Towards Tight Bounds for Streaming Attention
The attention mechanism is a cornerstone of modern transformer architectures. However, its expressive power comes at the cost of quadratic runtime and linear space usage. In particular, the classical transformer architecture explicitly stores all previously seen input elements (tokens) in order to generate the next one. The problem of implementing a transformer in limited space, known as KV cache compression, has received much interest over the past few years, spurring the development of powerful heuristics. Recent works of Haris et al, COLT'25 and Kochetkova et al, NeurIPS'25, formalized KV cache compression as the streaming attention approximation problem, providing both upper bounds (b...
| Authors | Justin Y. Chen, Ying Feng, Piotr Indyk et al. |
|---|---|
| Primary category | cs.DS |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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