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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Agent Workflow Memory · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2409.07429/</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/2409.07429/feed.xml" rel="self" type="application/rss+xml" /><item><title>Agent Workflow Memory — mentioned by mkw5053</title><link>https://news.ycombinator.com/item?id=42937757</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.07429:42937757</guid><pubDate>Tue, 04 Feb 2025 19:59:09 GMT</pubDate><description>&lt;p&gt;This reminds me of the Agent Workflow Memory (AWM) paper [1], which also tries to find optimal decision paths for LLM-based agents but relies on in-context learning, whereas DeepRAG fine-tunes models to decide when to retrieve external knowledge. I’ve been thinking about how modifying AWM to use fine-tuning or an external knowledge system (RAG) might work—capturing the ‘good’ workflows it discovers rather than relying purely on prompting. [1] https://arxiv.org/abs/2409.07429 - Agent Workflow Memory (Wang et al., 2024)&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.07429/"&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>
