<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Posts on 608z.com</title>
    <link>https://608z.com/posts/</link>
    <description>Recent content in Posts on 608z.com</description>
    <generator>Hugo</generator>
    <language>en-us</language>
    <lastBuildDate>Fri, 02 Oct 2026 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://608z.com/posts/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Recursive Self-Improvement: A Research Overview</title>
      <link>https://608z.com/posts/recursive-self-improvement/</link>
      <pubDate>Fri, 02 Oct 2026 00:00:00 +0000</pubDate>
      <guid>https://608z.com/posts/recursive-self-improvement/</guid>
      <description>&lt;h2 id=&#34;where-the-field-stands&#34;&gt;Where the field stands&lt;/h2&gt; &lt;p&gt;Fragments of the self-improvement loop are industrial practice; the closed loop is not. The search term is &lt;strong&gt;recursive self-improvement (RSI)&lt;/strong&gt;, sometimes &amp;ldquo;seed AI&amp;rdquo; or &amp;ldquo;self-referential learning&amp;rdquo;.&lt;/p&gt;&#xA;&lt;p&gt;The best entry point is a July 2026 survey of 1,250 arXiv papers from 2024–2026, which organises the literature by what the system improves (its deployment behaviour, its policy through training, its evaluator, or the research process itself) and by how closed the loop is. Its central distinction is worth adopting (&lt;a href=&#34;https://arxiv.org/abs/2607.07663&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Chen, Wang &amp;amp; Qu 2026&lt;/a&gt; ).&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
