Large language models (LLMs) display a unified "general factor" of capability across 10 benchmarks (a finding confirmed by our factor analysis of 156 models), yet they still struggle with simple, trivial tasks for humans. This is because current benchmarks focus on task completion, failing to probe the foundational cognitive abilities that highlight these behaviors. We address this by introducing the NeuroCognition benchmark, grounded in three adapted neuropsychological tests targeting distinct foundational cognitive components: Raven's Progressive Matrices (abstract relational reasoning), Spatial Working Memory (goal-directed spatial updating), and the Wisconsin Card Sorting Test (cognitive flexibility). Our evaluation reveals that while models perform strongly on text, their performance degrades for images and with increased complexity. Comparison with a human baseline shows that LLMs and humans fail at different parts of the same tasks. Furthermore, we observe that complex reasoning is not universally beneficial, whereas simple, human-like strategies yield partial gains. We also find that NeuroCognition correlates positively with standard general-capability benchmarks, while still measuring distinct cognitive abilities beyond them. Overall, NeuroCognition emphasizes where current LLMs align with human-like intelligence and where they lack core adaptive cognition, showing the potential to serve as a verifiable, scalable source for improving LLMs.
ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities
School of Psychology, South China Normal University（南方科技大学心理学院）；Information Hub, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息中心）；School of AI, Guangzhou University（广州大学人工智能学院）；College of Cyber Security, Jinan University（济南大学网络安全学院）
Beyond Scores: Diagnostic LLM Evaluation via Fine-Grained Abilities
College of Computer Science；Technology, National University of Defense Technology, Changsha, Hunan, China；State Key Laboratory of Complex \& Critical Software Environment, Changsha, Hunan, China；National Key Laboratory of Parallel；School of Humanities；Social Sciences, School of Public Administration, Beihang University, Beijing, China
AI Models Can Predict and Collaboratively Modulate Human Memory Search
Champalimaud Research（尚帕利莫德研究院）；Champalimaud Centre for Restorative Neurotechnology（尚帕利莫德修复神经技术中心）；New York University Tandon（纽约大学坦登工程学院）；Indiana University（印第安纳大学）
Vision Language Models See What You Want but not What You See
Johns Hopkins University（约翰霍普金斯大学）；University of California San Diego（加州大学圣地亚哥分校）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Michigan（密歇根大学）；Carnegie Mellon University（卡内基梅隆大学）
Caption This, Reason That: VLMs Caught in the Middle
Integrated Program in Neuroscience (IPN) McGill University（神经科学联合计划 麦吉尔大学）；Mila, University of Montreal（蒙特利尔大学Mila）；Microsoft Research USA（微软研究院美国总部）；Department of Physiology McGill University（生理学系 麦吉尔大学）
GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments
Yongjiang Laboratory（甬江实验室）；University of Nottingham Ningbo China（宁波诺丁汉大学）；The University of Hong Kong（香港大学）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges
Kyoto University（京都大学）；Indiana University（印第安纳大学）；National Taiwan Normal University（台湾师范大学）；Georgia Institute of Technology（佐治亚理工学院）；Hong Kong University of Science（香港科学大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Cornell University（康奈尔大学）；University of Liverpool, UK（利物浦大学）；University of Edinburgh, UK（爱丁堡大学）；Zhejiang University（浙江大学）；Purdue University（普渡大学）；Emory University（埃默里大学）；West China Biomedical Big Data Center, West China Hospital, Sichuan University（四川大学西昌生物大数据中心、西昌医院）
Mind meets machine: Unravelling GPT-4's cognitive psychology
Nowrosjee Wadia College（诺罗斯吉·瓦迪亚学院）；Indian Institute of Tropical Meteorology（印度热带气象研究所）；Defence Institute Of Advanced Technology（国防先进技术研究所）；Queen Mary University of London（伦敦玛丽女王大学）
Visual Large Language Models Exhibit Human-Level Cognitive Flexibility in the Wisconsin Card Sorting Test
Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences（脑图谱与脑启发智能实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Inria centre at the university of Bordeaux, Neurodegenerative Diseases Institute（波尔多大学Inria中心，神经退行性疾病研究所）
Do Large Language Models Mirror Cognitive Language Processing?
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；College of Computer and Information Engineering, Tianjin Normal University（天津师范大学计算机与信息工程学院）
Validating the Effectiveness of a Large Language Model-based Approach for Identifying Children's Development across Various Free Play Settings in Kindergarten
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
Indian Institute of Technology Kharagpur（印度理工学院Khargapur分校）；Rochester Institute of Technology（罗切斯特理工学院）；Researcher, Fatima Fellowship（研究员，Fatima fellowship）；AI Institute, University of South Carolina（人工智能研究所，南卡罗来纳大学）；Amazon GenAI（亚马逊生成人工智能）；Stanford University（斯坦福大学）；James Silberrad Brown Center for AI, San Diego State University（詹姆斯·西伯拉德·布朗人工智能中心，圣地亚哥州立大学）
Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents
Department of Computer Science, Princeton University（普林斯顿大学计算机科学系）；Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology（麻省理工学院脑科学与认知科学系）；Zuckerman Mind Brain Behavior Institute（祖克曼心智大脑行为研究所）；Department of Psychiatry, Columbia University（哥伦比亚大学精神医学系）；Neuroscience Institute（神经科学研究所）；Department of Machine Learning, Carnegie Mellon University（卡内基梅隆大学机器学习系）；Department of Psychology, Princeton University（普林斯顿大学心理学系）
Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models
University of British Columbia（不列颠哥伦比亚大学）；Zuse School（Zuse学校）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
SocialAI 0.1: Towards a Benchmark to Stimulate Research on Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
Inria（法国国家信息与自动化研究所）；Microsoft Research（微软研究院）
构建能够参与与人类的社会互动的嵌入式自主智能体是人工智能的主要挑战之一。这个问题激发了许多关于具身语言使用的研究方向。目前的方法集中在语言作为一种沟通工具，在非常简单和非多样化的社会情况下：语言的“自然性”被降低到高词汇量和可变性的概念。在本文中，我们认为，要实现人类水平的人工智能，需要更广泛的关键社交技能：1）在复杂多变的社会环境中使用语言; 2）在不断发展的社会世界中，在多模态环境中进行复杂的具体沟通。在这项工作中，我们解释了认知科学的概念如何帮助人工智能绘制通往类人智能的路线图，重点是其社会维度。然后，我们研究了最近SOTA Deep RL方法在即将推出的SocialAI的第一个网格世界环境中进行测试时的局限性，SocialAI是评估Deep RL代理社交技能的基准。视频和代码可在https://sites.google.com/view/socialai01上获得。
Human-like Cognitive Generalization for Large Models via Brain-in-the-loop Supervision
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Lab of Brain-Machine Intelligence, Zhejiang University（浙江大学脑机智能全国重点实验室）
