<?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>Math on xun's Blog</title><link>https://xungirl.github.io/tags/math/</link><description>Recent content in Math on xun's Blog</description><generator>Hugo -- 0.168.0</generator><language>en</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://xungirl.github.io/tags/math/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Agents: How ReAct, Tool Use, and Planning Actually Work</title><link>https://xungirl.github.io/posts/llm-agents-react/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://xungirl.github.io/posts/llm-agents-react/</guid><description>A technical breakdown of LLM-based agents — the ReAct loop, tool calling, planning strategies, and the math behind why chain-of-thought helps.</description></item><item><title>How Transformers Work: Attention Is All You Need, Explained</title><link>https://xungirl.github.io/posts/transformer-architecture/</link><pubDate>Mon, 20 Apr 2026 00:00:00 +0000</pubDate><guid>https://xungirl.github.io/posts/transformer-architecture/</guid><description>A from-scratch walkthrough of the Transformer architecture — self-attention, multi-head attention, positional encoding, and why it replaced RNNs.</description></item></channel></rss>