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        <tabi:current_section>interpretable-factors</tabi:current_section>
    </tabi:metadata><link rel="extra-stylesheet" href="https://blog.sagamiyun.me/skins/teal.css?h=bd19e558a52d678a50de" /><title>Kit Kyo - interpretable-factors</title>
        <subtitle>Full-stack engineer · DevOps · agent development — currently going deep on ML &amp; infrastructure.</subtitle>
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    <generator uri="https://www.getzola.org/">Zola</generator><updated>2026-06-29T00:00:00+00:00</updated><id>https://blog.sagamiyun.me/tags/interpretable-factors/atom.xml</id><entry xml:lang="en">
        <title>Cheap factors, four domains: a scorecard — and why &#x27;no single factor works&#x27; isn&#x27;t &#x27;no signal&#x27;</title>
        <published>2026-06-29T00:00:00+00:00</published>
        <updated>2026-06-29T00:00:00+00:00</updated>
        <author>
            <name>Kit Kyo · A2O Labs</name>
        </author>
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        <summary type="html">We put interpretable, locally-computable factors from four mechanistically-different domains — software-defect prediction, paper acceptance, AI-text detection, and a world-model physics proxy — on one shared per-factor scorecard. Predictive power has a domain-dependent ceiling, the factors don&#x27;t transfer across domains, and a single-factor reading nearly had us call an industrial benchmark a &#x27;collapse&#x27; when the signal was just multivariate.</summary>
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