English(EN) Prism Attention: Proposal-Refined Index Sharing Mechanism for Efficient LLMs Inference

Prism Attention 通过新颖的稀疏机制提升 LLM 推理速度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员推出 Prism Attention，这是一种新颖的稀疏注意力机制，旨在提高大型语言模型 (LLM) 推理的效率，尤其是在长上下文场景下。与之前在层之间静态重用注意力索引的方法不同，Prism Attention 采用一种粗粒度到细粒度的策略，该策略适应于特定层的注意力动态。这种方法在受益于跨层共享的同时，仍保持了适应性。实验表明，Prism Attention 在推理和长上下文基准测试中取得了最先进的性能，在解码方面实现了显著的加速。 AI

影响提高了 LLM 推理效率，有望实现更快、更具成本效益的长上下文模型的部署。

排序理由该集群包含一篇详细介绍 LLM 推理新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zhenxu Tian, Kebin Liu, Zhengwu Yang, Yi Su, Qingqing Dang, Yanlin Sha, Kaipeng Deng, Dianhai Yu, Yanjun Ma, Juntao Li, Min Zhang · 2026-10-19 04:00

Prism Attention：用于高效 LLMs 推理的提案精炼索引共享机制

arXiv:2610.22985v1 Announce Type: cross Abstract: Long-context decoding in large language models (LLMs) is increasingly bottlenecked by the memory and computation required to attend over an ever-growing Key-Value (KV) cache at each autoregressive step. Although token-level sparse…

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报道来源 [1]

Prism Attention：用于高效 LLMs 推理的提案精炼索引共享机制

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