Journal of Advanced Artificial Intelligence Research
Journal of Advanced Artificial Intelligence Research is an international peer-reviewed journal dedicated to publishing high-quality research in artificial intelligence and its interdisciplinary applications. The journal covers theoretical foundations, algorithmic innovations, intelligent systems, machine learning, deep learning, natural language processing, computer vision, data intelligence, robotics, and AI-driven solutions across science, engineering, healthcare, finance, education, and industry. Its purpose is to provide a scholarly platform for researchers, practitioners, and professionals to share original findings, review emerging trends, and promote the responsible development and practical application of advanced artificial intelligence technologies.
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Large Language Model-Driven Intent-Aware Resource Allocation for 6G Semantic Communication Networks
The emergence of sixth-generation wireless systems redefines communication through semantic-aware architectures that prioritize meaning and task effectiveness over raw bit transmission. Simultaneously, large language models have demonstrated remarkable capabilities in interpreting ambiguous human intent expressed in natural language. This paper presents a comprehensive system-level investigation into the convergence of these two paradigms, proposing an intent-aware resource allocation framework for 6G semantic communication networks that leverages large language models as the core intent interpretation engine. Departing from conventional optimization formulations, we examine the structural trade-offs involved in embedding powerful generative language understanding within the network control plane. The discussion spans architectural decomposition, cross-layer semantic mapping, dynamic resource orchestration, and governance challenges. We analyze how an intent-driven, language-model-mediated control loop can translate high-level user goals into semantically coherent resource assignments across radio, computation, and content dimensions. The study further investigates tensions between inference latency and resource granularity, between centralized intelligence and edge autonomy, and between model adaptability and operational determinism. Sustainability, fairness, trustworthiness, and regulatory alignment are examined as integral design requirements rather than afterthoughts. By situating large language models at the nexus of semantic intent interpretation and infrastructure orchestration, the paper articulates a holistic research agenda that informs future 6G standardization, network operations, and the responsible integration of generative artificial intelligence into critical communication infrastructures.
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Machine Learning-Guided Design of Heterograft Bottlebrush Polymer Nanocarriers for Stimuli-Responsive Drug Delivery: Linking Pearl-Necklace Self-Assembly to Biomedical Transport Performance
The convergence of artificial intelligence and macromolecular engineering is enabling a new paradigm for designing advanced drug delivery systems with unprecedented precision. This paper presents a large-scale, system-level framework that integrates machine learning with the synthesis and characterization of heterograft bottlebrush polymer nanocarriers, focusing on how pearl-necklace self-assembly architectures govern stimuli-responsive transport performance in complex biological environments. We examine the structural trade-offs inherent in bottlebrush topology, grafting density, side-chain chemistry, and block sequence as designable parameters that collectively determine the morphological transition from extended pearl-necklace conformations to compact unimolecular micelles. A machine learning pipeline is architected to predict these transitions and their impact on drug encapsulation efficiency, release kinetics under pH, temperature, or enzymatic stimuli, and biodistribution patterns. Rather than focusing on molecular-level simulation details, the discussion centers on the systemic challenges of data infrastructure, model governance, robustness across heterogeneous patient populations, and the sustainable deployment of such nanocarriers within regulated healthcare ecosystems. Cross-domain comparisons with existing self-assembling polymeric systems and a detailed examination of fairness and policy implications reveal that linking self-assembly mechanisms to therapeutic outcomes through predictive models demands rigorous attention to reproducibility, interpretability, and equitable access. The paper argues that the pearl-necklace to unimolecular micelle pathway, although molecular in origin, must be understood and controlled as a socio-technical system variable, with implications for manufacturing scalability, regulatory approval, and long-term clinical sustainability. Through analytic discourse, we provide a roadmap for building transparent, resilient, and ethically aligned ML-guided design platforms for next-generation nanomedicines.
Personalized Prompt Learning for User-Centric Conversational AI Systems
The rapid proliferation of large language models has reshaped conversational artificial intelligence, yet a critical gap remains in adapting these systems to individual users without costly fine-tuning or static prompt engineering. Personalized prompt learning emerges as a system-level paradigm that dynamically modulates instruction representations to align with user preferences, interaction histories, and contextual goals. This paper presents an interdisciplinary analysis of personalized prompt learning architectures, situating the approach within the broader landscape of parameter-efficient adaptation, user modeling, and socio-technical infrastructure. We examine structural trade-offs between centralized prompt optimization and on-device personalization, highlighting the interplay of latency, privacy, and model capacity. Governance frameworks are proposed to address fairness, representation harm, and the risk of echo chambers when prompts are continuously tuned to user behavior. Deployment considerations span edge-cloud orchestration, sustainability metrics, and lifecycle management of prompt modules. Robustness is analyzed through adversarial user inputs, distributional shifts, and feedback loops that may amplify biases. We further discuss policy implications for transparency, auditability, and user agency in systems that learn from interpersonal interaction. By synthesizing advances in selective prompt insertion, instruction tuning, and federated learning, we outline a reference architecture that treats prompts as adaptive, user-specific mediators between foundation models and situated dialogue. The paper contributes a comprehensive systems perspective, moving beyond algorithmic novelty to address the infrastructural and ethical conditions under which personalized conversational AI can be responsibly realized.
Knowledge Graph Enhanced AIOps Framework for Root Cause Analysis in Network Slice Service Degradation
The provisioning of network slices in fifth-generation and beyond communication systems introduces unprecedented flexibility in delivering tailored services with distinct performance guarantees. However, the inherent complexity of virtualized infrastructures and the tight coupling between logical slices and shared physical resources significantly complicate the detection and diagnosis of service degradation. Artificial intelligence for IT operations has emerged as a promising paradigm to automate operational tasks, yet conventional data-driven root cause analysis often suffers from a lack of semantic context, leading to ambiguous fault localization and reactive remediation. This paper proposes a knowledge graph enhanced AIOps framework specifically designed for root cause analysis in network slice service degradation. By integrating heterogeneous telemetry data, topological models, service level agreement policies, and historical incident records into a unified knowledge graph, the framework enables context-aware reasoning over complex dependencies. We examine architectural considerations, including the layered design of the ingestion, knowledge graph persistence, inference engine, and decision support subsystems. The discussion extends to structural trade-offs between centralized and distributed knowledge graph deployments, the balance between model interpretability and diagnostic accuracy, and the integration of graph neural networks for anomaly propagation tracing. Furthermore, we address operational governance aspects such as federated knowledge sharing across domains, zero-trust security principles, and the policy implications of AI-driven decision-making in regulated telecommunications environments. The framework emphasizes sustainability through resource-efficient knowledge maintenance and fairness in cross-tenant diagnostics. A comprehensive cross-domain comparison situates the proposed approach within the broader landscape of intelligent network management. The paper concludes with a forward-looking perspective on the role of knowledge graphs in achieving autonomous and trustworthy network operations.
Knowledge-Augmented Spatial–Temporal Traffic Forecasting for Autonomous Network Management in 6G Systems
The vision of sixth-generation (6G) networks encompasses extreme heterogeneity, near-instantaneous reconfigurability, and autonomous closed-loop management capable of orchestrating resources without human intervention. Central to this ambition is the ability to forecast network traffic with high fidelity across both spatial and temporal dimensions, enabling proactive and predictive resource allocation. While pure data-driven deep learning models have demonstrated remarkable accuracy in spatial-temporal traffic prediction, they often operate as black boxes, neglecting rich external knowledge such as infrastructure semantics, event calendars, social dynamics, and physical environment constraints. Knowledge-augmented forecasting, which fuses structured and unstructured external knowledge with neural spatial-temporal models, has emerged as a promising alternative to overcome these limitations. This paper presents a system-level examination of knowledge-augmented spatial-temporal traffic forecasting for autonomous network management in 6G systems. We articulate an end-to-end architectural vision that integrates heterogeneous knowledge sources, including large language models and knowledge graphs, within frameworks such as the O-RAN RAN Intelligent Controller and ETSI Zero-touch Network and Service Management. The discussion systematically addresses structural trade-offs between centralized and edge-distributed knowledge processing, the computability-latency-accuracy envelope under 6G latency budgets, and the infrastructural requirements for sustainable deployment. An in-depth governance analysis treats robustness against knowledge poisoning and adversarial perturbations, and fairness in resource allocation when predictions incorporate unevenly distributed external knowledge. We further reflect on the sustainability footprint of augmented prediction pipelines and propose strategies for life-cycle-aware design. The paper concludes by outlining future research directions that merge system-level trustworthiness with high-performance predictive autonomy.
Reinforcement Learning Enabled Design of Electronic-State Engineering Strategies for Sustainable Oxygen Evolution Catalysis
The oxygen evolution reaction (OER) constitutes a critical bottleneck in sustainable energy conversion systems, yet the design of high-performance OER catalysts remains constrained by the vast and combinatorially complex landscape of electronic states that govern catalytic activity. Traditional approaches to electronic-state engineering rely on trial-and-error experimentation or density functional theory calculations that are ill-suited to navigate high-dimensional design spaces under operational constraints. This paper presents a systems-level framework for leveraging reinforcement learning (RL) to autonomously discover and optimize electronic-state engineering strategies for transition metal oxide catalysts. We conceptualize the catalyst design problem as a sequential decision-making process in which an RL agent modulates synthesis parameters, compositional levers, and defect chemistries to tune frontier electronic states such as metal-oxygen covalency, charge-transfer excitations, and correlated electron configurations. The architecture integrates a multi-fidelity simulation environment, active experimental validation loops, and a distributed computing infrastructure that balances exploration-exploitation trade-offs across heterogeneous computational resources. We provide a detailed examination of structural trade-offs in representing electronic state spaces, formulating reward signals that reconcile catalytic activity with long-term stability, and governing the socio-technical deployment of autonomous discovery platforms. Robustness considerations under epistemic uncertainty from both computational approximations and experimental variabilities are analyzed through the lens of domain randomization and ensemble policy methods. Furthermore, we address fairness and policy implications by proposing open-access benchmarking ecosystems and distributed manufacturing models that ensure globally equitable access to sustainably designed catalytic materials. This work reframes catalyst discovery as a large-scale adaptive system, offering governance principles and infrastructure blueprints that extend beyond OER to broader materials design challenges in the transition to a carbon-neutral energy infrastructure.
Hierarchical Reinforcement Learning Framework for End-to-End Network Slice Lifecycle Management
Network slicing has emerged as a cornerstone of 5G and beyond mobile systems, enabling the provisioning of customized logical networks over a shared physical infrastructure. The end-to-end lifecycle management of network slices—spanning preparation, commissioning, operation, and decommissioning—poses a complex sequential decision-making problem under uncertainty. Traditional orchestration approaches rely on heuristics and reactive control loops that struggle to meet dynamic quality-of-service requirements and resource efficiency goals across multiple administrative domains. This paper presents a hierarchical reinforcement learning framework for autonomous end-to-end slice lifecycle management. The proposed architecture decomposes the management plane into multiple temporal and functional abstraction levels, each handled by a reinforcement learning module operating on distinct timescales. A high-level meta-controller selects long-term slice objectives and policy options, intermediate controllers manage lifecycle state transitions and cross-domain coordination, while low-level controllers execute fine-grained resource allocation and fault mitigation actions. The framework leverages the options formalism and policy gradient methods to enable sample-efficient learning and scalable decision-making. We discuss architectural trade-offs, infrastructure implications, and system-level design choices that govern robustness, sustainability, and multi-tenant fairness. The paper further examines policy and governance dimensions, including trustworthiness, auditability, and regulatory compliance, arguing that hierarchical structure naturally accommodates human-intelligible control points and policy constraints. By integrating lifecycle-wide optimization with hierarchically abstracted control, the proposed framework addresses key shortcomings of flat reinforcement learning approaches and charts a path toward fully automated, zero-touch slice management in future communication networks.
Intelligent Edge-Assisted Network Slice Orchestration for Ultra-Reliable Low-Latency Communications in 5G Networks
The emergence of fifth-generation (5G) wireless systems has introduced a paradigm shift through network slicing, enabling the co-existence of diverse service verticals over a common physical infrastructure. Among the most demanding slice categories is ultra-reliable low-latency communication (URLLC), which necessitates deterministic performance guarantees for mission-critical applications such as autonomous driving, industrial automation, and tele-surgery. However, achieving end-to-end URLLC across geographically distributed edge sites requires a fundamental rethinking of slice orchestration mechanisms that extend beyond centralized resource management. This paper presents a comprehensive system-level analysis of intelligent edge-assisted network slice orchestration for URLLC in 5G networks. We examine the architectural decomposition of orchestration functions across core, edge, and far-edge layers, highlighting the structural trade-offs between centralized coordination and distributed autonomy. The discussion explores how edge intelligence, particularly through machine learning, can enable proactive slice adaptation, predictive fault management, and context-aware policy enforcement. Special attention is devoted to the governance of multi-domain resources, the interplay between computational and networking elements, and the resilience strategies required to sustain URLLC under dynamic conditions. We further evaluate deployment constraints, infrastructure scalability, sustainability considerations, and fairness implications when URLLC slices compete with other service types. The paper contributes a forward-looking perspective on how policy frameworks and standardization efforts can align technical orchestration with socioeconomic objectives. By integrating perspectives from systems engineering, artificial intelligence, and regulatory governance, this work provides a holistic reference for designing and operating next-generation network slices that simultaneously satisfy ultra-reliability and low-latency requirements.
Graph Neural Network-Based Structural Health Monitoring of Intelligent Laminated Damping Systems
The integration of structural health monitoring (SHM) with intelligent laminated damping systems presents a significant opportunity to enhance the resilience, safety, and longevity of critical civil, aerospace, and mechanical infrastructures. This paper investigates the system-level design and deployment of graph neural network (GNN) architectures for the real-time assessment and prognostics of such smart composite structures. Intelligent laminated damping systems combine viscoelastic or constrained-layer damping treatments with embedded sensor arrays, generating spatially distributed, high-dimensional dynamic data that pose unique challenges for conventional signal processing. GNNs, by virtue of their relational inductive bias, are inherently suited to model the complex topological dependencies among sensor nodes, damage-sensitive features, and laminate layup configurations. The paper provides a comprehensive examination of the architectural choices, integration patterns, and trade-offs involved in building GNN-driven monitoring frameworks. Emphasis is placed on the fusion of physics-informed representations, the translation of laminate-scale phenomena into graph abstractions, and the orchestration of data pipelines from edge sensing to cloud analytics. Beyond technical design, the discussion extends to life-cycle governance, sustainability of monitoring infrastructures, adversarial robustness of learned models, algorithmic fairness in multi-asset management, and policy implications for the adoption of intelligent SHM systems. Case illustrations from aerospace skins, wind turbine blades, and bridge retrofits are used to ground the analysis. The work articulates a forward-looking perspective on how GNN-based monitoring can evolve from isolated prototypes to trusted, scalable, and equitable decision-support layers within the next generation of adaptive structural systems.
Energy-Efficient Prompt Placement for Green Artificial Intelligence in Large-Scale NLP Systems
The rapidly expanding computational demands of large-scale natural language processing systems have elevated energy consumption into a central challenge for sustainable artificial intelligence. While parameter-efficient fine-tuning methods such as prompt tuning and prefix tuning have substantially reduced the costs associated with adapting foundation models, the placement and activation of these prompts within inference pipelines remain under-explored from a system-level energy perspective. This paper presents a holistic examination of energy-efficient prompt placement as a lever for green AI in large-scale deployment environments. By framing prompt activation as a dynamic resource allocation problem, we analyze the structural trade-offs between inference latency, model quality, and carbon footprint across cloud, edge, and hybrid infrastructures. We explore architectural design patterns that support selective prompt insertion, caching strategies for reusable prompt states, and carbon-aware scheduling of prompt-dependent computations. The discussion extends to governance challenges surrounding fairness, transparency, and accountability when energy-saving policies differentially impact user populations or linguistic groups. Through cross-domain comparisons with adaptive computation mechanisms such as mixture-of-experts routing and early-exit architectures, we identify shared principles that can guide the development of energy-proportional prompt management frameworks. The paper concludes with forward-looking perspectives on how prompt placement strategies can be integrated into broader sustainability roadmaps for industrial-scale NLP systems, emphasizing the need for standardized energy reporting, open benchmarking databases, and multi-stakeholder policy coordination.
Multimodal Prompt Insertion Strategies for Vision-Language Foundation Models
Vision-language foundation models have ushered in a new era of multimodal intelligence, enabling systems to generate coherent textual descriptions from images, reason across modalities, and perform complex comprehension tasks. The integration of prompt engineering into these models has emerged as a pivotal mechanism for guiding behaviour without full retraining. Yet the design space for inserting prompts, both textual and visual, across the layered architectures of large-scale vision-language models remains fragmented and underexplored from a systems perspective. This paper presents a comprehensive analysis of multimodal prompt insertion strategies, examining the structural, infrastructural, and socio-technical dimensions that shape their effectiveness, efficiency, and trustworthiness. We develop a taxonomy of insertion architectures ranging from early fusion and cross-modal prefix tuning to dynamic, learnable insertion policies. System-level trade-offs involving computational cost, memory footprint, latency, and modular governance are dissected. The discussion extends to robustness under distribution shift and adversarial interference, fairness across demographic groups embedded in multimodal data, and the policy implications of prompt-based model steering in high-stakes deployments. Through cross-domain comparisons spanning medical imaging, autonomous perception, and content moderation, we reveal how insertion strategies mediate between flexibility and control, between adaptation and foundational knowledge preservation. The paper further addresses sustainability concerns tied to prompt-tuning overhead and the risk of prompt injection vulnerabilities. We argue that advancing prompt insertion requires not only architectural innovation but also holistic infrastructure design, monitoring frameworks, and governance protocols attuned to the evolving landscape of foundation model ecosystems.
Foundation Model Empowered Autonomous Network Management for QoS Optimization in 5G-Advanced and 6G Networks
The move from fifth-generation advanced (5G-Advanced) systems to sixth-generation (6G) networks is predicated on hyper-specialised performance envelopes that demand granular, real-time quality of service (QoS) guarantees across extremely heterogeneous service classes. Traditional and even deep reinforcement learning based management approaches, while effective in narrow slices, encounter fundamental brittleness when confronted with domain shift, novel service descriptors, or the combinatorial complexity of multi-objective resource allocation in open multi-vendor environments. Foundation models, large-scale pre-trained neural architectures that acquire broad representational capabilities from vast and diverse data modalities, offer a paradigm shift toward generalist intelligence in autonomous network management. This paper presents a system-level architectural framework that embeds foundation models as the cognitive nuclei of closed-loop control, enabling intent-driven reasoning, cross-domain policy transfer, and anticipatory QoS assurance without exhaustive per-slice retraining. We examine the structural trade-offs that arise when unifying language-based policy interfaces, vision-based radio environment maps, and time-series telemetry through a shared model backbone, and we dissect the concomitant challenges of inference latency, energy consumption, and model governance. The discussion extends to fairness, accountability, and transparency in AI-driven resource allocation, as well as the deployment pathways that balance centralised model hosting against distributed inference at the network edge. By synthesising advances in model compression, retrieval-augmented generation, and responsible AI frameworks, we articulate a roadmap for foundation model empowered autonomous networks that are robust, sustainable, and aligned with the regulatory and ethical imperatives of the 6G era.
Generative AI-Enhanced Surrogate Modeling for Fast Dynamic Analysis of Smart Laminated Structures with Viscoelastic Damping
The increasing complexity of modern engineered systems demands rapid, high-fidelity structural analysis, particularly for smart laminated composites that incorporate viscoelastic damping materials and distributed sensing-actuation networks. Conventional full-order dynamic simulations, though accurate, are computationally prohibitive for real-time health monitoring, iterative design, and adaptive control. This paper presents a system-level investigation into generative artificial intelligence-enhanced surrogate modeling as a transformative approach to accelerate dynamic analysis of such multi-physics structures. Moving beyond purely computational mechanics, the work examines architectural trade-offs between data-driven generative models—generative adversarial networks and variational autoencoders—and physics-informed training strategies, emphasizing the joint role of accuracy, latency, and generalization. A comprehensive discussion is provided on integrating these surrogates within larger socio-technical infrastructures, including cloud-based digital twins, edge computing platforms, and automated decision pipelines. Governance challenges are examined through the lenses of data provenance, model drift, robustness against adversarial perturbations, and the ethical imperative of equitable access to advanced structural intelligence. Additionally, the paper addresses sustainability outcomes, regulatory policy considerations, and the certification hurdles that arise when AI-generated predictions inform safety-critical decisions. By synthesizing insights from smart materials, machine learning, systems engineering, and science and technology policy, this contribution delineates a pathway toward resilient, scalable, and responsible deployment of generative surrogate models for next-generation laminated structures.
Green AI-Based Network Traffic Optimization Using Large Language Models and Digital Twin Wireless Networks
The escalating energy footprint of next-generation wireless networks demands a fundamental rethinking of traffic optimization architectures. This paper proposes a novel framework that couples large language models with digital twin wireless networks to achieve green AI-driven network management. We examine the system-level integration of semantic reasoning and high-fidelity simulation, investigating the structural trade-offs between model expressiveness, computational overhead, and energy sustainability. The architecture leverages large language models to generate interpretable traffic engineering policies from high-level intents, while a continuously synchronized digital twin serves as a sandbox for stress-testing and zero-energy validation. A central theme is the tension between the substantial carbon cost of training and serving large models and the long-term energy savings realized through proactive, coordinated optimization. We analyze governance mechanisms, fairness preservation, and the robustness of AI-augmented control loops under realistic operational conditions. Furthermore, we explore policy pathways, standardization efforts, and lifecycle carbon accounting needed to embed sustainability into the core of intelligent network infrastructures. The discussion synthesizes insights from distributed intelligence, resource allocation theory, explainable AI, and regulatory design, advocating for an interdisciplinary approach in which performance, equity, and environmental stewardship co-evolve. By framing the integration as a socio-technical system, we highlight how green AI principles can be operationalized at scale, ensuring that next-generation network intelligence does not merely optimize throughput but does so within the planet’s ecological boundaries.
Federated Reinforcement Learning for Privacy-Preserving Resource Management in Multi-Domain 5G Network Slicing
The proliferation of 5G network slicing has enabled operators to instantiate multiple logical networks over a shared physical infrastructure, each tailored to distinct service requirements. However, orchestration of end‑to‑end slices across multiple administrative domains introduces formidable challenges in resource allocation, quality‑of‑service assurance, and data governance. Centralized machine learning approaches that gather sensitive traffic traces, user mobility patterns, and slice state information from participating domains raise significant privacy and regulatory concerns, particularly when data traverse jurisdictions with differing legal frameworks. Federated reinforcement learning offers a promising alternative by allowing domains to collaboratively train resource management policies without sharing raw operational data. This paper provides a comprehensive system‑level analysis of federated reinforcement learning architectures designed for privacy‑preserving multi‑domain 5G network slicing. We examine the structural trade‑offs between fully distributed and hierarchical aggregation models, the interplay between local policy generalization and global coordination, and the privacy‑preserving mechanisms that underpin secure federated optimization. The discussion extends to robustness against non‑identically distributed data, fairness in resource allocation across heterogeneous slice tenants, and the governance frameworks required to sustain trust among competing stakeholders. Deployment considerations encompassing edge‑cloud continuum orchestration, energy efficiency, and interoperability with existing 3GPP management interfaces are critically assessed. Policy implications regarding cross‑border data flow regulations, algorithmic accountability, and the evolution of service level agreements in a federated multi‑domain ecosystem are explored. By synthesizing advances in federated learning, deep reinforcement learning, and 5G slicing standards, the paper articulates a research roadmap for resilient, equitable, and privacy‑compliant resource management in next‑generation networks.
Federated Prompt Tuning with Selective Prompt Synchronization for Privacy-Preserving NLP
The rapid advancement of large language models has transformed natural language processing but simultaneously intensified concerns around data privacy and sovereignty. Federated learning provides a compelling framework for collaborative model training without exposing raw user data, yet deploying these massive models across decentralized clients remains impractical due to enormous communication costs. Prompt tuning, which freezes the pre-trained model and learns only a small set of continuous embeddings, has emerged as a parameter-efficient alternative with strong downstream performance. When prompt tuning is integrated into federated settings, however, all prompt parameters are typically synchronized across clients, which still incurs nontrivial communication overhead and creates new privacy vulnerabilities through the shared representation space. This paper proposes a federated prompt tuning architecture augmented with selective prompt synchronization, in which only a carefully chosen subset of prompt tokens is exchanged between local clients and the aggregation server. Drawing on selective prompt insertion concepts, the system learns to identify and transmit the most informative prompt dimensions while suppressing redundant or privacy-sensitive information. We present a comprehensive system-level analysis that examines the structural trade-offs among communication efficiency, model accuracy, privacy protection, fairness, and governance. The discussion extends to integration with differential privacy, robustness against adversarial clients, sustainability, and policy implications in cross-silo and cross-device deployments. This work offers a blueprint for building secure, efficient, and responsible federated natural language processing infrastructures.
Explainable Machine Learning Framework for Decoding Zhang–Rice Singlet Formation and Oxygen Evolution Reaction Kinetics
Deciphering the electronic structure origin of catalytic activity in the oxygen evolution reaction (OER) remains a central challenge for sustainable energy technologies. The Zhang–Rice singlet, a two-hole bound state on a single transition metal site, has emerged as a key descriptor linking local electronic structure to OER kinetics, yet its formation pathways under operando conditions are highly complex. This paper presents a system-level explainable machine learning framework that decodes Zhang–Rice singlet formation and OER kinetics from multimodal operando spectroscopy data. The architecture couples deep representation learning with post-hoc interpretability techniques, enabling the extraction of physically meaningful features while preserving high predictive fidelity. We examine structural trade-offs between model accuracy, transparency, and computational cost across a distributed data infrastructure built on cloud-native, FAIR-compliant pipelines. The analysis extends to governance and fairness concerns, including dataset bias, underrepresentation of catalyst families, and the need for federated learning to protect intellectual property while enabling collaborative model refinement. Robustness is addressed through uncertainty quantification and adversarial validation protocols, while sustainability considerations motivate efficiency-aware model selection and resource budgeting. Policy implications concerning open science mandates, reproducibility, and equitable access to advanced catalyst informatics tools are discussed. By integrating technical and socio-technical dimensions, the proposed framework establishes a principled pathway toward interpretable, responsible, and scalable data-driven discovery in electrocatalysis.
Federated DeepSeek-Assisted Traffic Prediction and Anomaly Detection in Edge-Enabled Mobile Networks
The exponential growth of mobile data traffic and the proliferation of latency-sensitive applications demand intelligent, real-time traffic forecasting and anomaly detection mechanisms that operate close to the data source. Centralized cloud-based learning architectures incur prohibitive communication overhead, privacy vulnerabilities, and regulatory friction. This paper presents a federated learning framework that integrates DeepSeek, a large language model with strong sequence modeling and spatial-temporal reasoning capabilities, to enable collaborative traffic prediction and anomaly detection across edge nodes in mobile networks. The proposed system architecture distributes model fine-tuning across base stations and user equipment, employing parameter-efficient adaptation and communication-efficient aggregation to manage the large model footprint and heterogeneous client resources. We dissect the structural trade-offs between global prediction fidelity and communication cost, examining hierarchical aggregation, differential privacy, and resilience against adversarial perturbations. By coupling federated learning with DeepSeek, mobile operators can exploit complex long-range temporal dependencies and geographic correlations while preserving data locality, aligning with data governance frameworks, and enhancing infrastructure robustness. The analysis extends to fairness issues arising from non-identically distributed traffic patterns across diverse urban and rural regions, and discusses policy implications for cross-jurisdictional deployments. Deployment considerations address resource-constrained edge hardware, energy sustainability, and system reliability under dynamic network conditions. This study provides an interdisciplinary blueprint for a secure, scalable, and responsible socio-technical system that harnesses large-scale pre-trained models for next-generation mobile network intelligence, emphasizing the interplay between algorithmic innovation, governance, and operational sustainability.
Foundation Model-Assisted Structural State Estimation for Smart Composite Laminates under Uncertain Environments
The increasing deployment of smart composite laminates in aerospace, energy, and civil infrastructure demands robust structural state estimation capable of coping with severe environmental and operational uncertainties. Traditional physics-based model updating and purely data-driven deep learning approaches each exhibit brittleness under distribution shift, motivating the search for more adaptive architectures. This paper investigates the integration of foundation models—large-scale pretrained time-series models—into a system-level framework for real-time strain, damage, and damping state inference in laminated composite structures instrumented with embedded sensor networks. We articulate a hybrid architecture that couples a foundation model backbone with physics-informed priors, domain-specific fine-tuning, and edge-cloud orchestration to reconcile representational generality with task-specific precision. The analysis foregrounds structural trade-offs including latency versus accuracy, data provenance and sensor heterogeneity, uncertainty quantification, and the tension between centralized model governance and on-device adaptation. The discussion extends to socio-technical dimensions, examining how foundation model-assisted estimation reshapes certification pathways, algorithmic fairness across heterogeneous structural populations, and the regulatory implications of automated decision-making in safety-critical systems. By centering the system-level perspective rather than algorithmic novelty, we provide a comprehensive roadmap for deploying foundation model capabilities in physically-grounded, uncertainty-aware infrastructure health management.
Adversarially Robust Prompt Tuning through Selective Prompt Diversification and Injection
The paradigm of prompt tuning has emerged as a highly parameter-efficient means of adapting large pre-trained language models to downstream tasks, yet its vulnerability to adversarial perturbations threatens deployment in safety-critical and high-stakes environments. This paper presents a system-level framework for adversarially robust prompt tuning grounded in two interlocking mechanisms: selective prompt diversification and controlled prompt injection. The framework moves beyond static soft prompt representations by stochastically generating a candidate pool of diverse prompts and then selecting a subset according to robustness-aware criteria before integrating them into the model’s forward pass. We analyze the resulting architecture through the lenses of structural trade-offs, inference-time overhead, deployment infrastructure, governance, fairness, and long-term sustainability, arguing that robust prompt tuning is as much a systems design problem as it is a machine learning optimization challenge. The discussion delineates how diversification strategies can be calibrated to balance adversarial resilience against computational cost, how injection controllers can be implemented as lightweight gating modules suitable for edge and cloud environments, and how the interplay between prompt selection and input semantics raises novel fairness and interpretability concerns. Without introducing mathematical formalisms, the paper provides a conceptual blueprint for next-generation robust language interfaces and situates the proposal within broader socio-technical discourse on trustworthy artificial intelligence.
Explainable AI-Based Decision Framework for Transparent Network Slice Resource Management
Network slicing has become a foundational paradigm for fifth-generation and beyond mobile networks, enabling multiple logical networks to coexist on a shared physical infrastructure. Efficient resource management across slices is essential to meet diverse quality-of-service requirements, yet the growing adoption of artificial intelligence in orchestration decisions introduces opacity that can undermine trust, accountability, and regulatory compliance. This paper presents a system-level explainable AI-based decision framework for transparent network slice resource management that reconciles high-performance automation with human-intelligible governance. The framework integrates interpretable machine learning models, post-hoc explanation modules, and a dedicated governance layer to translate local feature attributions into auditable resource allocation actions. We examine the structural trade-offs between predictive accuracy and explainability, describe how the architecture can embed within existing ETSI management and orchestration platforms, and analyze the implications for fairness, operational robustness, and energy sustainability. Through cross-domain comparisons and deployment scenarios, the discussion illuminates how transparency mechanisms can enable stakeholder confidence, facilitate fault diagnosis, and support regulatory oversight without compromising real-time performance. The paper positions explainable AI as a governance instrument that aligns algorithmic decision-making with organizational accountability and long-term infrastructure evolution, arguing that transparent slice resource management is not merely a technical desideratum but a socio-technical necessity for trustworthy network automation.
Digital Twin-Based Network Slice Optimization Using Deep Reinforcement Learning for Next-Generation Mobile Networks
Next-generation mobile networks require dynamic and fine-grained resource management to accommodate highly diverse service requirements under stringent performance constraints. Network slicing, underpinned by digital twin technology and deep reinforcement learning, presents a transformative paradigm for autonomous slice optimization at scale. This paper presents a comprehensive system-level examination of digital twin-based network slice optimization using deep reinforcement learning, focusing on architectural design, deployment challenges, governance, sustainability, robustness, and fairness. We propose a closed-loop control architecture in which a high-fidelity digital twin continuously mirrors the physical radio access and core network infrastructure, enabling proactive, conflict-free resource allocation. The twin’s predictive capabilities are combined with a centralized policy learning agent that uses proximal policy optimization to resolve the complex combinatorial resource allocation problem while respecting strict service-level agreements. Through an analysis of system integration, edge-cloud orchestration, and twin synchronization latency, the paper delineates the structural trade-offs between real-time responsiveness and modeling fidelity. Further, we discuss multi-tenant governance, regulatory implications, and the role of digital twins in ensuring transparent, auditable slice management. The study addresses robustness against twin model drift and adversarial perturbations, as well as fairness criteria that prevent resource starvation of lower-priority slices. An extended discussion covers energy footprint accounting, lifecycle carbon costs, and the alignment of slice optimization with sustainable networking initiatives. By synthesizing cross-domain insights from communications, artificial intelligence, distributed systems, and public policy, this work offers a forward-looking roadmap for deploying responsible, resilient, and equitable digital twin-driven network slicing platforms in 5G-Advanced and 6G ecosystems.
PathShield-Med: Interpretable Safety Control for Clinical Large Language Models through Internal Reasoning Path Intervention
The integration of large language models into clinical decision-making has introduced transformative opportunities for diagnostic support, treatment planning, and patient communication. However, the high-stakes nature of medical practice demands exceptionally rigorous safety guarantees that surpass those required in general-domain applications. Existing safety mechanisms predominantly rely on external filtering, output moderation, or prompt-level constraints, which remain vulnerable to circumvention and lack the depth needed for clinical interpretability. This paper presents PathShield-Med, a novel framework for safety control in clinical large language models through targeted intervention upon internal reasoning pathways. Rather than treating the model as a black box, PathShield-Med identifies and modulates safety-critical reasoning trajectories within the model's forward computation, enabling precise, transparent, and auditable safety enforcement without sacrificing medical accuracy. The architecture integrates a reasoning path analyzer, a dynamic intervention engine, and a clinical knowledge verifier, all operating under a clinician-centered explainability layer that provides post-hoc and real-time justifications for every safety-relevant decision. Through extensive structural analysis, we examine the trade-offs between intervention granularity and clinical fluency, the governance implications of path-level control, and the infrastructure requirements for deploying such systems in regulated healthcare environments. We further discuss robustness under adversarial clinical queries, fairness across patient demographic representations, and alignment with evolving regulatory frameworks for artificial intelligence-based medical devices. PathShield-Med represents a shift from surface-level safety filters to deep, interpretable control embedded within the reasoning fabric of clinical language models.
Explainable Cultural Bias Auditing Framework for Text-to-Image Generative Models Across Multilingual Contexts
The proliferation of text-to-image generative models has introduced profound challenges in understanding and mitigating cultural biases that manifest across languages and visual representations. Current bias auditing remains fragmented, often focusing on isolated demographic dimensions in a single language, without systematically explaining why culturally skewed outputs emerge or how they propagate across multilingual prompts. This paper proposes the Explainable Cultural Bias Auditing Framework, an integrative system architecture designed to detect, interpret, and govern cultural bias in text-to-image pipelines spanning multiple languages. The framework combines multilingual prompt curation, cross-modal concept probing, gradient-based and attention-driven explainability layers, and governance interfaces that connect internal model behavior with external fairness standards. We examine the structural trade-offs between audit depth, computational scalability, and real-time deployment; analyze the interdependency between data provenance, model architecture, and cultural representational gaps; and situate the framework within broader policy landscapes, including the EU AI Act and the NIST AI Risk Management Framework. Through a systems-level discussion, we highlight how infrastructure choices, sustainability constraints, and explainability granularity collectively shape the robustness of cultural bias detection. Rather than proposing a single algorithm, the paper articulates a principled architectural paradigm that integrates fairness-aware infrastructure, multilingual semantic alignment, and interpretability-first design, providing a scaffold for future auditing systems in generative AI ecosystems.
Path-Aware Defense Against Prompt Injection Attacks in Retrieval-Augmented Large Language Models
Retrieval-augmented generation (RAG) architectures have rapidly become a dominant paradigm for grounding large language model outputs in external knowledge, yet they introduce novel attack surfaces through prompt injection that threaten the integrity, confidentiality, and reliability of downstream applications. This paper presents a comprehensive system-level analysis of path-aware defense mechanisms designed to counter adversarial prompt injections in RAG pipelines. Departing from conventional input-filtering approaches, path-aware strategies operate on the provenance and trajectory of information as it moves from retrieval sources through fusion and generation stages, enabling fine-grained attribution, anomaly detection, and targeted intervention. The discussion examines structural trade-offs between defense granularity, latency, scalability, and model performance, and it locates path-aware methods within broader architectural patterns for robust AI infrastructure. The paper further addresses governance frameworks, fairness implications, operational resilience, and long-term sustainability, highlighting how path-level visibility transforms incident response, auditability, and regulatory compliance. By synthesizing insights from distributed systems, adversarial machine learning, and information retrieval, this work articulates a research roadmap for building resilient RAG ecosystems in which safety is not an afterthought but a property of the retrieval-generation pathway itself.
Knowledge-Grounded Cultural Alignment Strategies for Safe and Inclusive Visual Content Generation
Recent advances in text-conditioned generative models have enabled the creation of high-fidelity visual content at an unprecedented scale, yet they have concurrently surfaced profound challenges concerning cultural misrepresentation, stereotyping, and the erosion of cultural specificity. This paper presents a system-level analysis of knowledge-grounded cultural alignment as a strategic pathway toward safe and inclusive visual content generation. Moving beyond fine-tuning on crowd-sourced preference data, we examine the integration of structured and semi-structured cultural knowledge into the architectural fabric of image generation pipelines as a means of embedding pluralistic cultural awareness. We develop a conceptual framework that situates cultural alignment within the broader lifecycle of generative systems, linking data governance, model conditioning, inference-time interventions, and post-hoc auditing. The analysis foregrounds the structural trade-offs among representational fidelity, inference latency, fairness metrics, and the scalability of intervention mechanisms. Drawing on governance scholarship, we discuss the infrastructural and policy requirements for sustaining cultural inclusivity across diverse deployment contexts and regulatory environments. Robustness to adversarial cultural prompts, fairness across underrepresented cultural groups, and the long-term sustainability of alignment strategies in the face of evolving cultural norms are examined as interdependent system properties. The paper reframes cultural alignment not as a static optimisation target but as an ongoing socio-technical negotiation that demands continuous knowledge curation, participatory audit infrastructure, and multi-level governance arrangements. Through this lens, we articulate design principles and deployment considerations that can inform the next generation of culturally responsive visual generation platforms.
Federated Auditing of Cultural Fairness in Privacy-Preserving Text-to-Image Generation Models
The rapid proliferation of text-to-image generative models has surfaced deep structural concerns regarding cultural fairness, representation, and inclusion. These models, trained on vast and often uncurated web-scale datasets, exhibit significant cultural biases that risk homogenizing visual expression and marginalizing underrepresented communities. Simultaneously, regulatory frameworks and community expectations increasingly demand rigorous auditing of such systems without compromising the data sovereignty of the cultural groups involved. This paper proposes a federated auditing architecture that enables the systematic evaluation of cultural fairness in text-to-image models while preserving the privacy and local control of culturally sensitive data. We present a system-level analysis of the trade-offs inherent in distributing fairness auditing across heterogeneous cultural nodes, discussing architectural design, secure aggregation protocols, governance structures, and policy alignment. Through a detailed conceptual framework, we examine how federated fairness auditing can reconcile the competing demands of statistical rigor, cultural authenticity, and privacy preservation. The discussion extends to deployment infrastructure, robustness against adversarial manipulation, and the integration of community-defined fairness metrics. By centering the infrastructural and governance dimensions, this work provides a comprehensive pathway toward accountable and culturally aware generative artificial intelligence systems, highlighting the necessity of interdisciplinary collaboration between systems engineers, cultural stakeholders, and policy makers.
Path-Level Intervention for Secure Autonomous AI Agents: Enhancing Robustness, Explainability, and Ethical Decision-Making
The rapid integration of autonomous artificial intelligence agents into large-scale socio-technical infrastructures has introduced profound challenges in safety, transparency, and ethical alignment. Traditional safety mechanisms, rooted in input filtering, output scrubbing, or static fine-tuning, operate at granularities that are increasingly mismatched to the sequential, context-dependent decision-making characteristic of modern agentic systems. This paper presents a comprehensive examination of path-level intervention as a transformative paradigm for securing autonomous AI agents. Path-level intervention refers to the dynamic monitoring, analysis, and modification of an agent's decision trajectory—the sequence of states, actions, and intermediate computations—before final execution. We argue that shifting the locus of control from isolated data points to entire pathways yields structural advantages across multiple dimensions: robustness against adversarial manipulation and distributional shift, richer forms of explainability grounded in counterfactual trace analysis, and more faithful operationalization of ethical principles through trajectory-constrained decision-making. The paper systematically explores the architectural foundations, deployment infrastructure, governance implications, and systemic trade-offs inherent in path-level intervention frameworks. By drawing on cross-domain case illustrations from autonomous vehicles, healthcare decision support, and financial trading, we delineate how this approach can be integrated into existing agent architectures as a non-intrusive supervision layer. We further address pressing concerns of scalability, latency, composability in multi-agent settings, and the long-term sustainability of path-level governance. The analysis reveals that while path-level intervention introduces new complexities—such as intervention logic vulnerabilities and the challenge of encoding normative principles—it offers a uniquely powerful mechanism for achieving verifiable, auditable, and adaptive safety in increasingly autonomous AI ecosystems. The paper concludes with a forward-looking research agenda emphasizing the need for interdisciplinary collaboration across systems engineering, ethics, law, and machine learning to realize the full potential of this paradigm.
Adversarial Prompt Robustness and Cultural Representation Consistency in Large-Scale Text-to-Image Systems
Large-scale text-to-image generative systems have rapidly evolved into sociotechnical infrastructures that shape visual culture and public imagination at global scale. This paper examines two interconnected yet underexplored systemic properties of these platforms: adversarial prompt robustness and cultural representation consistency. Adversarial prompt robustness refers to the capacity of a system to withstand maliciously crafted textual inputs designed to circumvent safety filters, induce toxic imagery, or exploit model biases. Cultural representation consistency denotes the system’s ability to produce outputs that equitably and accurately depict diverse cultural contexts, traditions, and identities without systematically erasing certain groups or reinforcing stereotypical portrayals. We analyze these properties not as isolated technical fixes but as emergent outcomes of complex trade-offs across model architecture, training data curation, alignment pipelines, moderation infrastructure, and governance frameworks. The discussion foregrounds how central architectural choices, including contrastive language-image pretraining, latent diffusion, and classifier-free guidance, create latent pathways that simultaneously affect robustness and representation. We argue that adversarial prompt vulnerability and cultural erasure are deeply entangled with the data supply chain and the incentive structures of large-scale deployment. Furthermore, we probe the policy implications of treating these properties as continuous monitoring problems rather than binary compliance checkpoints. The paper advocates for a polycentric governance model that integrates technical red-teaming, cultural auditing, and participatory feedback mechanisms, while remaining attentive to the energy and economic costs of remediation strategies. By reframing prompt robustness and cultural consistency as dual requirements of trustworthy generative infrastructure, the analysis offers a structured lens for researchers, platform architects, and regulators to navigate the next phase of responsible text-to-image system deployment.
Continual Learning Framework for Long-Sequence Video Understanding with Memory-Aware Visual Representations
Continuous video streams generated by autonomous vehicles, surveillance networks, and interactive media platforms demand machine perception systems that sustain high performance over indefinitely long temporal horizons without catastrophic forgetting. Traditional architectures designed for short video clips struggle to reconcile memory efficiency with the preservation of long-range dependencies, while static representations fail to adapt to shifting visual statistics. This paper proposes a continual learning framework that integrates memory-aware visual representations for long-sequence video understanding. The framework treats memory as an architectural primitive rather than a passive storage buffer, coupling compressible episodic representations with dynamic parameter allocation strategies. We examine structural trade-offs among external memory banks, generative replay, and elastic weight consolidation in the context of streaming video. The discussion extends to system-level considerations including distributed training infrastructure, privacy-preserving federated adaptation across distributed cameras, energy-aware deployment on edge devices, and governance mechanisms for accountability in continuously evolving perception pipelines. Through a detailed analysis of architecture robustness, fairness under non-stationary class distributions, and policy implications for monitored public spaces, we argue that memory-awareness must be elevated from a component-level optimization to a first-class design principle. The paper articulates a holistic research agenda in which visual representations, learning dynamics, and operational infrastructure co-evolve to support trustworthy, sustainable continual video intelligence.