Mixture-of-Top-$k$ Attention: Efficient Attention as Scalable Fast Weights

The vanilla self-attention mechanism in Transformers can be viewed as a two-layer fast-weight MLP, whose weights are dynamically induced by inputs and whose hidden dimension is equal to the sequence length $N$. As the context extends, the expressive capacity of such an $N$-width MLP increases, but it becomes unscalable for extremely long sequences. Recently, this fast-weight perspective has motivated the Mixture-of-Experts (MoE) attention, which partitions the sequence into rigid blocks, treats them as fast-weight experts, and sparsely routes the tokens to them. In this paper, we elevate this perspective to a unifying framework for efficient attention mechanisms, interpreting them as making fast weights scalable through either routing or compression, and organizing them into a five-dimensional taxonomy. Then, we propose \textbf{Mi}xture-of-\textbf{T}op-$k$ \textbf{A}ttention (\textbf{MiTA}), which employs a small set of landmark queries to gather top-$k$ attended key-value pairs as query-aware and deformable routed experts, while compressing the $N$-width MLP into a narrower shared expert. Consequently, MiTA improves the flexibility of prior MoE attention, from rigid to deformable fast-weight experts, as well as the scalability of prior top-$k$ attention, from query-specific set to reusable top-$k$ set. Our experiments on vision tasks demonstrate the superior effectiveness and efficiency of MiTA, while also uncovering intriguing properties such as an emergent token-pruning effect and easy generalization from standard attention.