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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Uni3D-MoE: Scalable Multimodal 3D Scene Understanding via Mixture of Experts · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2505.21079/</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/2505.21079/feed.xml" rel="self" type="application/rss+xml" /><item><title>Uni3D-MoE: Scalable Multimodal 3D Scene Understanding via Mixture of Experts — mentioned by viraptor</title><link>https://news.ycombinator.com/item?id=44541679</link><guid isPermaLink="false">urn:hackerpapers:mention:2505.21079:44541679</guid><pubDate>Sat, 12 Jul 2025 12:46:12 GMT</pubDate><description>&lt;p&gt;Sounds like dumping the routing information from programming questions would answer that... I guess I can do a dump from qwen or deepseek locally. You&amp;#x27;d think someone would created that kind of graph already, but I couldn&amp;#x27;t find one. What I did find instead is that some MoE models are explicitly domain-routed (MoDEM), but it doesn&amp;#x27;t apply to deepseek which is just equally load balanced, so it&amp;#x27;s unlikely to apply to Kimi. On the other hand, https://arxiv.org/html/2505.21079v1 shows modality preferences between experts, even in mostly random training. So maybe there&amp;#x27;s something there.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2505.21079/"&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>
