# Full-Duplex Dialogue

Full-duplex spoken dialogue models drift over long sessions, becoming slower to respond, less coherent and less consistent in voice, and long-session stability had not been targeted by dedicated training or architecture. A closed-loop simulated user quantifies this minute by minute across four public models, then privileged-context self-distillation applies lightweight LoRA adaptation and a two-resolution context (TRC) compresses older history. Across three architecturally distinct models, self-distillation mitigates degradation within the ten-minute training horizon, and adding TRC reduces spoken-content and voice degradation by 71–96% relative to baseline while extending the gains to twenty-minute sessions.

Full-duplex spoken dialogue models drift over long sessions, becoming slower to respond, less coherent and less consistent in voice, and long-session stability had not been targeted by dedicated training or architecture. A closed-loop simulated user quantifies this minute by minute across four public models, then privileged-context self-distillation applies lightweight LoRA adaptation and a two-resolution context (TRC) compresses older history. Across three architecturally distinct models, self-distillation mitigates degradation within the ten-minute training horizon, and adding TRC reduces spoken-content and voice degradation by 71–96% relative to baseline while extending the gains to twenty-minute sessions.