Modern Large Language Models (LLMs) exhibit impressive zero-shot and few-shot generalization capabilities across complex natural language tasks, enabling their widespread use as virtual assistants for diverse applications such as translation and summarization. Despite being trained solely on large corpora of text without explicit supervision on author intent, LLMs appear to infer the underlying meaning of textual interactions. This raises a fundamental question: can LLMs model and reason about the intentions of others, i.e., do they possess a form of theory of mind? Understanding other's intentions is crucial for effective collaboration, which underpins human societal success and is essential for cooperative interactions among multiple agents, including humans and autonomous systems. In this work, we investigate the theory of mind in LLMs through the lens of cooperative multi-agent reinforcement learning (MARL), where agents learn to collaborate via repeated interactions, mirroring human social reasoning. Our approach aims to enhance artificial agent's ability to adapt and cooperate with both artificial and human partners. By leveraging LLM-based agents capable of natural language interaction, we move towards creating hybrid human-AI systems that can foster seamless collaboration, with broad implications for the future of human-artificial interaction.
NOVA-LINCS；Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa（新里斯本大学科学与技术学院）；INESC-ID；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高级技术学院）；ATP-Group（ATP集团）
Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination
DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；Army Research Laboratory-Research Associateship Program（陆军研究实验室研究副研究员项目）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Advancing Audio-Visual Navigation Through Multi-Agent Collaboration in 3D Environments
Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center（新疆多模态智能处理与信息安全工程技术研究中心）；School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；School of Electrical Engineering and Automation, Tianjin University of Technology（天津理工大学电气工程与自动化学院）
Evolving Interpretable Constitutions for Multi-Agent Coordination
College of Engineering Shibaura Institute of Technology（Shibaura Institute of Technology 工程学院）；Faculty of Arts and Sciences The University of Tokyo（东京大学 文理学部）；Department of EECS University of California, Berkeley（加州大学伯克利分校 电子工程与计算机科学系）；Department of Informatics Università degli Studi di Milano-Bicocca（米兰-比科卡大学 信息学系）
