Targeted Recovery of Weight-Space Mechanisms From Neural Networks
Parameter decomposition (PD) decomposes neural networks into interpretable components but is computationally expensive for large models. The proposed targeted PD (tPD) introduces a high-rank catch-all component to handle non-target data, enabling efficient recovery of circuits for specific inputs. tPD extracts a CSS-only submodel from a 4-block transformer using 7% of the FLOPs of published decomposition, and surgically ablates memorized sequences in a 12-block transformer with negligible side effects. Accepted at the Mechanistic Interpretability Workshop, ICML 2026.
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[Submitted on 19 Jun 2026]
Title:Targeted Recovery of Weight-Space Mechanisms From Neural Networks
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Abstract:Parameter decomposition (PD) decomposes neural networks into interpretable computational components that faithfully reflect the original network's operations. However, scaling PD to large models requires vast compute, making it a costly and risky endeavor. Here we propose targeted PD (tPD), which identifies only the components that process specific inputs of interest -- from isolated prompts to large subtasks -- by introducing a high-rank catch-all component that handles all non-target data. We validate tPD on toy models and on transformer language models trained on The Pile, where it recovers reproducible, mechanistically faithful circuits. We extract a CSS-only submodel of a 4-block transformer using 7% of the FLOPs of its published decomposition, and in a 12-block transformer we surgically ablate and rewire memorized sequences, with negligible side effects on other inputs.
Comments: Accepted at the Mechanistic Interpretability Workshop, ICML 2026
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.13047 [cs.LG]
(or arXiv:2607.13047v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.13047
arXiv-issued DOI via DataCite
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From: Antoine Vigouroux [view email] [v1] Fri, 19 Jun 2026 19:07:38 UTC (3,565 KB)
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