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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>On the Complexity of Neural Computation in Superposition · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2409.15318/</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/2409.15318/feed.xml" rel="self" type="application/rss+xml" /><item><title>On the Complexity of Neural Computation in Superposition — mentioned by sometimelurker</title><link>https://news.ycombinator.com/item?id=48871585</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.15318:48871585</guid><pubDate>Sat, 11 Jul 2026 12:46:53 GMT</pubDate><description>&lt;p&gt;LLM + scale = more intelligent, this can be proven more than empirically, https://arxiv.org/abs/2409.15318 shows that neural nets can fit a number of independent AND-gate operations in their weights, - if you have a system that is large enough to store, lets say 10^12 AND gates (all frontier llms can do this) - and this system can produce outputs based on previous things it has outputed its turning complete, and RLVR on it is optimization over the space of algorithms. If an algorithm exists to do a task, and the task can be verifiably done, this finds the algorithm to solve the task most…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.15318/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CC</category></item></channel></rss>
