The source describes MDFB, built from 2,207 real-world meetings and 24,794 future-facing queries. Given a transcript prefix and an active question, a model generates a multi-turn continuation in one call. The evaluation measures utility (progress toward the question) and human-likeness (conversational flow and role consistency) without requiring exact reproduction of the observed future. GLARE adapts adversarial imitation learning to conditional language generation: a discriminator ranks the observed continuation above samples from the current actor, and its score supplies a KL-regularized policy reward. The source reports GLARE attains average human-evaluated win rates of 0.66 on utility and 0.70 on human-likeness, outperforming SFT and SPIN while remaining below the observed human continuation. The benchmark is also presented as a social reasoning arena for comparing general-purpose models.