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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2404.05902/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:27:26 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2404.05902/feed.xml" rel="self" type="application/rss+xml" /><item><title>WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents — HN submission</title><link>https://news.ycombinator.com/item?id=42724616</link><guid isPermaLink="false">urn:hackerpapers:submission:2404.05902:42724616</guid><pubDate>Thu, 16 Jan 2025 13:07:44 GMT</pubDate><description>&lt;p&gt;Show HN: Skyvern 2.0 – open-source AI Browser Agent scoring 85.8% on WebVoyager&lt;/p&gt;&lt;p&gt;Hey HN, We’re Suchintan and Shu from Skyvern (https://www.skyvern.com). We’re building an open source AI Agent that can browse the web and take actions. Our open source repo can be found at https://github.com/Skyvern-AI/Skyvern. We’ve re-built Skyvern with a Planner-Actor-Validator agent architecture and achieved 85.8% state of the art (SOTA) on the WebVoyager Benchmark. You can see the results for yourself here: https://eval.skyvern.com/ For reference, here were the previous SOTA results: 83.5% - Google Mariner (https://deepmind.google/technologies/project-mariner/) 73.1% - AgentE…&lt;/p&gt;&lt;p&gt;9 points; 3 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2404.05902/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
