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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The Computational Complexity of Structure-Based Causality · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1412.3076/</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/1412.3076/feed.xml" rel="self" type="application/rss+xml" /><item><title>The Computational Complexity of Structure-Based Causality — mentioned by nyrikki</title><link>https://news.ycombinator.com/item?id=39661640</link><guid isPermaLink="false">urn:hackerpapers:mention:1412.3076:39661640</guid><pubDate>Sun, 10 Mar 2024 19:04:49 GMT</pubDate><description>&lt;p&gt;LLMs are trained to sound confident. But they can also only do negation through exhaustion, known unknowns, future unknowns, etc... That is the pain of the Entscheidungsproblem. Even in Presburger arithmetic, Natural numbers will addition and equality, which is decidable, still has a double factorial time complexity to prove. That is worse than factorial time for those who&amp;#x27;ve not dealt with it. Add in multiplication then you are undecidable. Even if you decided to use the dag like structure of transformers, causality is very very hard. https://arxiv.org/abs/1412.3076 LLMs only have cheap…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1412.3076/"&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>
