https://hackerpaper.kaipfister.de/p/2603.12288/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=47415349urn:hackerpapers:mention:2603.12288:47415349Tue, 17 Mar 2026 17:00:48 GMT<p>Hello HN, I&#x27;m Terry, the first author. I spent the last 2.5+ years formalizing this theory to explain a strange anomaly I kept encountering in industry: models trained on vast, incredibly dirty, uncurated datasets were sometimes achieving state-of-the-art predictive performance, completely defying the &quot;Garbage In, Garbage Out&quot; mantra. The TL;DR of the paper [https://arxiv.org/abs/2603.12288] is a formal mathematical proof showing why adding more error-prone variables can actually beat cleaning fewer variables to perfection. The key is recognizing that complex systems often generate data…</p><p><a href="https://hackerpaper.kaipfister.de/p/2603.12288/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LG