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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Continuous Thought Machines · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2505.05522/</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/2505.05522/feed.xml" rel="self" type="application/rss+xml" /><item><title>Continuous Thought Machines — mentioned by granitepail</title><link>https://news.ycombinator.com/item?id=48416985</link><guid isPermaLink="false">urn:hackerpapers:mention:2505.05522:48416985</guid><pubDate>Fri, 05 Jun 2026 19:21:45 GMT</pubDate><description>&lt;p&gt;A great deal of their research has been focused on zigging where others zag. Their paper &amp;quot;Continuous Thought Machines&amp;quot; (https://arxiv.org/abs/2505.05522, presented at NeurIPS) was posed specifically under the framing of there needing to be more fundamental research beyond squeezing as much as we can out of relatively vanilla transformer stacks. It is very biologically inspired and unique. Now that models are getting stronger at agentic work, it is very natural that many labs are chasing some form of auto-research.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2505.05522/"&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>Continuous Thought Machines — mentioned by FuckButtons</title><link>https://news.ycombinator.com/item?id=47442604</link><guid isPermaLink="false">urn:hackerpapers:mention:2505.05522:47442604</guid><pubDate>Thu, 19 Mar 2026 17:08:14 GMT</pubDate><description>&lt;p&gt;You might be interested in this paper: https://arxiv.org/abs/2505.05522, in essence they demonstrate that a novel architecture that incorporates bounded convolution at the block level that has some variable time horizon on the number of iterations through the convolutional loop can be very effective and solves problems in a way that is much more similar to how humans do.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2505.05522/"&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>Continuous Thought Machines — HN submission</title><link>https://news.ycombinator.com/item?id=46093256</link><guid isPermaLink="false">urn:hackerpapers:submission:2505.05522:46093256</guid><pubDate>Sun, 30 Nov 2025 03:10:49 GMT</pubDate><description>&lt;p&gt;Continuous Thought Machines&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2505.05522/"&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>
