Dynamic Linear Attention
The scalability of Large Language Models (LLMs) to long contexts is fundamentally constrained by the quadratic complexity of standard attention, motivating the adoption of linear attention mechanisms with sub-quadratic cost. To improve representation capacity under long contexts, recent approaches organize memory in a multi-state manner. However, existing multi-state linear attention methods rely on fixed state merging policies that cannot adapt to dynamically varying token importance, irreversibly obscuring critical tokens and causing severe error accumulation over long sequences. To address this limitation, we propose DLA, a dynamic memory modeling framework for multi-state linear atten...
| Authors | Xin Wang, Hui Shen, Boyuan Zheng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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