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arXiv · 2503.09511

TRACE: Real-Time Multimodal Common Ground Tracking in Situated Collaborative Dialogues

Hannah VanderHoeven·Brady Bhalla·Ibrahim Khebour·Austin Youngren·Videep Venkatesha·Mariah Bradford·Jack Fitzgerald·Carlos Mabrey·Jingxuan Tu·Yifan Zhu·Kenneth Lai·Changsoo Jung·James Pustejovsky·Nikhil Krishnaswamy
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Abstract

We present TRACE, a novel system for live *common ground* tracking in situated collaborative tasks. With a focus on fast, real-time performance, TRACE tracks the speech, actions, gestures, and visual attention of participants, uses these multimodal inputs to determine the set of task-relevant propositions that have been raised as the dialogue progresses, and tracks the group's epistemic position and beliefs toward them as the task unfolds. Amid increased interest in AI systems that can mediate collaborations, TRACE represents an important step forward for agents that can engage with multiparty, multimodal discourse.

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Hannah VanderHoeven, Brady Bhalla, Ibrahim Khebour, Austin Youngren, Videep Venkatesha, Mariah Bradford, Jack Fitzgerald, Carlos Mabrey, Jingxuan Tu, Yifan Zhu, Kenneth Lai, Changsoo Jung, James Pustejovsky, Nikhil Krishnaswamy. 2025-03-12. TRACE: Real-Time Multimodal Common Ground Tracking in Situated Collaborative Dialogues. https://arxiv.org/abs/2503.09511

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