对比两种量化方法，发现RaBitQ在多数场景下优于TurboQuant。
Revisiting RaBitQ and TurboQuant: A Symmetric Comparison of Methods, Theory, and Experiments
- 构建对称实验框架，公平比较两种量化方法的理论与实测表现。
- 在内积估计、最近邻搜索等任务中，RaBitQ性能普遍优于TurboQuant。
- 指出TurboQuant论文部分结果无法复现，存在可复现性问题。
本文在统一的比较框架下重新审视了RaBitQ与TurboQuant之间的关系。通过可复现、透明且对称的设置，从方法论、理论保障和实验性能三方面对比两者。结果表明，尽管TurboQuant声称具有优势，但在内积估计、最近邻搜索及键值缓存量化等多种测试场景中，其表现均劣于RaBitQ。此外，我们发现原TurboQuant论文中若干运行时间和召回率结果无法基于其公开实现与声明配置复现。本文厘清了二者共享结构与真实差异，同时记录了TurboQuant论文实验结果中的可复现性问题。
原文摘要 · Abstract (English)
This technical note revisits the relationship between RaBitQ and TurboQuant under a unified comparison framework. We compare the two methods in terms of methodology, theoretical guarantees, and empirical performance, using a reproducible, transparent, and symmetric setup. Our results show that, despite the claimed advantage of TurboQuant, TurboQuant performs worse than RaBitQ in most tested settings of inner-product estimation, nearest-neighbor search and KV cache quantization. We further find that several reported runtime and recall results in the TurboQuant paper could not be reproduced from the released implementation under the stated configuration. Overall, this note clarifies the shared structure and genuine differences between the two lines of work, while documenting reproducibility issues in the experimental results reported by the TurboQuant paper.
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