Sparse Attention Is Matrix Approximation, Not Choosing from a Bag of Values
Large Language Models (LLMs) achieve strong performance across many domains, but their efficiency is limited by the quadratic cost of attention with respect to prompt length. Sparse attention reduces this cost by retaining only a small fraction of query-key interactions to approximate the full attention matrix. However, existing methods are trapped in a mathematically wrong view: they simply keep large scalar entries or high-mass regions of the attention matrix. This treats the attention matrix as a bag of values, ignoring that it is used as a structured matrix whose entries jointly determine the attention output through multiplication with value vectors. We argue that this is the core co...
| Authors | Fang Wan, Xufeng Liu, Fan Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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