for collaborative perception, improving 3D detection while cutting overhead.
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How Can Mamba Learn In Context with Outliers and Generalize Provably?
A simplified one-layer Mamba provably learns in-context binary classification tolerating outlier fractions approaching 1, whereas a linear Transformer can only tolerate α < 1/2.
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Change of Thought: Adaptive Test-Time Computation
A transformer layer that iteratively refines its attention matrix to a fixed point is claimed to improve accuracy with no extra parameters, but the benchmark evidence is not reproducible.
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