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LLM Sparsity Prior for Robust Feature Selection
利用大模型蕴含的稀疏性先验，为高维数据特征选择提供鲁棒策略，理论贡献显著。
arXiv:2605.23102v1 Announce Type: cross Abstract: Large language models (LLMs) offer a scalable mechanism to elicit domain-informed prior information …
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Beyond Explained Variance: A Cautionary Tale of PCA
PCA中解释方差并非万能指标，本文通过实例警示其潜在陷阱，值得数据分析者关注。
arXiv:2605.13520v2 Announce Type: replace-cross Abstract: We address shortcomings of principal component analysis (PCA) for visualizing high-dimension…
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Matrix-Decoupled Concentration for Autoregressive Sequences: Dimension-Free Guarantees for Sparse Long-Context Rewards
自回归序列的矩阵解耦集中不等式，为稀疏长上下文奖励提供无维度保证，理论创新突破。
arXiv:2605.06017v2 Announce Type: replace Abstract: Sequence-level evaluations in autoregressive Large Language Models (LLMs) rely on highly dependent…
4
Masking Causality and Conditional Dependence
探索因果关系与条件依赖的掩盖机制，为因果推断理论提供新见解
arXiv:2603.06984v2 Announce Type: replace-cross Abstract: Many regulatory and analytic problems require that a prohibited variable influence a decisio…