Ido Levy, Segev Shlomov, Leshem Choshen · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29722 · open-access PDF
Modern pipelines combine embedding, chunking, retrieval, reeranking, query augmentation, context compression, and agentic verification, yet these techniques are typically evaluated one at a time.It remains unclear whether their isolated gains survive composition, whether retrieval quality and answer faithfulness favor the same design choices, and whether conclusions from small corpora hold on a large scale.We pre-declarate three hypotheses and introduce LASAGNA, a open-source framework for controlled RAG ev
Machine translation (Meta M2M100); the English original follows.
Retrieval Augmented Generation (RAG) grounds large language models in external knowledge. Modern pipelines combine embedding, chunking, retrieval, reranking, query augmentation, context compression, and agentic verification, yet these techniques are typically evaluated one at a time. It remains unclear whether their isolated gains survive composition, whether retrieval quality and answer faithfulness favor the same design choices, and whether conclusions from small corpora hold at large scale. We pre-declare three hypotheses and introduce LASAGNA, an open-source framework for controlled RAG ev
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