Residual Stream

Residual streams, the difference between input and output activations in neural network layers, are a central focus in current research across diverse applications, from natural language processing to image restoration and physics-informed modeling. Researchers are investigating how these streams represent information flow, exploring their use in model interpretability, and developing techniques to manipulate or analyze them for improved model performance, robustness, and safety, often employing architectures like transformers and convolutional neural networks. This work has significant implications for enhancing model efficiency, mitigating adversarial attacks, and improving the accuracy and reliability of AI systems across various domains.