Component and Dimension Sparsity in Transformer Refusal Mechanisms
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Computer Science > Computation and Language
arXiv:2610.06903 (cs)
[Submitted on 30 Sep 2026]
Title:Component and Dimension Sparsity in Transformer Refusal Mechanisms
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Abstract:Activation steering manipulates large language model behavior by intervening on internal activations, but the mechanistic basis of these interventions remains poorly understood. We decompose refusal steering into component-level interventions across four open-weight models, identifying the sparse subsets of attention and MLP components whose steering suffices to reproduce the full behavioral effect. We find that refusal directions concentrate in sparse component mechanisms comprising 28--48\% of upstream components, retaining 88--101\% of steering effectiveness. Within these mechanisms, effective steering further concentrates in approximately 50\% of residual stream dimensions, retaining 85--98\% of the component-mechanism baseline, consistent with a privileged basis structure. Sparsity thus operates at two levels: which components are steered, and which dimensions within those components carry the signal. Together these findings show that refusal is not diffusely encoded across a transformer but assembled by a structured, identifiable mechanism, providing a foundation for mechanistic understanding of how refusal behaviors are represented and steered. To facilitate reproducibility, we release all code and raw experimental results in this https URL.
| Comments: | Accepted to COLM 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.06903 [cs.CL] |
| (or arXiv:2610.06903v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.06903
arXiv-issued DOI via DataCite
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View a PDF of the paper titled Component and Dimension Sparsity in Transformer Refusal Mechanisms, by Vincent Siu and 5 other authors
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