SkillTFM：用于表格基础模型无训练适应的门控技能演化
SkillTFM: Gated Skill Evolution for Training-Free Adaptation of Tabular Foundation Models
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中文总结 AI 辅助
SkillTFM是一种无训练系统，通过可验证可扩展的技能库将表格基础模型的适应转向技能门控演化，在电价预测等任务中提升了AUC，且在不同骨干网络上均有效。
中文摘要 AI 辅助
表格数据在现实应用中无处不在，对科学、工业、金融、医疗和公共服务等领域的数据驱动预测与决策至关重要。表格基础模型（Tabular Foundation Models, TFMs）已成为通用表格学习的有前景范式，可在不同数据集间提供可复用的预测器，大幅减少对特定任务训练、调优及模型开发的需求。然而，其实际部署仍受限于分布偏移、异构特征语义及特定任务模式，这些问题若不进行代价高昂的微调或额外标注数据则难以捕捉。为此，我们提出SkillTFM，一种将TFM适应从参数更新转向智能体技能门控演化的无训练系统。SkillTFM的核心是可验证且可扩展的技能库，其将边界证据识别与门控技能演化相结合：前者表征任务结构与基础模型的失败模式，后者在显式验证约束下检索并扩展可复用技能。在模拟边界设置及真实世界电价预测任务中，SkillTFM使AUC提升0.128至0.142，将非线性边界AUC从0.699提升至0.898。此外，在不同TFM骨干网络上的实验验证了SkillTFM的有效性与通用性。
英文摘要
Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance, healthcare, and public services. Tabular foundation models (TFMs) have emerged as a promising paradigm for general-purpose tabular learning, offering reusable predictors across diverse datasets and substantially reducing the need for task-specific training, tuning, and model development. However, their practical deployment remains constrained by distribution shifts, heterogeneous feature semantics, and task-specific patterns that are difficult to capture without costly fine-tuning or additional labeled data. To this end, we propose SkillTFM, a training-free system that shifts TFM adaptation from parameter updates to the gated evolution of agentic skills. The core of SkillTFM is a verifiable and extensible skill bank that couples boundary evidence identification with gated skill evolution: the former characterizes task structure and base-model failure patterns, whereas the latter retrieves and extends reusable skills subject to explicit validation. Across simulated boundary settings and real-world electricity-price forecasting, SkillTFM improves AUC by 0.128--0.142, raises nonlinear-boundary AUC from 0.699 to 0.898. Furthermore, experiments across TFM backbones demonstrate the effectiveness and generality of SkillTFM.
