International Journal of Artificial Intelligence Engineering and Systems
Journal Summary International Journal of Artificial Intelligence Engineering and Systems is an international academic journal dedicated to research in artificial intelligence, engineering applications, and intelligent systems. The journal publishes original research articles, review papers, and technical studies covering machine learning, deep learning, intelligent algorithms, knowledge engineering, data-driven systems, automation, robotics, computer vision, natural language processing, AI security, and AI-enabled engineering solutions. The journal aims to provide a platform for researchers, engineers, and practitioners to present theoretical advances, practical methods, and system-level innovations that contribute to the development and application of artificial intelligence technologies across engineering and interdisciplinary domains.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Neuro-Symbolic Prompt Tuning for Trustworthy Reasoning in Large Language Models
Large language models have exhibited remarkable generative capabilities, yet their deployment in high-stakes domains is constrained by persistent concerns regarding trustworthiness, interpretability, and systematic reasoning fidelity. Prompt tuning has emerged as a parameter-efficient adaptation strategy, but conventional approaches typically treat the prompt space as a continuous set of vectors optimized solely for task performance, neglecting the structured constraints that underpin reliable inference. This paper proposes a neuro-symbolic prompt tuning framework that integrates symbolic reasoning modules directly into the prompt optimization pipeline, thereby aligning the continuous adaptation of prompts with logical, rule-based verification and knowledge structures. We provide a system-level examination of this architecture, focusing on structural trade-offs among modular neuro-symbolic components, the governance of hybrid reasoning workflows, and the infrastructure required for sustainable and fair deployment. We analyze how symbolic guardrails, injected at the prompt tuning stage, can mitigate hallucination, improve consistency across diverse demographic contexts, and enable auditable decision pathways. The discussion extends to infrastructure considerations, including compute partitioning, energy-aware scheduling, and the incorporation of human-in-the-loop oversight to ensure that symbolic knowledge bases remain aligned with evolving societal norms. Policy implications of such reasoning-augmented models are examined through the lens of the EU AI Act and emerging fairness regulations, emphasizing the need for transparent documentation of symbolic rule provenance. The paper concludes by outlining a roadmap for transforming prompt tuning from a purely statistical optimization into a sociotechnically grounded engineering discipline, balancing performance, fairness, robustness, and accountability.
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems https://creativecommons.org/licenses/by/4.0
Digital Twin and Deep Reinforcement Learning Integrated Framework for Predictive Maintenance of Smart Damped Structures
Smart damped structures that embed viscoelastic layers and active elements are increasingly deployed in aerospace, civil, and automotive sectors to suppress vibration and extend service life. Their long-term resilience, however, depends on predictive maintenance strategies that can anticipate degradation of damping properties and structural integrity under stochastic operational loads. This paper presents a system-level integrated framework that couples high-fidelity digital twins with deep reinforcement learning agents to enable autonomous, condition-based maintenance planning for such complex mechatronic assemblies. The digital twin fuses physics-based modeling, in situ sensing, and continual model updating to mirror the evolving state of the structure, while the deep reinforcement learning module translates this state representation into maintenance policies that balance inspection cost, downtime, and residual life. We discuss the architecture and data-flow design of the twin–agent coupling, emphasizing modularity, real-time feedback, and edge-cloud orchestration. The analysis further examines structural trade-offs concerning governance, security, cross-organizational data sharing, and the robustness of decisions under epistemic and aleatory uncertainty. Policy dimensions, including fairness in asset fleet management and algorithmic accountability, are explored alongside implications for lifecycle sustainability and circular economy transitions. By integrating prognostics, control, and autonomous decision-making, the proposed framework offers a pathway toward self-aware damped structures that not only perceive their own health but also continuously optimize their maintenance in a resource-efficient and ethically grounded manner.
Hierarchical Prompt Placement Strategies for Efficient Parameter-Efficient Fine-Tuning in Transformer Architectures
The rapid proliferation of large-scale transformer architectures has driven an urgent need for parameter-efficient fine-tuning (PEFT) methods that can adapt pre-trained models to diverse downstream tasks without incurring prohibitive computational, memory, and carbon costs. Within the PEFT landscape, prompt-based strategies have emerged as a lightweight alternative to full model updating, yet their effectiveness remains tightly coupled to the placement of soft prompts within the network topology. This paper presents a comprehensive system-level analysis of hierarchical prompt placement strategies, conceptualizing the design space as a multi-scale governance problem that balances expressiveness, modularity, and efficiency. We argue that the strategic allocation of learnable prompt parameters across transformer layers, attention heads, and feed-forward blocks constitutes a form of architectural policy-making, with far-reaching implications for robustness, fairness, sustainability, and deployment logistics. Drawing upon insights from adapter-based methods, prefix tuning, low-rank adaptation, and selective insertion techniques, we develop a taxonomy of placement policies ranging from static layer-wise injection to dynamic, context-dependent routing. The discussion extends beyond accuracy metrics to examine how hierarchical placement influences bias amplification, cross-task interference, energy consumption, and model auditability in real-world infrastructure. By reframing prompt placement as a system-level resource allocation challenge, this work bridges the gap between algorithmic innovation and the socio-technical demands of large-scale AI systems. We conclude with forward-looking perspectives on governance frameworks for PEFT deployment, emphasizing the necessity of transparency, reproducibility, and lifecycle accounting in the design of efficient adaptation mechanisms.
Explainable Traffic Prediction in Intelligent Transportation and Vehicular Networks via Large Language Model Reasoning
Accurate traffic prediction is a foundational capability for next-generation intelligent transportation systems and connected vehicular networks, yet the opaque nature of state-of-the-art deep learning models erodes trust among operators, regulators, and road users. While graph neural networks and spatiotemporal transformers have dramatically improved forecasting accuracy, their capacity to produce human-intelligible explanations remains limited to attention heatmaps or saliency maps that rarely translate into actionable operational narratives. This paper presents a system-level investigation into the integration of large language model reasoning as a prospective explanatory layer atop conventional traffic prediction architectures. It charts the structural trade-offs between deterministic sensor-driven forecasts and generative natural language rationales, articulating how a multi-stage pipeline can convert latent spatiotemporal representations into contextualized explanations that reference infrastructure status, recurrent congestion patterns, weather conditions, and social event calendars. The discussion extends beyond model accuracy to encompass governance challenges, data provenance auditing, robustness under adversarial sensor perturbations, fairness across heterogeneous urban districts, sustainability of large-model deployments, and policy frameworks for automated decision support in traffic management centers. Through extended architectural analysis and cross-domain comparison with explainability techniques in finance and healthcare, the paper identifies where LLM-based reasoning can add genuine decision-making value and where it introduces new failure modes, including hallucinated causal inferences and disproportionate computational cost. The analysis is structured to inform both system designers and regulatory stakeholders about the conditions under which language-driven explainability can enhance the resilience, transparency, and public accountability of intelligent vehicular infrastructures.
Fairness-Aware Prompt Tuning: Selective Prompt Allocation for Bias Reduction in NLP Models
Prompt tuning has emerged as a parameter-efficient alternative to full model fine-tuning, enabling large language models to be adapted to downstream tasks through learned soft prompts while keeping pretrained weights frozen. Despite its efficiency advantages, the fairness implications of prompt tuning remain underexplored. Standard prompt tuning applies a single prompt uniformly across all instances, implicitly assuming that a monolithic prompt representation can serve all demographic subgroups equally well. This assumption is at odds with extensive evidence that language models amplify societal biases embedded in training data. In this paper, we introduce the concept of fairness-aware prompt tuning through selective prompt allocation, a system-level framework in which inputs are routed to distinct fairness-optimized prompts based on auxiliary signals that capture protected attribute proxies or bias-sensitive features. We analyze the structural trade-offs involved in partitioning the prompt space for fairness, including prompt catalog design, allocation policy learning, and the tension between group fairness and aggregate accuracy. We further examine infrastructure requirements for deploying selective prompt allocators in production pipelines, touching on latency, storage overhead, and the risks of re-identification when conditioning on sensitive attributes. The governance dimension is discussed in terms of regulatory compliance, transparency obligations, and the necessity of auditability in prompt-based adaptation layers. Through cross-domain comparisons with adversarial debiasing, data augmentation, and output calibration methods, we illustrate how selective prompt allocation creates a new operating point in the bias-accuracy landscape. We conclude by identifying challenges related to long-term fairness drift, adversarial prompt manipulation, and sustainable maintenance, suggesting that selective prompt allocation represents a promising middleware approach for fairness injection in large-scale NLP systems without compromising the efficiency gains of prompt tuning.
Federated Learning-Enabled Damage Detection in Smart Composite Laminates with Viscoelastic Layers
Smart composite laminates incorporating viscoelastic layers represent a significant advancement in lightweight engineering structures, offering both load-bearing functionality and intrinsic vibration damping capabilities. Effective damage detection within such heterogeneous multilayer systems is critical for structural integrity and lifecycle management, yet it introduces considerable challenges related to data volume, sensor heterogeneity, privacy of operational data, and the need for continuous model improvement across distributed assets. This paper presents a systems-level investigation into federated learning as an enabling paradigm for collaborative damage detection in smart composite laminates with viscoelastic layers. Departing from centralized machine learning approaches, federated learning allows multiple structural nodes or edge devices to jointly train damage inference models without sharing raw sensor data, thereby addressing data sovereignty concerns inherent in aerospace, automotive, and civil infrastructure applications. The architectural design of federated systems for this domain is examined, encompassing edge-cloud orchestration, secure aggregation protocols, and strategies for managing the highly non-identically distributed data arising from diverse structural configurations and environmental conditions. Data governance frameworks are analyzed, highlighting the intersection of technical privacy guarantees and regulatory compliance. Further, the paper discusses model robustness under adversary scenarios, fairness in diagnostic performance across heterogeneous fleets of composite structures, and the sustainability dimensions of decentralized learning workflows. Through a synthesis of structural health monitoring, composite mechanics, and decentralized artificial intelligence, this work identifies critical trade-offs and proposes governance-aware, resource-efficient deployment pathways. The analysis underscores that federated learning not only facilitates privacy-preserving damage detection but also opens new policy questions regarding liability, certification of continuously updating models, and cross-organizational data collaboration.
Deep Reinforcement Learning and LLM Collaborative Architecture for QoS-Aware Traffic Engineering in 5G Core Networks
The proliferation of latency-sensitive, bandwidth-intensive, and ultra-reliable applications across fifth-generation mobile networks has fundamentally transformed the requirements placed upon traffic engineering in the core network. The 5G core, built on service-based architectures and network slicing, demands dynamic, fine-grained, and quality-of-service-aware routing decisions that operate at unprecedented scale and complexity. Traditional optimization heuristics and early machine learning approaches have struggled to maintain both performance and adaptability under such conditions. This paper presents a novel collaborative architecture that integrates deep reinforcement learning with large language models to achieve continuous, context-aware traffic engineering governed by explicit quality-of-service objectives. In this design, a deep reinforcement learning agent learns forwarding policies through interaction with a high-fidelity simulation of the core network, while a large language model functions as a semantic reasoning module, interpreting natural language operator intent, generating policy templates, and providing high-level guidance to constrain the reinforcement learning action space. The architecture is examined from system-level perspectives including modular composition, training stability, inference latency trade-offs, and the division of labor between learned control and symbolic reasoning. Particular attention is devoted to the implications of such a dual-model system for infrastructure sustainability, governance, fairness, and robustness in multi-tenant network environments. The paper further analyzes how collaborative intelligence architectures can absorb emerging traffic prediction models, referencing recent advances in spatial-temporal intelligence, and addresses deployment pathways that align with the ongoing softwarization of telecom infrastructure. Critical policy dimensions such as algorithmic transparency, slice isolation guarantees, and resource allocation equity are discussed as intrinsic design requirements rather than afterthoughts. The analysis suggests that hybrid architectures combining subsymbolic learning with the compositional reasoning capabilities of large language models represent a viable trajectory for the next generation of autonomous network management systems.
Explainable Artificial Intelligence for Vibration Analysis and Model Updating of Smart Laminated Structures
The integration of smart laminated composite structures into critical engineering systems has transformed the landscape of structural health monitoring, yet it also introduces substantial complexity in interpreting sensor data and updating numerical models. As artificial intelligence becomes central to vibration-based damage detection and model updating, the opacity of many high-performance machine learning models presents a barrier to trust, regulatory compliance, and informed decision-making. This paper presents a systems-level analysis of explainable artificial intelligence applied to vibration analysis and model updating of smart laminated structures. It investigates the architectural and infrastructural demands of embedding explainability within the full lifecycle of data-driven structural assessment, from signal acquisition and feature extraction through model updating and deployment. The discussion delineates fundamental trade-offs between predictive accuracy and interpretability, examines the influence of post-hoc explanation methods on engineering confidence, and highlights the challenges associated with the non-uniqueness and ill-posedness inherent in model updating of laminated composites. Deployment considerations are addressed through the lens of edge computing, low-power sensing networks, and long-term sustainability of monitoring systems. Robustness and adversarial vulnerabilities of explainable models are scrutinized, as are the governance and fairness dimensions that arise when AI-informed decisions affect public safety and resource allocation across diverse communities. By synthesizing perspectives from structural dynamics, machine learning, human-computer interaction, and policy studies, the paper articulates a forward-looking framework for responsible and interpretable AI in next-generation smart infrastructure. The analysis underscores that explainability must be treated as a system property rather than a post-processing accessory, demanding co-design across hardware, algorithms, and institutional protocols to achieve safe, equitable, and transparent structural health management.
Digital Twin-Driven Operando Monitoring of Electronic Structure Reconstruction in Transition Metal Oxide Electrocatalysts
The rational design of transition metal oxide electrocatalysts for sustainable energy conversion demands a mechanistic understanding of dynamic electronic structure reconstruction under operating conditions, yet the experimental platforms for capturing such transient phenomena remain fragmented and profoundly underdetermined by static post-mortem analysis. This paper proposes a large-scale research infrastructure that interweaves digital twin architectures with high-throughput operando spectroscopy to achieve continuous, in silico–in situ coupling of catalyst electronic evolution. We establish a system framework in which multi-modal synchrotron-based X-ray absorption and emission data streams are ingested by a continuously updating computational replica of the electrode–electrolyte interface, enabling real-time inference of oxidation state dynamics, orbital hybridization changes, and the emergence of many-body electronic configurations. The architecture is examined through the lenses of data orchestration, transmission latency, cyber-physical synchronization, and semantic interoperability between experimental beamline control systems and predictive machine learning backbones. We dissect structural trade-offs between model fidelity and computational tractability, and outline governance protocols that address data provenance, algorithmic fairness across materials families, and the long-term sustainability of shared digital twin repositories. Particular attention is given to the challenge of capturing rare electronic events—such as the formation of Zhang-Rice singlet states—that decisively influence catalytic turnover but are easily missed in traditional intermittent characterization. By mapping the full lifecycle of operando data onto a digital twin continuum, this work articulates the socio-technical requirements for transforming solitary laboratory discoveries into a federated, robust, and equitable knowledge ecosystem for electrocatalysis, with implications extending to national synchrotron facilities, materials acceleration platforms, and global energy policy frameworks.
Cross-Domain Traffic Intelligence: Integrating Large Language Models and Graph Neural Networks for Wireless Network Forecasting and Optimization
The escalating complexity of next-generation wireless networks demands intelligent forecasting and optimization mechanisms capable of assimilating heterogeneous data streams across multiple spatial and temporal scales. This paper investigates a cross-domain traffic intelligence paradigm that integrates large language models and graph neural networks to unlock unprecedented capabilities in wireless network management. Large language models offer powerful semantic reasoning over unstructured contextual information including social media, event schedules, and weather reports, while graph neural networks excel at capturing structural dependencies among network nodes and spatiotemporal traffic patterns. We present a system-level architectural framework that orchestrates these two technologies through a hybrid pipeline, addressing critical design trade-offs in data fusion, computational partitioning, and model synchronization. The study delves deeply into governance, infrastructure, and deployment dimensions that are often overlooked in purely algorithmic treatments, covering privacy-preserving data sharing, federated learning across administrative domains, and regulatory compliance. Sustainability is examined through the lens of energy consumption and lifecycle management of large-scale models, while fairness and robustness are analyzed as fundamental socio-technical requirements for operational deployments. A cross-domain comparative analysis contrasts the proposed integration against conventional statistical and deep learning baselines across cellular, vehicular, and IoT ecosystems. The paper concludes by outlining a forward-looking research agenda that emphasizes foundation models for wireless networks, interpretable intelligence, and responsible automation governed by human-centric policies. The findings provide a blueprint for building resilient, equitable, and efficient traffic intelligence systems in the 6G era and beyond.
Cross-Lingual Prompt Transfer via Adaptive Prompt Position Optimization
Large-scale multilingual language models have enabled remarkable zero-shot cross-lingual transfer, yet parameter-efficient adaptation mechanisms such as prompt tuning remain constrained by their reliance on fixed prompt positions. The syntactic diversity, morphological richness, and divergent word order patterns across languages challenge the assumption that a single prompt placement, typically a prefix, can serve all target languages effectively. This paper presents a systems-oriented investigation of adaptive prompt position optimization for cross-lingual prompt transfer. We propose a conceptual architecture in which a lightweight position controller learns to determine the most favorable insertion point for soft prompt embeddings conditioned on language identity, typological features, and task semantics. The work examines structural trade-offs involved in designing the controller, the coordination between a shared multilingual backbone and dynamic prompt injection, and the implications for large-scale deployment. Beyond architectural considerations, we explore governance dimensions such as fairness across resource-disparate languages, robustness to syntactic perturbation, and the alignment with sustainable and equitable multilingual AI policies. By framing prompt positioning as a control problem within a socio-technical infrastructure, the paper provides a forward-looking perspective on how adaptive mechanisms can reconcile computational efficiency with linguistic inclusivity in multilingual natural language processing systems.
Cultural Safety and Value Alignment in Foundation Models: A Trace-Based Analysis of Reasoning Path Divergence
The rapid proliferation of foundation models across global digital infrastructures has intensified concerns regarding cultural safety and value alignment. These models, trained on vast and heterogeneous corpora, often exhibit reasoning paths that diverge in subtle yet consequential ways when confronted with culturally situated prompts. This paper advances a trace-based analytical framework to examine how internal reasoning trajectories within foundation models deviate when processing value-laden inputs across diverse cultural contexts. Rather than relying solely on surface-level output auditing, we employ path-level analysis to uncover hidden misalignments that persist despite reinforcement learning from human feedback and other alignment interventions. We discuss the structural, architectural, and governance dimensions of deploying trace-based safety mechanisms at scale, highlighting the trade-offs between model autonomy and intervention fidelity. Through a systems-theoretic lens, we evaluate the implications of path-level interventions for fairness, robustness, and the preservation of cultural pluralism. The analysis reveals that reasoning path divergence is not simply a failure mode but a reflection of the model’s inductive biases and training data imbalances, necessitating new governance frameworks that account for cultural safety as a dynamic, multi-stakeholder requirement. We argue that trace-based approaches offer a promising complement to output-level alignment by enabling targeted, context-sensitive corrections without sacrificing the model’s general capabilities. The paper concludes with a roadmap for integrating path-level safety mechanisms into the full lifecycle of foundation model deployment, emphasizing sustainability, transparency, and culturally informed policy design.
From Hallucination to Reliability: Path-Level Mechanisms for Trustworthy Knowledge Generation in Foundation Models
Foundation models have demonstrated remarkable generative capabilities across modalities, yet their propensity to produce factually incorrect or logically inconsistent outputs—commonly termed hallucination—threatens their deployment in safety-critical and knowledge-intensive domains. This paper reframes the problem of hallucination not merely as a training data or fine-tuning deficiency but as a systemic challenge rooted in the architectural pathways through which internal representations are composed into outputs. We argue that achieving trustworthy knowledge generation requires moving beyond surface-level output filtering to path-level mechanisms that intervene directly on the internal circuits, attention patterns, and activation subspaces responsible for factual reasoning. Drawing on recent advances in mechanistic interpretability, activation steering, and circuit-level editing, we examine how such path-level strategies reconfigure the reliability trade space. We present a system-level analysis of the structural trade-offs involved in implementing path-level interventions, including computational overhead, latency, modularity, and compatibility with existing alignment pipelines. The discussion extends to infrastructure requirements for real-time path-level monitoring, governance challenges related to auditability and transparency, and fairness implications arising from representational steering. We further consider the deployment lifecycle, sustainability of intervention mechanisms under distribution shift, and the role of regulatory frameworks in certifying path-level safety guarantees. By synthesizing cross-disciplinary perspectives from systems engineering, machine learning interpretability, and AI policy, this paper offers a comprehensive roadmap for transitioning foundation models from hallucination-prone generators to reliable, auditable, and controllable knowledge systems. We conclude that path-level mechanisms represent a necessary architectural evolution for foundation model trustworthiness, demanding new infrastructural investments, governance protocols, and interdisciplinary evaluation standards.
Fairness-Aware Benchmarking of Cross-Cultural Visual Generation in Foundation Models
As foundation models increasingly mediate visual culture across global audiences, their capacity to fairly represent diverse cultural contexts has emerged as a pressing socio-technical challenge. Current text-to-image generation systems exhibit profound demographic and cultural biases, often defaulting to Western, high-resource visual norms while erasing or stereotyping underrepresented communities. This paper presents a system-level analysis of fairness-aware benchmarking for cross-cultural visual generation, arguing that effective evaluation requires far more than dataset curation or prompt engineering. We articulate how benchmark design must integrate infrastructure scalability, metric pluralism, governance scaffolding, and sustainability considerations into a coherent sociotechnical architecture. Through a critical examination of existing evaluation pipelines, we identify structural trade-offs between cultural granularity and computational tractability, between automated metrics and culturally grounded human evaluation, and between centralised model auditing and community-driven oversight. Drawing on insights from recent model deployments, we propose a layered benchmarking architecture that couples large-scale automated analysis with distributed cultural annotation networks, enabling continuous monitoring of representation gaps. We further discuss the role of regulatory frameworks, model cards, and participatory design in shaping accountability for cross-cultural fairness. Throughout, we emphasise that cross-cultural fairness in generative AI is not a static property to be optimised but a dynamic, contested terrain that demands ongoing institutional commitment, reflexive evaluation practices, and infrastructure that supports pluralism rather than erasing it. The paper contributes a comprehensive framework for thinking about fairness-aware benchmarking as a system of measurement, governance, and cultural negotiation.
Explaining Toxicity Emergence in Multimodal Foundation Models through Internal Computational Path Analysis
The rapid deployment of multimodal foundation models across high-stakes sociotechnical systems has brought the challenge of toxicity emergence to the forefront of AI safety research. Unlike earlier unimodal systems, these models integrate text, vision, and other modalities to form joint representations, creating novel internal computational pathways through which toxic content can be generated or amplified. This paper presents a system-level framework for explaining toxicity emergence through internal computational path analysis, a methodological orientation rooted in mechanistic interpretability that traces how inputs, attention flows, and cross-modal interactions combine to produce harmful outputs. We argue that toxicity in multimodal foundations is not a static property of training data but an emergent phenomenon shaped by architectural design choices, training objectives, and the dynamic routing of information across modality-specific and cross-modal circuits. Drawing on recent advances in circuit analysis and representation editing, we examine the structural trade-offs inherent in scaling, fine-tuning, and alignment procedures that often mask but do not eliminate toxic predispositions. Through detailed conceptual analysis, case illustrations, and cross-domain comparisons, we critically assess the promise of path-level interventions as a robust safety mechanism. We further discuss ramifications for governance, robustness, fairness, and the sustainability of safety measures in production systems. The analysis situates computational path tracing as a necessary complement to surface-level filtering, offering a principled lens for auditing, repairing, and certifying large multimodal models before and after deployment.
Evaluating Cultural Hallucinations in Multimodal Generative AI: A Transparency and Trustworthiness Perspective
The rapid advancement of multimodal generative artificial intelligence has introduced capabilities for synthesizing images, text, and video with unprecedented fidelity. While substantial effort has focused on factual hallucinations in language models and object-level hallucinations in vision-language systems, a more insidious class of failure remains underexamined: cultural hallucination, defined as the systematic distortion, stereotyping, or erasure of cultural symbols, practices, and visual identities. This paper provides a system-level analysis of cultural hallucinations through the intertwined lenses of transparency and trustworthiness. We argue that cultural hallucinations arise not merely from insufficient data representation but from a confluence of architectural design choices, opaque curation pipelines, and evaluation protocols that prioritize object-centric accuracy over culturally grounded semantic fidelity. Drawing on interdisciplinary literature spanning machine learning, science and technology studies, and algorithmic auditing, we examine how contrastive pre-training objectives, dataset homogenization, and prompt-to-image decoding amplify hegemonic visual narratives. We further assess the limitations of existing transparency instruments, including model cards and datasheets, in capturing culturally specific failure modes, and propose extensions that incorporate culture-aware auditing, participatory red-teaming, and interpretability probes sensitive to symbolic meaning. Deployment-level consequences are analyzed, highlighting how cultural hallucinations erode public trust, perpetuate symbolic marginalization, and constrain the safe integration of generative models into education, media, and cross-cultural communication. We conclude by outlining a governance and research infrastructure framework that treats cultural veracity as a first-class dimension of model trustworthiness, calling for multi-stakeholder observatories, culturally stratified evaluation benchmarks, and continuous monitoring mechanisms that transcend static accuracy metrics.
A Risk-Aware Governance Framework for Cultural Diversity Preservation in Next-Generation Text-to-Image Foundation Models
The rapid proliferation of text-to-image foundation models has introduced transformative capabilities for creative production and knowledge communication, yet it has simultaneously surfaced deep structural risks associated with the systematic erosion of cultural diversity. This paper proposes a risk-aware governance framework that integrates cultural preservation safeguards directly into the lifecycle of next-generation text-to-image systems, from data curation and training pipelines to deployment infrastructure and post-deployment monitoring. The framework is grounded in a systems-level analysis that treats cultural diversity not as an isolated fairness metric but as a dynamic, multiscalar property shaped by interactions among training data provenance, model architecture, prompt ingestion mechanisms, inference acceleration stacks, and the socio-technical contexts of use. The discussion articulates critical structural trade-offs, including those between representational completeness and computational efficiency, between centralized content moderation and decentralized cultural sovereignty, and between open-ended generative capability and the precautionary containment of cultural harms. Governance mechanisms such as stratified risk registers, culture-aware red-teaming, data documentation protocols, and continuous auditing loops are examined as components of an infrastructural intervention. The paper further explores the sustainability and robustness dimensions of cultural diversity preservation under continual learning and model updating scenarios, and it assesses cross-domain policy implications for AI regulation, digital heritage protection, and international technology governance. By foregrounding architectural, operational, and policy-level levers, the framework advances a holistic approach to ensuring that next-generation foundation models become sites of cultural pluralism rather than agents of cultural flattening.
Energy-Efficient Safety Alignment of Large Foundation Models via Selective Reasoning Path Regulation
The alignment of large foundation models with human values and safety constraints is a computationally and energetically expensive undertaking, as it often involves exhaustive reinforcement learning procedures, repeated safety audits, or auxiliary shielding models that substantially increase the carbon footprint of both training and inference. This paper presents a system-oriented analysis of energy-efficient safety alignment through selective reasoning path regulation, a paradigm in which internal chain-of-thought trajectories are dynamically modulated to suppress unsafe reasoning while avoiding the full computational overhead of post-hoc safety filtering or alignment fine-tuning. We examine the structural trade-offs involved in designing gating mechanisms that intercept reasoning paths at the token, layer, or trajectory level and evaluate the implications for latency, throughput, and energy consumption. The discussion spans architecture design, deployment infrastructure, and the interplay between sparse activation optimization, early exiting, and energy-proportional hardware. Robustness and fairness considerations are addressed, including the risk that selective path interventions may inadvertently introduce distributional biases or be circumvented by adversarially crafted reasoning shortcuts. The paper further extends into governance and policy dimensions, arguing that sustainability reporting for foundation model providers should encompass the energy costs of safety alignment regimes and that auditability of reasoning path interventions is essential for regulatory compliance. By framing selective reasoning path regulation as a cross-layer systems challenge, we contribute a multi-perspective analysis that connects sustainable AI, safety engineering, and institutional accountability.
Cooperative Edge Intelligence and Network Slicing for Autonomous Vehicle Communication Systems
The proliferation of autonomous vehicles demands communication infrastructures capable of supporting extreme requirements for latency, reliability, and data throughput. Cooperative edge intelligence, realized through multi-access edge computing and distributed learning paradigms, and network slicing, as a softwarized 5G and beyond capability, together form a promising coalition to meet these demands. This paper presents a system-level investigation into the architectural integration of cooperative edge intelligence and network slicing for autonomous vehicle communication systems. It examines the structural trade-offs inherent in orchestrating computation and communication resources across multi-tier edge clouds while maintaining stringent service-level agreements through logical network partitions. The analysis extends to governance models for slice lifecycle management, resource isolation policies, and fairness mechanisms that address diverse stakeholder interests. Further, deployment challenges such as mobility-induced session continuity, energy efficiency, resilience under infrastructure degradation, and security vulnerabilities are critically evaluated. Policy and regulatory implications concerning spectrum management, data sovereignty, and inter-operator coordination are discussed in the context of ongoing standardization and real-world deployments. By synthesizing advances from distributed intelligence, softwarized networking, and autonomous driving ecosystems, the paper provides a forward-looking perspective on constructing robust, sustainable, and equitable vehicular communication systems. The discussion is firmly grounded in the relevant literature and draws cross-domain comparisons to highlight the unique constraints and opportunities of this converged infrastructure.
Blockchain-Assisted Trustworthy Resource Management for AI-Driven 5G Network Slicing Architectures
Fifth-generation mobile networks are engineered around the concept of network slicing, which enables multiple logical networks to coexist on a shared physical infrastructure, each tailored to distinct service requirements. The dynamic complexity of resource allocation across slices has motivated the integration of artificial intelligence techniques, particularly deep reinforcement learning, to automate and optimize decision-making in real time. However, the opacity of AI-driven control loops and the multi-stakeholder nature of modern network ecosystems introduce significant trust deficits, including concerns about fairness, verifiability, and adversarial robustness. This paper presents a system-level analysis of how blockchain technology can be harnessed to establish a trustworthy resource management framework for AI-driven 5G network slicing architectures. We examine the structural trade-offs inherent in combining distributed ledger mechanisms with machine learning pipelines at the orchestration layer, focusing on governance models, auditability of slice lifecycle events, and decentralized enforcement of service level agreements. The discussion covers the integration of smart contracts for automated compliance, the role of consensus protocols in maintaining resource state integrity, and the challenges of scaling blockchain components to meet carrier-grade transaction volumes. Architectural considerations are explored through the lens of infrastructure sustainability, cross-domain federation, and long-term policy evolution. The paper argues that blockchain-assisted architectures, when carefully designed to complement rather than duplicate AI functions, can provide a verifiable trust substrate that aligns economic incentives, mitigates resource contention inequities, and strengthens the resilience of next-generation network slicing platforms.
AI-Enabled Energy-Efficient Network Slicing and Resource Scheduling in 5G-Advanced Wireless Systems
The transition to 5G-Advanced and beyond represents a fundamental shift in wireless system design, demanding infrastructures that simultaneously support heterogeneous service requirements, dynamic resource allocation, and stringent energy efficiency targets. Network slicing has emerged as a cornerstone architectural principle, enabling multiple logical networks to coexist on a shared physical substrate. However, the operational complexity of orchestrating slices with diverse quality-of-service profiles under time-varying traffic and channel conditions pushes traditional heuristic and optimization-based resource scheduling to their limits. Artificial intelligence, particularly deep reinforcement learning, offers a transformative pathway toward adaptive, self-optimizing slice management that can jointly pursue energy conservation and performance guarantees. This paper provides a comprehensive systems-level analysis of AI-enabled energy-efficient network slicing and resource scheduling in 5G-Advanced wireless systems. It examines the architectural enablers, the integration of machine learning into slice orchestration, and the structural trade-offs between efficiency, fairness, robustness, and sustainability. Discussions span from centralized and distributed learning paradigms to model interpretability, deployment feasibility, and cross-domain governance. The analysis emphasizes infrastructure-scale implications, including life-cycle energy impacts, resource isolation, and the alignment of AI-driven decisions with regulatory and standardization frameworks. By synthesizing perspectives from telecommunications, systems engineering, and socio-technical governance, the paper articulates a forward-looking research agenda that prioritizes not only algorithmic innovation but also systemic resilience, policy coherence, and long-term environmental accountability in next-generation networks.
AI-Driven Joint Communication and Computing Resource Optimization in Edge-Enabled 5G Networks
The convergence of fifth-generation mobile networks and multi-access edge computing creates a distributed infrastructure in which communication and computational resources are deeply intertwined. Optimizing these resources in isolation yields suboptimal performance, especially under the demanding latency, throughput, and reliability requirements of emerging applications such as autonomous vehicles, immersive virtual reality, and industrial automation. This paper presents a system-level examination of artificial intelligence-driven joint communication and computing resource optimization in edge-enabled 5G networks. It argues that AI, particularly through deep reinforcement learning and multi-agent architectures, can serve as a systemic coordinating layer that continuously reconciles the tight coupling between radio spectrum, transmission power, and edge processing capacity. The discussion moves beyond algorithmic novelty to scrutinize structural trade-offs among centralization, distribution, and hierarchical governance models, highlighting how each choice affects convergence speed, resilience to network dynamics, and fairness across tenants. The paper further analyses infrastructure robustness when AI orchestrators themselves become points of failure or exhibit opaque behaviours, and it explores the sustainability implications of energy-aware joint optimization in large-scale deployments. Policy and governance dimensions are addressed, including the need to align AI-driven resource allocation with regulatory frameworks for spectrum sharing, neutrality, and service level assurance. By drawing together perspectives from communication engineering, distributed systems, and socio-technical governance, the paper offers a forward-looking synthesis intended to guide both design and regulation of future AI-native network architectures.
Deep Reinforcement Learning for Satellite–Terrestrial Integrated Network Slice Resource Scheduling
The convergence of non-terrestrial and terrestrial communication infrastructures into a unified satellite–terrestrial integrated network represents a defining architectural ambition for sixth-generation systems. This integration promises ubiquitous coverage, enhanced resilience, and flexible service provisioning, yet it simultaneously introduces formidable resource orchestration challenges. Network slicing, which enables the coexistence of multiple logical networks over a shared physical substrate, is essential for delivering differentiated quality of service across such a heterogeneous domain. Traditional resource allocation heuristics struggle to cope with the high-dimensional state spaces, multi-timescale dynamics, and stringent latency constraints inherent to hybrid satellite–terrestrial slices. This paper presents a system-level investigation into the application of deep reinforcement learning for end-to-end slice resource scheduling in integrated space–ground architectures. The discussion focuses on structural trade-offs, deployment implications, multi-objective fairness, governance frameworks, and sustainability considerations rather than on mathematical formulations. An extensive analysis is offered of how centralized and distributed learning agents can reason about spectrum, beam, storage, and computational resources across moving satellite nodes and stationary terrestrial cells. The paper further examines robustness under orbital mobility and sudden link interruptions, explores governance mechanisms for cross-operator slicing, and positions future developments around digital twins, zero-trust security, and intent-based networking. The study provides a broad architectural discourse intended to guide the engineering of autonomous, resilient, and equitable slice schedulers in next-generation satellite–terrestrial systems.
Deep Learning-Based Traffic Prediction and Adaptive Slice Scaling for Massive IoT Communications
The proliferation of massive Internet of Things (IoT) deployments introduces unprecedented spatial and temporal heterogeneity in network traffic, challenging the static resource provisioning assumptions on which current mobile architectures are built. Network slicing offers a path toward isolated, service-tailored logical networks, yet existing slice management frameworks rely predominantly on reactive threshold-based scaling that cannot track bursty, non-stationary IoT traffic patterns. This paper presents a system-level investigation of deep learning-based traffic prediction coupled with adaptive slice scaling as a unified control paradigm for massive IoT communications. We argue that the integration of advanced spatiotemporal prediction models with policy-driven reinforcement learning scaling agents constitutes a foundational shift from static over-provisioning toward anticipatory, self-optimizing network infrastructures. Our analysis spans the end-to-end system architecture, discussing orchestration layering, data pipelines, and the interplay between centralized inference and edge-native model execution. We examine the structural trade-offs among prediction accuracy, scaling reactivity, slice isolation guarantees, inter-tenant fairness, and energy sustainability. Critical attention is given to robustness under adversarial perturbations, model drift, and the lifecycle management of deep learning components in carrier-grade environments. Governance frameworks, standardization efforts, and economic mechanisms for multi-domain slice coordination are explored, highlighting the need for transparent, auditable decision-making. The proposed integrative viewpoint contributes a forward-looking research agenda that connects algorithmic innovation with the operational realities of large-scale, socio-technical communication systems.
Deep Reinforcement Learning-Driven Dynamic Resource Allocation Framework for QoS-Aware 5G Network Slicing
The proliferation of diverse 5G services with stringent and heterogeneous quality of service (QoS) requirements has elevated network slicing to a foundational architectural principle. Realizing the full potential of network slicing demands dynamic, automated resource allocation mechanisms that can adapt to rapidly fluctuating traffic and service-level demands across shared physical infrastructures. This paper presents a comprehensive framework for QoS-aware 5G network slicing driven by deep reinforcement learning, specifically leveraging the proximity policy optimization (PPO) algorithm as a central decision-making engine. Departing from conventional optimization or heuristic-based approaches, the framework treats slice resource allocation as a sequential decision-making problem under uncertainty, enabling continuous learning and adaptation in real time. The discussion is oriented toward system-level analysis, examining the architectural decomposition of the framework into monitoring, policy, and orchestration components, and evaluating structural trade-offs among centralization, scalability, and response latency. Critical dimensions such as multi-tenancy governance, sustainability through energy-aware policy shaping, robustness against non-stationary network conditions, and fairness among competing slices are analyzed in depth. Particular attention is given to deployment pragmatics, including the integration with existing software-defined networking and network function virtualization control planes, the role of digital twins for pre-deployment policy validation, and the challenge of maintaining slice isolation under dynamic resource fluctuations. Policy implications are explored with respect to regulatory frameworks for infrastructure sharing and net neutrality, arguing that intelligent resource brokers must be designed to balance commercial incentives with equitable access. The analysis draws on cross-domain insights from cloud orchestration, autonomous systems, and industrial control, positioning the proposed framework as a pathway toward self-optimizing, resilient, and ethically aligned 5G networks. By synthesizing advances in deep reinforcement learning with architectural and governance considerations, the paper contributes a holistic perspective on future-ready slice orchestration.
SafeRec-FL: Robust Federated Recommendation Learning Against Data Poisoning and Backdoor Attacks in Multi-Tenant E-Commerce Infrastructure
The rapid expansion of multi-tenant e-commerce infrastructures has created an urgent need for recommendation systems that can operate across diverse, independently managed data silos while preserving user privacy and platform security. Federated learning offers a promising paradigm for collaborative model training without centralizing sensitive user data, yet it simultaneously introduces a broad attack surface for adversaries seeking to manipulate recommendation outputs through data poisoning or backdoor injection. This paper presents SafeRec-FL, a robust federated recommendation learning framework designed to withstand such threats in a multi-tenant e-commerce environment. The framework integrates cryptographic trust anchors, Byzantine-robust aggregation protocols, and dynamic anomaly detection mechanisms to ensure that even a substantial fraction of malicious tenants cannot degrade recommendation quality or implant targeted backdoors. Beyond technical defenses, SafeRec-FL incorporates governance-oriented design choices—including compliance-by-design data handling, incentive structures aligned with honest participation, and standardized API interfaces that facilitate adoption by small and medium-sized businesses. We provide a comprehensive system-level analysis of architectural trade-offs between robustness, latency, fairness, and sustainability, and we draw on empirical evaluations from simulated multi-tenant deployments to demonstrate that SafeRec-FL maintains high recommendation accuracy while reducing attack success rates to negligible levels. The paper concludes with a discussion of policy implications and future directions for trustworthiness in decentralized socio-technical infrastructures.
Secure Preference Learning for Social E-Commerce: Integrating Adversarial Robustness, Differential Privacy, and Interpretable User-Intent Modeling
The convergence of social media and e-commerce has given rise to social e-commerce platforms where user engagement, content sharing, and transaction behaviors are deeply intertwined. Personalization systems on these platforms rely on learning user preferences from rich, high-dimensional interaction data. However, such systems face critical challenges related to data privacy, adversarial manipulation, and the opacity of learned models. This paper presents a comprehensive framework for secure preference learning in social e-commerce that integrates three complementary pillars: adversarial robustness, differential privacy, and interpretable user-intent modeling. We examine the structural trade-offs between privacy guarantees, model accuracy, and resilience to malicious inputs, and propose a socio-technical architecture that balances these objectives at the system level. The framework leverages federated learning to distribute model training across user devices, thereby reducing central data aggregation risks. Differential privacy mechanisms are embedded at both local and global levels to limit information leakage from user data and model updates. Adversarial training and input sanitization strategies are employed to defend against poisoning attacks and gradient manipulation. Interpretability is addressed through attention-based intent attribution and counterfactual explanations that provide actionable insights for platform stakeholders. We analyze governance considerations, including fairness across demographic groups, regulatory compliance, and the sustainability of privacy budgets over long-term deployment. Cross-domain comparisons with traditional recommendation systems and collaborative filtering approaches are drawn to highlight the unique challenges of social e-commerce contexts. The paper concludes by outlining future directions for privacy-preserving, transparent, and robust preference learning in dynamic social commerce environments.
Memory-Guided Video Scene Understanding for Embodied AI Navigation and Interactive Perception
Embodied artificial intelligence systems that operate in dynamic real-world environments must continuously parse streaming visual information, retain context over extended temporal horizons, and adapt their behavior in response to changing scenes. This paper presents a system-level analysis of memory-guided video scene understanding as a foundational capability for embodied navigation and interactive perception. We examine the architectural space of memory mechanisms—ranging from differentiable external stores and episodic controllers to transformer-based long-range context models—and trace their integration into video understanding pipelines that serve navigation, manipulation, and multimodal reasoning. Rather than focusing on algorithmic novelty, the discussion foregrounds structural trade-offs in capacity, retrieval latency, training stability, and deployment footprint, and it situates these technical considerations within broader sociotechnical frameworks. The paper dissects the interplay between spatial-topological mapping, object-centric memory, and continual adaptation in navigation tasks, and it extends the analysis to interactive perception where memory of past interactions shapes affordance learning and task planning. System-level challenges are then explored, including edge-cloud partitioning, energy efficiency, robustness under sensor degradation, and the tension between persistent memory and privacy. Governance, fairness, and safety implications are addressed through the lenses of bias auditing, regulatory compliance, and inclusive design, emphasizing that memory-guided embodied agents straddle the boundary between high-stakes autonomy and socially embedded operation. By synthesizing architectural insights with deployment and policy perspectives, the paper provides a holistic reference for researchers, system architects, and policymakers engaged in the development of trustworthy embodied AI.
DriveAgent-WM: World Model-Driven Multimodal Autonomous Driving Agents with Chain-of-Thought Planning and Interactive Decision Making
The pursuit of safe and reliable autonomous driving has increasingly turned toward holistic architectures that integrate perception, planning, and control within a unified representational framework. This paper introduces DriveAgent-WM, a novel autonomous driving agent that leverages a world model-driven design to fuse multimodal sensor streams, perform explicit chain-of-thought planning, and enable interactive decision making under uncertain, dynamic traffic conditions. The system constructs an internal predictive model of the environment—a world model—that jointly encodes visual, LiDAR, radar, and trajectory data, enabling the agent to simulate future states and reason over possible action sequences. Chain-of-thought planning translates the latent predictions into interpretable reasoning steps, aligning the decision pipeline with human-like deliberation while preserving responsiveness. Interactive decision making is realized through closed-loop negotiation with other road users, governed by a policy optimization layer that continuously refines behavior based on simulated rollouts. The paper provides a comprehensive analysis of the architectural components, emphasizing the structural trade-offs between end-to-end learning and modular decomposition, the computational infrastructure required for real-time latent imagination, and the governance mechanisms necessary for safe deployment. Particular attention is directed toward robustness against distributional shift, fairness in interaction protocols, sustainability of compute-intensive world model training, and the broader policy implications of deploying cognitively autonomous agents on public roads. Through this systems-oriented lens, DriveAgent-WM is positioned not merely as a technical artifact but as a socio-technical construct that demands careful balancing of performance, interpretability, equity, and accountability.
Explainable Artificial Intelligence for Clothing-Change Person Re-Identification via Semantic Feature Attribution
Clothing-change person re-identification (CC-ReID) poses a formidable challenge for surveillance systems, as individuals deliberately or incidentally alter their appearance, rendering traditional appearance-based matching unreliable. While deep learning models have achieved considerable accuracy in controlled benchmarks, their opacity undermines trust, accountability, and operational safety in high-stakes deployments such as law enforcement and border control. This paper presents a system-level investigation into explainable artificial intelligence (XAI) for CC-ReID through the lens of semantic feature attribution. We argue that the path toward trustworthy CC-ReID lies not in isolated algorithmic post-hoc explanation but in architecting inherently interpretable attribution pipelines that expose how models associate identity across clothing boundaries. The paper dissects the structural trade-offs among global and local explanation modalities, the integration of attention and part-based feature decoupling as attribution sources, and the infrastructure requirements for real-time explainability at scale. A comprehensive analysis is provided of how semantic attribution surfaces robustness vulnerabilities, enables fairness audits across demographic subgroups, and informs governance frameworks for biometric surveillance. We further examine the deployment continuum from centralized cloud analytics to privacy-preserving edge inference, discussing energy sustainability and the tension between model complexity and explanatory fidelity. By synthesizing perspectives from computer vision, systems engineering, and regulatory policy, this work offers a blueprint for next-generation CC-ReID platforms where explanation is not an add-on but a fundamental design constraint. The paper concludes with an agenda for open benchmarking, standardized attribution validation, and cross-disciplinary collaboration to align technical capability with societal values.