Does the Model Read the Room? Probing and Patching Partner Expertise in Collaborative LLM Dialogue

Does a language model's internal representation of its conversational partner's expertise actually drive how it responds? We investigate this for multi-turn collaborative dialogue---a setting where partner expertise must be actively inferred across turns, not read from a prompt. Using ExpertCollab, a controlled corpus of dialogues between LLM-played personas at four expertise levels, we show that partner expertise is linearly decodable from early residual-stream layers via linear probing, peaking at layer~8 before degrading to near-chance well before the network's midpoint. We then ask whether those early-layer representations are causally active. Counterfactual activation patching---replacing residual-stream activations at a candidate layer with those from a different expertise condition---reveals a sharp dissociation: injecting the expertise difference at probe-peak layers leaves a fixed late-layer readout essentially unchanged, while the same difference injected past the network's midpoint propagates nearly completely, an 11x separation confirmed to be content-specific rather than a generic perturbation artifact. This gap reaches the model's output: late-layer patches shift roughly one in three top predicted tokens; probe-peak patches leave the output essentially unchanged, with two independent diagnostics converging on the same transition point. Probing alone would mislocalize the intervention target by over half the network depth---a structural constraint on any attempt to steer partner-conditioned behavior. All results are for a single model family on a synthetic corpus; generalizability across architectures and naturalistic dialogue remains an open question.