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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gibbs randomness-compression proposition · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2505.23869/</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/2505.23869/feed.xml" rel="self" type="application/rss+xml" /><item><title>Gibbs randomness-compression proposition — mentioned by northlondoner</title><link>https://news.ycombinator.com/item?id=49802938</link><guid isPermaLink="false">urn:hackerpapers:mention:2505.23869:49802938</guid><pubDate>Tue, 22 Sep 2026 15:29:50 GMT</pubDate><description>&lt;p&gt;Reminded me Google&amp;#x27;s earlier paper, &amp;quot;Language Modeling Is Compression&amp;quot; https://arxiv.org/abs/2309.10668 Learning being a compression is also recently proposed as Gibbs compression proposition. See Gibbs randomness-compression proposition https://arxiv.org/abs/2505.23869v5&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2505.23869/"&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>
