Search Results (198)

This paper presents a scalable and low-cost Retrieval Augmented Generation (RAG) architecture designed to enhance learning in university-level courses, with a particular focus on supporting students from economically disadvantaged backgrounds. Recent advances in large language models (LLMs) have demonstrated considerable potential in educational contexts; however, their adoption is often limited by computational costs and the need for stable broadband access, issues that disproportionately affect low-income learners. To address this challenge, we propose a lightweight, mobile, and friendly RAG system that integrates the LLaMA language model with the Milvus vector database, enabling efficient on device retrieval and context-grounded generation using only modest hardware resources. The system was implemented in a university-level Data Mining course and evaluated over four semesters using a quasi-experimental design with randomized assignment to experimental and control groups. Students in the experimental group had voluntary access to the RAG assistant, while the control group followed the same instructional schedule without exposure to the tool. The results show statistically significant improvements in academic performance for the experimental group, with p < 0.01 in the first semester and p < 0.001 in the subsequent three semesters. Effect sizes, measured using Hedges g to account for small cohort sizes, increased from 0.56 (moderate) to 1.52 (extremely large), demonstrating a clear and growing pedagogical impact over time. Qualitative feedback further indicates increased learner autonomy, confidence, and engagement. These findings highlight the potential of mobile RAG architectures to deliver equitable, high-quality AI support to students regardless of socioeconomic status. The proposed solution offers a practical engineering pathway for institutions seeking inclusive, scalable, and resource-efficient approaches to AI-enhanced education. Full article

The rapid spread of climate change misinformation across digital platforms undermines scientific literacy, public trust, and evidence-based policy action. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) create new opportunities for automating the detection and correction of misleading climate-related narratives. This study presents a multi-stage system that employs state-of-the-art large language models such as Generative Pre-trained Transformer 4 (GPT-4), Large Language Model Meta AI (LLaMA) version 3 (LLaMA-3), and RoBERTa-large (Robustly optimized BERT pretraining approach large) to identify, classify, and generate scientifically grounded corrections for climate misinformation. The system integrates several complementary techniques, including transformer-based text classification, semantic similarity scoring using Sentence-BERT, stance detection, and retrieval-augmented generation (RAG) for evidence-grounded debunking. Misinformation instances are detected through a fine-tuned RoBERTa–Multi-Genre Natural Language Inference (MNLI) classifier (RoBERTa-MNLI), grouped using BERTopic, and verified against curated climate-science knowledge sources using BM25 and dense retrieval via FAISS (Facebook AI Similarity Search). The debunking component employs RAG-enhanced GPT-4 to produce accurate and persuasive counter-messages aligned with authoritative scientific reports such as those from the Intergovernmental Panel on Climate Change (IPCC). A diverse dataset of climate misinformation categories covering denialism, cherry-picking of data, false causation narratives, and misleading comparisons is compiled for evaluation. Benchmarking experiments demonstrate that LLM-based models substantially outperform traditional machine-learning baselines such as Support Vector Machines, Logistic Regression, and Random Forests in precision, contextual understanding, and robustness to linguistic variation. Expert assessment further shows that generated debunking messages exhibit higher clarity, scientific accuracy, and persuasive effectiveness compared to conventional fact-checking text. These results highlight the potential of advanced LLM-driven pipelines to provide scalable, real-time mitigation of climate misinformation while offering guidelines for responsible deployment of AI-assisted debunking systems. Full article

Artificial Intelligence (AI) is increasingly reshaping how cultural heritage institutions design and deliver digital visitor experiences, particularly through adaptive Augmented Reality (AR) applications. However, most existing AR deployments in museums and galleries remain static, rule-based, and insufficiently responsive to visitors’ contextual, behavioral, and emotional diversity. This paper presents ARIA (Augmented Reality for Interpreting Artefacts), a conceptual and architectural framework for AI-supported, adaptive AR experiences in cultural heritage settings. ARIA is designed to address current limitations in personalization, affect-awareness, and ethical governance by integrating multimodal context sensing, lightweight affect recognition, and AI-driven content personalization within a unified system architecture. The framework combines Retrieval-Augmented Generation (RAG) for controlled, knowledge-grounded narrative adaptation, continuous user modeling, and interoperable Digital Asset Management (DAM), while embedding Human-Centered Design (HCD) and Fairness, Accountability, Transparency, and Ethics (FATE) principles at its core. Emphasis is placed on accountable personalization, privacy-preserving data handling, and curatorial oversight of narrative variation. ARIA is positioned as a design-oriented contribution rather than a fully implemented system. Its architecture, data flows, and adaptive logic are articulated through representative museum use-case scenarios and a structured formative validation process including expert walkthrough evaluation and feasibility analysis, providing a foundation for future prototyping and empirical evaluation. The framework aims to support the development of scalable, ethically grounded, and emotionally responsive AR experiences for next-generation digital museology. Full article

Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing a mobile AI solution equipped with multimodal perception, task decomposition, memory, reasoning, and multi-agent collaboration capabilities. The proposed system integrates computer vision, multi-object tracking, natural language processing, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to construct a Pipeline-based Traffic Analysis System (PTAS). The PTAS can produce real-time statistics on pedestrian and vehicle flows at intersections, incorporating potential risk factors such as traffic accidents, construction activities, and weather conditions for multimodal data fusion analysis, thereby providing forward-looking traffic insights. Experimental results demonstrate that the enhanced DuCRG-YOLOv11n pre-trained model, equipped with our proposed new activation function $\beta silu$, can accurately identify various vehicle types in object detection, achieving a frame rate of 68.25 FPS and a precision of 91.4%. Combined with ByteTrack, it can track over 90% of vehicles in medium- to low-density traffic scenarios, obtaining a 0.719 in MOTA and a 0.08735 in MOTP. In traffic flow analysis, the RAG of Vertex AI, combined with Claude Sonnet 4 LLMs, provides a more comprehensive view, precisely interpreting the causes of peak-hour congestion and effectively compensating for missing data through contextual explanations. The proposed method can enhance the efficiency of urban traffic regulation and optimizes decision support in intelligent transportation systems. Full article

A cultural heritage augmented reality system overlays virtual information onto real-world heritage sites, enabling intuitive exploration and interpretation with spatial and temporal contexts. This study presents the design and implementation of a cognitive Mobile Augmented Reality (MAR) system that integrates KCHDM-based ontologies with large language models (LLMs) to facilitate intelligent exploration of urban heritage. While conventional AR guides often rely on static data, our system introduces a Semantic Retrieval-Augmented Generation (RAG) pipeline anchored in a structured knowledge base modeled after the Korean Cultural Heritage Data Model (KCHDM). This architecture enables the LLM to perform dynamic contextual reasoning, transforming heritage data into adaptive question-answering (Q&A) and interactive knowledge-testing quizzes that are precisely grounded in both historical and spatial contexts. The system supports on-site AR exploration and map-based remote exploration to ensure robust usability and precise spatial alignment of virtual content. To deliver a rich, multisensory experience, the system provides multimodal outputs, integrating text, images, models, and audio narration. Furthermore, the integration of a knowledge sharing repository allows users to review and learn from others’ inquires. This ontology-driven LLM-integrated MAR design enhances semantic accuracy and contextual relevance, demonstrating the potential of MAR for socially enriched urban heritage experiences. Full article

This paper presents the design, implementation and evaluation of an agentic virtual assistant (VA) for a medical clinic, combining large language models (LLMs) with retrieval-augmented generation (RAG) technology and multi-agent artificial intelligence (AI) frameworks to enhance reliability, clinical accuracy and explainability. The assistant has multiple functionalities and is built around an orchestrator architecture in which a central agent dynamically routes user queries to specialized tools for retrieval-augmented question answering (Q&A), document interpretation and appointment scheduling. The implementation combines LangChain and LangGraph with interactive visualizations to track reasoning steps, prompts using Gemini 2.5 Flash defines tool usage and strict formatting rules, maintaining reliability and mitigating hallucinations. Prompt engineering has an important role in the implementation and thus, it is designed to assist the patient in the human–computer interaction. Evaluation through qualitative and quantitative metrics, including ROUGE, BLEU, LLM-as-a-judge and sentiment analysis, confirmed that the multi-agent architecture enhances interpretability, accuracy and context-aware performance. Evaluation shows that the multi-agent architecture improves reliability, interpretability and alignment with medical requirements, supporting diverse clinical tasks. Furthermore, the evaluation shows that Gemini 2.5 Flash combined with clinic-specific RAG significantly improves response quality, grounding and coherence compared with earlier models. SBERT analyses confirm strong semantic alignment across configurations, while LLM-as-a-judge scores highlight the superior relevance and completeness of the 2.5 RAG setup. Although some limitations remain, the updated system provides a more reliable and context-aware solution for clinical question answering. Full article

The complexity of modern cyber–physical systems is steadily increasing as their functional scope expands and as regulations become more demanding. To cope with this complexity, organizations are adopting methodologies such as model-based systems engineering (MBSE). By creating system models, MBSE promises significant advantages such as improved traceability, consistency, and collaboration. On the other hand, the adoption of MBSE faces challenges in both the introduction and the operational use. In the introduction phase, challenges include high initial effort and steep learning curves. In the operational use phase, challenges arise from the difficulty of retrieving and reusing information stored in system models. Research on the support of MBSE through artificial intelligence (AI), especially generative AI, has so far focused mainly on easing the introduction phase, for example by using large language models (LLMs) to assist in creating system models. However, generative AI could also support the operational use phase by helping stakeholders access the information embedded in existing system models. This study introduces an LLM-based multi-agent system that applies a Graph Retrieval-Augmented Generation (GraphRAG) strategy to access and utilize information stored in MBSE system models. The system’s capabilities are demonstrated through a chatbot that answers questions about the underlying system model. This solution reduces the complexity and effort involved in retrieving system model information and improves accessibility for stakeholders who lack advanced knowledge in MBSE methodologies. The chatbot was evaluated using the architecture of a battery electric vehicle as a reference model and a set of 100 curated questions and answers. When tested across four large language models, the best-performing model achieved an accuracy of 93 percent in providing correct answers. Full article

Large Language Models (LLMs) promise to enhance clinical decision-making, yet empirical studies reveal a paradox: physician performance with LLM assistance shows minimal improvement or even deterioration. This failure stems from an “acquiescence problem”: current LLMs passively confirm rather than challenge clinicians’ hypotheses, reinforcing cognitive biases such as anchoring and premature closure. To address these limitations, we propose a Dialogic Reasoning Framework that operationalizes Dialogical AI principles through a prototype implementation named “Diagnostic Dialogue” (DiDi). This framework operationalizes LLMs into three user-controlled roles: the Framework Coach (guiding structured reasoning), the Socratic Guide (asking probing questions), and the Red Team Partner (presenting evidence-based alternatives). Built upon Retrieval-Augmented Generation (RAG) architecture for factual grounding and traceability, this framework transforms LLMs from passive information providers into active reasoning partners that systematically mitigate cognitive bias. We evaluate the feasibility and qualitative impact of this framework through a pilot study (DiDi) deployed at Centro Chirurgico Toscano (CCT). Through purposive sampling of complex clinical scenarios, we present comparative case studies illustrating how the dialogic approach generates necessary cognitive friction to overcome acquiescence observed in standard LLM interactions. While rigorous clinical validation through randomized controlled trials remains necessary, this work establishes a methodological foundation for designing LLM-based clinical decision support systems that genuinely augment human clinical reasoning. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

Procedural knowledge is essential in specialized domains, and natural language tools for retrieving procedural knowledge are necessary for non-expert users to facilitate their understanding and learning. In this study, we focus on function decomposition trees, a framework for representing procedural knowledge, and propose a natural language interface leveraging Retrieval-Augmented Generation (RAG). The natural language interface converts the user’s inputs into SPARQL queries, retrieving relevant data and subsequently presenting them in an accessible and chat-based format. Such a flexible and purpose-driven search facilitates users’ understanding of functions of artifacts or human actions and their performance of these actions. We demonstrate that the tool effectively retrieves actions, goals, and dependencies using an illustrative real-world example of a function decomposition tree. In addition, we evaluated the system by comparing it with ChatGPT 4o and Microsoft GraphRAG. The results suggest that the system can deliver responses that are both necessary and sufficient for users’ needs, while the outputs of other systems lack the key elements and return redundant information. Full article

Large language models (LLMs) have rapidly transformed artificial intelligence applications across industries, yet their integration into production systems has unveiled critical security vulnerabilities, chief among them prompt injection attacks. This comprehensive review synthesizes research from 2023 to 2025, analyzing 45 key sources, industry security reports, and documented real-world exploits. We examine the taxonomy of prompt injection techniques, including direct jailbreaking and indirect injection through external content. The rise of AI agent systems and the Model Context Protocol (MCP) has dramatically expanded attack surfaces, introducing vulnerabilities such as tool poisoning and credential theft. We document critical incidents including GitHub Copilot’s CVE-2025-53773 remote code execution vulnerability (CVSS 9.6) and ChatGPT’s Windows license key exposure. Research demonstrates that just five carefully crafted documents can manipulate AI responses 90% of the time through Retrieval-Augmented Generation (RAG) poisoning. We propose PALADIN, a defense-in-depth framework implementing five protective layers. This review provides actionable mitigation strategies based on OWASP Top 10 for LLM Applications 2025, identifies fundamental limitations including the stochastic nature problem and alignment paradox, and proposes research directions for architecturally secure AI systems. Our analysis reveals that prompt injection represents a fundamental architectural vulnerability requiring defense-in-depth approaches rather than singular solutions. Full article

With the rapid evolution of cloud-native platforms, microservice-based systems have become increasingly large-scale and complex, making fast and accurate root cause localization and recovery a critical challenge. Runtime signals in such systems are inherently multimodal—combining metrics, logs, and traces—and are intertwined through deep, dynamic service dependencies, which often leads to noisy alerts, ambiguous fault propagation paths, and brittle, manually curated recovery playbooks. To address these issues, we propose GALR, a graph- and LLM-based framework for root cause localization and recovery in microservice-based business middle platforms. GALR first constructs a multimodal service call graph by fusing time-series metrics, structured logs, and trace-derived topology, and employs a GAT-based root cause analysis module with temporal-aware edge attention to model failure propagation. On top of this, an LLM-based node enhancement mechanism infers anomaly, normal, and uncertainty scores from log contexts and injects them into node representations and attention bias terms, improving robustness under noisy or incomplete signals. Finally, GALR integrates a retrieval-augmented LLM agent that retrieves similar historical cases and generates executable recovery strategies, with consistency checking against expert-standard playbooks to ensure safety and reproducibility. Extensive experiments on three representative microservice datasets demonstrate that GALR consistently achieves superior Top-k accuracy and mean reciprocal rank for root cause localization, while the retrieval-augmented agent yields substantially more accurate and actionable recovery plans compared with graph-only and LLM-only baselines, providing a practical closed-loop solution from anomaly perception to recovery execution. Full article

With the increasing complexity of software systems, the number of security vulnerabilities contained within software has risen accordingly. The existing shift-left security concept aims to detect and fix vulnerabilities during the software development cycle. While CodeQL stands as the premier static code analysis tool currently available on the market, its high barrier to entry poses challenges for meeting the implementation requirements of shift-left security initiatives. While large language model (LLM) offers potential assistance in QL code development, the inherent complexity of code generation tasks often leads to persistent issues such as syntactic inaccuracies and references to non-existent modules, which consequently constrains their practical applicability in this domain. To address these challenges, this paper proposes CQLLM (CodeQL-enhanced Large Language Model), a novel framework for automating the generation of CodeQL security vulnerability detection code by leveraging LLM. This framework is designed to enhance both the efficiency and the accuracy of automated QL code generation, thereby advancing static code analysis for a more efficient and intelligent paradigm for vulnerability detection. First, retrieval-augmented generation (RAG) is employed to search the vector database for dependency libraries and code snippets that are highly similar to the user’s input, thereby constraining the model’s generation process and preventing the import of invalid modules. Then, the user input and the knowledge chunks retrieved by RAG are fed into a fine-tuned LLM to perform reasoning and generate QL code. By integrating external knowledge bases with the large model, the framework enhances the correctness and completeness of the generated code. Experimental results show that CQLLM significantly improves the executability of the generated QL code, with the execution success rate improving from 0.31% to 72.48%, outperforming the original model by a large margin. Meanwhile, CQLLM also enhances the effectiveness of the generated results, achieving a CWE (Common Weakness Enumeration) coverage rate of 57.4% in vulnerability detection tasks, demonstrating its practical applicability in real-world vulnerability detection. Full article

Effective knowledge management is critical for preserving institutional expertise and improving the efficiency of workforce training in state transportation agencies. Traditional approaches, such as static documentation, classroom-based instruction, and informal mentorship, often lead to fragmented knowledge transfer, inefficiencies, and the gradual loss of expertise as senior engineers retire. Moreover, given the enormous volume of technical manuals, guidelines, and research reports maintained by these agencies, it is increasingly challenging for engineers to locate relevant information quickly and accurately when solving field problems or preparing for training tasks. These limitations hinder timely decision-making and create steep learning curves for new personnel in maintenance and construction operations. To address these challenges, this paper proposes a Retrieval-Augmented Generation (RAG) framework with a multi-agent architecture to support knowledge management and decision-making. The system integrates structured document retrieval with real-time, context-aware response generation powered by a large language model (LLM). Unlike conventional single-pass RAG systems, the proposed framework employs multiple specialized agents for retrieval, answer generation, evaluation, and query refinement, which enables iterative improvement and quality control. In addition, the system incorporates an open-weight vision-language model to convert technical figures into semantic textual representations, which allows figure-based knowledge to be indexed and retrieved alongside text. Retrieved text and figure-based context are then provided to an open-weight large language model, which generates the final responses grounded in the retrieved evidence. Moreover, a case study was conducted using over 500 technical and research documents from multiple State Departments of Transportation (DOTs) to assess system performance. The multi-agent RAG system was tested with 100 domain-specific queries covering pavement maintenance and management topics. The results demonstrated Recall@3 of 94.4%. These results demonstrate the effectiveness of the system in supporting document-based response generation for DOT knowledge management tasks. Full article

With smart buildings being widely adopted in urban digital transformation, interactive semantic question answering (QA) systems serve as a crucial bridge between user intent and environmental response. However, they still face substantial challenges in semantic understanding and dynamic reasoning. Most existing systems rely on static frameworks built upon Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), which suffer from rigid prompt design, breakdowns in multi-step reasoning, and inaccurate generation. To tackle these issues, we propose LoopRAG, a multi-agent RAG architecture that incorporates a Plan–Do–Check–Act (PDCA) closed-loop optimization mechanism. The architecture formulates a dynamic QA pipeline across four stages: task parsing, knowledge extraction, quality evaluation, and policy feedback, and further introduces a semantics-driven prompt reconfiguration algorithm and a heterogeneous knowledge fusion module. These components strengthen multi-source information handling and adaptive reasoning. Experiments on HotpotQA, MultiHop-RAG, and an in-house building QA dataset demonstrate that LoopRAG significantly outperforms conventional RAG systems in key metrics, including context recall of 90%, response relevance of 72%, and answer accuracy of 88%. The results indicate strong robustness and cross-task generalization. This work offers both theoretical foundations and an engineering pathway for constructing trustworthy and scalable semantic QA interaction systems in smart building settings. Full article