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.
Temporal Difference Learning with Adaptive Horizon Control for Stable Multi-Step Collaborative Decisions
Instability in long-horizon decision-making often arises from improper credit assignment across extended time steps. This study explores an adaptive horizon control mechanism integrated with temporal difference (TD) learning to improve stability in multi-step collaborative tasks. Instead of using a fixed discount factor, the method dynamically adjusts the effective planning horizon based on reward sparsity and task progression signals. The approach is validated on 10,300 multi-step decision sequences with horizon lengths ranging from 10 to 50 steps. Compared with standard TD learning, the proposed method reduces cumulative reward variance by 25.9% and improves final task success rate by 14.6%. Furthermore, convergence is achieved with fewer training iterations, indicating improved learning efficiency. The results suggest that adaptive horizon control is a practical solution for stabilizing long-range coordination.
Copyright (c) 2026 Wei Chen, Jun Jie Tan, Li Wang (Author) https://creativecommons.org/licenses/by/4.0
Uncertainty-Aware Reinforcement Learning for Robust Decision Making in LLM-Agent Collaboration
Decision-making processes involving LLM agents are often susceptible to uncertainty arising from ambiguous inputs and stochastic generation behavior. To address this issue, this study proposes an uncertainty-aware reinforcement learning framework that incorporates Bayesian reward estimation and entropy-regularized policy updates. The approach is validated on a benchmark consisting of 9,600 tasks with varying levels of input ambiguity, including incomplete instructions and conflicting objectives. Results indicate that the proposed method reduces error propagation by 28.7% and improves decision robustness, with task success rates increasing from 68.9% to 80.2%. Additionally, calibration metrics such as expected calibration error (ECE) decrease by 19.5%, demonstrating improved reliability in agent outputs. The framework provides a systematic solution for enhancing robustness in LLM-based collaborative systems under uncertainty.
Copyright (c) 2026 Maximilian R. Müller, Tobias F. Weber, Anna K. Schneider (Author) https://creativecommons.org/licenses/by/4.0
Research on 3D Object Detection for Quadruped Robot in Railway Maintenance Based on Improved PV-RCNN
As the core infrastructure of national comprehensive transportation system, the safe operation of railway lines is crucial. Quadruped robots have become ideal carriers for railway autonomous inspection due to their excellent terrain adaptability. High-precision environmental perception is the core prerequisite for their autonomous operation. Aiming at the defects of traditional PV-RCNN algorithm in point cloud feature extraction for railway scenarios, this paper proposes an improved TS-PV-RCNN algorithm for 3D object detection using LiDAR point cloud. By introducing transform-equivariant strategy, dual attention mechanism, and Transformer feature extraction module, the feature extraction effect is optimized. Experiments on KITTI dataset and railway field dataset show that compared with the benchmark PV-RCNN algorithm, the average precision (AP) of railway equipment/vehicles, pedestrians, and foreign objects is improved by 1.35%, 16.83%, and 9.24% respectively under medium difficulty. The proposed algorithm provides a feasible technical scheme for autonomous inspection of railway lines.
Copyright (c) 2026 Taotao Li, Dianyun Luo, Ruixiang Liu, Yuhang Cai, Yunjia Chen, Shenghui Zheng (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Recent Advances in Intelligent MPPT Algorithms for Photovoltaic Systems: Performance Comparison and Application Analysis
Maximum power point tracking (MPPT) plays a vital role in improving the energy conversion efficiency of photovoltaic systems. In recent years, intelligent MPPT algorithms have advanced rapidly; however, existing studies still lack a sufficiently integrated comparison of their performance characteristics, application suitability, and engineering practicality. This paper reviews recent advances in intelligent MPPT algorithms for photovoltaic systems and classifies them into four categories: rule-based control, prediction-learning, search-optimization, and hybrid-collaborative methods. These methods are comparatively analyzed in terms of tracking speed, tracking accuracy, tracking stability, computational complexity, and application adaptability, with further discussion of typical operating scenarios such as dynamic irradiance variation, partial shading, and resource-constrained control platforms. The review indicates that rule-based methods are generally easier to implement in real-time environments, prediction-learning methods show advantages under dynamic conditions, search-optimization methods are more suitable for multi-peak conditions such as partial shading, and hybrid-collaborative methods offer stronger overall potential under complex operating conditions. Overall, the selection of an MPPT method should be made in accordance with specific operating conditions, control objectives, and engineering constraints
Copyright (c) 2026 Xia Wei, Tadiwa Elisha Nyamasvisva (Author) https://creativecommons.org/licenses/by/4.0
Entropy-Based Anomaly Detection for Memory Integrity Preservation in Multi-Agent Systems
Memory poisoning often alters statistical properties of shared state representations. This study proposes an entropy-based monitoring mechanism to detect anomalous memory distributions in collaborative agent environments. Shannon entropy and conditional entropy metrics are computed over memory state vectors at each synchronization step. A divergence threshold based on Kullback–Leibler distance identifies suspicious memory updates. Experiments were conducted on distributed planning simulations with 150 agents and controlled poisoning injection rates from 5% to 30%. Detection precision reached 93.1% and recall 88.7% at a divergence threshold of 0.27. Early intervention reduced system-wide contamination by 48.3% compared with no monitoring. Entropy-driven detection provides a lightweight and scalable solution for preserving memory integrity.
Copyright (c) 2026 Lukas Meier, Camille Morel, Adrian Schmid (Author) https://creativecommons.org/licenses/by/4.0
Robust Memory Update Mechanisms Against Poisoning Attacks in Multi-Agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), shared replay buffers and inter-agent memory exchange introduce vulnerability to memory poisoning attacks. This work proposes a confidence-weighted memory validation mechanism integrated into the experience sharing pipeline. Each memory entry is assigned a trust score derived from temporal consistency and reward deviation metrics. A Bayesian filtering process excludes anomalous transitions before propagation to peer agents. Experiments were conducted on cooperative navigation and resource allocation benchmarks with 12–24 agents. Under a 20% poisoning injection rate, baseline MARL performance dropped by 37.8%, whereas the proposed defense limited degradation to 11.4%. Convergence time improved by 23.6% compared to anomaly-blind training.The method effectively mitigates adversarial memory contamination in collaborative reinforcement learning environments.
Copyright (c) 2026 James Thompson, Oliver Bennett, Daniel Carter (Author) https://creativecommons.org/licenses/by/4.0/
CANAO: A Cloud-Aware Native Agentic AI Framework for Adaptive Task Orchestration in Cloud-Native Environments
Agentic AI has emerged as a promising paradigm for autonomous reasoning and execution in complex AI-driven applications; however, its effective deployment in cloud-native environments remains challenging due to the lack of unified platform architectures that jointly support task decomposition, multi-agent collaboration, and adaptive cloud resource orchestration. In practical scenarios such as automated data analytics, AI DevOps, and MLOps pipelines, Agentic AI systems must operate over dynamic containerized infrastructures where resource availability, execution cost, and failure conditions continuously change. Existing approaches typically decouple agent-level decision making from cloud-native scheduling, resulting in limited scalability and poor robustness. To address these limitations, this paper proposes CANAO, a Cloud-Aware Native Agentic AI framework for adaptive task orchestration in cloud-native environments. CANAO models complex AI workloads as dynamically reconfigurable task dependency graphs and enables coordinated collaboration among Planner, Executor, and Critic agents. By incorporating real-time cloud resource awareness into the agent orchestration loop, CANAO supports adaptive scheduling, partial task re-planning, and self-healing execution on Kubernetes-based platforms. A prototype system is implemented using cloud-native technologies and evaluated on representative automated data analysis and AI DevOps workflows. Experimental results show that CANAO significantly outperforms baseline orchestration methods under dynamic cloud conditions. Compared with static DAG-based scheduling, CANAO reduces end-to-end task execution time by approximately 34.3% and cloud resource cost by nearly 30%, while lowering the task failure rate by over 34%. These improvements demonstrate the effectiveness of cloud-aware agent collaboration and adaptive task orchestration in large-scale cloud-native AI workflows.
Copyright (c) 2026 Jiawei Li, Peifan Zeng, Pinghao Luo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Empowering Intercultural Performative Competence: An Empirical Study of LLM-Assisted Vocal Learning in a Multicultural Context
Multicultural vocal education requires students to deeply understand foreign cultural contexts and linguistic nuances, which often poses a significant cognitive challenge. Traditional empirical learning often leads to mechanical imitation rather than authentic emotional connection. This study investigates the impact of Large Language Models (LLMs) as cognitive pedagogical tools on enhancing vocal students' intercultural performative competence, specifically focusing on textual comprehension and aesthetic expression rather than vocal mechanics. A 2×2 within-subjects crossover experiment was conducted with eight Chinese vocal majors (four postgraduates and four undergraduates). Participants were tasked with learning two Western classical art songs (Italian and German) under two conditions: traditional desk work and LLM-assisted learning. Performance was evaluated through double-blind expert scoring based on aesthetic dimensions, triangulated with subjective student questionnaires. The results indicate that the LLM intervention significantly improved students' language diction, textual comprehension, and emotional empathy (p < 0.05). Qualitative feedback revealed that LLMs effectively reduced cognitive load and helped students decode deep cultural metaphors, thereby transforming mere linguistic memorization into authentic aesthetic expression. Based on these empirical findings, this paper proposes a three-stage "Perception-Internalization-Expression" pedagogical strategy, providing a novel and highly adaptable framework for integrating generative AI into multicultural music education.
Copyright (c) 2026 Li Kehang, Wang Zimeng, Ji Wen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Interpretable and Lightweight Predictive Modelingfor Congestive Heart Failure Using ICU Electronic Health Records
Congestive Heart Failure (CHF) is a prevalent cardiovascular condition and a major contributor to hospitalizations and mortality worldwide. Early identification of CHF risk from electronic health records (EHR) can support proactive clinical monitoring and intervention. This paper presents an interpretable and lightweight predictive modeling workflow for CHF prediction using structured ICU data from the MIMIC-III database. A patient-level dataset of approximately 44,000 adult ICU patients with 115 demographic and laboratory-derived features is used to evaluate classical tabular machine learning models, including logistic regression, stochastic gradient descent classifiers, Random Forest, Gradient Boosting, and XGBoost. Tree-based ensemble models achieve the strongest performance, with XGBoost reaching an accuracy of 0.858 and sensitivity of 0.861 for CHF detection. The study also examines interpretability through logistic regression coefficients and feature-importance analysis, and compares these models with a prompted small language model baseline. The findings suggest that compact and interpretable machine learning models provide an effective and deployable approach for disease risk prediction using structured EHR data, especially in resource-constrained clinical environments.
Copyright (c) 2026 Ziwei Wang, Haoyun Zhang, Di Zhu, Chen Xie (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
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