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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Open Problems in Mechanistic Interpretability · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2501.16496/</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/2501.16496/feed.xml" rel="self" type="application/rss+xml" /><item><title>Open Problems in Mechanistic Interpretability — mentioned by nl</title><link>https://news.ycombinator.com/item?id=50174138</link><guid isPermaLink="false">urn:hackerpapers:mention:2501.16496:50174138</guid><pubDate>Tue, 20 Oct 2026 23:31:26 GMT</pubDate><description>&lt;p&gt;&amp;gt; I counterclaim that open-weights, open-training, open-usage models are understood. Knowing what is in the model is a long way from understanding it. The entire field of mechanistic interpretability exists to try to understand models. Read for example &amp;quot;Open Problems in Mechanistic Interpretability&amp;quot;[1]. To quote: &amp;gt; Developers only need to design the training process; they do not need to – and in almost all cases, do not – understand the neural mechanisms underlying the capabilities learned by an AI system.[1] (emphasis mine) Here&amp;#x27;s a practical example relevant to this case: Is it possible to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2501.16496/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Open Problems in Mechanistic Interpretability — mentioned by camelmel</title><link>https://news.ycombinator.com/item?id=48379235</link><guid isPermaLink="false">urn:hackerpapers:mention:2501.16496:48379235</guid><pubDate>Wed, 03 Jun 2026 02:36:01 GMT</pubDate><description>&lt;p&gt;LLM written article. It&amp;#x27;s also not accurate; the fact that language models have human-interpretable representations and neurons has been known since BERT. Circuits research also does not come from Anthropic. Mech interp is a huge field in academia and most of the core circuit analysis papers were from OpenAI/GDM/academia. However, Anthropic tends to produce a lot of blog posts where they draw poorly supported but hype-able analogies between LLMs and biological intelligence. It&amp;#x27;s wild. For a better understanding of mech interp and circuits, including what we actually do know about LLM…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2501.16496/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Open Problems in Mechanistic Interpretability — HN submission</title><link>https://news.ycombinator.com/item?id=46931046</link><guid isPermaLink="false">urn:hackerpapers:submission:2501.16496:46931046</guid><pubDate>Sun, 08 Feb 2026 03:24:56 GMT</pubDate><description>&lt;p&gt;Open Problems in Mechanistic Interpretability&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2501.16496/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Open Problems in Mechanistic Interpretability — HN submission</title><link>https://news.ycombinator.com/item?id=44012060</link><guid isPermaLink="false">urn:hackerpapers:submission:2501.16496:44012060</guid><pubDate>Sat, 17 May 2025 04:35:06 GMT</pubDate><description>&lt;p&gt;Open Problems in Mechanistic Interpretability&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2501.16496/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Open Problems in Mechanistic Interpretability — HN submission</title><link>https://news.ycombinator.com/item?id=42864437</link><guid isPermaLink="false">urn:hackerpapers:submission:2501.16496:42864437</guid><pubDate>Wed, 29 Jan 2025 13:03:17 GMT</pubDate><description>&lt;p&gt;Open Problems in Mechanistic Interpretability&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2501.16496/"&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>
