Data-driven Circuit Discovery for Interpretability of Language Models
Circuit study aims to explain how language models (LMs) implement a task by localizing and interpreting a circuit: a computational subgraph responsible for a specific model's behavior. Existing methods for circuit localization are largely hypothesis-driven: they define a task through a dataset, assume that all examples in the task are implemented by the same mechanism, and apply circuit discovery to obtain a single circuit. This workflow does not account for mechanistic diversity within a task, where different examples may rely on different mechanisms. We first show that when a dataset mixes tasks processed through distinct mechanisms, current circuit discovery can combine these mechanism...
| Authors | Daking Rai, Mor Geva, Ziyu Yao |
|---|---|
| Primary category | cs.AI |
| Published | 9 May 2026 |
| Last updated at source | 12 October 2026 |
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