Mamba (State Space Model)
A sequence modeling architecture based on selective state spaces that processes sequences in linear time, offering an alternative to the quadratic complexity of transformer attention.
Why It Matters
Transformers' attention mechanism scales quadratically with sequence length (O(n^2)). Mamba processes sequences in linear time (O(n)), making it dramatically more efficient for very long sequences while maintaining competitive quality.
How It Works
Mamba uses structured state space models (S4) with a selection mechanism that allows the model to selectively remember or forget information based on the input, similar to how attention selectively focuses on relevant tokens.
Current Status
Mamba and hybrid architectures (combining Mamba layers with attention layers) are being explored as alternatives to pure transformers, especially for applications requiring very long context like genomics, audio, and code.