What are State Space Models (SSMs) and why are they considered alternatives to Transformers?

State Space Models are sequence modeling architectures designed to efficiently process long contexts with linear scaling, offering an alternative to Transformer attention mechanisms.

Transformers dominate modern AI, but their quadratic attention complexity creates major scalability bottlenecks.

State Space Models (SSMs) emerged as a promising alternative.

Core intuition: Instead of computing pairwise token interactions using attention, SSMs compress sequence information into evolving hidden states.

Mathematical foundation: SSMs originate from control theory and dynamical systems.

Continuous state equations: x'(t) = Ax(t) + Bu(t) y(t) = Cx(t) + Du(t)

Where: