Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysis

The article presents the "agentic hybrid RAG," an evidence-grounded retrieval-augmented generation framework specifically designed for muon collider research. This framework integrates a hybrid retriever that combines sparse lexical and dense semantic retrieval with an agentic reasoning module for query decomposition and evidence expansion. The authors also introduce a benchmark for retrieval-augmented scientific question answering in the muon collider domain, demonstrating that their framework outperforms existing retrieval and RAG baselines in retrieval effectiveness, answer quality, and evidence grounding, thus providing a robust tool for researchers in high-energy physics to analyze large-scale scientific literature.