Hot eventLive
Self-Indexing Attention稀疏长上下文推理框架论文
1 reports1 sources4 hr ago updated
Get the story
AI overview
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。
Generated from reports · updated 3 hr ago
Timeline
Follow the coverage from different angles.
Oct 8, 2026
- arXiv · Information RetrievalSelf-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。
Heat trend
Current heat 9·Comparable peak 10(Oct 8)·Comparable change over 24 hours –
The trend compares only the same participants observed continuously; its range may be smaller than the current heat count. Move or click on the chart to inspect hourly heat; use the left and right arrow keys to switch.