<?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>LLM on Xu'Blog</title><link>https://xuquant.com/tags/llm/</link><description>Recent content in LLM on Xu'Blog</description><image><title>Xu'Blog</title><url>https://xuquant.com/images/profile.jpg</url><link>https://xuquant.com/images/profile.jpg</link></image><generator>Hugo -- 0.152.2</generator><language>en</language><lastBuildDate>Wed, 29 Apr 2026 14:00:00 +0800</lastBuildDate><atom:link href="https://xuquant.com/tags/llm/index.xml" rel="self" type="application/rss+xml"/><item><title>Qwen3.5 vs Qwen3: A Deep Architectural Comparison</title><link>https://xuquant.com/posts/autodrive/qwen3-vs-qwen3-5-architecture/</link><pubDate>Wed, 29 Apr 2026 14:00:00 +0800</pubDate><guid>https://xuquant.com/posts/autodrive/qwen3-vs-qwen3-5-architecture/</guid><description>A deep architectural comparison of Qwen3.5 versus Qwen3, examining hybrid attention, native multimodal fusion, high-sparsity MoE, and partial RoPE across attention, vision, and MoE dimensions</description></item><item><title>CORAL: Autonomous Multi-Agent Evolution for Open-Ended Discovery</title><link>https://xuquant.com/posts/ai/coral-autonomous-multi-agent-evolution/</link><pubDate>Thu, 15 May 2025 10:00:00 +0800</pubDate><guid>https://xuquant.com/posts/ai/coral-autonomous-multi-agent-evolution/</guid><description>How delegating evolutionary search decisions to autonomous agents—rather than relying on fixed heuristics—enables faster convergence and stronger results across mathematical and systems optimization tasks.</description></item><item><title>Multi-Head Latent Attention: Efficient KV Cache Compression in DeepSeek-V2</title><link>https://xuquant.com/posts/autodrive/deepseek_series1_mla/</link><pubDate>Sat, 15 Feb 2025 10:00:00 +0800</pubDate><guid>https://xuquant.com/posts/autodrive/deepseek_series1_mla/</guid><description>Deep technical analysis of Multi-Head Latent Attention (MLA) from DeepSeek-V2, covering low-rank KV cache compression, decoupled RoPE design, and computational cost comparison with MHA, MQA, and GQA.</description></item></channel></rss>