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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AgentEvolver: Towards Efficient Self-Evolving Agent System · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2511.10395/</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/2511.10395/feed.xml" rel="self" type="application/rss+xml" /><item><title>AgentEvolver: Towards Efficient Self-Evolving Agent System — mentioned by jauntywundrkind</title><link>https://news.ycombinator.com/item?id=46063501</link><guid isPermaLink="false">urn:hackerpapers:mention:2511.10395:46063501</guid><pubDate>Wed, 26 Nov 2025 23:29:53 GMT</pubDate><description>&lt;p&gt;I might suggest looking at Alibaba&amp;#x27;s open source AgentEvolver. It doesn&amp;#x27;t specifically target video games, but it&amp;#x27;s an agentic system designed around a more OODA loop evolutionary system than the kind of train/inference system, has potential, could be exciting to see. I like how they classifythr sub problems of their work. Environment/ self questioning -&amp;gt; task / self questioning -&amp;gt; trajectory / self evaluation. OODA-esque. https://arxiv.org/abs/2511.10395 https://github.com/modelscope/AgentEvolver with thanks to Sung Kim who has been a great feed…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2511.10395/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
