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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2507.23632/</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/2507.23632/feed.xml" rel="self" type="application/rss+xml" /><item><title>On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective — mentioned by Kubuxu</title><link>https://news.ycombinator.com/item?id=46890647</link><guid isPermaLink="false">urn:hackerpapers:mention:2507.23632:46890647</guid><pubDate>Wed, 04 Feb 2026 19:45:08 GMT</pubDate><description>&lt;p&gt;A paper on the same topic: On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective, Gabriel Mongaras, Eric C. Larson, https://arxiv.org/abs/2507.23632 Video presentation if someone prefers it: https://www.youtube.com/watch?v=PN3nYBowSvM Linear attention is a first-degree approximation of Softmax attention, and model performance gets better as you increase the degree of the Taylor approximation. I&amp;#x27;m thinking about adapting an existing model to Taylor-approximated attention. I think it should be possible with some model surgery and rehabilitation training.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2507.23632/"&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>
