# Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration arXiv: https://arxiv.org/abs/2502.11882 ยท HTML: https://mlsanity.com/paper/2502.11882/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. - Builds on 21 of its 49 references that are in the map. - First posted on arXiv 17 Feb 2025. ## Placed in - LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/) ## Builds on (most cited first, 21 of 21) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023) - [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024) - [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022) - [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024) - [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019) - [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018) - [A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://mlsanity.com/paper/2402.18013/) (2024) - [Distilling System 2 into System 1](https://mlsanity.com/paper/2407.06023/) (2024) - [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021) - [Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization](https://mlsanity.com/paper/2402.17574/) (2024) - [Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.09207/) (2019) - [Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration](https://mlsanity.com/paper/2412.15701/) (2024) - [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024) - [Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased](https://mlsanity.com/paper/2302.01605/) (2023) - [Planning Like Human: A Dual-process Framework for Dialogue Planning](https://mlsanity.com/paper/2406.05374/) (2024) - [ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination](https://mlsanity.com/paper/2310.05208/) (2023) - [Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task](https://mlsanity.com/paper/2409.08811/) (2024) - [Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination](https://mlsanity.com/paper/2306.03034/) (2023) - [Efficient Human-AI Coordination via Preparatory Language-based Convention](https://mlsanity.com/paper/2311.00416/) (2023) ## Built on by (most cited first, 10 of 10) - [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025) - [Self-Harness: Harnesses That Improve Themselves](https://mlsanity.com/paper/2606.09498/) (2026) - [Real-Time Reasoning Agents in Evolving Environments](https://mlsanity.com/paper/2511.04898/) (2025) - [A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy](https://mlsanity.com/paper/2506.09420/) (2025) - [Adapting Like Humans: A Metacognitive Agent with Test-time Reasoning](https://mlsanity.com/paper/2511.23262/) (2025) - [Can large language models interpret unstructured chat data on dynamic group decision-making processes? Evidence on joint destination choice](https://mlsanity.com/paper/2601.05582/) (2026) - [CoWork-X: Experience-Optimized Co-Evolution for Multi-Agent Collaboration System](https://mlsanity.com/paper/2602.05004/) (2026) - [System Switch: When Should a Fast Decision Model Stop and Think?](https://mlsanity.com/paper/2610.09683/) (2026) - [Structured In-context Environment Scaling for Large Language Model Reasoning](https://mlsanity.com/paper/2509.23330/) (2025) - [PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning](https://mlsanity.com/paper/2601.08679/) (2026) ## Abstract (arXiv) 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. DPT-Agent can effectively help LLMs convert correct slow thinking and reasoning into executable actions, thereby improving performance. 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.