Tracking Equivalent Mechanistic Interpretations Across Neural Networks
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 defining and studying the problem of interpretive equivalence: determining whether two different models share a common interpretation, without requiring an explicit description of what that interpre...
| Authors | Alan Sun, Mariya Toneva |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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