Individual Parameters in Weight-Sparse Transformers Appear Interpretable
A central goal of mechanistic interpretability is to understand how neural networks work and what each individual component does. Dominant circuit-finding approaches focus on a specific behavior and reverse-engineer the role of components on the associated sub-distribution. However, past work has shown that components can have different functions that are active on different subsets of the input distribution. In this work we ask whether a single weight can be understood globally across the full training distribution by characterizing when it matters (the inputs on which ablating it changes the model's predictions). We introduce an automated LLM pipeline that writes a short, human-readable...
| Authors | Arnau Marin-Llobet, Stefan Heimersheim |
|---|---|
| Primary category | cs.LG |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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