MAMBA (S6) stands for a simplified neural network architecture that integrates selective state space models (SSMs) for sequence modelling. It's designed to be a more efficient and powerful alternative to Transformer models (like current LLMs, VLMs, ..) , particularly for long sequences. It is an evolution on classical S4 models.

By making the SSM parameters inputdependent, MAMBA can selectively focus on relevant information in a sequence, enhancing its modelling capability.

Does it have the potential to disrupt the transformer architecture, that almost all AI systems currently are based upon?

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