Spatial reasoning, an important faculty of human cognition with many practical applications, is one of the core commonsense skills that is not purely language-based and, for satisfying (as opposed to optimal) solutions, requires some minimum degree of planning. Existing benchmarks of Commonsense Spatial Reasoning (CSR) tend to evaluate how Large Language Models (LLMs) interpret text-based spatial $\textit{descriptions}$ rather than directly evaluate a plan produced by the LLM in response to a $\textit{specific}$ spatial reasoning problem. In this paper, we construct a large-scale benchmark called GRASP, which consists of 16,000 grid-based environments where the agent is tasked with an energy collection problem. These environments include 100 grid instances instantiated using each of the 160 different grid settings, involving five different energy distributions, two modes of agent starting position, and two distinct obstacle configurations, as well as three kinds of agent constraints. Using GRASP, we compare classic baseline approaches, such as random walk and greedy search methods, with advanced LLMs like GPT-3.5-Turbo, GPT-4o, and GPT-o1-mini. The experimental results indicate that even these advanced LLMs struggle to consistently achieve satisfactory solutions.
CDH-Bench: A Commonsense-Driven Hallucination Benchmark for Evaluating Visual Fidelity in Vision-Language Models
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（广东省新型安全智能技术重点实验室，网络空间安全研究院，计算机科学与技术学院，哈尔滨工业大学（深圳））
Estimating Commonsense Plausibility through Semantic Shifts
CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China（中国科学院网络数据科学与技术重点实验室，计算技术研究所，中国科学院，北京，中国）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）
Generative Visual Commonsense Answering and Explaining with Generative Scene Graph Constructing
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）
Utilizing Background Knowledge for Robust Reasoning over Traffic Situations
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；Bosch Research Pittsburgh（博世研究院匹兹堡分院）；Carnegie Mellon University（卡内基梅隆大学）
SCoRE: Benchmarking Long-Chain Reasoning in Commonsense Scenarios
Center for Chinese Linguistics, Department of Chinese Language and Literature, Peking University（北京大学中文语言文学系中文语言学中心）；School of Computer Science, State Key Laboratory of Multimedia Information Processing, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；Huawei Noah’s Ark Lab, China（华为诺亚实验室，中国）
EffiReason-Bench: A Unified Benchmark for Evaluating and Advancing Efficient Reasoning in Large Language Models
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology（香港科技大学）；Singapore Institute of Manufacturing Technology, A*STAR（新加坡制造技术研究所，A*STAR）
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
Information Sciences Institute, Viterbi School of Engineering, University of Southern California（南加州大学维特比工程学院信息科学研究所）；Department of Computer Science, Faculty of Science, Vrije Universiteit Amsterdam（阿姆斯特丹自由大学理学院计算机科学系）；Tencent AI Lab（腾讯AI实验室）
Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving
Department of Electronic Engineering, Tsinghua University, Beijing, China（清华大学电子工程系）；College of AI, Tsinghua University, Beijing, China（清华大学人工智能学院）；School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China（北京邮电大学计算机科学学院）；iFLYTEK Research, Hefei, China（iFLYTEK研究院）
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
Institute of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Shanghai AI Laboratory（上海人工智能实验室）；Huazhong University of Science and Technology（华中科技大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
Cornell University（康奈尔大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）；University of Pittsburgh（匹兹堡大学）；University of California, Berkeley（加州大学伯克利分校）
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models
Huawei Poisson Laboratory（华为泊松实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Hong Kong University of Science and Technology（香港科技大学）
