To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAG
Researchers demonstrate that multi-agent document assessment for retrieval-augmented generation (RAG) systems can be significantly optimized through model-adaptive routing rather than expensive scoring mechanisms. The study reveals that weaker models benefit primarily from document isolation rather than quality assessment, while MADARA, a proposed adaptive architecture, generalizes across different model families with zero-shot capability, reducing computational overhead.