Multi-Agent Collaborative Knowledge Question Answering for Mineralogy

When large language models are applied to professional geological fields such as mineralogy, they frequently suffer from knowledge obsolescence, factual hallucinations, and invalid complex logical reasoning. Existing retrieval-augmented generation (RAG) methods mostly adopt a static pipeline architecture, which easily causes logical breaks and semantic fragmentation when processing complex geological queries, and can hardly realize collaborative utilization of structured knowledge and unstructured text. To address the above shortcomings, this paper proposes a Multi-agent Mineral RAG (MaM-RAG) approach for mineralogy-oriented collaborative retrieval-augmented generation. This method constructs a collaborative closed loop of intent recognition-multi-source execution-decision fusion. An intent recognition agent performs non-destructive decoupling of complex queries while completely preserving the internal logical structure. Multi-source retrieval agents acquire knowledge graphs and text corpora in parallel to break information isolation among heterogeneous data. A decision fusion agent conducts semantic consistency verification on multi-source evidence and triggers reflective retrieval in case of evidence inconsistency, completing self-verification and self-correction of the reasoning process. Experimental results show that the proposed method achieves significantly higher accuracy than traditional RAG and general large language models on mineralogy professional question answering tasks. It can effectively suppress factual hallucinations and improve answer accuracy and robustness, providing a feasible solution for professional intelligent question answering in the geological domain.