Title: On Structured State-Space Duality
Primary area: foundation or frontier models, including LLMs
Keywords: mamba, mamba2, structured state-space model, transformer, attention

Abstract: Structured {S}tate-{S}pace {D}uality (SSD)  [Dao \& Gu, ICML 2024] is an equivalence between a simple Structured {S}tate-{S}pace {M}odel (SSM) and a masked attention mechanism. 
In particular, a state-space model with a scalar-times-identity state matrix is equivalent to a masked self-attention with a $1$-semiseparable causal mask.
Consequently, the same sequence transformation (model) has two algorithmic realizations: 
a linear-time $O(T)$ recurrence or as a quadratic-time $O(T^2)$ attention.
In this work, we formalize and generalize this duality:
(i) 
we extend SSD from the scalar‑identity case to general diagonal SSMs (diagonal state matrices); 
(ii) we show that these diagonal SSMs match the scalar case's training complexity lower bounds while supporting richer dynamics;
(iii) we establish a necessary and sufficient condition under which an SSM is equivalent to $1$-semiseparable masked attention; 
and
(iv) we provide a negative result that such duality is impossible to extend to standard softmax attention due to rank explosion.
Together, these results strengthen the theoretical bridge between recurrent SSMs and Transformers, and widen the design space for expressive yet efficient models.