We propose a localized conformal model selection framework that integrates local adaptivity with post-selection validity for distribution-free prediction. By performing model selection symmetrically across calibration points using upper and lower surrogate intervals, we construct a data-dependent safe index set that contains the oracle model and preserves exchangeability. The resulting ensemble procedure retains exact finite-sample marginal coverage while adapting to spatial heterogeneity and model complexity. Simulations demonstrate substantial reductions in interval length compared to the best fixed model, especially in heterogeneous and low-noise settings.

翻译：我们提出了一种局部化共形模型选择框架，该框架将局部自适应性分布自由预测的事后选择有效性相结合。通过使用上下代理区间在校准点间对称地执行模型选择，我们构建了一个包含最优模型并保持可交换性的数据依赖安全索引集。由此产生的集成方法在适应空间异质性与模型复杂度的同时，保持了精确的有限样本边际覆盖度。仿真实验表明，相较于最佳固定模型，该方法能显著缩短区间长度，在异质性与低噪声场景中尤为明显。