@inproceedings{hanna-etal-2025-circuit,
    title = "Circuit-Tracer: A New Library for Finding Feature Circuits",
    author = "Hanna, Michael  and
      Piotrowski, Mateusz  and
      Lindsey, Jack  and
      Ameisen, Emmanuel",
    editor = "Belinkov, Yonatan  and
      Mueller, Aaron  and
      Kim, Najoung  and
      Mohebbi, Hosein  and
      Chen, Hanjie  and
      Arad, Dana  and
      Sarti, Gabriele",
    booktitle = "Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.blackboxnlp-1.14/",
    doi = "10.18653/v1/2025.blackboxnlp-1.14",
    pages = "239--249",
    ISBN = "979-8-89176-346-3",
    abstract = "Feature circuits aim to shed light on LLM behavior by identifying the features that are causally responsible for a given LLM output, and connecting them into a directed graph, or *circuit*, that explains how both each feature and each output arose. However, performing circuit analysis is challenging: the tools for finding, visualizing, and verifying feature circuits are complex and spread across multiple libraries.To facilitate feature-circuit finding, we introduce `circuit-tracer{`}, an open-source library for efficient identification of feature circuits. `circuit-tracer{`} provides an integrated pipeline for finding, visualizing, annotating, and performing interventions on such feature circuits, tested with various model sizes, up to 14B parameters. We make `circuit-tracer{`} available to both developers and end users, via integration with tools such as Neuronpedia, which provides a user-friendly interface."
}