Right Answer, Wrong Mechanism: Detecting Pernicious Divergence in Causal Interventions
Causal interventions such as activation patching and distributed alignment search (DAS) are the main tool for making mechanistic claims about neural networks. Recent work showed that these interventions routinely push representations off the model's natural distribution, and that such divergence is sometimes harmless and sometimes pernicious: it can recruit pathways the model never uses on natural inputs, so that an intervention produces the expected answer through the wrong mechanism. No method currently tells the two cases apart. We make this question testable by planting hidden pathways inside pretrained language models; the pathways are silent on every benchmark prompt by construction...
| Authors | Beiming Liu, Minjie Chen |
|---|---|
| Primary category | cs.LG |
| Published | 30 September 2026 |
| Last updated at source | 4 October 2026 |
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