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RAG suffers from a scattered-answer problem because the current retrieval approach scores relevance between a query and individual chunks, making it difficult to guarantee that multiple complementary chunks are retrieved simultaneously.
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8/23/2026
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UnverifiedIn RAG systems, retrieval results are typically sorted by relevance score and concatenated in order, meaning the most relevant chunk is not necessarily placed in the most prominent position.74% similarUnverifiedVector retrieval is inherently weak at multi-hop reasoning; when an answer requires synthesizing scattered information fragments from multiple documents, pure similarity ranking often fails to retrieve all necessary context.73% similarUnverifiedThe bottleneck of a RAG system often lies not in retrieval itself, but in how to tell the model via the prompt how to utilize the retrieved content.72% similarUnverified大多数RAG（检索增强生成）实现只解决了'检索到什么'的问题，却忽视了检索到的内容是否可信、是否互相矛盾的更深层问题70% similarUnverified网格搜索穷举所有参数组合结果全面但耗时，随机搜索随机采样效率更高69% similar
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