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Mamba, a new architecture leveraging State-Space Models (SSMs), particularly Structured State Space (S4) models, offers a breakthrough in processing long sequences efficiently, outperforming traditional Transformer-based models with linear complexity scaling. This advancement enables handling tasks like genomic analysis and long-form content generation without memory or compute bottlenecks. Recent papers introduce extensions like EfficientVMamba for resource-constrained deployment, Cobra for multi-modal reasoning, and SiMBA for stability in scaling, showcasing Mamba's architectural flexibility and potential in various domains.

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