RunningTensor: Generalizing Linear Attention to Higher-Order Recurrent States

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. It also improves performance on time-series classification and forecasting, suggesting that higher-order recurrent state can provide useful additional memory capacity beyond matrix-valued state.