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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2401.05618/</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/2401.05618/feed.xml" rel="self" type="application/rss+xml" /><item><title>The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models — mentioned by AIrtemis</title><link>https://news.ycombinator.com/item?id=49107608</link><guid isPermaLink="false">urn:hackerpapers:mention:2401.05618:49107608</guid><pubDate>Thu, 30 Jul 2026 09:13:28 GMT</pubDate><description>&lt;p&gt;Author here. I enjoyed the original idea behind the Caveman skill. I tried to log and do RLHF over my own mistakes and corrections in the LLM conversations and realized the skills were slowing everything down. Hence, I&amp;#x27;ve this opinionated system prompt that aims to add a few engineering principles. Caveman testing ok. Autistic caveman better. Problem: person is one. Need more people test. It builds on the same concise-CoT research behind the original Caveman prompt (https://arxiv.org/html/2401.05618v3) The biggest lift has been speed due to faster tool-calling and preserving my codex budget…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2401.05618/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models — mentioned by JBrussee-2</title><link>https://news.ycombinator.com/item?id=47650509</link><guid isPermaLink="false">urn:hackerpapers:mention:2401.05618:47650509</guid><pubDate>Sun, 05 Apr 2026 15:38:54 GMT</pubDate><description>&lt;p&gt;Author here. A few people are arguing against a stronger claim than the repo is meant to make. As well, this was very much intended to be a joke and not research level commentary. This skill is not intended to reduce hidden reasoning / thinking tokens. Anthropic’s own docs suggest more thinking budget can improve performance, so I would not claim otherwise. What it targets is the visible completion: less preamble, less filler, less polished-but-nonessential text. Therefore, since post-completion output is “cavemanned” the code hasn’t been affected by the skill at all :) Also surprising to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2401.05618/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models — mentioned by xyz7786</title><link>https://news.ycombinator.com/item?id=43420295</link><guid isPermaLink="false">urn:hackerpapers:mention:2401.05618:43420295</guid><pubDate>Thu, 20 Mar 2025 06:29:14 GMT</pubDate><description>&lt;p&gt;After reading both papers, I have to disagree. Chain of Draft seems &amp;quot;identical&amp;quot; to Concise Chain-of-Thought (CCoT) https://arxiv.org/abs/2401.05618. Additionally, the CoD authors seem to mention that it is cognitive inspired but don&amp;#x27;t give any actual basis for that. Sketch-of-Thought, while similar in terms of its goal, seems to be based on cognitive science principles and has better extensibility than CoD and CCoT (they basically just say &amp;quot;answer in X words&amp;quot; and leave it at that).&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2401.05618/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
