<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Causal-Graph on Noureddine RAMDI</title><link>https://ramdi.fr/tags/causal-graph/</link><description>Recent content in Causal-Graph 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/causal-graph/index.xml" rel="self" type="application/rss+xml"/><item><title>LuaN1aoAgent: Autonomous penetration testing with P-E-R multi-agent causal graph reasoning</title><link>https://ramdi.fr/github-stars/luan1aoagent-autonomous-penetration-testing-with-p-e-r-multi-agent-causal-graph-reasoning/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/luan1aoagent-autonomous-penetration-testing-with-p-e-r-multi-agent-causal-graph-reasoning/</guid><description>LuaN1aoAgent uses a P-E-R multi-agent framework and causal graph reasoning to achieve 90.4% autonomous success on penetration tests with low exploit cost. Key for AI-driven pentesting.</description></item></channel></rss>