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"先读历史清单：本篇只有“未评审”等条目，没有完整旧评。以下针对2602.06909v1的十九页正文与附录；会议、引用量不参与评分。论文的实用价值是把时间序列基础模型的架构比较重新放回数据、训练预算和目标设计的背景，而不是提供一种新的注意力。模型仍是PatchTST式编码器，加入残差输入输出头、分位数预测、掩码感知归一化和连续块掩码，以一次前向重建未来。这个组合清楚且有可复用性，但不能把组合后表现强直接等同于已经证明架构创新不重要。\n\n最重要的阅读边界是两个模型不能合并讲述。第4页说明Pretrained使用含评测重叠的TSMixup，涉及97个测试设置中的23个；Zero-shot使用清理后的混合数据。图3中Pretrained的MASE为0.696、CRPS为0.480，Zero-shot分别为0.714、0.492，而Chronos-2分别为0.698、0.485。因此本作提供了强零样本基线，却没有在这些总体数值上取得严格零样本第一。作者明确披露污染并分别报告，值得肯定；摘要和结论中较宽泛的领先表述仍容易让读者把受污染模型的最好结果迁移给干净版本。\n\n第14页排除23个重叠设置后的结果很有价值，但不消除所有混杂。在剩余74个设置上Pretrained的CRPS为0.468，略优于Chronos-2的0.469；其MASE为0.682，却不及后者0.676。干净模型为0.692、0.474。排除直接重叠能够削弱“全由记忆测试集解释”的判断，不能把差异唯一归因于数据多样性：清理前后混合数据规模分别为一千万与四百万，内容、数量和分布一起变了，论文未给出足以复核的逐项重叠清单。真实数据、合成数据与混合数据的表1也不是完整析因对照，所以“互补”和“正则化”应保留为解释而非已识别因果机制。\n\n消融的强处是覆盖模型宽深、训练步数、上下文长度、分位数数量、掩码块和掩码比例，信息远超过只报排行榜。连续块大小由1变8时MASE由0.750降至0.696，是明显的现象；掩码比例20%、40%、60%、80%的差别则小得多，没有多随机种子误差条，不能据此宣布40%具有稳定唯一优势。更长上下文也增加计算，按相同步数比较并不意味着等计算预算。四个深度或宽度设置展示了缩放趋势，却不足以建立严格的标度定律。深窄模型参数效率更好，与计算、吞吐效率更好亦不同。\n\n方法定义总体可读，归一化只用可见数据避免了显然的未来统计量渗入，但附录第12页的MASE公式把缩放项写在测试目标相邻差分上，和常用训练期季节性朴素缩放不一致。这是需要限定的数学报告问题；作者采用GIFT-Eval，不代表本次已核实实际实现采用错误公式，不能由排版直接判定全部数值无效。图3说明把全部指标写为几何平均，正文又把排名写为算术平均，也应区分正文定义和图注简写。概率预测给出九个评估分位点及区间图，但未展示校准误差、分位数交叉等证据，图15—17页的可视化不能替代总体概率可靠性。\n\n效率证据具有明确适用条件。第19页采用RTX6000 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