Reasoning and strategic behavior in social interactions is a hallmark of intelligence. This form of reasoning is significantly more sophisticated than isolated planning or reasoning tasks in static settings (e.g., math problem solving). In this paper, we present Strategic Planning, Interaction, and Negotiation (SPIN-Bench), a new multi-domain evaluation designed to measure the intelligence of strategic planning and social reasoning. While many existing benchmarks focus on narrow planning or single-agent reasoning, SPIN-Bench combines classical PDDL tasks, competitive board games, cooperative card games, and multi-agent negotiation scenarios in one unified framework. The framework includes both a benchmark as well as an arena to simulate and evaluate the variety of social settings to test reasoning and strategic behavior of AI agents. We formulate the benchmark SPIN-Bench by systematically varying action spaces, state complexity, and the number of interacting agents to simulate a variety of social settings where success depends on not only methodical and step-wise decision making, but also conceptual inference of other (adversarial or cooperative) participants. Our experiments reveal that while contemporary LLMs handle basic fact retrieval and short-range planning reasonably well, they encounter significant performance bottlenecks in tasks requiring deep multi-hop reasoning over large state spaces and socially adept coordination under uncertainty. We envision SPIN-Bench as a catalyst for future research on robust multi-agent planning, social reasoning, and human--AI teaming. Project Website: https://spinbench.github.io/
SocialHarmBench: Revealing LLM Vulnerabilities to Socially Harmful Requests
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Toronto Metropolitan University（多伦多都会大学）；IIT, Roorkee（印度理工学院鲁尔基分校）；University of Michigan（密歇根大学）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）
Are LLMs Socially Adaptive? Contrasting Belief Evolution in Large Language Models and Humans
Tsinghua University（清华大学）；Department of Psychological and Cognitive Sciences（心理与认知科学系）；College AI（人工智能学院）；School of Management（管理学院）；Fudan University（复旦大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Northwestern University（西北大学）；University of Oxford（牛津大学）
NICE: A Theory-Grounded Diagnostic Benchmark for Social Intelligence of LLMs
Department of Psychology and Behavioral Sciences, Zhejiang University（浙江大学心理学与行为科学系）；College of Artificial Intelligence, Zhejiang University（浙江大学人工智能学院）；Human Machine Interaction Lab, Huawei Technologies Co., Ltd.（华为技术有限公司人机交互实验室）；Zhejiang Key Laboratory of Neurocognitive Development and Mental Health（浙江省神经认知发展与心理健康重点实验室）
StratMem-Bench: Evaluating Strategic Memory Use in Virtual Character Conversation Beyond Factual Recall
School of Computer Science and Engineering, Southeast University, China（东南大学计算机科学与工程学院，中国）；Key Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其交叉应用国家重点实验室（东南大学），中华人民共和国教育部，中国）；College of Design and Innovation, Tongji University, China（同济大学设计与创新学院，中国）
PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media
School of Computer and Information Technology, Shanxi University, China（山西大学计算机与信息学院）；Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, China（教育部计算智能与中文信息处理重点实验室，山西大学，中国）；Joint Laboratory of Tourism Big Data in Shanxi Province, China（山西省旅游大数据联合实验室）
University of Alcalá（阿尔卡拉大学）；European Commission, Joint Research Center（欧盟委员会联合研究中心）
Autonomous vehicles navigate in dynamically changing environments under a wide variety of conditions, being continuously influenced by surrounding objects. Modelling interactions among agents is essential for accurately forecasting other agents' behaviour and achieving safe and comfortable motion planning. In this work, we propose SCOUT, a novel Attention-based Graph Neural Network that uses a flexible and generic representation of the scene as a graph for modelling interactions, and predicts socially-consistent trajectories of vehicles and Vulnerable Road Users (VRUs) under mixed traffic cond…
We present ProxEmo, a novel end-to-end emotion prediction algorithm for socially aware robot navigation among pedestrians. Our approach predicts the perceived emotions of a pedestrian from walking gaits, which is then used for emotion-guided navigation taking into account social and proxemic constraints. To classify emotions, we propose a multi-view skeleton graph convolution-based model that works on a commodity camera mounted onto a moving robot. Our emotion recognition is integrated into a mapless navigation scheme and makes no assumptions about the environment of pedestrian motion. It achi…
Beihang University（北京航空航天大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）
Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment. The MAB framework, however, overlooks the scenario where the decision-maker cannot take actions (e.g., pulling arms) directly. It is a practically important scenario in many applications such as spectrum sharing, crowdsensing, and edge computing. In these applications, the decision-maker would incentivize other selfish agents to carry out desired actions (i.e., pulling arms on the decision-maker's behalf). This paper establishes the incentivized online learning…
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks still struggle to generalize in closed-loop predictions (e.g., output colliding trajectories). This issue largely arises from the non-i.i.d. nature of sequential prediction in conjunction with ill-distributed training data. Intuitively, if the training data only comes from human behaviors in safe spaces, i.e., from "positive" examples, it is dif…
Training Socially Aligned Language Models on Simulated Social Interactions
Google DeepMind（谷歌DeepMind）；University of British Columbia（不列颠哥伦比亚大学）；Stanford University（斯坦福大学）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；Dartmouth College（达特茅斯学院）
GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory
ETH Zürich（苏黎世联邦理工学院）；Berea College（贝雷学院）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Max Planck Institute for Intelligent Systems, Tübingen, Germany（图宾根德国智能系统马克斯·普朗克研究所）
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）
We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games -- environments wherein an agent perceives and interacts with a world through natural language. Such interactive agents are often trained via reinforcement learning to optimize task performance, even when such rewards may lead to agent behaviors that violate societal norms -- causing harm either to the agent itself or other entities in the environment. Social value alignment refers to creating agents whose behaviors conform to expected moral and social norms…
The spread of disinformation on social platforms is harmful to society. This harm may manifest as a gradual degradation of public discourse; but it can also take the form of sudden dramatic events such as the 2021 insurrection on Capitol Hill. The platforms themselves are in the best position to prevent the spread of disinformation, as they have the best access to relevant data and the expertise to use it. However, mitigating disinformation is costly, not only for implementing detection algorithms or employing manual effort, but also because limiting such highly viral content impacts user enga…
