ClusterAttention: A training-free speedup of bidirectional attention
We introduce ClusterAttention, a general training-free speedup of bidirectional attention at large token counts. We point out two common assumptions in contemporary training-free methods; attention sparsity, and context that can be leveraged, such as structure in the input or multiple similar forward passes, and show when they fail. Our proposed method utilizes a fast attention-aware recursive clustering method, and compensation of excluded clusters through their mean. The clustering method gives power-of-two cluster sizes, allowing block-sparse attention to match dense attention in GPU throughput. On TabPFN-3 arXiv:2605.13986, a model where none of the assumptions hold, ClusterAttention ...
| Authors | Kasper Nordenram, Amelie Dittmann |
|---|---|
| Primary category | cs.LG |
| Published | 27 August 2026 |
| Last updated at source | 29 September 2026 |
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