RunningTensor: Generalizing Linear Attention to Higher-Order Recurrent States

Abstract:Linear attention and state-space models provide linear-time sequence modeling, but their recurrent memory remains a second-order tensor (a matrix), limiting the order of interactions that can be represented in the state. We introduce the RunningTensor, which generalizes this memory to an order-$o$ tensor, updated by a rank-1 outer product and read by contracting against $o-1$ vector queries. Order $2$ recovers linear attention; we study order $3$ as a proof of concept, retaining both recurrent and parallel forms while remaining linear in sequence length $T$ and improving working memory capacity from $\mathcal{O}(W^2)$ to $\mathcal{O}(W^o)$. On synthetic multi-query associative recall, RunningTensor outperforms linear-attention and SSM baselines. After pretraining, it also improves performance on language-understanding and non-synthetic retrieval tasks, suggesting that higher-order recurrent state can provide useful additional memory capacity beyond matrix-valued state.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.12814 [cs.LG]

(or arXiv:2609.12814v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.12814 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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