The computational demands of modern AI services are increasingly shifting execution beyond centralized clouds toward a computing continuum spanning edge and end devices. However, the scale, heterogeneity, and cross-layer dependencies of these environments make resilience difficult to maintain. Existing fault-management methods are often too static, fragmented, or heavy to support timely self-healing, especially under noisy logs and edge resource constraints. To address these limitations, this paper presents NeSy-Edge, a neuro-symbolic framework for trustworthy self-healing in the computing continuum. The framework follows an edge-first design, where a resource-constrained edge node performs local perception and reasoning, while a cloud model is invoked only at the final diagnosis stage. Specifically, NeSy-Edge converts raw runtime logs into structured event representations, builds a prior-constrained sparse symbolic causal graph, and integrates causal evidence with historical troubleshooting knowledge for root-cause analysis and recovery recommendation. We evaluate our work on representative Loghub datasets under multiple levels of semantic noise, considering parsing quality, causal reasoning, end-to-end diagnosis, and edge-side resource usage. The results show that NeSy-Edge remains robust even at the highest noise level, achieving up to 75% root-cause analysis accuracy and 65% end-to-end accuracy while operating within about 1500 MB of local memory.
Department of Computer Science and Leuven.AI, KU Leuven, Belgium（1 计算机科学系和 Leuven.AI，KU Leuven，比利时）；Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Sweden（3 应用自主传感器系统中心（AASS），Örebro大学，瑞典）
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
DISI, University of Trento（特伦托大学DISI学院）；DI, University of Pisa（比萨大学DI学院）；University of Edinburgh（爱丁堡大学）；CIMeC&DISI, University of Trento（特伦托大学CIMeC&DISI学院）
A Learnability Analysis on Neuro-Symbolic Learning
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）
NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing
State Key Laboratory for Novel Software Technology, Nanjing University, China（新型软件技术国家重点实验室，南京大学）；School of Artifical Intelligence, Nanjing University, China（人工智能学院，南京大学）；School of Intelligence Science and Technology, Nanjing University, China（智能科学与技术学院，南京大学）
NeSyGeo: A Neuro-Symbolic Framework for Multimodal Geometric Reasoning Data Generation
School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）；National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）；School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）
Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How
Institute for Computational Biomedicine, RWTH Aachen University（计算生物医学研究所，亚琛工业大学）；Center for Computational Life Sciences, RWTH Aachen University（计算生命科学中心，亚琛工业大学）
A homotopy-type-theoretic generalization of neurosymbolic inference
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；KAUST Center of Excellence for Smart Health (KCSH)（KAUST智能健康卓越中心）；KAUST Center of Excellence for Generative AI（KAUST生成式人工智能卓越中心）
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）
LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation
Carnegie Mellon University（卡内基梅隆大学）；University of Toronto（多伦多大学）；University at Buffalo（布法罗大学）；University of Pennsylvania（宾夕法尼亚大学）；Centaur AI Institute（半人马人工智能研究所）；Mila（米拉研究所）
Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
Department of Information Engineering and Computer Science, University of Trento, Italy（特伦托大学信息工程与计算机科学系）；CIMeC, University of Trento, Rovereto, Italy（特伦托大学罗韦雷托CIMeC）
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）
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
University at Buffalo（布法罗大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Carnegie Mellon University（卡内基梅隆大学）；ETH Zürich（苏黎世联邦理工学院）；Purdue University（普渡大学）
Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing
University of Bologna（博洛尼亚大学）；I Tatti — The Harvard University Center for Italian Renaissance Studies（哈佛大学意大利文艺复兴研究中心（I Tatti））；University of Copenhagen（哥本哈根大学）
