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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>OpenZL: Using Graphs to Compress Smaller and Faster · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2605.09928/</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/2605.09928/feed.xml" rel="self" type="application/rss+xml" /><item><title>OpenZL: Using Graphs to Compress Smaller and Faster — mentioned by pella</title><link>https://news.ycombinator.com/item?id=48699006</link><guid isPermaLink="false">urn:hackerpapers:mention:2605.09928:48699006</guid><pubDate>Sat, 27 Jun 2026 15:17:48 GMT</pubDate><description>&lt;p&gt;&amp;quot;The unreasonable effectiveness of our first foray into training leads us to believe that the graph model is uniquely positioned to facilitate ML-guided generation of compressors. We are tempted to view this as “the next big thing” in production-scale compression. Whereas compression research has up to now eluded those without domain expertise, we believe the future of application-specific compressors will be unlocked via investment in automated learning methods.&amp;quot; https://arxiv.org/abs/2605.09928 [11 May 2026] OpenZL: Using Graphs to Compress Smaller and Faster&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2605.09928/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.IR</category></item></channel></rss>
