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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DatBench: Discriminative, Faithful, and Efficient VLM Evaluations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2601.02316/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:27:26 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2601.02316/feed.xml" rel="self" type="application/rss+xml" /><item><title>DatBench: Discriminative, Faithful, and Efficient VLM Evaluations — HN submission</title><link>https://news.ycombinator.com/item?id=46515648</link><guid isPermaLink="false">urn:hackerpapers:submission:2601.02316:46515648</guid><pubDate>Tue, 06 Jan 2026 17:43:03 GMT</pubDate><description>&lt;p&gt;DatBench: Discriminative, faithful, and efficient VLM evaluations&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;18 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2601.02316/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
