AI Research
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 sha...
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ArXiv CS.AI
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ArXiv CS.AI
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LISA tackles the quadratic complexity of standard attention in large language models by introducing a hybrid linear and sparse attention mechanism. This approach allows for longer context windows and faster inference, making advanced reasoning capabilities more practical for real-world applications.
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