SMat-Attention: Structured Long-Context Sequence Modeling

Researchers introduced Structured Matrix Attention (SMat-Attention), a long-context sequence modeling method that uses a family of causal masks with structured long-range routing whose row supports have VC-dimension d, according to a paper posted as arXiv:2609.36062v1. The construction recovers the standard causal mask at d=1, and for sequences of length T the hard-routing variant takes O(T^{2-3/d}+T) work despite a dense mask, with constant-time per-token decoding after the distant prefix using O(T^{1-1/d}) cached states. Extensions to Mamba-2 and Gated DeltaNet using learned routing with top-k query reads retain subquadratic prefill and improve recall accuracy over the backbones in several settings.

arXiv:2609.36062v1 Announce Type: new Abstract: Long-context sequence models face a fundamental tradeoff: softmax attention uses flexible token-level interactions at quadratic cost, whereas linear attention obtains linear-time training and constant-time decoding by compressing history into a fixed-size state. In this work, we ask whether we can connect these regimes through a tunable notion of structure. To this end, we introduce Structured Matrix Attention SMat-Attention via a family of causal masks with structured long-range routing whose row supports have VC-dimension $d$. In our construction, $d=1$ recovers the standard causal mask, and increasing $d$ permits richer subset-routing patterns. We give chunkwise forward and backward algorithms to enable hardware-efficiency. For sequences of length $T$, the hard-routing construction takes $O T^{2-3/d}+T $ work, despite the mask being dense, for our prescribed family. In fixed-horizon streaming, decoding after the distant prefix takes constant time per token using $O T^{1-1/d} $ cached states. SMat-Attention therefore makes VC-dimension an explicit knob governing access-pattern complexity, prefill cost, and decoding memory. Empirically, subset-routing and rule-assisted multi-key retrieval experiments illustrate the masks' routing expressiveness. Extensions to Mamba-2 and Gated DeltaNet using learned routing with top-$k$ query reads retain subquadratic prefill, improve recall accuracy over the backbones in several settings, and achieve comparable small-scale language-modeling performance.