<?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>Axiom on SciDraft | A Hugo Theme</title><link>https://www.zhumengyao.com/scidraft/tags/axiom/</link><description>Recent content in Axiom on SciDraft | A Hugo Theme</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 21 Sep 2026 15:22:00 +0900</lastBuildDate><atom:link href="https://www.zhumengyao.com/scidraft/tags/axiom/index.xml" rel="self" type="application/rss+xml"/><item><title>Inner-Product Axioms (axiom)</title><link>https://www.zhumengyao.com/scidraft/notes/demo-note-axiom-inner-product/</link><pubDate>Mon, 21 Sep 2026 15:22:00 +0900</pubDate><guid>https://www.zhumengyao.com/scidraft/notes/demo-note-axiom-inner-product/</guid><description>&lt;p&gt;&lt;strong&gt;Demo note.&lt;/strong&gt; This note demonstrates what the &lt;strong&gt;SciDraft&lt;/strong&gt; Hugo theme can do
for academic and scientific publishing — for research institutions, researchers
and students. Its content is demonstration material only: readers should ignore
whether the demo content is correct.&lt;/p&gt;
&lt;p&gt;Attention is built on a quantity that looks like a similarity but is defined by three
axioms: an inner product is symmetric, linear in each argument, and positive definite.
Those three properties are what allow a score to be read as &amp;ldquo;how much one vector points
along another&amp;rdquo;.&lt;/p&gt;</description></item><item><title>Kolmogorov Axioms for a Token Distribution (axiom)</title><link>https://www.zhumengyao.com/scidraft/notes/demo-note-axiom-probability/</link><pubDate>Mon, 21 Sep 2026 13:32:00 +0900</pubDate><guid>https://www.zhumengyao.com/scidraft/notes/demo-note-axiom-probability/</guid><description>&lt;p&gt;&lt;strong&gt;Demo note.&lt;/strong&gt; This note demonstrates what the &lt;strong&gt;SciDraft&lt;/strong&gt; Hugo theme can do
for academic and scientific publishing — for research institutions, researchers
and students. Its content is demonstration material only: readers should ignore
whether the demo content is correct.&lt;/p&gt;
&lt;p&gt;An axiom is an assumption taken as given, and the ones that hold up a language model
are Kolmogorov&amp;rsquo;s three: probabilities are non-negative, the whole space has probability
one, and disjoint events add. Everything else about token distributions is derived from
them rather than assumed.&lt;/p&gt;</description></item></channel></rss>