降维向量能否讲清大模型训练数据？这篇论文给出了实证对比，值得关注。
arXiv:2601.16651v3 Announce Type: replace Abstract: Gradient-based methods for instance-based explanation for large language models (LLMs) are hindere…
重尾数据下PCA的经典方法失效？这篇论文提出了一种鲁棒主成分分析方法，突破二阶矩限制。
arXiv:2603.11308v3 Announce Type: replace Abstract: Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical…
FSD、Robotaxi共用一套模型
用降维技术揭示大模型内部隐藏的几何结构，直观理解语言模型如何表征知识。
arXiv:2511.21594v3 Announce Type: replace Abstract: Large language models (LLMs) achieve state-of-the-art results across many natural language tasks, …
提出连续域降维新范式，用神经算子嵌入离散点云，突破传统方法瓶颈
arXiv:2605.11970v2 Announce Type: replace Abstract: Most dimensionality reduction methods treat data as discrete point clouds, ignoring the continuous…
用主成分分析高效检测月球陨石坑，为天体图像识别领域带来新思路。
arXiv:2605.17125v1 Announce Type: cross Abstract: Optical navigation is a critical component for lunar orbiter and lander missions. Image-based crater…