diff --git a/TPU_ISSUES_RECORD.md b/TPU_ISSUES_RECORD.md
index 992ac7b..248d333 100644
--- a/TPU_ISSUES_RECORD.md
+++ b/TPU_ISSUES_RECORD.md
@@ -350,6 +350,42 @@ adjusted_lens = ((n_time_steps.float() - self.args['model']['patch_size']) / sel
 
 **Key Insight**: Mixed precision training requires explicit dtype management for ALL tensor operations, even intermediate calculations.
 
+## New Issue: Features Tensor DType Mismatch (2025-10-12 17:00)
+
+### Error Description
+```
+Status: INVALID_ARGUMENT: Call parameter must match argument; got parameter 0 shape: f32[32,7168], argument shape: bf16[32,7168].
+```
+
+### Root Cause Analysis
+After fixing the `adjusted_lens` dtype issue, a new mismatch emerged in the `features` tensor dimensions `[32, 7168]` representing (batch_size=32, neural_dim×patch_size=512×14=7168). Under `accelerator.autocast()` with mixed precision `bf16`, input tensors are automatically converted to bfloat16, but model parameters remained in float32 after removing hardcoded dtype specifications, creating a mismatch at the model input level.
+
+### Problem Code
+```python
+# Inside accelerator.autocast() context:
+# features becomes bf16 automatically by autocast
+logits = self.model(features, day_indicies, None, False, 'inference')
+# Model expects f32 parameters but receives bf16 input → mismatch
+```
+
+### Solution
+Add explicit dtype conversion before all model calls to ensure consistency:
+
+```python
+# Ensure features tensor matches model parameter dtype for TPU compatibility
+if self.accelerator.mixed_precision == 'bf16':
+    # In mixed precision mode, ensure features match the expected precision
+    features = features.to(torch.float32)
+```
+
+### Fixed Locations
+- `rnn_trainer.py:582-584` - Training loop model call
+- `rnn_trainer.py:760-763` - Validation loop model call
+- `rnn_trainer.py:839-842` - Inference method model call
+- `rnn_trainer.py:863-866` - Inference batch method model call
+
+**Key Insight**: Mixed precision autocast converts inputs but not necessarily model parameters. When removing hardcoded dtypes, explicit conversion ensures compatibility between autocast inputs and model parameters.
+
 ## Lessons Learned
 - **Root Cause**: TPU XLA compiler requires strict dtype consistency across all tensors
 - **Key Insight**: `torch.eye()` and `torch.zeros()` default to f32 - must explicitly specify dtype
diff --git a/model_training_nnn/rnn_trainer.py b/model_training_nnn/rnn_trainer.py
index 82e3476..c41cb89 100644
--- a/model_training_nnn/rnn_trainer.py
+++ b/model_training_nnn/rnn_trainer.py
@@ -578,6 +578,11 @@ class BrainToTextDecoder_Trainer:
 
                 # Get phoneme predictions using inference mode during training
                 # (We use inference mode for simplicity - only clean logits are used for CTC loss)
+                # Ensure features tensor matches model parameter dtype for TPU compatibility
+                if self.accelerator.mixed_precision == 'bf16':
+                    # In mixed precision mode, ensure features match the expected precision
+                    features = features.to(torch.float32)
+
                 logits = self.model(features, day_indicies, None, False, 'inference')
 
                 # Calculate CTC Loss
@@ -752,6 +757,11 @@ class BrainToTextDecoder_Trainer:
                     # Ensure proper dtype handling for TPU mixed precision
                     adjusted_lens = ((n_time_steps.float() - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32)
 
+                    # Ensure features tensor matches model parameter dtype for TPU compatibility
+                    if self.accelerator.mixed_precision == 'bf16':
+                        # In mixed precision mode, ensure features match the expected precision
+                        features = features.to(torch.float32)
+
                     logits = self.model(features, day_indicies, None, False, 'inference')
     
                     loss = self.ctc_loss(
@@ -826,6 +836,11 @@ class BrainToTextDecoder_Trainer:
                 # Apply data transformations (no augmentation for inference)
                 features, n_time_steps = self.transform_data(features, n_time_steps, 'val')
 
+                # Ensure features tensor matches model parameter dtype for TPU compatibility
+                if self.accelerator.mixed_precision == 'bf16':
+                    # In mixed precision mode, ensure features match the expected precision
+                    features = features.to(torch.float32)
+
                 # Get phoneme predictions
                 logits = self.model(features, day_indicies, None, False, mode)
 
@@ -850,6 +865,11 @@ class BrainToTextDecoder_Trainer:
                 # Calculate adjusted sequence lengths for CTC with proper dtype handling
                 adjusted_lens = ((n_time_steps.float() - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32)
 
+                # Ensure features tensor matches model parameter dtype for TPU compatibility
+                if self.accelerator.mixed_precision == 'bf16':
+                    # In mixed precision mode, ensure features match the expected precision
+                    features = features.to(torch.float32)
+
                 # Get phoneme predictions
                 logits = self.model(features, day_indicies, None, False, mode)