University of Seoul

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.

A real-time OD demand calibration framework couples a data-driven demand-prediction network with a black-box microscopic traffic simulator in both directions, using an analytically tractable metamodel to carry gradients back so network parameters and demand estimates update together at each time step. In a 3×3 grid network, after link B1C1 closed at hour 4 on day 10, density MSE was 21.6 [veh/km]² versus 57.9 [veh/km]² for LimEKF, a 63% reduction; on the Tokyo expressway corridor during the 2021 Games travel-demand-management day, the online-updated version reached 0.66 [veh/km]² against 1.15 [veh/km]² for LimEKF.

A real-time OD demand calibration framework couples a data-driven demand-prediction network with a black-box microscopic traffic simulator in both directions, using an analytically tractable metamodel to carry gradients back so network parameters and demand estimates update together at each time step. In a 3×3 grid network, after link B1C1 closed at hour 4 on day 10, density MSE was 21.6 [veh/km]² versus 57.9 [veh/km]² for LimEKF, a 63% reduction; on the Tokyo expressway corridor during the 2021 Games travel-demand-management day, the online-updated version reached 0.66 [veh/km]² against 1.15 [veh/km]² for LimEKF.