Subliminal Prompting Beyond Static Geometry: Causal Depth and Multi-Token Confounds
Subliminal learning shows that language models can transmit a hidden trait through outputs that appear unrelated to it. One proposed explanation, token entanglement, links animal and number tokens through the model's output vocabulary. Yet existing measurements answer different questions: whether outputs co-vary, fixed output vectors align, an answer can be read from a hidden state, or that state causally controls the answer. We measure each separately in a fixed animal-number prompting protocol. From Llama-3.1-8B to 70B, fixed output-vector similarity predicts behavior less well: the paired mean correlation change is -0.080 (95% CI [-0.127, -0.035]). A fixed output-head readout shows no ...
| Authors | Barath Velmurugan |
|---|---|
| Primary category | cs.CL |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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