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提出了运动等变卷积 LSTM（MELSTM），将序列数据中的运动视为规范对称性（gauge symmetry），通过速度模型作为规范场实现对任意运动的可证明等变性，在未来帧预测、运动数字分类和动作识别任务上以更少状态超越 ConvLSTM 与流量等变基线。
This paper introduces Statistically Meaningful Geometry (SMG), a geometric framework for modeling over-parameterized learning systems as infinite-dimensional non-parametric Orlicz fiber bundles. It proposes that under out-of-distribution stimuli, the system undergoes a gauge symmetry break, leading to the emergence of new causal axes that can distinguish genuine scientific discovery from hallucinations.