A Negative Result on Cross-Model Activation Transfer in a Pythia Multi-Hop Setting
Recent work shows that language models can transmit behavioural traits through hidden signals in generated data during training. We ask whether a different activation-mediated channel is viable: can one language model communicate a useful intermediate reasoning state to another at inference time through a post-hoc linear activation bridge, rather than through a textual or structured-token relay? We test this question in a controlled Pythia-160M to Pythia-410M multi-hop reasoning setting. A linear translation layer learns a strong normalized-space map between sender and receiver hidden states, with normalized cosine similarity near 0.97 across seeds. However, when the translated activation...
| Authors | Peiyan Zhang, Jason Xin |
|---|---|
| Primary category | cs.AI |
| Published | 2 June 2026 |
| Last updated at source | 5 June 2026 |
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