State-space self-attention is a hybrid deep learning mechanism that combines structured state-space models with self-attention to process sequential data efficiently. In this design, self-attention is typically employed to capture fine-grained, short-range relationships within localized segments or tokens, while state-space operations model long-range temporal dependencies across the broader sequence. By integrating the expressive representational power of attention mechanisms with the linear computational complexity and memory efficiency of state-space formulations, this approach enables neural networks to reason over extended context windows without incurring the prohibitive computational overhead of standard full-sequence self-attention.
