I’ve spent a few hours reading the Mamba paper, examining the code, and watching Yannick’s YouTube dive into the paper. If you took discrete time signal process

I’ve spent a few hours reading the Mamba paper, examining the code, and watching Yannick’s YouTube dive into the paper. If you took discrete time signal processing in school, you will have what you need to work through the math. Here is my intuition about Mamba. It’s a linear discrete time signal filter where the filter blocks are conditioned on the input at each time step as well as receiving the input as a normal filter would. Think of the predecessor to Mamba as a discrete time filter without this conditioning. That’s the first innovation. The other innovation is some lovely algebra allowing them to reorganize things so they can be computed much more efficiently on current GPU hardware, with respect to slow and fast memory. State space models are very simple conceptually. It’s kind of amazing that Mamba does so well on long sequence modeling because the architecture is ridiculously simple. But when you consider that the state matrices are conditioned on the input, it all makes sense. Somehow, they learn how to adapt the “filter” based on the input. That’s where the intelligence lies.