Unverified60% confidenceFactExact time
In 2023, Gu and Dao proposed Mamba, introducing an input-dependent selective scan mechanism that achieves language modeling performance comparable to or better than Transformers while maintaining linear time complexity.
2
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60%
Confidence
Long-term
Relevance
8/6/2026
First Seen
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Related Claims
UnverifiedMamba 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.72% similarUnverifiedThe Mamba state space model was proposed by Albert Gu and Tri Dao in 2023, with both computational complexity and memory usage at O(1) during inference68% similarUnverifiedMamba's memory footprint during inference remains constant and does not grow linearly with sequence length, narrowing the gap with Transformers on language modeling tasks.66% similarUnverifiedThe fusion of Mamba-style state space models with Transformers is moving from papers to usable open-source weights.64% 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.63% similar
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