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CoSA introduces efficient sparse attention for long-context LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CoSA, a novel sparse attention mechanism designed to accelerate inference for long-context language models. CoSA employs a two-stage, training-free approach that couples a Kernel-Aware Proxy (KAP) with an Ordered-Skipping Kernel (OSK). This method allows for more accurate block selection and efficient kernel computation, leading to significant speedups in attention and reduced time-to-first-token, even under tight computational budgets and extended context lengths. AI

IMPACT CoSA's efficiency gains could enable broader adoption of long-context models in resource-constrained environments.

RANK_REASON Academic paper detailing a new technical approach to LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CoSA introduces efficient sparse attention for long-context LLMs

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Academic paper detailing a new technical approach to LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yufei Xue, Lin Niu, Hong Liu, Siran Liu, Hanyong Shao, Wei Liu, Guanghua Yu, Jianchen Zhu, Jun Zhang ·

    CoSA: Accelerating Long-Context Inference via Proxy-Kernel Co-Designed Sparse Attention

    arXiv:2607.25291v1 Announce Type: new Abstract: The quadratic cost of self-attention makes long-context inference prohibitively expensive, and proxy-based block-sparse attention has become a practical remedy. Existing methods typically rely on a proxy to predict a binary sparse m…

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