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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>NGM: A Plug-and-Play Training-Free Memory Module for LLMs · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2605.16893/</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/2605.16893/feed.xml" rel="self" type="application/rss+xml" /><item><title>NGM: A Plug-and-Play Training-Free Memory Module for LLMs — mentioned by pjankiewicz</title><link>https://news.ycombinator.com/item?id=49264331</link><guid isPermaLink="false">urn:hackerpapers:mention:2605.16893:49264331</guid><pubDate>Tue, 11 Aug 2026 20:54:13 GMT</pubDate><description>&lt;p&gt;That was my thinking as well mainly to increase the speed of training. But it may turn out that the simple statistics that you can capture like this may account for 1% of the training and are likely to be captured as the first thing during the training. But actually these techniques are used but they are hidden as speculative decoding with increasing complexity of approximations. For example you can have a part of the network that predicts the next word based on the markov chain, the next approximation is more complex etc. This paper proposes something similar where you can inject memory…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2605.16893/"&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>
