LISA: Linear-Indexed Sparse Attention for Efficient Long-Context Reasoning
Recent advances in long chain-of-thought reasoning models such as DeepSeek-R1 have led to increasingly longer inference context lengths under the test-time scaling paradigm. However, the O(n^2) computational complexity of standard self-attention causes inference costs to grow sharply with long sequences, limiting the deployment of long-CoT reasoning in production settings. To address this, we propose LISA (Linear-Indexed Sparse Attention), a plug-and-play attention replacement module that requires no pretraining from scratch. LISA integrates two lightweight components in parallel within the original model: (1) a Linear Attention module that provides long-range memory with O(n) time comple...
| Authors | Yu Zhao, Zekun Zhang, Fan Jiang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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