BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning
School of AI for Science, Peking University（科学人工智能学院，北京大学）；School of Electronic and Computer Engineering, Peking University（电子与计算机工程学院，北京大学）；School of Computer Science, Peking University（计算机科学学院，北京大学）
Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs. In these irreversible environments, probabilistic hallucinations are not merely incorrect; they can cause equipment damage or experimental failure. We propose BioProAgent, a neuro-symbolic framework that anchors probabilistic planning in a deterministic Finite State Machine (FSM). We introduce a State-Augmented Planning mechanism that enforces a rigorous Design-Verify-Rectify workflow, ensuring hardware compliance before execution. Furthermore, we address the context bottleneck inherent in complex device schemas by Semantic Symbol Grounding, reducing token consumption by ~6* through symbolic abstraction. In the extended BioProBench benchmark, BioProAgent achieves 95.6% physical compliance (compared to 21.0% for ReAct), demonstrating that neuro-symbolic constraints are essential for reliable autonomy in irreversible physical environments. Code: https://github.com/YuyangSunshine/bioproagent | Website: https://yuyangsunshine.github.io/BioPro-Project.
KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction
National Institute of Technology Kurukshetra（国立库鲁克舍特拉理工学院）；Indian Institute of Information Technology Manipur（印度曼尼普尔信息技术学院）；Purdue University（普渡大学）；Indian Institute of Technology Indore（印度印多尔理工学院）
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World
Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
University of Trento（特伦托大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Sapienza University of Rome（罗马大学）；University of Edinburgh（爱丁堡大学）；Huawei Labs（华为实验室）
Exploring the Paradigm Shift from Grounding to Skolemization for Complex Query Answering on Knowledge Graphs
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Data Science, Lingnan University（岭南大学数据科学学院）；School of Science and Technology, Hong Kong Metropolitan University（香港理工大学科技学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
State Key Lab of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；University of Toronto（多伦多大学）；Birkbeck, University of London（伦敦大学伯贝克学院）
Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
Golden Gate University, San Francisco Foundation（金门大学，旧金山基金会）；AgenticOS (FAOS)（AgenticOS（FAOS））；Associate Director, Data, Digital & IT Novartis Healthcare Pvt. Ltd.（数据、数字与IT部门，诺华健康有限公司）；Novartis Healthcare Pvt. Ltd., Hyderabad, India（诺华健康有限公司，海得拉巴，印度）
NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
Department of Computer Science, AI Institute, University of South Carolina（南卡罗来纳大学计算机科学系，人工智能研究所）；Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校计算机科学与电气工程系）；Department of Computer Science and Engineering, The Ohio State University（俄亥俄州立大学计算机科学与工程系）
Protect$^*$: Steerable Retrosynthesis through Neuro-Symbolic State Encoding
Deep Forest Sciences（深林科技）；Departament de Farmacologia, Toxicologia i Química Terapèutica, Universitat de Barcelona（巴塞罗那大学药理学、毒理学与治疗化学系）；Institut de Nanociència i Nanotecnologia IN2UB, Universitat de Barcelona（巴塞罗那大学纳米科学与纳米技术研究所）
GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning
Technical University of Darmstadt（达姆施塔特技术大学）；Google Intrinsic（谷歌内在）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Centre for Cognitive Science, TU Darmstadt（达姆施塔特技术大学认知科学中心）
Neurosymbolic Retrievers for Retrieval-augmented Generation
Dept. of CSEE University of Maryland Baltimore County, Maryland, USA（电子工程系大学马里兰大学巴尔的摩县）；Dept. of CSEE University of Maryland Baltimore County, MD, USA（电子工程系大学马里兰大学巴尔的摩县）
Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities
Department of Information Systems, University of Maryland, Baltimore County（信息系统系，马里兰大学巴尔的摩分校）；Department of Computer Science and Engineering, University at Buffalo（计算机科学与工程系，布法罗大学）；Department of Computer Science, University of Colorado Boulder（计算机科学系，科罗拉多大学博尔德分校）；Laboratory for Cybersecurity Dynamics, Department of Computer Science, University of Colorado Colorado Springs（网络安全动力实验室，科罗拉多大学科罗拉多斯普林斯分校）
State Key Lab of CAD&CG, Zhejiang University（浙江大学计算机辅助设计与图形学国家重点实验室）；Polytechnic Institute, Zhejiang University（浙江大学多科大学院）；Hangzhou Research Institute of AI and Holographic Technology（杭州人工智能与全息技术研究 institutes）；Volkswagen Group Innovation（大众集团创新）；School of Mathematical Science, Zhejiang University（浙江大学数学科学学院）
IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）
