Reference-Guided Turn-Taking Style Transfer for Full-Duplex Spoken Language Models

arXiv:2610.20763v1 Announce Type: cross Abstract: Full-duplex spoken language models decide at every frame whether to take the floor, defining a conversational personality -- yet each set of weights encodes exactly one, which human speakers bracket rather than share. Existing controls mostly act from outside; none identifies the quantity that sets how much the model speaks. We identify it: a single late-layer residual-stream direction, the initiative vector, extracted from the model's own conversations without training or annotation. The same direction reads the decision, anticipating onsets u

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