Introduces a rigorous algorithm to determine if two different neural networks share the same underlying 'algorithmic interpretation' without needing to manually define the circuits.
April 1, 2026
Original Paper
Tracking Equivalent Mechanistic Interpretations Across Neural Networks
arXiv · 2603.30002
The Takeaway
Mechanistic interpretability has lacked a precise way to compare findings across models. This framework allows researchers to verify if an insight found in a small model actually generalizes to a larger one, creating a foundation for automated and scalable interpretability.
From the abstract
Mechanistic interpretability (MI) is an emerging framework for interpreting neural networks. Given a task and model, MI aims to discover a succinct algorithmic process, an interpretation, that explains the model's decision process on that task. However, MI is difficult to scale and generalize. This stems in part from two key challenges: there is no precise notion of a valid interpretation; and, generating interpretations is often an ad hoc process. In this paper, we address these challenges by d