Most work on physical reasoning, both in artificial intelligence and in cognitive science, has focused on closed-world reasoning, in which it is assumed that the problem specification specifies all relevant objects and substance, all their relations in an initial situation, and all exogenous events. However, in many situations, it is important to do open-world reasoning; that is, making valid conclusions from very incomplete information. We have implemented in Prolog an open-world reasoner for a toy microworld of containers that can be loaded, unloaded, sealed, unsealed, carried, and dumped.
University of Greifswald（格赖夫斯瓦尔德大学）；Université du Luxembourg（卢森堡大学）；Freie Universität Berlin（柏林自由大学）；Fraunhofer FOKUS（弗劳恩霍夫开放通信系统研究所）；University of Miami（迈阿密大学）；University of Bamberg（班贝格大学）
Non-classical logics are used in a wide spectrum of disciplines, including artificial intelligence, computer science, mathematics, and philosophy. The de-facto standard infrastructure for automated theorem proving, the TPTP World, currently supports only classical logics. Similar standards for non-classical logic reasoning do not exist (yet). This hampers practical development of reasoning systems, and limits their interoperability and application. This paper describes the latest extension of the TPTP World, which provides languages and infrastructure for reasoning in non-classical logics. The…
Many cognitive systems deploy multiple, closed, individually consistent models which can represent interpretations of the present state of the world, moments in the past, possible futures or alternate versions of reality. While they appear under different names, these structures can be grouped under the general term of worlds. The Xapagy architecture is a story-oriented cognitive system which relies exclusively on the autobiographical memory implemented as a raw collection of events organized into world-type structures called {\em scenes}. The system performs reasoning by shadowing current eve…
Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities
The Nueva School（新维学校）；University of Southern California（南加州大学）；Notre Dame College（诺特大学）；Arizona State University（亚利桑那州立大学）；National University of Singapore（新加坡国立大学）
University of Bielefeld（比勒费尔德大学）；Ruhr University Bochum（波鸿鲁尔大学）；University of Waterloo（滑铁卢大学）；Vector Institute（矢量研究所）；University of Toronto（多伦多大学）；Stanford University（斯坦福大学）；Georgia Institute of Technology（佐治亚理工学院）
TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Zhongguancun Academy（北京中关村科学城）；Nanyang Technological University（南洋理工大学）；Guangdong Institute of Artificial Intelligence and Advanced Computing（广东省人工智能与先进计算研究院）
A fundamental question is whether Turing machines can model all reasoning processes. We introduce an existence principle stating that the perception of the physical existence of any Turing program can serve as a physical causation for the application of any Turing-computable function to this Turing program. The existence principle overcomes the limitation of the outputs of Turing machines to lists, that is, recursively enumerable sets. The principle is illustrated by productive partial functions for productive sets such as the set of the Goedel numbers of the Turing-computable total functions.…
Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；ETH Zurich（苏黎世联邦理工学院）；ETH AI Center（苏黎世联邦理工学院人工智能中心）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；Yinwang Intelligent Technology Co., Ltd.（云网智能技术有限公司）；Peking University and Beijing International Center for Mathematical Research（北京大学和北京国际数学研究中心）
Self-supervised Hierarchical Visual Reasoning with World Model
Department of Electronic Engineering and Information Science, University of Science and Technology of China（电子工程与信息科学系，中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning
1 Taobao \& Tmall Group of Alibaba, 2 Institute of Software, Chinese Academy of Science, 3 University of Chinese Academy of Sciences, 4 Renmin University of China, 5 Informatics Department, PUC-Rio
This paper presents a Bayesian framework for assessing the adequacy of a model without the necessity of explicitly enumerating a specific alternate model. A test statistic is developed for tracking the performance of the model across repeated problem instances. Asymptotic methods are used to derive an approximate distribution for the test statistic. When the model is rejected, the individual components of the test statistic can be used to guide search for an alternate model.
TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；National University of Singapore（新加坡国立大学）；Zhongguancun Academy（中关村学院）；Xiamen University（厦门大学）；Xi’an Jiaotong University（西安交通大学）；Nanyang Technological University（南洋理工大学）；Nanjing University（南京大学）
Reasoning about the behaviour of physical objects is a key capability of agents operating in physical worlds. Humans are very experienced in physical reasoning while it remains a major challenge for AI. To facilitate research addressing this problem, several benchmarks have been proposed recently. However, these benchmarks do not enable us to measure an agent's granular physical reasoning capabilities when solving a complex reasoning task. In this paper, we propose a new benchmark for physical reasoning that allows us to test individual physical reasoning capabilities. Inspired by how humans a…
In this work we propose a novel task framework under which a variety of physical reasoning puzzles can be constructed using very simple rules. Under sparse reward settings, most of these tasks can be very challenging for a reinforcement learning agent to learn. We build several simple environments with this task framework in Mujoco and OpenAI gym and attempt to solve them. We are able to solve the environments by designing curricula to guide the agent in learning and using imitation learning methods to transfer knowledge from a simpler environment. This is only a first step for the task framew…
We present a program that manages a database of temporally scoped beliefs. The basic functionality of the system includes maintaining a network of constraints among time points, supporting a variety of fetches, mediating the application of causal rules, monitoring intervals of time for the addition of new facts, and managing data dependencies that keep the database consistent. At this level the system operates independent of any measure of belief or belief calculus. We provide an example of how an application program mi9ght use this functionality to implement a belief calculus.
Do LLMs Build World Models From Text? A Multilingual Diagnostic of Spatial Reasoning
University of New South Wales（新南威尔士大学）；Essential Energy；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；Tsinghua University（清华大学）
HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds
Artyom Sorokin（AXXX）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Higher School of Economics（高等经济学院）；Kurchatov Institute（库尔斯克研究所）；Independent Researcher（独立研究者）
PhyX: Does Your Model Have the "Wits" for Physical Reasoning?
The University of Hong Kong（香港大学）；University of Michigan（密歇根大学）；Independent（独立研究者）；University of Toronto（多伦多大学）；University of Waterloo（滑铁卢大学）；The Ohio State University（俄亥俄州立大学）
University of the Basque Country (UPV/EHU)（巴斯克地区大学）
Most commonly, the Open World Assumption is adopted as a standard strategy for the design, construction and use of ontologies. This strategy limits the inferencing capabilities of any system because non-asserted statements (missing knowledge) could be assumed to be alternatively true or false. As we will demonstrate, this is especially the case of first-order logic (FOL) ontologies where non-asserted statements is nowadays one of the main obstacles to its practical application in automated commonsense reasoning tasks. In this paper, we investigate the application of the Closed World Assumption…
NASA Ames Research Center（NASA艾姆斯研究中心）；University of California, Berkeley（加州大学伯克利分校）；ISRO Satellite Center（印度空间研究组织卫星中心）
This paper presents a new technique for the design of approximate reasoning based controllers for dynamic physical systems with interacting goals. In this approach, goals are achieved based on a hierarchy defined by a control knowledge base and remain highly interactive during the execution of the control task. The approach has been implemented in a rule-based computer program which is used in conjunction with a prototype hardware system to solve the cart-pole balancing problem in real-time. It provides a complementary approach to the conventional analytical control methodology, and is of subs…
Institute for Artificial Intelligence（人工智能研究所）；University of Bremen（不来梅大学）
A key problem in the application of first-order probabilistic methods is the enormous size of graphical models they imply. The size results from the possible worlds that can be generated by a domain of objects and relations. One of the reasons for this explosion is that so far the approaches do not sufficiently exploit the structure and similarity of possible worlds in order to encode the models more compactly. We propose fuzzy inference in Markov logic networks, which enables the use of taxonomic knowledge as a source of imposing structure onto possible worlds. We show that by exploiting this…
MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
Beihang University（北京航空航天大学）；Northwestern Polytechnical University（西北工业大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；National University of Singapore（新加坡国立大学）；Peking University（北京大学）
National University of Singapore（新加坡国立大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究院）；New York University（纽约大学）；University of California Santa Cruz（加利福尼亚大学圣克鲁兹分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Recent work in computer vision and cognitive reasoning has given rise to an increasing adoption of the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by infant psychology, researchers are now evaluating a model's ability to label scenes as either expected or surprising with knowledge of only expected scenes. However, existing VoE-based 3D datasets in physical reasoning provide mainly vision data with little to no heuristics or inductive biases. Cognitive models of physical reasoning reveal infants create high-level abstract representations of objects and interactions. …
TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）
Seeing is Not Reasoning: MVPBench for Graph-based Evaluation of Multi-path Visual Physical CoT
Central South University（中南大学）；University of Electronic Science and Technology of China（电子科技大学）；Peng Cheng Laboratory（鹏城实验室）；Nanjing University（南京大学）；Microsoft（微软公司）
School of Computing, The Australian National University（澳大利亚国立大学计算学院）
Humans are well-versed in reasoning about the behaviors of physical objects and choosing actions accordingly to accomplish tasks, while it remains a major challenge for AI. To facilitate research addressing this problem, we propose a new testbed that requires an agent to reason about physical scenarios and take an action appropriately. Inspired by the physical knowledge acquired in infancy and the capabilities required for robots to operate in real-world environments, we identify 15 essential physical scenarios. We create a wide variety of distinct task templates, and we ensure all the task te…
Physical reasoning requires forward prediction: the ability to forecast what will happen next given some initial world state. We study the performance of state-of-the-art forward-prediction models in the complex physical-reasoning tasks of the PHYRE benchmark. We do so by incorporating models that operate on object or pixel-based representations of the world into simple physical-reasoning agents. We find that forward-prediction models can improve physical-reasoning performance, particularly on complex tasks that involve many objects. However, we also find that these improvements are contingent…
Denoising Hamiltonian Network for Physical Reasoning
Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；Harvard-Smithsonian Center for Astrophysics（哈佛-史密森尼天体物理中心）；Northeastern University（东北大学）
A common approach to solving physical reasoning tasks is to train a value learner on example tasks. A limitation of such an approach is that it requires learning about object dynamics solely from reward values assigned to the final state of a rollout of the environment. This study aims to address this limitation by augmenting the reward value with self-supervised signals about object dynamics. Specifically, we train the model to characterize the similarity of two environment rollouts, jointly with predicting the outcome of the reasoning task. This similarity can be defined as a distance measur…
Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. We test several modern learning algorithms on PHYRE and find that these algorithms fall short in solving the puzzles efficiently. We expect that PHYRE will encourage the development of novel sample-efficient agents that learn eff…
Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Microsoft（微软）；Asan Medical Center, University of Ulsan College of Medicine（釜山大学医学院阿桑医疗中心）；Samsung Medical Center, Sungkyunkwan University School of Medicine（成均馆大学医学院三星医疗中心）；Seoul National University Bundang Hospital, Seoul National University College of Medicine（首尔国立大学医学院首尔国立大学医院）
SemEval-2026 Task 12: Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China（认知与决策智能复杂系统重点实验室，自动化研究所，中国科学院，北京，中国）；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China（人工智能学院，中国科学院大学，北京，中国）
Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）
In order for AI to be safely deployed in real-world scenarios such as hospitals, schools, and the workplace, it must be able to robustly reason about the physical world. Fundamental to this reasoning is physical common sense: understanding the physical properties and affordances of available objects, how they can be manipulated, and how they interact with other objects. Physical commonsense reasoning is fundamentally a multi-sensory task, since physical properties are manifested through multiple modalities - two of them being vision and acoustics. Our paper takes a step towards real-world phys…
IoT-LLM: a framework for enhancing Large Language Model reasoning from real-world sensor data
MARS Lab, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore（MARS实验室，机械与航空航天工程学院，南洋理工大学，新加坡）；School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore（机械与航空航天工程学院，南洋理工大学，新加坡）
