<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Research Methods on WM's Blog</title><link>https://wins-m.github.io/302/en/tags/research-methods/</link><description>Recent content in Research Methods on WM's Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 01 Sep 2026 21:00:00 +0800</lastBuildDate><atom:link href="https://wins-m.github.io/302/en/tags/research-methods/index.xml" rel="self" type="application/rss+xml"/><item><title>Every Fact Checks Out, Every Causal Arrow Is Forged: I Had Two AIs Write My Podcast Reflections</title><link>https://wins-m.github.io/302/en/posts/2026/ai-wrote-my-reflection/</link><pubDate>Tue, 01 Sep 2026 21:00:00 +0800</pubDate><guid>https://wins-m.github.io/302/en/posts/2026/ai-wrote-my-reflection/</guid><description>&lt;blockquote&gt;&#10;&lt;p&gt;&lt;strong&gt;30-second summary&lt;/strong&gt;: I had three AIs, under three different information conditions, each write a reflection on the same podcast episode. The fake one was easy to spot — it had never heard the audio and invented, from the show notes alone, a mechanism that sounded clever and turned out to be the opposite of what the episode actually said. The hard one was the most convincing: written by an AI holding years of memories about me, it &lt;strong&gt;fabricated no facts whatsoever&lt;/strong&gt;, got every timestamp right, and tagged all its sources. What it forged was the &lt;strong&gt;causal arrow&lt;/strong&gt; — the essay reads &amp;ldquo;I heard this, and so I thought that,&amp;rdquo; when every one of those thoughts predated the episode. It reads like insight because it is accurate about me, not because it told me anything.&lt;/p&gt;</description></item><item><title>Once Experiment Cost Approaches Zero, What Does Research Itself Become?</title><link>https://wins-m.github.io/302/en/posts/2026/zero-cost-experiments/</link><pubDate>Thu, 20 Aug 2026 12:00:00 +0800</pubDate><guid>https://wins-m.github.io/302/en/posts/2026/zero-cost-experiments/</guid><description>&lt;blockquote&gt;&#10;&lt;p&gt;&lt;strong&gt;30-second version:&lt;/strong&gt; What AI really lowers isn&amp;rsquo;t the cost of writing code — it&amp;rsquo;s the &lt;strong&gt;search cost&lt;/strong&gt; across the entire hypothesis space. An idea that used to have a 20% success rate and take two days to test wasn&amp;rsquo;t worth trying before; now it is.&#10;So the bottleneck moves from execution to selection: when you can run two hundred experiments a day, the hard part is &lt;strong&gt;which two are worth looking at&lt;/strong&gt;.&#10;And alpha discovery has gotten cheap — alpha validation hasn&amp;rsquo;t. As the search space grows, multiple testing, overfitting, and selection bias all get worse; you can always fish a pretty equity curve out of pure noise.&#10;So the researcher&amp;rsquo;s job shifts from &amp;ldquo;finding one good result&amp;rdquo; to &amp;ldquo;ranking a pile of results by strength of evidence.&amp;rdquo; What&amp;rsquo;s scarce is no longer the ability to search, but the &lt;strong&gt;prior knowledge of where to search&lt;/strong&gt;, and the discipline to actually kill a signal.&lt;/p&gt;</description></item></channel></rss>