<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Research on Noureddine RAMDI</title><link>https://ramdi.fr/tags/research/</link><description>Recent content in Research 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/index.xml" rel="self" type="application/rss+xml"/><item><title>awesome-phd-advice: a community-curated collection of PhD guidance</title><link>https://ramdi.fr/github-stars/awesome-phd-advice-a-community-curated-collection-of-phd-guidance/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/awesome-phd-advice-a-community-curated-collection-of-phd-guidance/</guid><description>A comprehensive community-maintained collection of PhD advice links covering admissions, research, writing, and academic career preparation. No code, just curated wisdom.</description></item><item><title>Formalizing academic paper writing as a programmable pipeline with Claude Code skills</title><link>https://ramdi.fr/github-stars/formalizing-academic-paper-writing-as-a-programmable-pipeline-with-claude-code-skills/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/formalizing-academic-paper-writing-as-a-programmable-pipeline-with-claude-code-skills/</guid><description>This repo implements academic paper planning and writing as a two-phase Claude Code skill pipeline with a 35-point quality rubric enforced by Python scripts at each stage.</description></item><item><title>gnnpapers: the definitive curated reading list for graph neural network research</title><link>https://ramdi.fr/github-stars/gnnpapers-the-definitive-curated-reading-list-for-graph-neural-network-research/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/gnnpapers-the-definitive-curated-reading-list-for-graph-neural-network-research/</guid><description>gnnpapers is a curated, community-recognized bibliography of 800+ must-read graph neural network papers. It organizes GNN research evolution and applications without any code.</description></item><item><title>LiveTradeBench: Evaluating LLM-driven trading agents in live markets</title><link>https://ramdi.fr/github-stars/livetradebench-evaluating-llm-driven-trading-agents-in-live-markets/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/livetradebench-evaluating-llm-driven-trading-agents-in-live-markets/</guid><description>LiveTradeBench benchmarks LLM trading agents like GPT and Claude in live US equity and prediction markets with real-time news and sentiment integration.</description></item><item><title>LLM4Pentest: A curated knowledge hub on large language models for automated penetration testing</title><link>https://ramdi.fr/github-stars/llm4pentest-a-curated-knowledge-hub-on-large-language-models-for-automated-penetration-testing/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/llm4pentest-a-curated-knowledge-hub-on-large-language-models-for-automated-penetration-testing/</guid><description>LLM4Pentest aggregates 40+ research papers and tools tracking the evolving role of LLMs in automated penetration testing, highlighting progress and limitations.</description></item><item><title>Navigating the evolving landscape of LLM-based multi-agent systems: A survey paper repository</title><link>https://ramdi.fr/github-stars/navigating-the-evolving-landscape-of-llm-based-multi-agent-systems-a-survey-paper-repository/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/navigating-the-evolving-landscape-of-llm-based-multi-agent-systems-a-survey-paper-repository/</guid><description>A curated and frequently updated bibliography accompanying the IJCAI 2024 survey paper on LLM-based multi-agent systems, organizing research into five key categories and revealing emerging trends.</description></item><item><title>Understanding Awesome-GraphRAG: A Curated Survey and Benchmark for Graph-Based Retrieval-Augmented Generation</title><link>https://ramdi.fr/github-stars/understanding-awesome-graphrag-a-curated-survey-and-benchmark-for-graph-based-retrieval-augmented-generation/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/understanding-awesome-graphrag-a-curated-survey-and-benchmark-for-graph-based-retrieval-augmented-generation/</guid><description>Awesome-GraphRAG is a curated repository organizing graph-based retrieval-augmented generation methods, with a taxonomy, benchmark, and original research from DEEP-PolyU.</description></item><item><title>llm-wiki: orchestrating multi-agent LLM research into persistent knowledge bases</title><link>https://ramdi.fr/github-stars/llm-wiki-orchestrating-multi-agent-llm-research-into-persistent-knowledge-bases/</link><pubDate>Tue, 05 May 2026 13:37:39 +0000</pubDate><guid>https://ramdi.fr/github-stars/llm-wiki-orchestrating-multi-agent-llm-research-into-persistent-knowledge-bases/</guid><description>llm-wiki is a shell-based orchestration layer that turns LLM agents into a persistent, multi-agent research wiki. Supports up to 10 agents, deep investigations, and durable provenance tracking.</description></item><item><title>open-researcher: AI-powered web research assistant with integrated scraping and summarization</title><link>https://ramdi.fr/github-stars/open-researcher-ai-powered-web-research-assistant-with-integrated-scraping-and-summarization/</link><pubDate>Tue, 05 May 2026 13:37:39 +0000</pubDate><guid>https://ramdi.fr/github-stars/open-researcher-ai-powered-web-research-assistant-with-integrated-scraping-and-summarization/</guid><description>open-researcher is a TypeScript app combining AI APIs and web scraping to assist research workflows. It offers an extensible setup and local dev server for experimentation.</description></item><item><title>StereoWorld: stereo vision-based 3D-consistent video generation from binocular inputs</title><link>https://ramdi.fr/github-stars/stereoworld-stereo-vision-based-3d-consistent-video-generation-from-binocular-inputs/</link><pubDate>Tue, 05 May 2026 13:37:39 +0000</pubDate><guid>https://ramdi.fr/github-stars/stereoworld-stereo-vision-based-3d-consistent-video-generation-from-binocular-inputs/</guid><description>StereoWorld uses binocular stereo vision cues to guide 3D-consistent stereo video generation, offering a biologically inspired approach to scene geometry understanding.</description></item><item><title>A practical taxonomy for large language model ensembles: Exploring the Awesome-LLM-Ensemble repository</title><link>https://ramdi.fr/github-stars/a-practical-taxonomy-for-large-language-model-ensembles-exploring-the-awesome-llm-ensemble-repository/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/a-practical-taxonomy-for-large-language-model-ensembles-exploring-the-awesome-llm-ensemble-repository/</guid><description>The Awesome-LLM-Ensemble repo catalogs research on combining multiple LLMs with a clear three-phase taxonomy: before, during, and after inference ensemble methods.</description></item><item><title>PAT3D: orchestrating text-to-3D simulation-ready scenes through a multi-stage AI and physics pipeline</title><link>https://ramdi.fr/github-stars/pat3d-orchestrating-text-to-3d-simulation-ready-scenes-through-a-multi-stage-ai-and-physics-pipeline/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/pat3d-orchestrating-text-to-3d-simulation-ready-scenes-through-a-multi-stage-ai-and-physics-pipeline/</guid><description>PAT3D composes a 9-stage pipeline combining LLMs, vision models, 3D asset generators, and physics simulation to produce physically plausible, simulation-ready 3D scenes from text prompts.</description></item></channel></rss>