让AI实时理解人类意图，提升人机协作效率。
DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration
- 基于双系统认知理论构建多尺度心智模型
- 在无直接沟通下仍能准确推断人类领域意图
- 适合需要快速响应的交互式AI系统
实时人机协作至关重要却充满挑战，尤其当AI需适应动态场景中多样且未见过的人类行为时。现有大语言模型代理往往难以准确建模复杂的人类心理特征，如领域意图，尤其是在缺乏直接沟通的情况下。为此，我们提出一种新型双过程多尺度心智（DPMT）框架，借鉴认知科学中的双过程理论。该框架引入多尺度心智模块，通过心理特征推理实现稳健的人类伙伴建模。实验结果表明，DPMT显著提升了人机协作效果，消融实验证明了其慢系统中多尺度心智模块的关键贡献。
原文摘要 · Abstract (English)
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.
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