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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fixed Point Diffusion Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2401.08741/</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/2401.08741/feed.xml" rel="self" type="application/rss+xml" /><item><title>Fixed Point Diffusion Models — mentioned by radarsat1</title><link>https://news.ycombinator.com/item?id=47327788</link><guid isPermaLink="false">urn:hackerpapers:mention:2401.08741:47327788</guid><pubDate>Tue, 10 Mar 2026 19:31:13 GMT</pubDate><description>&lt;p&gt;it also reminds me a bit of this diffusion paper [1] which proposes having an encoding layer and a decoding layer but repeats the middle layers until a fixed point is reached. but really there is a whole field of &amp;quot;deep equilibrium models&amp;quot; that is similar. it wouldn&amp;#x27;t be surprising if large models develop similar circuits naturally when faced with enough data. finding them on the other hand is not easy! as you&amp;#x27;ve shown, i guess brute force is one way.. it would be nice to find a short cut but unfortunately as your diagrams show, the landscape isn&amp;#x27;t exactly smooth. I would also hypothesize that…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2401.08741/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
