<?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>Evaluation on Eugene Oleinik</title>
    <link>https://evoleinik.com/tags/evaluation/</link>
    <description>Recent content in Evaluation on Eugene Oleinik</description>
    <image>
      <title>Eugene Oleinik</title>
      <url>https://evoleinik.com/og-image.png</url>
      <link>https://evoleinik.com/og-image.png</link>
    </image>
    <generator>Hugo -- 0.152.2</generator>
    <language>en-us</language>
    <lastBuildDate>Tue, 03 Nov 2026 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://evoleinik.com/tags/evaluation/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Automatic Research Loops Need an Honest Scorer</title>
      <link>https://evoleinik.com/posts/automatic-research-loops-honest-scorer/</link>
      <pubDate>Tue, 03 Nov 2026 00:00:00 +0000</pubDate>
      <guid>https://evoleinik.com/posts/automatic-research-loops-honest-scorer/</guid>
      <description>Karpathy&amp;rsquo;s autoresearch and DeepMind&amp;rsquo;s AlphaEvolve improve things with one simple loop: propose a change, score it, keep it only if it wins. I ran the same loop three ways: on trading strategies with a backtest as the scorer, on a spec and a strategy with LLM judges as the scorer, and by hand through 56 LLM review panels. The backtest loop inflated its own results 3 to 4 times. The LLM-judged loops stalled and learned to flatter the judges. Here is what each failure teaches, and how to build a loop that works.</description>
    </item>
  </channel>
</rss>
