这篇论文提出了一个能同时预测多个生存时间分位数还不打架的模型，在乳腺癌和死亡率数据上都比老方法更准，做医学预测的可以看看。
该论文提出Censored Non-crossing Quantile（CNQ）框架，联合估计多个条件生存分位数，并通过构造保证分位数曲线不交叉。框架支持Kolmogorov-Arnold和Transformer两种骨干网络，并给出联合覆盖所有分位水平的有限样本超额风险界。在27个模拟设置和6个队列中，当条件分布不对称时，CNQ的钉球损失低于分位数、风险和树基竞争方法，且区间覆盖率更接近名义水平。METABRIC和FLCHAIN两个临床案例显示，CNQ能恢复随生存分布变化的协变量效应，并生成一致的个体化分位里程碑。
Non-Crossing Deep Quantile Regression for Distributional Survival Prediction
In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number. Quantile-based modeling instead describes the full conditional distribution on the original time scale, but existing censored-data methods are either inflexible or produce logically inconsistent crossing quantile curves. We propose a Censored Non-crossing Quantile (CNQ) framework for right-censored data that jointly estimates several conditional survival quantiles and guarantees valid ordering by construction, with flexibility supplied by Kolmogorov-Arnold and Transformer backbones, and we establish a finite-sample excess-risk bound holding jointly across all fitted quantile levels. Across 27 simulation settings and six cohorts the framework attains lower pinball loss than quantile-, hazard- and tree-based competitors whenever the conditional distribution is asymmetric, with interval coverage closer to nominal on all six. In two clinical case studies (METABRIC, breast cancer; FLCHAIN, population mortality) it recovers covariate effects that vary across the survival distribution and would be hidden by a single hazard ratio, and yields coherent individualized quantile milestones. Code: https://github.com/BIG-S2/deepcnq