DeFT-RAG decomposes multi-hop questions for RAG
DeFT-RAG reduces LLM calls by 12.9% through QA cache reuse while achieving top accuracy on multi-hop benchmarks.
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DeFT-RAG reduces LLM calls by 12.9% through QA cache reuse while achieving top accuracy on multi-hop benchmarks.
LLM pipelines lose up to 40.5 accuracy points at stage interfaces; re-grounding after the loss helps.
Study of 600 production deep-research traces finds agent-tree decomposition loses findings at rate N^(1-δ); flat architectures maximize yield.
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