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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2505.17190/</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/2505.17190/feed.xml" rel="self" type="application/rss+xml" /><item><title>Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms — mentioned by kingstnap</title><link>https://news.ycombinator.com/item?id=45698420</link><guid isPermaLink="false">urn:hackerpapers:mention:2505.17190:45698420</guid><pubDate>Fri, 24 Oct 2025 19:48:46 GMT</pubDate><description>&lt;p&gt;There are many many problems with attention. The softmax has issues regarding attention sinks [1]. The softmax also causes sharpness problems [2]. In general this decision boundary being Euclidean dot products isn&amp;#x27;t actually optimal for everything, there are many classes of problem where you want polyhedral cones [3]. Positional embedding are also janky af and so is rope tbh, I think Cannon layers are a more promising alternative for horizontal alignment [4]. I still think there is plenty of room to improve these things. But a lot of focus right now is unfortunately being spent on…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2505.17190/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
