<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Research-Framework on Noureddine RAMDI</title><link>https://ramdi.fr/tags/research-framework/</link><description>Recent content in Research-Framework on Noureddine RAMDI</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 23 May 2026 20:41:27 +0000</lastBuildDate><atom:link href="https://ramdi.fr/tags/research-framework/index.xml" rel="self" type="application/rss+xml"/><item><title>Graph-R1: Reinforcement learning to train LLMs for reasoning over knowledge graphs</title><link>https://ramdi.fr/github-stars/graph-r1-reinforcement-learning-to-train-llms-for-reasoning-over-knowledge-graphs/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/graph-r1-reinforcement-learning-to-train-llms-for-reasoning-over-knowledge-graphs/</guid><description>Graph-R1 trains large language models with reinforcement learning to reason over knowledge graphs, cycling through think-query-retrieve-rethink steps for complex knowledge tasks.</description></item><item><title>Meta-Harness: evolving the scaffolding around large language models for optimized task performance</title><link>https://ramdi.fr/github-stars/meta-harness-evolving-the-scaffolding-around-large-language-models-for-optimized-task-performance/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/meta-harness-evolving-the-scaffolding-around-large-language-models-for-optimized-task-performance/</guid><description>Meta-Harness from Stanford IRIS Lab automates the search for optimal harness configurations around LLMs, evolving memory, retrieval, and context systems for better task-specific performance.</description></item></channel></rss>