A Mathematical Framework for Transformer Circuits (2021)

This foundational 2021 research paper from Anthropic introduces a mathematical framework designed to interpret the internal workings of Transformer-based language models. By decomposing the model into specific components such as 'induction heads' and 'OV circuits,' the authors provide a mechanistic approach to understanding how these models process information and perform complex tasks. The framework moves beyond treating neural networks as 'black boxes,' offering a rigorous method to map individual weights and activations to specific computational functions. This work has become a cornerstone in the field of mechanistic interpretability, enabling researchers to better understand the internal logic of large-scale models. By identifying how circuits interact to form predictions, the paper provides essential insights for improving model transparency, safety, and reliability. It remains a critical resource for developers and researchers aiming to demystify the complex internal architectures that power modern generative AI systems.