<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>硅基认知 on AI 实战派 · 从技术到赚钱</title><link>https://guijiagi.com/categories/%E7%A1%85%E5%9F%BA%E8%AE%A4%E7%9F%A5/</link><description>Recent content in 硅基认知 on AI 实战派 · 从技术到赚钱</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Wed, 28 Oct 2026 07:17:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/categories/%E7%A1%85%E5%9F%BA%E8%AE%A4%E7%9F%A5/index.xml" rel="self" type="application/rss+xml"/><item><title>参数里固化与上下文里流动：硅基知识的两种存储哲学</title><link>https://guijiagi.com/posts/2026-10-28-12-silicon-knowledge-two-storage-philosophies/</link><pubDate>Wed, 28 Oct 2026 07:17:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-28-12-silicon-knowledge-two-storage-philosophies/</guid><description>大模型把知识焊进参数、又在推理时靠上下文临时装配，解读这两种存储方式的分工与权衡。</description></item><item><title>想得久比长得大更聪明：推理时计算的崛起</title><link>https://guijiagi.com/posts/2026-10-28-11-test-time-compute-deeper-thinking/</link><pubDate>Wed, 28 Oct 2026 07:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-28-11-test-time-compute-deeper-thinking/</guid><description>o系列模型之后，通过延长推理时计算让模型「深思熟虑」成为新的能力杠杆，解读test-time compute如何重塑智能竞赛。</description></item><item><title>下一个词预测的免费午餐：为什么逼出了世界模型</title><link>https://guijiagi.com/posts/2026-10-28-10-next-token-prediction-world-model/</link><pubDate>Wed, 28 Oct 2026 07:03:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-28-10-next-token-prediction-world-model/</guid><description>一个看似幼稚的训练目标——预测下一个token——为何逼出了对物理、因果与常识的内部表征，解读预测学习的意外深度。</description></item><item><title>机械可解释性进入深水区：我们开始读到硅基大脑的内部电路</title><link>https://guijiagi.com/posts/2026-10-28-09-mechanistic-interpretability-circuits/</link><pubDate>Wed, 28 Oct 2026 06:56:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-28-09-mechanistic-interpretability-circuits/</guid><description>过去两年机械可解释性研究从识别单个神经元走向解析完整计算电路，解读人类如何第一次看懂大模型的内部工作机制。</description></item></channel></rss>