Tabular anomaly detection (TAD) aims to identify samples that deviate from the majority in tabular data and is critical in many real-world applications. However, existing methods follow a ``one model for one dataset (OFO)'' paradigm, which relies on dataset-specific training and thus incurs high computational cost and yields limited generalization to unseen domains. To address these limitations, we propose OFA-TAD, a generalist one-for-all (OFA) TAD framework that only requires one-time training on multiple source datasets and can generalize to unseen datasets from diverse domains on-the-fly. To realize one-for-all tabular anomaly detection, OFA-TAD extracts neighbor-distance patterns as transferable cues, and introduces multi-view neighbor-distance representations from multiple transformation-induced metric spaces to mitigate the transformation sensitivity of distance profiles. To adaptively combine multi-view distance evidence, a Mixture-of-Experts (MoE) scoring network is employed for view-specific anomaly scoring and entropy-regularized gated fusion, with a multi-strategy anomaly synthesis mechanism to support training under the one-class constraint. Extensive experiments on 34 datasets from 14 domains demonstrate that OFA-TAD achieves superior anomaly detection performance and strong cross-domain generalizability under the strict OFA setting. The source code is available at https://github.com/Shiy-Li/OFA-TAD.

翻译：表格异常检测（TAD）旨在识别表格数据中偏离大多数样本的数据点，在众多实际应用中至关重要。然而，现有方法遵循"一个数据集对应一个模型（OFO）"的范式，依赖于特定数据集训练，导致计算成本高昂且对未见领域的泛化能力有限。为解决这些局限，我们提出OFA-TAD，一种通用的"一劳永逸"（OFA）TAD框架，只需在多个源数据集上一次性训练，即可实时泛化至来自不同领域的未见数据集。为实现在线表格异常检测，OFA-TAD提取邻域距离模式作为可迁移线索，并引入来自多个变换诱导度量空间的多视角邻域距离表示，以缓解距离分布对变换的敏感性。为自适应组合多视角距离证据，采用混合专家（MoE）评分网络进行视角特定异常评分与熵正则化门控融合，并配备多策略异常合成机制以支持单类约束下的训练。在来自14个领域的34个数据集上进行的大量实验表明，OFA-TAD在严格的OFA设置下实现了优越的异常检测性能与强跨领域泛化能力。源代码已开源：https://github.com/Shiy-Li/OFA-TAD。