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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Can LLM Graph Reasoning Generalize beyond Pattern Memorization? · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2406.15992/</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/2406.15992/feed.xml" rel="self" type="application/rss+xml" /><item><title>Can LLM Graph Reasoning Generalize beyond Pattern Memorization? — mentioned by jokethrowaway</title><link>https://news.ycombinator.com/item?id=42565796</link><guid isPermaLink="false">urn:hackerpapers:mention:2406.15992:42565796</guid><pubDate>Wed, 01 Jan 2025 13:08:50 GMT</pubDate><description>&lt;p&gt;I don&amp;#x27;t think llm generalise much, that&amp;#x27;s why they&amp;#x27;re not creative and can&amp;#x27;t solve novel problems. It&amp;#x27;s pattern matching with a huge amount of data. Study on the topic: https://arxiv.org/html/2406.15992v1 This would explain o1 poor performance with problems with variations. o3 seems to be expensive brute forcing in latent space followed by verification which should yield better results - but I don&amp;#x27;t think we can call it generalisation. I think we need to go back to the drawing board.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2406.15992/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
