Dissociating Decodability and Causal Use in Bracket-Sequence Transformers
When trained on tasks requiring an understanding of hierarchical structure, transformers have been found to represent this hierarchy in distinct ways: in the geometry of the residual stream, and in stack-like attention patterns maintaining a last-in, first-out ordering. However, it remains unclear whether these representations are causally used or merely decodable. We examine this gap in transformers trained on the Dyck language (a formal language of balanced bracket sequences), where the hierarchical ground truth is explicit. By probing and intervening on the residual stream and attention patterns, we find that depth, distance, and top-of-stack signals are all decodable, yet their causal...
| Authors | Aryan Sharma, Cutter Dawes, Shivam Raval |
|---|---|
| Primary category | cs.CL |
| Published | 24 April 2026 |
| Last updated at source | 16 June 2026 |
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