Mathematical Framework for Transformer Circuits Published 2021

Researchers have released a new framework for dissecting transformer neural networks, titled **“Transformer Circuits.”** The paper, published in 2021 and hosted on the domain transformer‑circuits.pub, proposes a systematic method for breaking down the attention and feed‑forward components of transformers into reusable functional modules, or “circuits.” By mapping these circuits across layers and model variants, the authors aim to clarify how transformers process information and why they exhibit certain behaviors.

The framework introduces a set of diagnostic tools that identify and visualize recurring circuit patterns, such as positional encoding, layer‑norm interactions, and multi‑head attention dynamics. The authors demonstrate that many well‑known transformer phenomena can be expressed as combinations of a limited set of circuits, providing a more interpretable view