表格模型遇到数据偏移不用微调，SkillTFM靠技能库自动适配，AUC最多涨0.14，一篇很实用的论文。
SkillTFM提出一种无需训练的表格基础模型（TFM）适配系统，将参数更新转变为智能体技能的门控演化。其核心是可验证的技能库，结合边界证据识别与门控技能演化，无需微调或额外标注数据即可应对分布偏移。在模拟边界设置和真实电价预测任务中，SkillTFM将AUC提升0.128至0.142，非线性边界AUC从0.699升至0.898。跨多个TFM骨干网络的实验验证了该方法的有效性和通用性。
SkillTFM: Gated Skill Evolution for Training-Free Adaptation of Tabular Foundation Models
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.