While large language models demonstrate remarkable capabilities across numerous domains, social intelligence - the capacity to perceive social cues, infer mental states, and generate appropriate responses - remains a critical challenge, particularly for enabling effective human-AI collaboration and developing AI that truly serves human needs. Current models often rely on superficial patterns rather than genuine social reasoning. We argue that cultivating human-like social intelligence requires training with challenging cases that resist shortcut solutions. To this end, we introduce ToMBench-Hard, an adversarial benchmark designed to provide hard training examples for social reasoning. Building on this, we propose Social-R1, a reinforcement learning framework that aligns model reasoning with human cognition through multi-dimensional rewards. Unlike outcome-based RL, Social-R1 supervises the entire reasoning process, enforcing structural alignment, logical integrity, and information density. Results show that our approach enables a 4B parameter model to surpass much larger counterparts and generalize robustly across eight diverse benchmarks. These findings demonstrate that challenging training cases with trajectory-level alignment offer a path toward efficient and reliable social intelligence.
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）
R^3-VQA: "Read the Room" by Video Social Reasoning
School of Intelligence Science and Technology, Peking University（北京理工大学智能科学与技术学院）；College of Artificial Intelligence, Xi’an Jiaotong University（西安交通大学人工智能学院）；State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence（一般人工智能国家重点实验室）；Yuanpei College, Peking University（北京大学元培学院）；Tsinghua University（清华大学）；University of California, Los Angeles（加州大学洛杉矶分校）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）
Cognitive Chain-of-Thought (CoCoT): Structured Multimodal Reasoning about Social Situations
Seoul National University（首尔国立大学）；Human-Computer Interaction Institute, Carnegie Mellon University（人机交互研究所，卡内基梅隆大学）；Language Technologies Institute, Carnegie Mellon University（语言技术研究所，卡内基梅隆大学）
How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs
The University of Osaka（大阪大学）；UC Davis（加州大学戴维斯分校）；UC Berkeley（加州大学伯克利分校）；Peking University（北京大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Shanghai Jiao Tong University（上海交通大学）；NII（信息学研究所（日本））；City University of Hong Kong（香港城市大学）
Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving
The Hong Kong University of Science and Technology（香港科学与技术大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Zhejiang University（浙江大学）；Peking University（北京大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Surrey（萨里大学）
InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
Nankai University（南开大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai AI Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Johns Hopkins University（约翰霍普金斯大学）；Wuhan University（武汉大学）；University of Science and Technology of China（中国科学技术大学）
Acting Flatterers via LLMs Sycophancy: Combating Clickbait with LLMs Opposing-Stance Reasoning
Yangzhou University（扬州大学）；Auburn University（亚伯拉罕大学）；University of South Dakota（南达科他大学）；Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（巴黎-罗quentourt研究所）；Rajiv Gandhi University（拉贾夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒尔研究实验室）
Unraveling SITT: Social Influence Technique Taxonomy and Detection with LLMs
Warsaw University of Technology（华沙技术大学）；Wrocław University of Science and Technology（沃拉布大学科学技术学院）；University of Wrocław（沃拉布大学）；Lincoln University College, Petaling Jaya, Malaysian（林肯大学学院，吉隆坡，马来西亚）
