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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2509.24914/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2509.24914/feed.xml" rel="self" type="application/rss+xml" /><item><title>Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws — mentioned by hackpert</title><link>https://news.ycombinator.com/item?id=46540285</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.24914:46540285</guid><pubDate>Thu, 08 Jan 2026 12:30:29 GMT</pubDate><description>&lt;p&gt;These metaphorical database analogies bug me, and from what it seems like, a lot of other people in comments! So far some of the most reasonable explanations I have found that take training dynamics into account are from Lenka Zdeborova&amp;#x27;s lab (albeit in toy, linear attention settings but it&amp;#x27;s easy to see why they generalize to practical ones). For instance, this is a lovely paper: https://arxiv.org/abs/2509.24914&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.24914/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
