Unverified50% confidenceOpinionExact time
The quality bottleneck of a RAG pipeline often lies on the retrieval side rather than the generation side; if the recalled passages are semantically far from the question, the model will struggle to provide accurate answers.
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9/28/2026
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UnverifiedBounding boxes generated by the model often have offsets; even if the extracted values are accurate, the bounding boxes may not be fully precise.77% similarUnverifiedIn multi-hop scenarios, intermediate nodes are semantically distant from the original question and are not adjacent in the vector space, causing the retriever to fail to include them in the candidate set.75% similarVerifiedWhen deploying RAG in engineering, the quality bottleneck is usually not the model itself, but the design quality of the retrieval pipeline.73% similarUnverified无梯度方法样本效率低，在高维参数空间中缺乏梯度指引意味着需要海量函数评估才能收敛71% similarUnverified模型性能的瓶颈不仅在于参数量，更在于表示的质量，嵌入空间坍缩的模型即使参数再多也无法充分发挥潜力71% similar
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