diff --git a/model_training_nnn_tpu/trainer_tf.py b/model_training_nnn_tpu/trainer_tf.py
index b5f92b1..cc6f7ba 100644
--- a/model_training_nnn_tpu/trainer_tf.py
+++ b/model_training_nnn_tpu/trainer_tf.py
@@ -77,8 +77,7 @@ from dataset_tf import (
     BrainToTextDatasetTF,
     DataAugmentationTF,
     train_test_split_indices,
-    create_input_fn,
-    analyze_dataset_shapes
+    create_input_fn
 )
 
 
@@ -728,63 +727,36 @@ class BrainToTextDecoderTrainerTF:
         initial_tpu_status = self._get_detailed_tpu_status()
         self.logger.info(f"Initial TPU Status: {initial_tpu_status}")
 
-        # ========================= DATASET SHAPE ANALYSIS =========================
-        # Perform one-time full dataset analysis for FIXED shapes (critical for XLA)
-        self.logger.info("🚀 Performing one-time full dataset analysis for FIXED shapes...")
-        self.logger.info("    This is CRITICAL for resolving both CTC compatibility and graph structure issues")
+        # ========================= 终极解决方案：批处理优先 =========================
+        # 使用经过验证的"先批处理，后增强"方法，消除数据增强与形状分析的时间悖论
+        self.logger.info("🚀 Using FINAL 'batch-first, augment-after' approach")
+        self.logger.info("    This eliminates the time paradox between data augmentation and shape analysis")
 
-        # Analyze training dataset (all data for accurate max shapes)
-        train_analysis_start = time.time()
-        train_max_shapes = analyze_dataset_shapes(self.train_dataset_tf, sample_size=-1)
-        train_analysis_time = time.time() - train_analysis_start
-        self.logger.info(f"✅ Training dataset analysis completed in {train_analysis_time:.2f}s")
-
-        # Analyze validation dataset (all data for accurate max shapes)
-        val_analysis_start = time.time()
-        val_max_shapes = analyze_dataset_shapes(self.val_dataset_tf, sample_size=-1)
-        val_analysis_time = time.time() - val_analysis_start
-        self.logger.info(f"✅ Validation dataset analysis completed in {val_analysis_time:.2f}s")
-
-        # Use maximum shapes across both datasets for consistent padding
-        final_max_shapes = {
-            'max_time_steps': max(train_max_shapes['max_time_steps'], val_max_shapes['max_time_steps']),
-            'max_phone_seq_len': max(train_max_shapes['max_phone_seq_len'], val_max_shapes['max_phone_seq_len']),
-            'max_transcription_len': max(train_max_shapes['max_transcription_len'], val_max_shapes['max_transcription_len']),
-            'n_features': train_max_shapes['n_features']
-        }
-
-        self.logger.info(f"📊 Final FIXED shapes for TPU training (eliminates XLA dynamic shape issues):")
-        self.logger.info(f"   Time steps: {final_max_shapes['max_time_steps']}")
-        self.logger.info(f"   Phone sequence length: {final_max_shapes['max_phone_seq_len']}")
-        self.logger.info(f"   Transcription length: {final_max_shapes['max_transcription_len']}")
-        self.logger.info(f"   Features: {final_max_shapes['n_features']}")
-        # =====================================================================
-
-        # Create datasets using modern distribution API with FIXED shapes
-        def create_dist_dataset_fn(input_dataset_tf, training, max_shapes):
-            """Create distributed dataset function for modern TPU strategy with FIXED shapes"""
+        # 简化的数据集创建函数，不再需要 max_shapes
+        def create_dist_dataset_fn(input_dataset_tf, training):
+            """Create distributed dataset function for the final 'batch-first' approach."""
             def dataset_fn(input_context):
-                # create_input_fn now requires max_shapes parameter for FIXED shapes
+                # 调用新版的 create_input_fn，它不需要 max_shapes
                 return create_input_fn(
                     input_dataset_tf,
                     self.args['dataset']['data_transforms'],
-                    max_shapes=max_shapes,  # Pass pre-analyzed FIXED shapes
                     training=training
                 )
             return self.strategy.distribute_datasets_from_function(dataset_fn)
 
-        # Distribute datasets using modern API with FIXED shapes
-        self.logger.info("🔄 Distributing training dataset across TPU cores...")
+        # 使用新的、简化的函数签名创建数据集
+        self.logger.info("🔄 Distributing training dataset (batch-first approach)...")
         dist_start_time = time.time()
-        train_dist_dataset = create_dist_dataset_fn(self.train_dataset_tf, training=True, max_shapes=final_max_shapes)
+        train_dist_dataset = create_dist_dataset_fn(self.train_dataset_tf, training=True)
         train_dist_time = time.time() - dist_start_time
         self.logger.info(f"✅ Training dataset distributed in {train_dist_time:.2f}s")
 
-        self.logger.info("🔄 Distributing validation dataset across TPU cores...")
+        self.logger.info("🔄 Distributing validation dataset (batch-first approach)...")
         val_start_time = time.time()
-        val_dist_dataset = create_dist_dataset_fn(self.val_dataset_tf, training=False, max_shapes=final_max_shapes)
+        val_dist_dataset = create_dist_dataset_fn(self.val_dataset_tf, training=False)
         val_dist_time = time.time() - val_start_time
         self.logger.info(f"✅ Validation dataset distributed in {val_dist_time:.2f}s")
+        # =====================================================================
 
         self.logger.info("Created distributed training and validation datasets")
         # Training metrics