@inproceedings{zhang-etal-2025-leveraging,
    title = "Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-{AI} Collaboration",
    author = "Zhang, Shao  and
      Wang, Xihuai  and
      Zhang, Wenhao  and
      Li, Chaoran  and
      Song, Junru  and
      Li, Tingyu  and
      Qiu, Lin  and
      Cao, Xuezhi  and
      Cai, Xunliang  and
      Yao, Wen  and
      Zhang, Weinan  and
      Wang, Xinbing  and
      Wen, Ying",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.206/",
    pages = "4081--4108",
    ISBN = "979-8-89176-251-0",
    abstract = "Agents built on large language models (LLMs) have excelled in turn-by-turn human-AI collaboration but struggle with simultaneous tasks requiring real-time interaction. Latency issues and the challenge of inferring variable human strategies hinder their ability to make autonomous decisions without explicit instructions. Through experiments with current independent *System 1* and *System 2* methods, we validate the necessity of using Dual Process Theory (DPT) in real-time tasks. We propose DPT-Agent, a novel language agent framework that integrates *System 1* and *System 2* for efficient real-time simultaneous human-AI collaboration. DPT-Agent{'}s *System 1* uses a Finite-state Machine (FSM) and code-as-policy for fast, intuitive, and controllable decision-making. DPT-Agent{'}s *System 2* integrates Theory of Mind (ToM) and asynchronous reflection to infer human intentions and perform reasoning-based autonomous decisions. We demonstrate the effectiveness of DPT-Agent through further experiments with rule-based agents and human collaborators, showing significant improvements over mainstream LLM-based frameworks. To the best of our knowledge, DPT-Agent is the first language agent framework that achieves successful real-time simultaneous human-AI collaboration autonomously. Code of DPT-Agent can be found in https://github.com/sjtu-marl/DPT-Agent."
}