Dissecting Jet-Tagger Through Mechanistic Interpretability
Mechanistic interpretability seeks to reverse engineer a trained neural network by identifying the minimal subset of internal components. We perform a mechanistic interpretability analysis of the Particle Transformer architecture, trained on the Top Quark Tagging reference dataset, with the goal of identifying the computational circuit responsible for jet classification and characterizing the physical content of its internal representations. Combining zero ablation, path patching with two complementary on-manifold corruption strategies and linear probing of the residual stream, we identify a sparse six-head circuit that recovers the great majority of the full model performance while admit...
| Authors | Saurabh Rai, Sanmay Ganguly |
|---|---|
| Primary category | hep-ph |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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