Successful human-agent teaming relies on an agent being able to understand instructions given by a (human) principal. In many cases, an instruction may be incomplete or ambiguous. In such cases, the agent must infer the unspoken intentions from their shared context, that is, it must exercise the principal's Theory of Mind (ToM) and infer the mental states of its principal. We consider the prospects of effective human-agent collaboration using large language models (LLMs). To assess ToM in a dynamic, goal-oriented, and collaborative environment, we introduce a novel task, Instruction Inference, in which an agent assists a principal in reaching a goal by interpreting incomplete or ambiguous instructions. We present Tomcat, an LLM-based agent, designed to exhibit ToM reasoning in interpreting and responding to the principal's instructions. We implemented two variants of Tomcat. One, dubbed Fs-CoT (Fs for few-shot, CoT for chain-of-thought), is based on a small number of examples demonstrating the requisite structured reasoning. One, dubbed CP (commonsense prompt), relies on commonsense knowledge and information about the problem. We realized both variants of Tomcat on three leading LLMs, namely, GPT-4o, DeepSeek-R1, and Gemma-3-27B. To evaluate the effectiveness of Tomcat, we conducted a study with 52 human participants in which we provided participants with the same information as the CP variant. We computed intent accuracy, action optimality, and planning optimality to measure the ToM capabilities of Tomcat and our study participants. We found that Tomcat with Fs-CoT, particularly with GPT-4o and DeepSeek-R1, achieves performance comparable to the human participants, underscoring its ToM potential for human-agent collaboration.
Mind the Gap: Conformative Decoding to Improve Output Diversity of Instruction-Tuned Large Language Models
School of Computing University of Kent（肯特大学计算机学院）；Leiden Institute of Advanced Computer Science Universiteit Leiden（莱顿先进计算机科学研究所）；Cheriton School of Computer Science University of Waterloo（滑铁卢大学查里顿计算机科学学院）
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；ARC Lab, Tencent PCG（腾讯PCG ARC实验室）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）
Multi-Agent Collaboration for Multilingual Code Instruction Tuning
Alibaba Group（阿里巴巴集团）；Shanghai Jiao Tong University（上海交通大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）
From Mind to Machine: The Rise of Manus AI as a Fully Autonomous Digital Agent
Department of Electrical and Computer Engineering, Virginia Tech（弗吉尼亚理工学院电气与计算机工程系）；Department of Computer Science, Brown University（布朗大学计算机科学系）；Department of Computer Science, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Khoury College of Computer Sciences, Northeastern University（东北大学科里尔计算机科学学院）
Enhancing Speech Instruction Understanding and Disambiguation in Robotics via Speech Prosody
IT University of Copenhagen（丹麦技术大学）；University of Florida（佛罗里达大学）；Brown University（布朗大学）；Intelligent Agents Research Group（智能代理研究组）；Intelligent Robot Lab（智能机器人实验室）
Mind-Omni: A Unified Multi-Task Framework for Brain-Vision-Language Modeling via Discrete Diffusion
NeuBCI Lab, State Key Laboratory of Brain Cognition；Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China；School of Future Technology, University of Chinese Academy of Sciences, Beijing, China；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China；Zhongguancun Academy, Beijing, China；Peking University, Beijing, China
Mind the Rarities: Can Rare Skin Diseases Be Reliably Diagnosed via Diagnostic Reasoning?
Carnegie Mellon University（卡内基梅隆大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Australian National University（澳大利亚国立大学）；University of Western Australia（西澳大学）；POSTECH；Yale University（耶鲁大学）
Mind the Gap: A Review of Arabic Post-Training Datasets and Their Limitations
Refine AI；ASAS AI；University of Bisha（比沙大学）；University College London（伦敦大学学院）；King Salman Global Academy for Arabic（萨勒曼全球阿拉伯学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；King Abdulaziz University（阿卜杜勒阿齐兹大学）；HUMAIN
本文综述 Hugging Face Hub 上的阿拉伯语后训练数据集，从能力、可引导性、对齐和鲁棒性等维度评估其质量与局限，并提出未来建设建议。
Falcon-UI: Understanding GUI Before Following User Instructions
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；College of Computer Science, Nankai University（南开大学计算机学院）；Department of Automation, Tsinghua University（清华大学自动化系）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States
UC San Diego（加州大学圣迭戈分校）；KU Leuven（鲁汶大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；The Ohio State University（俄亥俄州立大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Few-shot Language Coordination by Modeling Theory of Mind
Carnegie Mellon University（卡内基梅隆大学）
$\textit{No man is an island.}$人类通过在简短的对话中与不同的对话者进行协调来与大型社区进行交流。这种能力已经被构建神经通信代理的研究所忽视。我们研究了Few-Shot $\textit{language coordination}$的任务：智能体快速适应他们的会话伙伴的语言能力。与目前的自我游戏训练的沟通代理不同，我们需要领导代理与具有不同语言能力的代理$\textit{population}$协调，快速适应与人口中看不见的代理进行沟通。这就需要有能力模仿伴侣的信念，这是人类沟通的重要组成部分。从心理理论（ToM; Premack& Woodruff（1978））中获得灵感，我们研究了说话者明确地模拟听者的心理状态的效果。正如我们的实验所示，说话者获得了预测其伙伴反应的能力，这有助于它生成简明表达其交际目标的指令。我们研究了我们的假设，即与心理理论建模生成的指令产生更好的通信性能在参考游戏和语言导航任务。从我们的实验中的积极结果暗示的重要性，明确建模通信作为一个社会语用的进步。
