<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>长上下文 on LoopRouter Blog</title><link>https://blog.looprouter.com/tags/%E9%95%BF%E4%B8%8A%E4%B8%8B%E6%96%87/</link><description>Recent content in 长上下文 on LoopRouter Blog</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Sun, 20 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.looprouter.com/tags/%E9%95%BF%E4%B8%8A%E4%B8%8B%E6%96%87/index.xml" rel="self" type="application/rss+xml"/><item><title>长上下文推理越跑越贵?On-Demand Attention 让模型「需要时才回看历史」</title><link>https://blog.looprouter.com/docs/on-demand-attention-long-context-inference-cost/</link><pubDate>Sun, 20 Sep 2026 00:00:00 +0000</pubDate><guid>https://blog.looprouter.com/docs/on-demand-attention-long-context-inference-cost/</guid><description>&lt;blockquote&gt;&#10;&lt;p&gt;本文基于 arXiv 论文《On-Demand Attention: Language Models Know When to Recall》(2026-09-17 提交)的公开摘要整理与二次分析,加入 LoopRouter 的接入/成本视角;原文见文末链接。文中不含摘要之外的量化数字,具体收益请以完整论文与自有负载复测为准。&lt;/p&gt;</description></item><item><title>DeepSeek V4.1-Flash 参数翻倍却更省:一文看懂 KV Cache 重构如何压低长上下文推理成本</title><link>https://blog.looprouter.com/docs/deepseek-v4-1-flash-kv-cache-cost/</link><pubDate>Fri, 11 Sep 2026 00:00:00 +0000</pubDate><guid>https://blog.looprouter.com/docs/deepseek-v4-1-flash-kv-cache-cost/</guid><description>&lt;blockquote&gt;&#10;&lt;p&gt;本文基于 InfoQ 中文(2026-09-10,作者褚杏娟)对 DeepSeek 技术报告的报道整理与二次分析,加入 LoopRouter 的模型选型与成本视角;数据均转述自该报道,笔者未独立复现原始报告。原文见文末链接。&lt;/p&gt;</description></item></channel></rss>