Frontiers in Artificial Intelligence Research
Frontiers in Artificial Intelligence Research is a scholarly journal dedicated to advancing the field of artificial intelligence (AI) through high-quality research and interdisciplinary collaboration. The journal serves as a platform for AI experts, computer scientists, data analysts, and professionals from various domains to share the latest innovations, trends, and applications in AI technology.
Urban Sustainability Assessment through Multi-Modal Learning: A Vision Transformer–Graph Neural Network Framework Integrating Remote Sensing and Economic Indicators
Urban sustainability assessment is a crucial challenge in achieving balanced economic growth and environmental protection in modern cities. Traditional statistical evaluation methods often overlook spatial heterogeneity and environmental patterns that can be captured from remote sensing imagery. To address this limitation, this study proposes a multi-modal deep learning framework that integrates high-resolution remote sensing data with socioeconomic indicators for comprehensive urban sustainability assessment. Specifically, a Vision Transformer (ViT) is employed to extract fine-grained spatial and environmental representations—such as vegetation coverage, surface temperature, and built-up density—from Sentinel-2 satellite imagery, while a Graph Neural Network (GNN) models the spatial and economic dependencies between cities, enabling cross-modal and inter-city information fusion. The proposed ViT–GNN framework effectively captures both environmental and socioeconomic dynamics to generate a composite sustainability score. Experiments conducted on a dataset covering 120 major Chinese cities from 2019 to 2023 demonstrate that the model achieves an MSE of 0.012, an MAE of 0.071, and an () of 0.931, outperforming existing regression and CNN-based baselines. The results highlight that the model can accurately evaluate urban sustainability levels, providing an interpretable and data-driven tool for policymakers and planners to support sustainable urban development, resource allocation, and green policy formulation.
Copyright (c) 2026 Jiajiang Shen, Huaiyu Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Multi-Agent Reinforcement Learning for Language-Based Social Deduction
This study applies MARL to train LLM agents for language-based social deduction, where communication directly influences multi-agent outcomes. Speaking policies are optimized using rewards that capture the causal impact of messages on other agents’ beliefs, while listening models predict hidden state information from dialogue. Training on 12,000 simulated game episodes results in a 2.1× increase in win rate over standard RL baselines and demonstrates emergent strategic behaviors such as coordinated accusations and evidence sharing.
Copyright (c) 2026 David R. Miller, Sophie L. Gagnon, James P. Wilson (Author) https://creativecommons.org/licenses/by/4.0/
Large Language Models as Decision Support Tools in Procurement and Supplier Selection
Large language models (LLMs) have emerged as transformative technologies in enterprise decision-making, offering unprecedented capabilities in natural language processing (NLP), information synthesis, and predictive analytics. This review examines the application of LLMs as decision support tools in procurement and supplier selection processes. Procurement professionals face increasingly complex challenges in supplier evaluation, risk assessment, contract analysis, and market intelligence gathering. Traditional decision support systems (DSS) often struggle with unstructured data and nuanced linguistic information inherent in supplier communications, contracts, and market reports. LLMs, powered by transformer architectures and trained on vast corpora, demonstrate remarkable abilities in understanding context, extracting insights from diverse data sources, and generating actionable recommendations. This paper systematically reviews recent developments in LLM applications for procurement, analyzing their capabilities in supplier risk assessment, performance prediction, contract compliance monitoring, and market trend analysis. We examine how models such as GPT-4, Claude, and domain-specific variants are being integrated into procurement workflows to enhance decision quality, reduce processing time, and improve supplier relationship management. The review identifies key technical challenges including data privacy concerns, hallucination risks, integration with existing enterprise resource planning (ERP) systems, and the need for domain-specific fine-tuning. We also explore emerging research on multi-agent LLM systems for collaborative procurement decisions, retrieval-augmented generation (RAG) for supplier database queries, and interpretability mechanisms that enhance trust in LLM-generated recommendations. Our analysis reveals that while LLMs offer substantial benefits in processing efficiency and insight generation, successful implementation requires careful consideration of organizational readiness, data governance frameworks, and human oversight mechanisms.
Copyright (c) 2026 Jingyi Liu, Pan Li (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Transformer Networks Enable Nanoscale Defect Detection at Advanced Semiconductor Nodes
The relentless scaling of semiconductor manufacturing toward sub-5nm process nodes has rendered traditional optical and rule-based inspection systems increasingly inadequate for detecting nanoscale structural anomalies. This paper proposes a transformer-based deep learning framework for automated nanoscale defect detection in advanced semiconductor fabrication environments. The proposed architecture leverages self-attention mechanisms and multi-head feature extraction to capture long-range spatial dependencies across scanning electron microscopy (SEM) images of wafer surfaces, enabling high-sensitivity identification of defects including voids, bridging, line-edge roughness, and particle contamination. A hybrid pipeline combining a convolutional feature extractor with a Vision Transformer (ViT) encoder is developed and evaluated on a multi-class wafer defect dataset. Experimental results demonstrate that the proposed model achieves a defect detection accuracy of 97.3%, a false negative rate of 1.8%, and a mean average precision of 95.6%, outperforming conventional convolutional neural network (CNN)-based and support vector machine (SVM)-based baselines. The findings confirm that transformer architectures offer a compelling pathway for scalable, high-throughput semiconductor quality control, particularly as device geometries continue to shrink toward physical limits.
Copyright (c) 2026 Zhixuan Gao, Haoyu Deng, Ethan Marshall (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Generative Feature Enhancement and Metric Learning for Few-Shot Defects in Aero-Engine Blades
The structural integrity of aero-engine blades is paramount to aviation safety, requiring rigorous inspection protocols to identify surface and sub-surface defects. While deep learning has revolutionized industrial defect detection, its efficacy is severely constrained in aerospace applications by the paucity of defect samples, a condition known as the few-shot learning problem. Standard convolutional neural networks struggle to generalize from limited data, leading to overfitting and poor recognition of rare defect classes. This paper proposes a novel framework that integrates Generative Feature Enhancement with Metric Learning to address the scarcity of labeled defect data in aero-engine blades. Unlike traditional data augmentation techniques that operate in the image space, our approach synthesizes diverse training samples in the high-dimensional feature space using a modified generative adversarial network. Concurrently, a metric learning module optimizes the embedding space to ensure that defects of the same class are clustered tightly while maintaining significant separation from non-defect samples and other defect categories. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art few-shot learning algorithms in identifying micro-cracks, erosion, and ablation on blade surfaces. The dual-branch architecture ensures robust generalization capabilities even when training examples are reduced to single digits per category.
Copyright (c) 2026 Thomas Anderson, Sarah Mitchell (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Reducing Annotation Cost in Vision Language Pedestrian Re Identification via Uncertainty Driven Sampling
Scaling pedestrian re-identification for autonomous driving is limited by the cost of identity labeling across large camera networks. Inspired by CLIP-based uncertainty modal modeling, this paper proposes an active learning approach that selects labeling candidates using uncertainty in the joint vision–language embedding space. The method combines (i) uncertainty sampling for ambiguous matches, (ii) diversity sampling based on embedding coverage, and (iii) batch acquisition with redundancy control. Experiments are conducted on a large-scale dataset with 400,000 images and 50,000 identities under incremental labeling budgets from 5% to 30%. Compared with random sampling, core-set selection, and margin-based acquisition using TransReID embeddings, the proposed strategy reaches 95% of full-supervision mAP using 18%–22% fewer labeled identities, while reducing annotation time by an estimated 20%–25% under standard labeling workflows.
Copyright (c) 2026 Michael Anderson, Daniel Rodriguez, Yi Chen (Author) https://creativecommons.org/licenses/by-sa/4.0
Generative AI-Driven Optimization of Intelligent Supply Chain Decision-Making: Mechanisms and Applications
Modern supply chains face mounting complexity due to volatility in demand, frequent disruptions, and fragmented data systems. Conventional decision-making approaches, which are often rule based, siloed, and reactive, lack the adaptability required in today’s dynamic environment. Generative artificial intelligence (GenAI) offers a transformative solution by integrating heterogeneous data, generating realistic counterfactual scenarios, and enabling continuous, context-aware optimization. This paper outlines how GenAI enhances supply chain intelligence through four key mechanisms: probabilistic forecasting under uncertainty, proactive risk simulation, adaptive resource allocation using reinforcement learning, and automated generation of strategic plans from both structured and unstructured data. Real-world applications span procurement (e.g., resilient supplier selection), warehouse management (demand-responsive inventory planning), logistics (dynamic routing), and after-sales service (predictive maintenance). Looking forward, the convergence of GenAI with digital twins and multi-enterprise collaboration platforms will enable more autonomous, transparent, and resilient operations. However, effective deployment requires human-AI governance that ensures explainability, ethical alignment, and strategic oversight. By shifting supply chains from reactive problem-solving to anticipatory intelligence, GenAI paves the way for next-generation operational excellence.
Copyright (c) 2026 Yue Hao (Author) https://creativecommons.org/licenses/by/4.0
Graph Neural Networks for Detecting Anomalous Transaction Patterns in Enterprise Accounting Systems
The increasing complexity of enterprise accounting systems has created significant challenges in detecting fraudulent and anomalous transaction patterns. Traditional rule-based detection methods often fail to identify sophisticated fraud schemes that exploit network structures and temporal dependencies within transaction data. This paper proposes a graph neural network-based framework for detecting anomalous transaction patterns in enterprise accounting systems by modeling financial transactions as heterogeneous graphs. We demonstrate that graph-based representations can capture complex relationships between accounts, merchants, and transactions that are invisible to conventional point-based anomaly detection methods. Our approach leverages multi-level graph analysis to detect anomalies at transaction, account, and network levels, enabling comprehensive fraud detection across different organizational scales. The methodology incorporates temporal dynamics, heterogeneous node types, and structural features to identify suspicious patterns indicative of fraudulent activities. Experimental results demonstrate the superiority of graph neural networks over traditional machine learning approaches in detecting complex fraud schemes including collusion networks, money laundering chains, and coordinated attack patterns. The proposed framework achieves significant improvements in detection accuracy while maintaining interpretability through graph visualization and attention mechanisms that highlight suspicious substructures.
Copyright (c) 2026 Yitao Huang, Shenghan Li, Nathan Brooks (Author) https://creativecommons.org/licenses/by/4.0
Research on Interface Design Optimization of Online Learning Platforms for Universities Based on Cognitive Load Theory
Against the backdrop of deepening digital transformation in education, online teaching platforms have gradually become an important carrier connecting online and offline teaching. The interface design of university learning platforms significantly influences the distribution and regulation of cognitive load during the learning process, thus having a significant impact on learning efficiency and effectiveness. This paper, based on cognitive load theory, studies the interface design of university learning platforms. It analyzes the key design aspects affecting students' cognitive load, as well as the shortcomings of current interface design, explores the intrinsic relationship between interface characteristics and cognitive load, and proposes corresponding optimization strategies, providing theoretical and practical references for the optimization and upgrading of university learning platforms.
Copyright (c) 2026 Zhiqun Ren, Ahmadrashidi Bin Hasan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Workflow Restructuring Method for Aircraft Maintenance with AR Guidance and Version-Consistent Configuration Management
Aircraft maintenance often suffers from mismatched document versions and delays in updating task steps after software changes. This study introduces a method that delivers maintenance instructions through AR glasses and links each step to OTA version data. The method was tested in two airline hangars across 26 maintenance tasks involving junior and senior technicians. Results show that junior staff made 61.3% fewer errors, and average task time decreased by 27.5% when using AR guidance. After a software update, revised steps reached AR devices within 15 minutes, removing the usual delay found in paper and PDF procedures. These findings indicate that connecting AR guidance with OTA updates can reduce the risk of outdated instructions and improve task execution in routine maintenance. The study is limited to hangar work and a single device type, and broader testing is needed to assess performance in line maintenance and mixed fleet environments.
Copyright (c) 2026 Daniel Morrison, Sophie Reynolds, Liam Fraser (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Learning to Place Macros and Synthesize Power Grids Through Multi Agent Coordination
Modern chip design faces unprecedented challenges in optimizing macro placement and power grid synthesis simultaneously. Traditional Electronic Design Automation (EDA) approaches rely on sequential optimization strategies that fail to capture the complex interdependencies between macro positioning and power distribution networks. This paper presents a novel framework leveraging multi-agent reinforcement learning for coordinated macro placement and power grid synthesis. Our approach employs multiple specialized agents that collaboratively optimize placement objectives including wirelength minimization, congestion reduction, and power integrity. Through a hierarchical coordination mechanism, agents negotiate placement decisions while maintaining awareness of power delivery constraints. Experimental results on industrial benchmarks demonstrate that our multi-agent coordination framework achieves 12.3% improvement in wirelength, 15.7% reduction in congestion hotspots, and 18.2% enhancement in IR drop metrics compared to conventional single-agent and sequential optimization methods. The framework exhibits strong scalability properties, handling designs with over 1000 macros while maintaining solution quality. This work demonstrates that multi-agent coordination provides a promising paradigm for addressing the increasing complexity of modern chip design challenges.
Copyright (c) 2026 Ziyang Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Graph Neural Networks for Timing Optimization in Advanced Node Placement
The escalating complexity of modern integrated circuit design demands innovative approaches to address timing optimization challenges in advanced technology nodes. Graph Neural Networks (GNNs) have emerged as a transformative paradigm for modeling circuit representations and optimizing placement decisions. This paper presents a comprehensive investigation of GNN applications in timing-driven placement optimization for sub-10nm process technologies. We propose a novel framework that leverages GNN architectures to encode circuit connectivity patterns, predict timing metrics, and guide placement algorithms toward solutions that minimize critical path delays while maintaining acceptable wirelength overhead. Our methodology employs a two-stage GNN model integrating global placement refinement with local timing optimization subroutines. The framework captures spatial dependencies between circuit components through message passing mechanisms while incorporating timing constraints directly into the optimization objective. Experimental evaluations on industry benchmark circuits demonstrate that GNN-based timing optimization achieves 18-24% reduction in worst negative slack compared to conventional analytical placement methods, with runtime improvements of 3-5x over traditional static timing analysis iterations. The proposed approach maintains placement quality metrics including wirelength increase below 7% and demonstrates robust convergence across diverse circuit topologies ranging from processor cores to memory controllers. This research establishes GNNs as viable alternatives to conventional timing-driven placement algorithms and opens new directions for machine learning integration in electronic design automation workflows.
Copyright (c) 2026 Jiahao Liu, Pengfei Wu, Robert Klein (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Toward Evasion-Resistant LLM Attribution with Multi-Scale Watermarking and Cryptographic Verification
Large language models (LLMs) have transformed natural language generation capabilities across numerous applications, yet their proliferation raises critical concerns regarding content attribution, intellectual property protection, and potential misuse. Watermarking techniques have emerged as promising solutions for embedding verifiable signals into LLM outputs, but existing approaches remain vulnerable to sophisticated evasion attacks that exploit detection mechanisms through adversarial modifications. This paper introduces a novel watermarking framework that integrates multi-scale semantic embedding with cryptographic verification to achieve robust attribution of LLM-generated text. Our approach operates across multiple granularity levels, from token-level perturbations to discourse-level structural patterns, while incorporating error-correcting codes and cryptographic signatures to ensure detection integrity even under aggressive tampering attempts. Through comprehensive evaluation on diverse text generation tasks, we demonstrate that our framework achieves superior robustness against paraphrasing attacks, token substitution, and deletion operations while maintaining high text quality with perplexity comparable to unwatermarked outputs. The integration of cryptographic primitives enables public verifiability without exposing watermarking keys, addressing critical security requirements for real-world deployment. Our results show detection accuracy exceeding 94 percent under various attack scenarios while preserving semantic coherence and stylistic naturalness of generated text.
Copyright (c) 2026 Pieter Janssen, Elisa Conti (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Domain-Specific Language Models with Knowledge Graph Injection and Graph Attention Networks
The rapid evolution of Large Language Models has revolutionized natural language processing, yet these models frequently exhibit limitations when deployed in specialized high-stakes domains such as medicine, law, and engineering. A primary deficiency is the propensity for hallucination and the inability to access up-to-date, structured factual knowledge that was not present or emphasized during the pre-training phase. This paper proposes a novel architecture that integrates Domain-Specific Knowledge Graphs with pre-trained language models utilizing Graph Attention Networks. By employing a dual-stream mechanism that processes textual input alongside structured graph data, we facilitate a deep injection of semantic relationships into the latent space of the language model. The Graph Attention Network component dynamically weighs the importance of neighboring entities within the knowledge graph, allowing the model to attend to the most relevant factual context corresponding to the input query. We evaluate this approach on two distinct domain-specific datasets involving biomedical and legal texts. Our experimental results demonstrate that this injection mechanism significantly outperforms standard fine-tuning approaches in terms of factual accuracy and reasoning capabilities. The proposed method offers a scalable pathway toward creating more reliable and logically sound domain-specific artificial intelligence systems.
Copyright (c) 2026 Jean Dupont, Marie Laurent, Michael Reynolds (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Integrating Symbolic Reasoning into Neural Networks: A Neuro-Symbolic Logic Programming Approach for Enhanced Explainability
The dichotomy between sub-symbolic connectionist approaches and symbolic logic-based systems constitutes a fundamental divide in the history of artificial intelligence. While deep neural networks have achieved unprecedented success in perceptual tasks such as image recognition and natural language processing, they continue to suffer from a lack of interpretability and a tendency to fail in scenarios requiring rigorous logical consistent reasoning. Conversely, symbolic systems offer high explainability and verifiable reasoning chains but struggle with the noise and ambiguity inherent in real-world sensory data. This paper proposes a unified Neuro-Symbolic Logic Programming framework that integrates differentiable logic layers within deep neural architectures. By mapping logical predicates to continuous real-valued tensors and relaxing Boolean operators into differentiable functions, we enable end-to-end training of systems that possess both the learning capability of neural networks and the reasoning structure of logic programming. Our experimental results demonstrate that this hybrid approach not only matches state-of-the-art performance in complex reasoning tasks but also significantly outperforms baseline models in terms of explainability and data efficiency. The framework allows for the extraction of explicit logical rules from trained networks, providing a window into the decision-making process of the model and bridging the gap between data-driven learning and knowledge-based reasoning.
Copyright (c) 2026 Arthur Hamilton, Claire Vance, Eleanor P. Wright (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Optimization of Adaptive Prompt Engineering for Large Language Models via Bayesian Inference in Low-Resource Settings
The rapid proliferation of Large Language Models (LLMs) has necessitated the development of effective prompt engineering strategies to harness their full potential. However, the stochastic nature of LLM outputs and the vast, discrete combinatorial space of natural language make manual prompt design a laborious and often suboptimal process. While automated prompt optimization techniques exist, they typically require significant computational resources, massive datasets, or access to model gradients, rendering them inaccessible for low-resource environments and edge computing applications. This paper proposes a novel framework for optimizing adaptive prompt engineering using Bayesian Inference. We introduce a sample-efficient methodology that treats prompt selection as a black-box optimization problem, utilizing Gaussian Processes to model the latent manifold of prompt effectiveness. By strategically balancing exploration and exploitation through acquisition functions, our approach converges on high-performing prompts with significantly fewer model queries than traditional grid search or reinforcement learning paradigms. We demonstrate that this method achieves state-of-the-art performance on reasoning benchmarks while reducing token consumption by an order of magnitude, making advanced prompt engineering feasible for academic laboratories and limited-budget applications.
Copyright (c) 2026 Arthur J. Williams, Katherine L. McDough, Thomas S. Halloway (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Theoretical Mechanisms of How AI-Enabled Driver Assistance Systems Influence Consumers’Automobile Choice Behavior
This study develops a theoretical mechanism-based framework to explain how artificial intelligence (AI)-enabled driver assistance systems influence consumers’ automobile choice behavior. While research on AI-enabled driver assistance has largely focused on engineering feasibility, system reliability, and safety performance, consumer research has not sufficiently theorized how AI systems’ autonomy-related properties reshape consumers’ cognitive evaluations and decision processes. Distinct from conventional vehicle attributes, AI-enabled driver assistance systems constitute intelligent socio-technical configurations that participate in driving decision-making, thereby introducing unique psychological concerns such as perceived autonomy, controllability, transparency, and responsibility ambiguity. Building on prior work in consumer choice, technology acceptance, and trust–risk theories, this study conceptualizes four key perceived attributes—perceived intelligence, perceived autonomy, perceived controllability, and perceived transparency—and argues that AI-enabled driver assistance affects automobile choice behavior primarily through mediated pathways of trust formation and risk/uncertainty appraisal. The framework further clarifies that the choice effects of AI-enabled driver assistance are not uniformly positive; rather, they depend on consumers’ perceived trade-offs between anticipated technological benefits and psychological uncertainty. By theorizing AI-enabled driver assistance as an autonomy-bearing technology element in consumption contexts, the study extends consumer choice theorizing into AI settings, refines conceptual distinctions between objective technical capability and subjective perceived attributes, and offers an integrative mechanism model that can guide future empirical work and managerial practice in intelligent vehicle design and communication.
Copyright (c) 2026 Yanqing Zhou, Bowen Shi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Security-Constrained Graph Neural Clustering for Industrial Manufacturing Systems under OT and Hardware Constraints
Industrial manufacturing systems increasingly rely on tightly coupled operational technology (OT) networks and heterogeneous hardware platforms, which exposes them to sophisticated multi-stage cyber attacks that propagate across devices, control logic, and production processes. Existing graph-based security analytics often overlook OT process semantics and hardware constraints, resulting in unstable clustering results and limited practical deployability. This paper proposes a Security-Constrained Graph Neural Clustering framework for industrial manufacturing systems that explicitly incorporates OT semantics and hardware constraints into attack-chain analysis. The manufacturing environment is modeled as a heterogeneous graph integrating OT assets, communication and control relationships, process-stage dependencies, and device-level resource limitations. A security-oriented clustering objective is designed to aggregate related assets into attack-chain communities while enforcing OT-consistent propagation and hardware-feasible inference. The proposed framework further enhances robustness under incomplete or noisy telemetry and provides interpretable community-level risk indicators to support security operations. Experimental results on industrial manufacturing datasets demonstrate that the proposed approach achieves more coherent and stable attack-chain communities than representative baselines, while maintaining computational efficiency suitable for deployment in resource-constrained OT environments.
Copyright (c) 2026 Fu Wang, Liang Zhang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Quantifying the Interplay Between Panic Propagation and Misinformation on Social Media Using Large Language Models
The digital age has catalyzed a phenomenon where information diffusion occurs at unprecedented velocities, often outpacing the capacity for verification. This paper investigates the symbiotic relationship between panic propagation and the spread of misinformation on social media platforms during crisis events. While traditional sentiment analysis and fact-checking systems have operated in isolation, we propose a novel framework that utilizes Large Language Models (LLMs) to jointly model these phenomena. By leveraging the semantic reasoning capabilities of state-of-the-art LLMs, we quantify the causality and temporal lag between exposure to falsified narratives and the subsequent escalation of collective anxiety. Our methodology introduces the Panic-Misinformation Interaction Index (PMII), a metric derived from high-dimensional embedding spaces, to measure the volatility of public discourse. We evaluate our approach on a massive dataset curated from social media feeds during recent global health emergencies. The results demonstrate that misinformation does not merely accompany panic but acts as a primary accelerant, with a quantifiable amplification factor. Furthermore, our LLM-driven approach outperforms baseline deep learning models in predictive accuracy regarding the trajectory of social hysteria.
Copyright (c) 2026 Chen Xie (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
SecHOT-GNC: Security-Oriented Hardware- and OT-Aware Graph Neural Clustering for Attack-Chain Community Detection in Industrial Fiber Manufacturing Systems
Industrial fiber manufacturing systems increasingly rely on tightly coupled operational technology (OT) networks and heterogeneous hardware controllers, making them vulnerable to multi-stage attack chains that traverse cyber–physical dependencies. Existing graph-based security analytics often ignore hardware constraints and OT process semantics, leading to unstable communities, weak attack-chain consistency, and limited deployability on shop-floor compute. This paper proposes SecHOT-GNC, a security-oriented, hardware- and OT-aware graph neural clustering framework for attack-chain community detection in industrial fiber manufacturing. We model the plant as a multi-layer heterogeneous graph that integrates OT assets (PLCs, HMIs, drives, sensors), communication flows, process topology, and hardware attributes (resource budgets, firmware/OS class, interface types, timing constraints). SecHOT-GNC couples OT-aware message passing with a security-driven clustering objective that aligns communities with plausible attacker paths by jointly optimizing (i) attack-chain consistency, (ii) OT-process coherence, (iii) hardware feasibility under on-device constraints, and (iv) robustness to noisy/partial telemetry. The framework further produces interpretable community rationales via edge/feature attributions and yields community-level risk scores to support prioritization of defense actions. Experiments on industrial fiber manufacturing datasets and attack simulations demonstrate that SecHOT-GNC improves attack-chain community quality and stability over representative baselines, while maintaining practical inference latency and memory footprints suitable for edge/plant deployment.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Learning Risk and Communities in Complex Systems: A Review of Temporal Models, Graph Neural Networks, and Fourier Approaches
Risk assessment and community detection are two central problems in data-driven decision systems, spanning finance, infrastructure, cybersecurity, transportation, and social networks. In modern settings, both tasks are increasingly defined by (i) temporal complexity (non-stationarity, regime shifts, delays), (ii) relational structure (interacting agents and cascading effects), and (iii) multi-scale frequency behavior (smooth trends vs. abrupt anomalies), motivating learning frameworks that unify deep temporal models, graph representation learning, and Fourier/spectral operators. This review synthesizes progress across three complementary axes: deep temporal learning (e.g., sequence models and transformers for forecasting, anomaly detection, and early-warning), graph-based learning (GNNs, graph transformers, spatio-temporal GNNs, and temporal graph neural networks), and Fourier/spectral learning (graph Fourier transform, spectral filters, wavelets, and frequency-aware graph architectures). We provide a taxonomy that maps model families to learning settings and objectives, compare methods under shared evaluation protocols, and highlight practical design trade-offs such as scalability, stability, interpretability, and robustness. Finally, we outline open challenges—dataset realism, dynamic community ground truth, distribution shift, and frequency-domain generalization—and propose a benchmarking checklist to support reproducible research across risk prediction and community discovery.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
A Hybrid Neural–Symbolic Framework for Interpretable Fault Diagnosis in Rotating Machinery
The advent of Industry 4.0 has precipitated a paradigm shift in the maintenance strategies of complex industrial systems, particularly rotating machinery, moving from reactive to predictive maintenance regimes. While deep learning models have achieved state-of-the-art performance in fault diagnosis due to their powerful feature extraction capabilities, they suffer from a critical lack of transparency, often described as the black-box problem. This opacity hinders their adoption in safety-critical environments where understanding the etiology of a fault is as significant as its detection. This paper proposes a novel Hybrid Neural–Symbolic Framework (HNSF) that integrates the perceptual capabilities of deep neural networks with the inferential transparency of symbolic logic. We utilize a One-Dimensional Convolutional Neural Network (1D-CNN) to extract high-level latent features from raw vibration signals, which are subsequently mapped to semantic concepts within a predefined knowledge graph. A differentiable reasoning layer then applies First-Order Logic rules to these concepts to deduce fault classes, ensuring that the model's predictions are consistent with domain knowledge. Experimental validation on the Case Western Reserve University (CWRU) bearing dataset demonstrates that our framework not only achieves classification accuracy comparable to pure deep learning models but also provides human-readable explanations for its decisions. The results suggest that bridging the subsymbolic and symbolic gap offers a robust pathway toward trustworthy industrial artificial intelligence.
Adversarially Robust Sequence Models with Frequency-Domain Consistency Regularization
The deployment of sequence models in safety-critical applications, ranging from automated financial trading to clinical narrative analysis, is currently hindered by their susceptibility to adversarial perturbations. While adversarial training has emerged as a rigorous defense mechanism, it predominantly operates in the time or token domain, often failing to account for the spectral characteristics of the input data where adversarial noise tends to concentrate. In this paper, we introduce Frequency-Domain Consistency Regularization (FDCR), a novel architectural constraint that enforces semantic invariance across the spectral decomposition of latent representations. By leveraging the Discrete Fourier Transform (DFT) within the training loop, FDCR penalizes discrepancies between the high-frequency components of clean and perturbed sequences, effectively filtering out non-robust features that contribute to model fragility. We provide a theoretical analysis demonstrating that spectral regularization tightens the generalization bound for recurrent architectures under min-max perturbation constraints. Extensive experiments on standard benchmarks verify that FDCR significantly outperforms state-of-the-art adversarial defense methods in maintaining robust accuracy while mitigating the trade-off with clean performance.
A Uncertainty-Calibrated Transformer for Long-Horizon Forecasting with Missing and Irregular Observations
The proliferation of Internet of Things (IoT) devices and distributed sensor networks has resulted in an explosion of time-series data, yet the utility of this data is frequently compromised by irregularities, such as missing observations, non-uniform sampling rates, and sensor failures. While Transformer-based architectures have established a new state-of-the-art in sequence modeling, standard implementations rely implicitly on fixed-interval discrete time steps, rendering them suboptimal for irregular temporal environments. Furthermore, long-horizon forecasting inherently involves accumulating errors, necessitating robust uncertainty quantification to support downstream decision-making processes. This paper introduces the Uncertainty-Calibrated Continuous Transformer (UCCT), a novel architecture designed to address these dual challenges simultaneously. We propose a continuous-time positional encoding mechanism that leverages the temporal timestamps directly, decoupling the model from the rigid index-based sequence assumption. Additionally, we integrate a probabilistic decoding head that outputs distributional parameters rather than point estimates, calibrated via a composite loss function balancing accuracy and aleatoric uncertainty estimation. Extensive experiments on real-world datasets, including energy consumption, meteorology, and healthcare telemetry, demonstrate that the proposed UCCT outperforms current deterministic and stochastic baselines. Specifically, the model achieves a reduction in Mean Squared Error by approximately 14% in scenarios with 50% missing data, while providing reliable confidence intervals that accurately capture the increasing variance over long forecast horizons.
Retrieval-Augmented Graph Reasoning with Large Language Models for Explainable Incident Diagnosis
The increasing complexity of modern distributed systems has created significant challenges in incident diagnosis and root cause analysis. Traditional approaches often lack explainability and struggle with the dynamic nature of system failures, while pure machine learning methods suffer from limited interpretability and contextual understanding. This paper proposes a novel framework that integrates Retrieval-Augmented Generation (RAG) with graph-based reasoning and Large Language Models (LLMs) to enable explainable incident diagnosis in complex systems. The proposed approach leverages knowledge graphs to capture causal relationships among system components, employs retrieval mechanisms to access relevant historical incident data, and utilizes LLMs to generate human-interpretable explanations for diagnosed incidents. Through comprehensive evaluation on real-world incident datasets, our method demonstrates superior performance in fault localization accuracy, achieving 92.3% precision while providing transparent reasoning paths that enhance engineer trust and accelerate remediation workflows. The framework addresses critical limitations in existing approaches by combining the structured reasoning capabilities of graph neural networks with the semantic understanding and generation abilities of large language models, thereby advancing the state-of-the-art in intelligent operations and system reliability engineering.
Adaptive Query Decomposition via Learned Symbolic Primitives in Retrieval-Augmented Systems
Retrieval-Augmented Generation systems have emerged as a promising paradigm for enhancing large language models with external knowledge retrieval capabilities. However, complex user queries often require sophisticated decomposition strategies to effectively retrieve relevant information from heterogeneous knowledge bases. This paper presents a novel framework for adaptive query decomposition that leverages learned symbolic primitives to intelligently break down complex queries into retrievable sub-components. Our approach combines neural representation learning with symbolic reasoning mechanisms to create a hybrid architecture that maintains interpretability while achieving robust performance across diverse query types. The proposed system employs a hierarchical decomposition strategy where queries are first analyzed to identify semantic components, which are then mapped to learned symbolic primitives representing fundamental information-seeking patterns through semantic pattern tree construction. These primitives guide the retrieval process through structured query reformulation and contextual expansion across distributed processing layers. Experimental evaluation demonstrates that our method achieves significant improvements in retrieval precision and answer quality compared to baseline neural ranking approaches, particularly for multi-hop reasoning tasks requiring integration of information from multiple sources.
Learning Driven Decision Intelligence for Autonomous Driving Through Multimodal Understanding World Modeling and Policy Optimization
Autonomous driving represents one of the most challenging applications of artificial intelligence (AI), requiring sophisticated decision-making capabilities that integrate perception, prediction, and planning under dynamic and uncertain conditions. Recent advances in learning-driven approaches have demonstrated remarkable potential in addressing these challenges through multimodal understanding, world modeling, and policy optimization. Deep learning (DL) techniques enable vehicles to process heterogeneous sensory inputs including camera images, LiDAR point clouds, and radar signals to construct comprehensive environmental representations. World models provide predictive frameworks that simulate future scenarios and potential outcomes, allowing autonomous systems to anticipate complex traffic dynamics and make informed decisions. Reinforcement learning (RL) and imitation learning methods optimize driving policies through interaction with real and simulated environments, progressively improving decision quality and safety. This review examines the current state of learning-driven decision intelligence in autonomous driving, analyzing how multimodal perception architectures extract meaningful features from diverse sensor modalities, how world modeling techniques enable forward-looking planning capabilities, and how policy optimization frameworks translate environmental understanding into safe and efficient driving behaviors. We synthesize recent developments in transformer-based architectures, neural rendering approaches, and end-to-end learning systems that directly map sensory inputs to control actions. The integration of these components presents both significant opportunities and substantial challenges, including handling distribution shifts between training and deployment scenarios, ensuring robustness to adversarial conditions, and achieving the safety guarantees required for real-world deployment.
Machine Learning Methods for Financial Forecasting in Enterprise Planning: Transitioning from Rule-Based Models to Predictive Analytics
The transition from traditional rule-based forecasting systems to machine learning (ML) approaches represents a fundamental shift in enterprise financial planning methodologies. This review examines the evolution of financial forecasting techniques, analyzing how ML algorithms have transformed predictive analytics in corporate environments. Traditional rule-based models, while offering interpretability and deterministic outputs, often struggle with complex non-linear patterns and dynamic market conditions. In contrast, ML methods including deep learning (DL), ensemble techniques, and hybrid models demonstrate superior performance in capturing intricate relationships within financial data. This paper synthesizes recent literature on ML applications in enterprise financial forecasting, evaluating methodologies such as recurrent neural networks (RNN), long short-term memory (LSTM) networks, gradient boosting machines, and transformer architectures. The review explores implementation challenges including data quality requirements, model interpretability concerns, regulatory compliance, and organizational change management. Empirical evidence suggests that ML-based forecasting systems can achieve accuracy improvements of 15-40% compared to traditional approaches, though success depends heavily on data infrastructure, talent capabilities, and strategic integration. The paper concludes by identifying emerging trends including explainable artificial intelligence (AI), automated machine learning (AutoML), and federated learning approaches that address current limitations while maintaining the predictive advantages of ML systems.
Infrastructure as Code and Observability Automation for Payment Systems in Cloud-Native Environments
The rapid adoption of cloud-native architectures has fundamentally transformed the deployment and operation of payment systems, necessitating automated approaches to infrastructure management and system monitoring. Infrastructure as Code (IaC) enables the declarative definition and version-controlled management of computational resources, while observability automation provides real-time insights into complex distributed payment workflows. This review examines the convergence of IaC and observability automation in modern payment infrastructures, analyzing their combined impact on system reliability, compliance, and operational efficiency. The integration of IaC frameworks with observability platforms addresses critical challenges in payment system management, including deployment consistency, security compliance, and performance optimization. Through systematic analysis of recent literature, this paper synthesizes current approaches to automated infrastructure provisioning, distributed tracing, metrics collection, and log aggregation within payment processing environments. The review identifies emerging patterns in declarative infrastructure management, automated monitoring configuration, and intelligent alerting mechanisms that collectively enhance the resilience of cloud-native payment systems. Findings indicate that organizations implementing comprehensive IaC and observability automation achieve significant improvements in deployment velocity, incident response times, and regulatory compliance adherence. This paper contributes to the understanding of how automation technologies reshape payment infrastructure management and provides insights into future research directions for autonomous system operations.
Building Billion-User Products from Zero to Scale: Full-Stack Engineering Patterns for Creator Monetization and Content Protection Systems
The rapid expansion of digital creator economies has necessitated sophisticated engineering approaches to build platforms capable of supporting billion-user scales while maintaining robust monetization and content protection mechanisms. This review examines the full-stack engineering patterns employed in developing large-scale creator platforms, focusing on the technical architectures that enable creator monetization and content protection systems. Modern platforms face unprecedented challenges in balancing user experience, creator compensation, and intellectual property protection while managing massive concurrent user loads. This paper synthesizes current research on microservices architecture (MSA), distributed systems, blockchain technology, machine learning (ML), and cloud-native infrastructure to present comprehensive engineering patterns for building scalable creator platforms. The review analyzes payment processing systems, content delivery networks (CDN), digital rights management (DRM), and fraud detection mechanisms that constitute the technical foundation of billion-user products. Through examination of contemporary platforms and emerging technologies, this paper identifies critical design patterns including event-driven architecture (EDA), eventual consistency models, horizontal scaling strategies, and zero-trust security frameworks. The findings reveal that successful billion-user creator platforms require careful orchestration of backend infrastructure, real-time data processing, intelligent caching strategies, and multi-layered security approaches. This review contributes to the understanding of engineering tradeoffs in building creator economy platforms and provides insights into future technological directions for supporting the next generation of digital content creation and distribution systems.