Vernier: Probing Representational Misalignment Behind Lexical Gaps in Causal Reasoning
Instruction-tuned language models can answer the same causal-reasoning question differently after its English variable names are replaced by type-preserving placeholders, although the structural causal model and the gold answer are unchanged. We ask whether this lexical gap reflects information loss in the placeholder view or a misaligned read-out from a representation that still carries answer-relevant content. Vernier uses a paired-view weight update as an instrument and then inspects the mechanism left after the gap closes. In the working regimes, the evidence favours representational misalignment. A variable-name probe becomes more accurate on the placeholder view, and activation patc...
| Authors | Zhenyu Yu |
|---|---|
| Primary category | cs.CL |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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