<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>稀疏激活 on AI内参</title>
    <link>https://www.neican.ai/tags/%E7%A8%80%E7%96%8F%E6%BF%80%E6%B4%BB/</link>
    <description>Recent content in 稀疏激活 on AI内参</description>
    <generator>Hugo</generator>
    <language>zh-cn</language>
    <lastBuildDate>Mon, 13 Jul 2026 23:40:14 +0800</lastBuildDate>
    <atom:link href="https://www.neican.ai/tags/%E7%A8%80%E7%96%8F%E6%BF%80%E6%B4%BB/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>超越算力堆叠：混合专家模型（MoE）如何重塑人工智能的效能边界</title>
      <link>https://www.neican.ai/insights/moe-20260713234014876-0/</link>
      <pubDate>Mon, 13 Jul 2026 23:40:14 +0800</pubDate>
      <guid>https://www.neican.ai/insights/moe-20260713234014876-0/</guid>
      <description>混合专家模型（MoE）通过稀疏激活的架构设计，实现了模型规模与推理效率的解耦。这种范式革命不仅大幅降低了企业部署大规模AI的成本，也标志着AI从单一通用模型向高度专业化的协作生态演进。</description>
    </item>
  </channel>
</rss>
