<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/">
  <channel>
    <title>Ephemerent News</title>
    <link>https://ephemerent.com/news</link>
    <description>Research briefings, technology, and evidence.</description>
    <language>en-us</language>
    <lastBuildDate>Fri, 04 Sep 2026 12:00:00 -0400</lastBuildDate>
    <atom:link href="https://ephemerent.com/news.xml" rel="self" type="application/rss+xml"/>
    <image>
      <url>https://ephemerent.com/assets/og-news-swc.png</url>
      <title>Ephemerent News</title>
      <link>https://ephemerent.com/news</link>
    </image>
    <item>
      <title>What a checked answer can promise.</title>
      <link>https://ephemerent.com/news/stochastic-witness-calculus</link>
      <guid isPermaLink="true">https://ephemerent.com/news/stochastic-witness-calculus</guid>
      <pubDate>Fri, 04 Sep 2026 12:00:00 -0400</pubDate>
      <author>kt@ephemerent.com (Kenju Tomita)</author>
      <category>Research Dispatch</category>
      <category>Automated reasoning</category>
      <description>An intuitive guide to Stochastic Witness Calculus and the boundary between learned proof search, exact certificates, and empirical evidence.</description>
      <content:encoded><![CDATA[
        <p>A model can help search an enormous space for a proof witness. It should not get to certify its own work.</p>
        <p><a href="https://ephemerent.com/news/stochastic-witness-calculus">Read the full Research Dispatch at Ephemerent News.</a></p>
        <p><a href="https://ephemerent.com/journal/preprint/stochastic-witness-calculus">Read the public v6 preprint at Ephemerent Research.</a> Updated September 5: exact-output receipts, complete coverage of 12,673 declared inputs, and 4,603 fresh generated inputs with zero failures. The availability result is specific to the recorded IID model and frozen solver; no physical deployment was evaluated. The paper, LaTeX source, and full evidence are downloadable.</p>
      ]]></content:encoded>
    </item>
    <item>
      <title>A simpler rule beat the hierarchy on GPT-2. Here’s what that means.</title>
      <link>https://ephemerent.com/news/drfsp-robust-compression</link>
      <guid isPermaLink="true">https://ephemerent.com/news/drfsp-robust-compression</guid>
      <pubDate>Sun, 09 Aug 2026 12:00:00 -0400</pubDate>
      <author>kt@ephemerent.com (Kenju Tomita)</author>
      <category>Research Dispatch</category>
      <category>Neural network compression</category>
      <description>A controlled experiment rewarded a sophisticated compression hierarchy. A small GPT-2 test did not. The reversal became the real result.</description>
      <content:encoded><![CDATA[
        <p>I built a sophisticated hierarchy to compress neural networks without erasing rare-domain behavior. It worked perfectly in a controlled setting. On GPT-2, a much simpler robust baseline did better at aggressive compression—and that became the real result.</p>
        <p><a href="https://ephemerent.com/news/drfsp-robust-compression">Read the full Research Dispatch at Ephemerent News.</a></p>
        <p><a href="https://ephemerent.com/journal/article/distribution-robust-functional-subset-projection-for-structured-neural-network-width-compression">Open the peer-reviewed research record at Ephemerent Research.</a></p>
      ]]></content:encoded>
    </item>
  </channel>
</rss>
