arXiv cs.AI· Mohamed Bouadi, Nassim Bouarour, Varun Kulkarni, Shivam Dubey, Aditya Tanna, Vinay Kumar Sankarapu·· 4 小时前AI 评分40
O'Prior：合成任务分布如何决定表格基础模型质量
Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality
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arXiv 论文提出 O'Prior，一种面向表格基础模型的组合式合成先验，由四个组件构成：分层 SCM 元生成器、覆盖异质边际/缺失/目标变换的真实性引擎、注入混淆与 support-query 失配的压力模块，以及防泄漏的课程化生成协议。在固定架构、优化器与算力、仅改变合成任务分布的条件下，O'Prior 在真实表格基准上带来一致的精度与鲁棒性提升，消融实验表明各组件贡献不可互相替代。
正文 · 原文
Abstract:What determines the quality of a tabular foundation model? Unlike language or vision, tabular foundation models acquire their inductive biases almost entirely from synthetic pretraining distributions, yet the design of these distributions remains poorly understood. Standard synthetic priors are too well-behaved: they omit the irregularities and failure modes that determine deployment robustness. We introduce O'Prior, a compositional realism prior built around four coupled components: a hierarchical SCM meta-generator spanning diverse functional families; a modular realism engine covering heterogeneous marginals, missingness, and target transforms; an explicit stress module injecting confounding and support-query mismatch; and a curriculum-governed, leakage-safe generation protocol. To isolate prior design as the scientific variable, we hold architecture, optimizer, and compute budget fixed and vary only the synthetic task distribution. O'Prior yields consistent and substantial improvements in downstream accuracy and robustness across real tabular benchmarks, with gains concentrated in regimes characterized by distributional irregularities. Ablations confirm that mechanism diversity, realism composition, and shift-aware stress each contribute independently, their effects are not interchangeable. These results establish synthetic prior construction as a first-order and largely overlooked determinant of tabular foundation model quality.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.18971 [cs.LG] |
| (or arXiv:2605.18971v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18971 arXiv-issued DOI via DataCite |
Submission history
From: Mohamed Bouadi [view email]
[v1]
Mon, 18 May 2026 18:00:45 UTC (4,988 KB)
[v2]
Fri, 23 Oct 2026 12:04:54 UTC (4,988 KB)
来源：arXiv cs.AI · arxiv.org