diff --git a/ML/nmWTAI-ML/src/models/time_conditioned_surrogate.py b/ML/nmWTAI-ML/src/models/time_conditioned_surrogate.py
index cde54e4..7e93160 100644
--- a/ML/nmWTAI-ML/src/models/time_conditioned_surrogate.py
+++ b/ML/nmWTAI-ML/src/models/time_conditioned_surrogate.py
@@ -1,12 +1,12 @@
 # -*- coding: utf-8 -*-
-"""时间条件代理模型网络结构。
+"""Time-conditioned surrogate network.
 
-TimeConditionedSurrogate 不一次性输出完整曲线，而是把“物理参数 + 流量制度 + 某个
-时间点特征”作为输入，预测该时间点的 log_pressure 和 log_derivative。它适合用于
-更灵活的时间采样、局部曲线重建以及需要按时间点加权的训练策略。
+The model predicts one point on the log-pressure and log-derivative curves:
 
-模型主体由参数编码器、制度编码器、时间编码器、融合投影和若干残差块组成。残差
-块让较深的全连接网络更容易优化，同时保留原始融合特征。
+    f(params, schedule, time_point) -> [log_pressure(t), log_derivative(t)]
+
+It is intended for flexible time grids, point-wise training, and later PSO
+candidate screening where only selected curves need full solver evaluation.
 """
 
 from __future__ import annotations
@@ -21,7 +21,7 @@ from torch import nn
 
 @dataclass(frozen=True)
 class TimeConditionedSurrogateConfig:
-    """时间条件代理模型配置。"""
+    """Network dimensions and architecture options."""
 
     param_dim: int
     schedule_dim: int
@@ -33,10 +33,9 @@ class TimeConditionedSurrogateConfig:
 
 
 class ResidualBlock(nn.Module):
-    """时间条件模型使用的全连接残差块，用于在较深网络中稳定传播特征。"""
+    """Fully connected residual block used in the fusion trunk."""
 
     def __init__(self, dim: int, dropout: float = 0.0):
-        """构造两层全连接残差块，并根据 dropout 参数决定是否启用随机失活。"""
         super().__init__()
         self.net = nn.Sequential(
             nn.LayerNorm(dim),
@@ -48,18 +47,12 @@ class ResidualBlock(nn.Module):
         self.act = nn.GELU()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """在保留原始隐藏表示的基础上叠加两层非线性修正。"""
-        # 残差连接让块学习修正量，而不是每层都重新表示完整特征。
+        """Apply a residual correction and activation."""
         return self.act(x + self.net(x))
 
 
 class TimeConditionedSurrogate(nn.Module):
-    """逐时间点预测的时间条件代理模型。
-
-    与 ForwardSurrogate 一次输出整条曲线不同，该模型每次只预测一个时间点的
-    log_pressure 和 log_derivative。训练数据通常由 PointCurveDataset 将 [N, T] 曲线
-    展开为 N*T 个样本，因此 batch 中每一行都带有自己的 time_x。
-    """
+    """Point-wise time-conditioned pressure/derivative predictor."""
 
     def __init__(
         self,
@@ -73,7 +66,6 @@ class TimeConditionedSurrogate(nn.Module):
         dropout: float = 0.05,
         use_schedule: bool = True,
     ):
-        """按配置组装时间条件代理模型的编码、融合和输出层。"""
         super().__init__()
         config = self._coerce_config(
             config=config,
@@ -129,7 +121,7 @@ class TimeConditionedSurrogate(nn.Module):
         dropout: float,
         use_schedule: bool,
     ) -> TimeConditionedSurrogateConfig:
-        """兼容配置对象式构造和旧版关键字参数式构造。"""
+        """Support both config-object and legacy keyword constructors."""
         if config is not None:
             return config
         if param_dim is None or schedule_dim is None or time_dim is None:
@@ -149,7 +141,6 @@ class TimeConditionedSurrogate(nn.Module):
 
     @staticmethod
     def _build_encoder(in_dim: int, out_dim: int) -> nn.Sequential:
-        """构建 Linear-LayerNorm-GELU 编码器。"""
         return nn.Sequential(
             nn.Linear(in_dim, out_dim),
             nn.LayerNorm(out_dim),
@@ -162,25 +153,18 @@ class TimeConditionedSurrogate(nn.Module):
         time_x: torch.Tensor,
         schedule_x: torch.Tensor | None = None,
     ) -> torch.Tensor:
-        """融合样本级特征和点级时间特征，输出双通道响应。
-
-        params_x 与 schedule_x 在同一条曲线的所有时间点上相同，time_x 则随时间点变化。
-        三类特征编码后拼接进入残差主干，最后输出 [B, 2]，分别对应标准化空间中的
-        log_pressure 和 log_derivative。
-        """
-        # params_x 和 schedule_x 是样本级特征；time_x 是展开后的点级特征。
+        """Fuse sample-level and point-level features into a two-channel output."""
         param_feat = self.param_encoder(params_x)
         time_feat = self.time_encoder(time_x)
 
         if self.use_schedule:
             if schedule_x is None:
-                raise ValueError("use_schedule=True，但 forward 没有传入 schedule_x")
+                raise ValueError("use_schedule=True, but schedule_x was not provided")
             schedule_feat = self.schedule_encoder(schedule_x)
             fused = torch.cat([param_feat, schedule_feat, time_feat], dim=-1)
         else:
             fused = torch.cat([param_feat, time_feat], dim=-1)
 
-        # 融合后的特征经过残差主干，输出 log_pressure 和 log_derivative 两个通道。
         hidden = self.input_proj(fused)
         hidden = self.blocks(hidden)
         return self.head(hidden)
@@ -189,5 +173,5 @@ class TimeConditionedSurrogate(nn.Module):
 def build_time_conditioned_surrogate(
     config: TimeConditionedSurrogateConfig,
 ) -> TimeConditionedSurrogate:
-    """根据配置创建时间条件代理模型。"""
+    """Create a time-conditioned surrogate from a config object."""
     return TimeConditionedSurrogate(config)