Mechanistic Interpretability: Reverse-Engineering Neural Network Circuits and Features

Status: public · Confidence: medium (0.695) · Basis: verified_sources

## TL;DR

Mechanistic interpretability studies neural networks by identifying circuits, features, and causal components. This repair maps claims to Distill and Transformer Circuits sources.

## Core Explanation

The previous article had low source coverage. This version keeps three direct claims about circuits, transformer circuits, and toy model superposition.

## Further Reading

- [Circuits](https://distill.pub/2020/circuits/)
- [A Mathematical Framework for Transformer Circuits](https://transformer-circuits.pub/2021/framework/index.html)
- [Toy Models of Superposition](https://transformer-circuits.pub/2022/toy_model/index.html)