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Encoded Early, Used Late: Where Transformers Begin to Act on an Inferred Partner's Expertise

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

A transformer can make an attribute linearly decodable in its residual stream at a depth where that attribute does not yet influence the output. This gap between where information is readable and where it is used has been shown for attributes stated directly in the input. We ask whether it also holds for an attribute the model must infer gradually over a conversation, namely how expert its dialogue partner is. Using ExpertCollab, a corpus of multi-turn research-planning dialogues between model-played personas at four expertise levels, we find that partner expertise is most decodable in the ear

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