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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Unexpected Benefits of Self-Modeling in Neural Systems · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2407.10188/</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/2407.10188/feed.xml" rel="self" type="application/rss+xml" /><item><title>Unexpected Benefits of Self-Modeling in Neural Systems — mentioned by jrosenblatt</title><link>https://news.ycombinator.com/item?id=46389233</link><guid isPermaLink="false">urn:hackerpapers:mention:2407.10188:46389233</guid><pubDate>Fri, 26 Dec 2025 04:27:36 GMT</pubDate><description>&lt;p&gt;Quite a lot of smart people are taking it seriously We are at AE Studio with research like https://ae.studio/research/self-referential and https://arxiv.org/abs/2407.10188 Anthropic is doing interesting work here, Eleos exists (https://eleosai.org/), and https://digitalminds.substack.com/p/digital-minds-in-2025-a-... is a great review of other compelling work The field is just beginning but it is worth taking a serious scientific approach to this work&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2407.10188/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Unexpected Benefits of Self-Modeling in Neural Systems — HN submission</title><link>https://news.ycombinator.com/item?id=41027287</link><guid isPermaLink="false">urn:hackerpapers:submission:2407.10188:41027287</guid><pubDate>Sun, 21 Jul 2024 19:00:33 GMT</pubDate><description>&lt;p&gt;Unexpected Benefits of Self-Modeling in Neural Systems&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 1 discussion reply on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2407.10188/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
