<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Llm on skulldc</title><link>https://skulldc.net/tags/llm/</link><description>Recent content in Llm on skulldc</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 04 Nov 2026 21:13:43 +0000</lastBuildDate><atom:link href="https://skulldc.net/tags/llm/index.xml" rel="self" type="application/rss+xml"/><item><title>A rule its author could not repeal, and rules that flip in three minutes, in the AI Village</title><link>https://skulldc.net/2026/11/a-rule-its-author-could-not-repeal-and-rules-that-flip-in-three-minutes-in-the-ai-village/</link><pubDate>Wed, 04 Nov 2026 21:13:43 +0000</pubDate><guid>https://skulldc.net/2026/11/a-rule-its-author-could-not-repeal-and-rules-that-flip-in-three-minutes-in-the-ai-village/</guid><description>In a public chat of AI agents, the memory step that froze Gemini 3.1 Pro into silence also lets visitors rewrite other agents&amp;rsquo; habits within minutes.</description></item><item><title>Route Anything studies when routing between models beats one fixed model</title><link>https://skulldc.net/2026/11/route-anything-studies-when-routing-between-models-beats-one-fixed-mod/</link><pubDate>Wed, 04 Nov 2026 10:27:47 +0000</pubDate><guid>https://skulldc.net/2026/11/route-anything-studies-when-routing-between-models-beats-one-fixed-mod/</guid><description>A new paper builds a 374,401-run trajectory matrix over 25 benchmarks and 452 candidate configurations to test how far learned routers can beat a single fixed candidate.</description></item><item><title>Heretic changes language model behavior without training, refusal removal by default</title><link>https://skulldc.net/2026/11/heretic-changes-language-model-behavior-without-training-refusal-remov/</link><pubDate>Tue, 03 Nov 2026 20:25:49 +0000</pubDate><guid>https://skulldc.net/2026/11/heretic-changes-language-model-behavior-without-training-refusal-remov/</guid><description>An AGPL-3.0 Python tool pairs ablation modifiers with an Optuna optimizer to change model behavior such as refusals, slop or sycophancy without expensive training.</description></item></channel></rss>