In July 2025, Hilmes developed a robust stateful inference capability for LSTM models in the rwth-i6/i6_models repository. By implementing a new forward_with_state method using Python and PyTorch, Hilmes enabled step-wise processing of input sequences while preserving and passing LSTM hidden and cell states. This approach allows for incremental inference, making the model suitable for streaming and long-sequence data scenarios. The work focused on deep learning and recurrent neural networks, ensuring backward compatibility and maintainability. Although no bugs were fixed during this period, the feature delivered depth and addressed a practical need for stateful sequence modeling in production environments.