A multi-stage RAG back-end with query expansion, rank fusion, MMR re-ranking and a dedicated canonical-knowledge layer attains 77.9% mean tag-recall on a 28-case OpenFOAM setup benchmark.
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IteraSim RAG: A Multi-Stage Retrieval-Augmented Agentic Back-End for OpenFOAM-Based Computational Fluid Dynamics
A multi-stage RAG back-end with query expansion, rank fusion, MMR re-ranking and a dedicated canonical-knowledge layer attains 77.9% mean tag-recall on a 28-case OpenFOAM setup benchmark.