DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915398177259520 |
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| author | Li, Xiyun Ding, Yining Jiang, Yuhua Zhao, Yunlong Xie, Runpeng Xu, Shuang Ni, Yuanhua Yang, Yiqin Xu, Bo |
| author_facet | Li, Xiyun Ding, Yining Jiang, Yuhua Zhao, Yunlong Xie, Runpeng Xu, Shuang Ni, Yuanhua Yang, Yiqin Xu, Bo |
| contents | Real-time human-artificial intelligence (AI) collaboration is crucial yet challenging, especially when AI agents must adapt to diverse and unseen human behaviors in dynamic scenarios. Existing large language model (LLM) agents often fail to accurately model the complex human mental characteristics such as domain intentions, especially in the absence of direct communication. To address this limitation, we propose a novel dual process multi-scale theory of mind (DPMT) framework, drawing inspiration from cognitive science dual process theory. Our DPMT framework incorporates a multi-scale theory of mind (ToM) module to facilitate robust human partner modeling through mental characteristic reasoning. Experimental results demonstrate that DPMT significantly enhances human-AI collaboration, and ablation studies further validate the contributions of our multi-scale ToM in the slow system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14088 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration Li, Xiyun Ding, Yining Jiang, Yuhua Zhao, Yunlong Xie, Runpeng Xu, Shuang Ni, Yuanhua Yang, Yiqin Xu, Bo Machine Learning Real-time human-artificial intelligence (AI) collaboration is crucial yet challenging, especially when AI agents must adapt to diverse and unseen human behaviors in dynamic scenarios. Existing large language model (LLM) agents often fail to accurately model the complex human mental characteristics such as domain intentions, especially in the absence of direct communication. To address this limitation, we propose a novel dual process multi-scale theory of mind (DPMT) framework, drawing inspiration from cognitive science dual process theory. Our DPMT framework incorporates a multi-scale theory of mind (ToM) module to facilitate robust human partner modeling through mental characteristic reasoning. Experimental results demonstrate that DPMT significantly enhances human-AI collaboration, and ablation studies further validate the contributions of our multi-scale ToM in the slow system. |
| title | DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.14088 |