AI 中文总结
针对数据表中重复行导致的评估歧义，提出SCOUT因子化异常检测器，分离复制不变支持证据与计数证据，实现复制鲁棒的异常检测，并在686个数据集上验证其有效性。
AI 中文摘要
发布的数据表通常被视为独立同分布样本，尽管其重复行可能编码业务频率、重复实体、连接、重采样或提取错误。我们表明，这种模糊性创建了一个隐藏的测量层，具有三个后果：特征相同的行施加了可达到的评估上限，行加权AUROC对复制敏感，行训练的检测器学习到多重性大小偏置的规律。对所有690个OddBench数据集的精确行审计发现，355个存在训练-测试重叠，147个存在特征相同的标签冲突，137个存在测试异常与训练正常样本相同。从行加权切换到支持加权，在四种经典检测器几何结构上，50-61个数据集的AUROC变化至少为0.05。我们引入了SCOUT（支持计数正交化无监督测试），这是一种因子化异常检测器，将复制不变的支持证据与暴露感知的计数证据分离。因子式分割共形校准实现了边际假阳性率控制，而支持通道对任意正行复制完全不变。在686个OddBench数据集和五个种子上，仅支持SCOUT在原始AUROC上不劣于逐行孤立森林，并提高了复制不变的AUROC。外部正常支持评估跟踪名义假阳性水平，四个骨干在受控复制下保持完全不变。半合成干预表明，仅在强速率异质性下，条件计数建模才有实质性帮助。这些结果明确了何时应将多重性视为信号、干扰，或在没有额外信息时不可解释。
英文摘要
A released table is often treated as an i.i.d. sample, although its repeated rows may encode business frequency, repeated entities, joins, resampling, or extraction errors. We show that this ambiguity creates a hidden measurement layer with three consequences: feature-identical rows impose an attained evaluation ceiling, row-weighted AUROC is sensitive to replication, and row-trained detectors learn a multiplicity-size-biased law. An exact-row audit of all 690 OddBench datasets finds train-test overlap in 355, feature-identical label conflict in 147, and a test anomaly identical to a training normal in 137. Switching from row to support weighting changes AUROC by at least 0.05 on 50-61 datasets across four classical detector geometries. We introduce SCOUT (Support-Count Orthogonalized Unsupervised Testing), a factorized anomaly detector that separates replication-invariant support evidence from exposure-aware count evidence. Factorwise split-conformal calibration yields marginal false-positive-rate control, while the support channel is exactly invariant to arbitrary positive row replication. On 686 OddBench datasets and five seeds, support-only SCOUT is non-inferior to row-wise Isolation Forest in raw AUROC and improves replication-invariant AUROC. External normal-support evaluations track nominal false-positive levels, and four backbones remain exactly unchanged under controlled replication. Semi-synthetic interventions show that conditional count modeling helps materially only under strong rate heterogeneity. These results specify when multiplicity should be treated as signal, nuisance, or uninterpretable without additional information.
Comments11 pages, 3 figures
