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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Frozen Transformers in Language Models Are Effective Visual Encoder Layers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2310.12973/</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/2310.12973/feed.xml" rel="self" type="application/rss+xml" /><item><title>Frozen Transformers in Language Models Are Effective Visual Encoder Layers — mentioned by vjsrinivas</title><link>https://news.ycombinator.com/item?id=47369420</link><guid isPermaLink="false">urn:hackerpapers:mention:2310.12973:47369420</guid><pubDate>Fri, 13 Mar 2026 20:29:25 GMT</pubDate><description>&lt;p&gt;Great work and love the detailed breakdown. This is kind of tangential, but it reminded me of this work: https://arxiv.org/pdf/2310.12973 (Frozen Transformers in Language Models are Effective Visual Encoder Layers). The paper puts out an interesting hypothesis that these LLM-derived transformer layers have the ability to &amp;quot;refine&amp;quot; any set of learned tokens, even in different modalities. I wonder if what you&amp;#x27;re seeing here is related?&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2310.12973/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
