CircuitKIT : Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretability

Abstract:Circuit analysis can support not only model explanation but also downstream interventions such as pruning, editing, steering, and selective fine-tuning. However, conducting such analyses currently requires stitching together separate implementations for discovery, evaluation, and intervention, as well as hand-authoring the contrastive prompts required by many discovery methods. This fragmentation makes methods difficult to compare and limits their application beyond canonical tasks. We introduce CircuitKIT, a source-available library that connects the circuit-analysis workflow through a typed, serializable representation. CircuitKIT provides a suite of discovery algorithms, declarative interfaces for mapping structured data into discovery tasks, complementary circuit diagnostics, and downstream application modules. Together, these components provide common infrastructure for conducting and comparing circuit analyses. The library, examples, notebooks, and documentation are released at this https URL .

Machine Learning (cs.LG); Computation and Language (cs.CL); Emerging Technologies (cs.ET)

arXiv:2607.19317 [cs.LG]

(or arXiv:2607.19317v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.19317 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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