<?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>Factor Research on AI + Quant Engineering</title><link>https://miasyster.github.io/en/tags/factor-research/</link><description>Recent content in Factor Research on AI + Quant Engineering</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 27 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://miasyster.github.io/en/tags/factor-research/index.xml" rel="self" type="application/rss+xml"/><item><title>Anti-Overfit Is Architecture, Not a Plugin</title><link>https://miasyster.github.io/en/posts/anti-overfit-is-architecture/</link><pubDate>Mon, 27 Apr 2026 00:00:00 +0000</pubDate><guid>https://miasyster.github.io/en/posts/anti-overfit-is-architecture/</guid><description>Most backtest systems treat anti-overfit as an optional add-on check — run the backtest, then test for overfitting if you feel like it. QuantGPT builds it into the scoring system and evolution engine: anti-overfit results directly affect factor scores, and the evolution engine reads anti-overfit metrics to decide its next strategy. Factors that haven&amp;rsquo;t proven robustness don&amp;rsquo;t even qualify for iteration.</description></item></channel></rss>