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Self-Indexing Attention稀疏长上下文推理框架论文

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2026年10月8日,研究者在arXiv(Information Retrieval类别,属一手来源)发表论文,提出Self-Indexing Attention框架。该框架面向长上下文大语言模型推理,主打稀疏化与压缩兼容,且无需训练。其核心思路是基于共享变换域的符号-幅度表示:用关键符号作为可复用的token级索引,并以此统一prefill阶段的分组选择与decode阶段的检索。目前公开信息仅涉及该论文的方法概述,尚无实验数据、代码发布或第三方复现结果的后续报道。

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Oct 8, 2026
  1. arXiv · Information Retrieval
    Self-Indexing Attention:兼容压缩的稀疏长上下文 LLM 推理

    研究者提出 Self-Indexing Attention,一种免训练框架,基于共享变换域符号-幅度表示,用关键符号作为可复用的 token 级索引,统一 prefill 分组选择与 decode 检索。

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