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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Introspective Diffusion Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2604.11035/</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/2604.11035/feed.xml" rel="self" type="application/rss+xml" /><item><title>Introspective Diffusion Language Models — mentioned by RandyOrion</title><link>https://news.ycombinator.com/item?id=49385225</link><guid isPermaLink="false">urn:hackerpapers:mention:2604.11035:49385225</guid><pubDate>Fri, 21 Aug 2026 08:09:22 GMT</pubDate><description>&lt;p&gt;In contrast to memory-bounded autoregressive decoding, diffusion decoding can be computation-bounded. Devices with a decent amount of compute but very small memory, e.g. nvidia consumer-grade GPUs, could benefit from diffusion decoding massively. Performance-wise, I don&amp;#x27;t think diffusion decoding should be much worse than autoregressive decoding, see https://arxiv.org/html/2604.11035 . As a local LLM user, I really want to see more small diffusion LLM/VLMs with decent performance coming.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2604.11035/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
