From the Readme:
Given a model with pre-trained transcoders, it finds the circuit / attribution graph; i.e., it computes the direct effect that each non-zero transcoder feature, transcoder error node, and input token has on each other non-zero transcoder feature and output logit.
Given an attribution graph, it visualizes this graph and allows you to annotate these features.
Enables interventions on a model's transcoder features using the insights gained from the attribution graph; i.e. you can set features to arbitrary values, and observe how model output changes.
The blog post:
https://www.anthropic.com/research/open-source-circuit-traci...
