Abstract
Successful group meetings, such as those implemented in group behavioral-change programs, work meetings, and other social contexts, must promote individual goal setting and execution while strengthening the social relationships within the group. Consequently, an ideal facilitator must be sensitive to the subtle dynamics of disengagement, difficulties with individual goal setting and execution, and interpersonal difficulties that signal a need for intervention. The challenges and cognitive load experienced by facilitators create a critical gap for an embodied technology that can interpret social exchanges while remaining aware of the needs of the individuals in the group and providing transparent recommendations that go beyond powerful but "black box" foundation models (FMs) that identify social cues. We address this important demand with a social robot co-facilitator that analyzes multimodal meeting data and provides discreet cues to the facilitator. The robot's reasoning is powered by an agentic concept bottleneck model (CBM), which makes decisions based on human-interpretable concepts like participant engagement and sentiments, ensuring transparency and trustworthiness. Our core contribution is a transfer learning framework that distills the broad social understanding of an FM into our specialized and transparent CBM. This concept-driven system significantly outperforms direct zero-shot FMs in predicting the need for intervention and enables real-time human correction of its reasoning. Critically, we demonstrate robust knowledge transfer: the model generalizes across different groups and successfully transfers the expertise of senior human facilitators to improve the performance of novices. By transferring an expert's cognitive model into an interpretable robotic partner, our work provides a powerful blueprint for augmenting human capabilities in complex social domains.
Abstract (translated)
成功的团队会议，例如在行为改变项目、工作会议和其他社交场合中实施的那些，必须促进个人目标设定和执行的同时加强团队内部的社会关系。因此，理想的主持人需要对参与者参与度下降、个人目标设定和执行困难以及人际问题等微妙动态保持敏感，这些信号表明需要介入。主持人面临的挑战和认知负荷为一种能够解读社会互动并关注群体成员需求的实体技术创造了一个关键缺口，这种技术可以提供超越强大但像“黑盒”一样的基础模型（FMs）所识别的社会线索的透明建议。 我们通过一个社交机器人共主持人来应对这一重要需求。该机器人分析多模式会议数据，并向主持人提供隐性提示。机器人的推理由代理概念瓶颈模型（CBM）驱动，基于参与者参与度和情感等人类可解释的概念进行决策，确保其透明性和可靠性。我们的核心贡献是一个迁移学习框架，它将基础模型的广泛社交理解提炼成我们专有的、透明的CBM。 这一概念驱动系统在预测介入需求方面显著优于直接零样本基础模型，并且能够实现实时的人类对其推理进行修正。尤为重要的是，我们证明了强大的知识转移：该模型可以在不同的团队之间推广并成功地将资深人类主持人的专业知识转移到新手身上以提高其表现。通过将专家的认知模型转化为一个可解释的机器人伙伴，我们的工作为在复杂社交领域增强人类能力提供了一个强有力的蓝图。
URL
https://arxiv.org/abs/2508.03998
