Mamba is a selective state-space model architecture that achieves linear-time sequence processing by learning input-dependent selection of which information to remember, competing with transformers on language tasks. Unlike fixed SSMs, Mamba's selection mechanism lets it focus on relevant tokens and ignore irrelevant ones, achieving strong performance with much lower inference cost on long sequences.