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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reasoning Models Better Express Their Confidence · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2505.14489/</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/2505.14489/feed.xml" rel="self" type="application/rss+xml" /><item><title>Reasoning Models Better Express Their Confidence — mentioned by martianlantern</title><link>https://news.ycombinator.com/item?id=44937603</link><guid isPermaLink="false">urn:hackerpapers:mention:2505.14489:44937603</guid><pubDate>Mon, 18 Aug 2025 05:09:54 GMT</pubDate><description>&lt;p&gt;Hey, really cool work love the idea of focusing on key decision points. I was curious though since confidence can be non monotonic during CoT[1], how does binary search handle cases where there are multiple ups and downs in confidence? It seems like there might be more than one &amp;quot;pivotal&amp;quot; token, so I wonder if there&amp;#x27;s a plan to support multi-token pivots or use a different approach than binary search? [1] - https://arxiv.org/abs/2505.14489&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2505.14489/"&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>
