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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Frontier Large Language Models Rival State-of-the-Art Planners · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2511.09378/</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/2511.09378/feed.xml" rel="self" type="application/rss+xml" /><item><title>Frontier Large Language Models Rival State-of-the-Art Planners — mentioned by parasubvert</title><link>https://news.ycombinator.com/item?id=47499165</link><guid isPermaLink="false">urn:hackerpapers:mention:2511.09378:47499165</guid><pubDate>Tue, 24 Mar 2026 06:16:36 GMT</pubDate><description>&lt;p&gt;I think you are vastly underestimating the emergent behaviours in frontier foundational models and should never say never. Remember, the basis of these models is unsupervised training, which, at sufficient scale, gives it the ability to to detect pattern anomalies out of context. For example, LLMs have struggled with generalized abstract problem solving, such as &amp;quot;mystery blocks world&amp;quot; that classical AI planners dating back 20+ years or more are better at solving. Well, that&amp;#x27;s rapidly changing: https://arxiv.org/html/2511.09378v1&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2511.09378/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
