Journal of Global Engineering Review: Advancing multidisciplinary engineering research through global collaboration. Peer-reviewed publications since 2023.
Reflexion-Guided Self-Correcting Tool Agents under API Selection and Execution Failures
Tool-using language agents fail through coupled errors: an incorrect API choice changes the available argument schema, while correct tool selection still fails when dialogue state, identifiers, temporal values, or required fields are bound incorrectly. This study evaluates a Reflexion-guided correction pipeline on a four-file API-Bank evaluation package containing 1,012 records, including 534 API-call targets from 261 dialogues. The first-pass agent ranks the provided API schemas with BM25 over the latest four dialogue events and binds arguments with deterministic local extraction rules. A benchmark feedback gateway rejects calls that are not execution-equivalent to the target and records a structured diagnosis. The repair agent then reconstructs the active trajectory segment, reselects the API with a state machine and global library ranking, and rebinds arguments using fold-local episodic profiles, value lexicons, and defaults. All memory objects were fitted in five GroupKFold splits grouped by dialogue, preventing any dialogue from contributing to its own repair memory. First-pass tool accuracy was 61.80%, strict call accuracy was 29.03%, and execution-normalized success was 36.52%. The gated correction pipeline reached 93.82% tool accuracy, 58.61% strict call accuracy, and 70.41% execution-normalized success. It repaired 181 of 339 initial failures and preserved all 195 initially successful calls, producing a 33.90-point paired gain (95% bootstrap confidence interval: 29.96–38.01; exact McNemar p=6.53×10⁻⁵⁵). Level-3 trajectories gained 44.44 points, and controlled failure injection yielded repair success from 67.04% for wrong tools to 100% for observed stale-state errors. The results show that trajectory state and argument reflection provide complementary gains and that value grounding remains the dominant residual failure.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
When Should RAG Use Graphs? Complexity-Aware GraphRAG Routing and Answer Quality Prediction on GraphRAG-Bench-H
Graph retrieval-augmented generation (GraphRAG) improves retrieval when questions require multi-hop relations, entity interactions, and evidence-level composition, but graph construction and traversal add cost when ordinary retrieval is sufficient. This paper presents CAR-GraphRAG, a complexity-aware routing and answer-quality prediction framework for deciding when a RAG query should use a graph. The evaluation uses a 4,072-row GraphRAG-Bench-H materialization with the fields id, source, question, answer, question_type, evidence, and evidence_relations. The dataset spans medical and novel domains and four task types: Fact Retrieval, Complex Reasoning, Contextual Summarization, and Creative Generation. We compare BM25-RAG, Dense-RAG, Hybrid-RAG, GraphRAG-Lite, and the proposed router using evidence recall@5, answer F1, ROUGE-L, faithfulness, accuracy, latency, and token cost. CAR-GraphRAG achieved 0.8665 mean answer F1, exceeding GraphRAG-Lite (0.8240), Hybrid-RAG (0.8028), Dense-RAG (0.7238), and BM25-RAG (0.6349). On the held-out test split, the router identified graph-beneficial queries with 0.9650 F1 and 0.9858 ROC-AUC, while the answer-quality predictor achieved 0.0298 MAE. The results show that graph retrieval is most valuable for relation-dense, evidence-heavy, high lexical-gap queries, whereas direct fact retrieval is better served by hybrid non-graph retrieval.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Nugget-Level Retrieval Coverage Prediction for Complete and Faithful RAG Answers
Retrieval-augmented generation (RAG) depends on retrieved passages to provide the evidence from which an answer is composed. Conventional retrieval metrics reward relevance, yet a highly relevant top-k list can repeatedly surface the same fact while omitting other information units required for a complete response. This study investigates nugget-level retrieval coverage prediction on CoverageBench, which comprises seven retrieval collections, 334 topics, and six retrieval configurations. Using the benchmark’s aggregate nDCG@20, alpha-nDCG@20, and Subtopic Recall at rank 20 (StRecall@20) measurements, we introduce NLCP-Ridge, a deterministic ridge-regression model that predicts coverage from relevance, diversity-aware relevance, and reranker identity. Leave-one-dataset-out validation over 42 held-out system-dataset cases yields MAE = 0.0464, RMSE = 0.0687, R2 = 0.9090, Pearson r = 0.9628, and Spearman rho = 0.9713. Relative to an nDCG-only predictor, NLCP-Ridge reduces MAE by 58.6%; relative to an alpha-nDCG-only predictor, it reduces MAE by 18.0%. The retrieval comparisons further show that the highest-nDCG configuration is not always the highest-coverage configuration: CAsT and CRUX-MultiNews select different winners under StRecall@20. The findings show that a compact coverage estimate can complement relevance evaluation and provide an evidence-sufficiency signal before answer generation.
Memory-Aware GPU Resource Scheduling Prediction for DLRM Inference Services with BiGRU-Transformer Models
Deep learning recommendation models (DLRMs) combine large sparse embeddings with latency-sensitive inference, making host-memory availability as important as accelerator capacity. This study examines per-instance memory-reservation prediction using the Alibaba cluster-trace-gpu-v2025 dataset. The trace contains 23,871 instances from 156 services, including 16,485 CPU-node (CN) instances and 7,386 heterogeneous GPU-node (HN) instances. A BiGRU-Transformer encodes the eight most recent instances of the same service and fuses that history with current CPU, GPU, RDMA, disk, role, and density specifications. It is compared with train-median, role-app median, ridge regression, random forest, gradient boosting, static MLP, GRU, and Transformer baselines under a chronological 70/15/15 split. On the 3,581-instance test period, the BiGRU-Transformer achieves 16.82 GiB MAE and 0.9811 R2, the strongest result among the neural sequence variants. RandomForest achieves the lowest overall MAE at 1.64 GiB and an R2 of 0.9979, indicating that tree partitions capture the trace's discrete reservation templates particularly well. A capacity-normalized scheduling replay converts prediction error into placement risk: RandomForest records 473.86 memory-overflow node-hours, compared with 4,656.53 for the role-app median baseline and 3,332.46 for the BiGRU-Transformer. The results show that same-service histories add predictive value to neural models, while tree ensembles provide more accurate reservations on this trace. A practical scheduler can therefore combine template-sensitive tabular prediction with sequence-based drift signals and role-aware safety margins.
Medical RAG Pipeline Selection with Retrieval Quality Prediction and LLM Safety Explanations
Medical retrieval-augmented generation (RAG) systems must match retrieval strategy to question structure, estimate whether the retrieved evidence is strong enough to support an answer, and communicate clearly when the evidence is insufficient. This paper presents RQ-SafeSelector, an evidence-aware pipeline-selection framework evaluated on the 420-question RAGCare-QA benchmark across six medical specialties, three complexity levels, and three annotated RAG pipeline classes. The framework compares sparse TF-IDF, multi-vector lexical–character retrieval, and graph-enhanced hybrid retrieval; predicts retrieval quality from out-of-fold scores; and routes each question to Basic RAG, Multi-vector RAG, or Graph-enhanced RAG with a structured safety explanation and abstention gate. Graph-enhanced Hybrid achieved the strongest overall Recall@3 (0.336) and nDCG@5 (0.339), while Multi-vector Hybrid obtained the highest Recall@5 (0.548). RQ-SafeSelector reached 0.933 accuracy and 0.833 macro-F1 for pipeline selection. The calibrated retrieval-quality predictor achieved ROC-AUC 0.686 with expected calibration error 0.036. Applying the quality gate reduced the audit-defined unsupported-answer risk to 0.000 while answering 30.7% of questions, with Success@3 of 0.527 among answered cases. The findings support evaluation of medical RAG as a routed, evidence-aware, and selectively answering system rather than as a single retriever followed by an unconstrained generator.
Legal RAG Error Decomposition: Predicting Retrieval Failure, Reasoning Failure, and Hallucination Risk on Legal RAG Bench
Retrieval-augmented generation (RAG) systems are attractive for legal assistance because they connect generative language models to authoritative source material, yet their failures are not monolithic. A pipeline may miss the controlling passage, retrieve it but apply it incorrectly, or produce a fluent answer that is unsupported by the supplied context. This paper presents an empirical error-decomposition study on Legal RAG Bench, a reasoning-intensive benchmark containing 4,876 legal passages and 100 expert-written questions. The analysis covers the complete six-cell factorial design of three retrievers and two generators, corresponding to 600 question-by-configuration outcomes. For each configuration, we examine correctness, groundedness, retrieval accuracy, hallucination, retrieval error, reasoning error, and a composite RAG accuracy that counts only correct and grounded answers as complete successes. The released cell totals are also represented as a 600-row binary analysis table for cross-configuration risk modeling. Retrieval is the dominant source of performance variation: Kanon 2 Embedder reaches 86.0% retrieval accuracy and 91.5% average RAG accuracy, compared with 52.0% and 72.5% for Text Embedding 3 Large and 53.0% and 68.0% for Gemini Embedding 001. Generator choice changes groundedness and hallucination risk, but it does not offset weak retrieval. Cross-fitted prediction is strongest for retrieval failure (AUROC = 0.663) and retrieval error (AUROC = 0.661), whereas reasoning failure is rare and difficult to predict (AUROC = 0.439). The results support reporting component-attributed legal RAG errors rather than a single end-to-end accuracy score.
Latency–Throughput Tradeoffs of ONNX Runtime, TensorRT-LLM, vLLM, and Triton: An Empirical Comparison on 1B–3B Parameter LLM Inference
Small-scale large language models with one to three billion parameters have emerged as a practical choice for latency-sensitive deployment, offering a balance between linguistic capability and operational cost. Serving these models efficiently depends heavily on the choice of inference engine, yet published comparisons often target 7B-scale or larger workloads and report metrics in isolation. This paper presents an empirical comparison of four widely adopted inference engines—ONNX Runtime, TensorRT-LLM, vLLM, and NVIDIA Triton configured with the TensorRT-LLM backend—on three open-weight checkpoints (TinyLlama-1.1B, Llama-3.2-1B, and Llama-3.2-3B). Using prompt distributions drawn from ShareGPT, LMSYS-Chat-1M, and HumanEval, we measure time-to-first-token, inter-token latency, sustained throughput across batch sizes from 1 to 64, GPU utilization, and peak memory footprint on an NVIDIA A100 40GB and an RTX 4090 24GB. Results indicate that TensorRT-LLM attains the lowest single-request latency (22.6 ms time-to-first-token on the 1.1B checkpoint), while vLLM delivers comparable throughput at large batch sizes with a 13.8% smaller memory footprint at the 3B scale due to its paged KV-cache layout. ONNX Runtime trails on both dimensions yet remains competitive for cross-platform deployment. The findings yield quantitative deployment guidance for cost-aware serving of small-scale language models.
Does an LLM-Based Feedback Agent Move the Needle? An Empirical Study of Student Correctness and Hint Dependency on the ASSISTments 2017 Interaction Logs
Large language model (LLM) feedback agents are increasingly proposed as substitutes for system-authored hints in intelligent tutoring environments, yet most existing evaluations rely on rubric-based offline judgments or small-scale human ratings rather than learner-behaviour evidence drawn from real interaction logs. This study presents an empirical analysis of three production-grade LLM feedback agents — GPT-4o-mini, Llama-3-70B-Instruct, and Gemini-1.5-Flash — evaluated on the ASSISTments 2017 click-stream corpus, which contains 942,816 records, 1,709 students, 3,162 problems, and 102 knowledge components. Three research questions are examined: whether LLM-generated feedback raises subsequent correctness, whether it reduces hint dependency, and whether automatic feedback-quality scores correlate with observed behavioural change. The evaluation pipeline pairs Prometheus and G-Eval as offline judges with a context-aware attentive knowledge-tracing counterfactual predictor, allowing each intervention to be scored both for textual quality and for predicted learning trajectory. Aggregate results show a moderate average correctness gain of 4.8 percentage points for GPT-4o-mini over the system-authored hint baseline, concentrated in conceptual knowledge components. Hint-request rates drop by 11.4 percent under GPT-4o-mini, while Llama-3-70B and Gemini-1.5 produce smaller and less stable shifts. The correlation between feedback-quality score and behavioural gain is moderate (Spearman ρ = 0.41, 95% CI [0.34, 0.47]), indicating that high rubric scores partially but not fully explain learner impact. Findings provide cautious empirical support for LLM feedback agents as supplements to template hints in middle-school mathematics tutoring and highlight open questions around procedural-knowledge components and ability-stratified responsiveness.
LLM feedback agent; automated formative feedback; knowledge tracing; Assessment’s
Passage, Sentence, or Proposition? An Empirical Comparison of Retrieval Granularity Effects on LLM Answer Accuracy in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has become a dominant paradigm for grounding large language model (LLM) outputs in external knowledge. While extensive research has focused on retriever architectures and generation strategies, the choice of retrieval granularity—the textual unit indexed and retrieved—remains insufficiently studied. This paper presents a controlled empirical comparison of four retrieval granularity levels: document, passage (100-word window), sentence, and proposition. Experiments are conducted across three open-domain question answering benchmarks (Natural Questions, TriviaQA, and HotpotQA) using two representative dense retrievers (DPR and Contriever) paired with LLaMA-2-7B-Chat as the reader. Results indicate that finer-grained retrieval units consistently improve retrieval recall, with proposition-level indexing achieving up to 6.8 absolute points higher Recall@20 than passage-level on Natural Questions under DPR. End-to-end answer accuracy follows a similar trend for single-hop factoid questions, where proposition-level retrieval yields the highest Exact Match scores. On multi-hop questions in HotpotQA, this advantage diminishes and passage-level retrieval produces comparable or slightly superior accuracy, suggesting that broader contextual units are beneficial when reasoning across multiple evidence pieces. These findings provide practical guidance for RAG pipeline design: retrieval granularity should be selected in accordance with question complexity, and no single granularity level dominates across all conditions.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
A Comparative Evaluation of URL-Sharing, Content Similarity, and Temporal Synchronicity Signals for Detecting Coordinated Inauthentic Behavior in Multilingual Political Discourse
Coordinated inauthentic behavior (CIB) on social media platforms poses a persistent threat to the integrity of political discourse. While multiple signal types have been proposed to detect such coordinated manipulation, no prior study has systematically compared their detection efficacy across languages. This paper presents a comparative evaluation of three widely adopted coordination signals — URL co-sharing, content cosine similarity, and temporal posting synchronicity — applied to English and Russian political CIB datasets derived from the Twitter Information Operations Archive and the Clemson Internet Research Agency corpus. For each signal type, we construct a coordination network, apply Leiden community detection, and measure precision, recall, and F1 against ground-truth information operation labels. Our results indicate that URL co-sharing achieves the highest F1 in both languages (0.802 in English, 0.787 in Russian), while content similarity suffers a 16.0% F1 degradation in Russian attributable to morphological complexity. Temporal synchronicity yields the most language-stable performance with only a 2.3% cross-language gap. A late-fusion combination of all three signals reaches an F1 of 0.891 in English and 0.824 in Russian. Parameter sensitivity analysis reveals that optimal time-window configurations differ across languages and signal types. These findings offer practical guidance for platform governance teams selecting detection indicators for multilingual CIB monitoring.
LLM-Grounded Visual Alerts for Humanitarian Food-Price Crisis Dashboards: Reproducible Anomaly Detection on WFP VAM Market Data
Humanitarian non-governmental organizations need food-price dashboards that turn volatile market data into alerts that field teams, donors, and accountability officers can read quickly. This paper presents and evaluates a reproducible food-price anomaly detection workflow for an NGO dashboard built on World Food Programme Vulnerability Analysis and Mapping market data. The experiment uses a pre-2022 public WFP food-price CSV subset covering Afghanistan, Benin, Burkina Faso, Burundi, Chad, the Democratic Republic of the Congo, Ethiopia, Kenya, and Lebanon from 2000 to 2021. After deterministic filtering, the dataset contains 76,822 valid retail price records, 526 eligible market-commodity time series, and 12,813 held-out observations for 2019-2021. A controlled anomaly benchmark injects 1,239 reproducible price shocks into the test period while preserving the original clean series for operational alert review. Seven detectors are evaluated: percent-change, exponentially weighted moving average residuals, six- and twelve-month rolling median absolute deviation, seasonal median absolute deviation, trend-seasonal residuals, and a rank ensemble. The exponentially weighted moving average detector achieves the strongest F1 score, 0.5146, with precision 0.8510, recall 0.3688, AUROC 0.9303, and AUPRC 0.7116. The paper also implements a bounded LLM-style warning-copy generator that verbalizes only measured variables, producing map labels, severity tags, and donor-facing summaries without adding unsupported claims. The resulting dashboard design prioritizes auditability: every alert links to country, market, commodity, month, price, anomaly score, month-to-month change, threshold, and verification text. The results show that a transparent, high-precision visual alert pipeline can support NGO food-security monitoring while limiting false escalation.
AI-Assisted Fragmented UI Repair: From Layer Chaos to Clean Interface Structure
Design-to-code tools fail when high-fidelity interface mockups contain fragmented layers: a button becomes a background rectangle, a text label, icons, shadows, and decoration; a card becomes many unrelated rectangles and texts; a navigation bar becomes an arbitrary stack of visual primitives. This paper presents EGFE-RePair, an AI-assisted layer repair method that converts chaotic layer sets into clean interface hierarchy nodes for button, card, navbar, and form-group components. The method combines pairwise spatial learning with a deterministic, LLM-compatible semantic repair stage that applies explicit grouping decisions to candidate layer sets. The study conducts full experimental evaluation on the packaged EGFE-v1 and EGFE-v2 datasets, containing 100 and 300 samples respectively, with JSON layer metadata and rendered interface assets. All reported values are measured by the included code over fixed train, validation, and test splits; no illustrative or estimated result is used. On EGFE-v1, EGFE-RePair reaches 0.899 pair F1 and 0.907 hierarchy accuracy. On the larger EGFE-v2 test split, it reaches 0.868 pair F1 and 0.869 hierarchy accuracy, outperforming name-pattern, proximity, visual-rule, and learned pair-only baselines. Ablation shows that removing the semantic repair stage lowers EGFE-v2 hierarchy accuracy from 0.869 to 0.564, confirming that layer grouping requires both local evidence and component-level UI semantics. The results demonstrate a reproducible path from fragmented design layers to maintainable UIUX structure.
LLM-as-Design-Critic: Empirical Alignment of AI Mobile UI Critiques with Human Graphic Design Judgment
This paper evaluates whether large-language-model-generated user-interface critiques align with human graphic design judgment. The experiment uses the public UICrit CSV distributed with source-labeled critique comments, bounding boxes, and five human rating dimensions for mobile screens. The raw file contains 2,981 annotator rows, 1,000 unique RICO screen identifiers, and 11,344 canonical critique source labels. We parsed the complete file, retained the 2,898 rows in which the comment and source-label lists were aligned, excluded three zero-critique rows for text experiments, and evaluated 11,112 source-aligned comments. The central comparison treats comments marked human or both as the human side and comments marked llm or both as the LLM side. We measured natural language similarity, topic-set agreement, bounding-box overlap, and the ability of LLM-side comments to predict human ratings under grouped five-fold cross-validation by RICO screen identifier. The LLM side reached a row-level TF-IDF cosine of 0.412, token Jaccard of 0.357, and topic-set Jaccard of 0.660 with human-side critiques. However, when shared comments marked both were removed, strict TF-IDF cosine fell to 0.057. Bounding boxes showed the same pattern: source-inclusive mean best IoU was 0.759, whereas strict LLM-only to human-only IoU was 0.323. For overall design quality, the LLM critique TF-IDF model achieved Pearson r = 0.185 and MAE = 0.786, matching the mean baseline in error, while LLM topic/count features improved to r = 0.243 and MAE = 0.757. Human critique text remained substantially stronger with r = 0.625 and MAE = 0.595. The empirical result is therefore specific: LLM critiques align with human designers at the level of broad design topics, but they do not yet reproduce the rating signal or the specific localized judgments of human design critique.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0
Temporal and Behavioural Feature Attribution Analysis for E-commerce Transaction Fraud Detection: A Comparative Study of Explainability Methods
Online transaction fraud imposes substantial financial losses on digital commerce ecosystems globally, with reported annual losses exceeding USD 48 billion as of 2024. While gradient boosting classifiers have achieved strong detection performance, the interpretability of their predictions remains inadequately addressed, particularly regarding the relative contribution of temporal and behavioral features to fraud identification. This study presents a systematic comparative analysis of three mainstream explainability methods—SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Permutation Feature Importance (PFI)—applied to XGBoost and LightGBM classifiers trained on two publicly available benchmark datasets: the ULB Credit Card Fraud Detection Dataset (284,807 transactions; fraud prevalence: 0.172%) and the IEEE-CIS Fraud Detection Dataset (590,540 transactions; fraud prevalence: 3.5%). A structured feature engineering pipeline constructs temporal interval features, cyclic time encodings, and rolling behavioral statistics. Class imbalance is addressed via Synthetic Minority Over-sampling Technique (SMOTE). Detection performance is evaluated using precision-recall area under the curve (PR-AUC) and F1-score. Cross-method attribution consistency is quantified via Spearman rank correlation across top-10 feature rankings. Results indicate that engineered temporal and behavioral features yield consistent PR-AUC improvements of 4.3–5.6 percentage points, and that SHAP demonstrates substantially higher attribution stability than LIME and PFI across all experimental configurations.
Comparative Evaluation of Zero-Shot and Few-Shot Performance of Large Language Models in Low-Resource Language Machine Translation
Large language models (LLMs) have demonstrated remarkable translation capabilities for high-resource languages, yet their effectiveness on low-resource languages under varying prompting conditions remains insufficiently understood. This study presents a comparative evaluation of four LLMs—GPT-4, GPT-3.5-Turbo, LLaMA-2-70B, and BLOOM-176B—alongside NLLB-200-3.3B as a supervised baseline, across ten translation directions spanning four resource levels. Using the FLORES-200 devtest set as the primary benchmark and NTREX-128 for cross-validation, we assess zero-shot, one-shot, five-shot, and eight-shot configurations with BLEU, chrF++, and COMET-22 metrics. Our results reveal three principal findings. The few-shot advantage is most pronounced for low-resource languages, with GPT-4 achieving an average BLEU gain of 5.3 points when moving from zero-shot to five-shot on low-resource pairs. One-shot prompting consistently degrades performance below zero-shot baselines, with an average BLEU reduction of 1.4 points across low-resource directions. The supervised NLLB-200 baseline outperforms all LLMs in zero-shot on eight of ten directions, while five-shot GPT-4 narrows this gap to within 1.0 BLEU on mid-resource pairs. These findings provide empirical guidance for practitioners selecting prompting strategies for LLM-based translation in resource-constrained settings.
Comparative Analysis of Pre-trained Language Models for ESG Financial News Sentiment Classification and Market Signal Correlation
Environmental, Social, and Governance (ESG) investing has become a central framework in modern capital markets, with the volume of ESG-related news growing at an unprecedented pace. Accurate sentiment extraction from ESG financial news constitutes a foundational capability for investment decision support, portfolio risk monitoring, and regulatory compliance analytics. This paper presents a systematic comparative study of pre-trained language model (PLM) approaches applied to ESG financial news sentiment classification, alongside an empirical analysis of their market signal predictive validity. Experiments are conducted across three publicly available benchmarks: FinancialPhraseBank (4,840 sentences; 75%-agreement subset: 3,453), FiQA Task 1 (1,174 entries), and the Twitter Financial News Sentiment dataset (11,932 entries). Market correlation analysis draws on S&P 500 component-level daily price data spanning January 2016 to July 2023, sourced from Yahoo Finance. The comparative evaluation covers lexicon-based methods (VADER, TextBlob, Loughran-McDonald) and domain-adapted transformer models (BERT-base, RoBERTa-base, FinBERT, FinBERT-Tone). Domain-adapted PLMs consistently outperform lexicon-based baselines by 12–19 weighted F1 percentage points on FinancialPhraseBank. Sector-level market correlation analysis reveals significant heterogeneity: Energy and Utilities sectors exhibit the strongest ESG sentiment–return linkages (Pearson r > 0.25), while Technology shows the weakest correlations (r < 0.16), providing sector-specific guidance for ESG-integrated quantitative investment strategies.
A Comparative Evaluation of Prompting Strategies for Code Generation with Large Language Models
Large language models have demonstrated remarkable capabilities in automated code generation, yet the relative effectiveness of different prompting strategies remains insufficiently characterized under zcontrolled experimental conditions. This study presents a systematic comparative evaluation of six prompting strategies — zero-shot, few-shot, chain-of-thought, structured chain-of-thought, self-debugging, and self-refine — across four representative models (GPT-4o, GPT-3.5-Turbo, DeepSeek-Coder-33B-Instruct, and CodeLlama-34B-Instruct) on three established benchmarks (HumanEval+, MBPP+, and LiveCodeBench). The evaluation encompasses functional correctness measured by pass@1, computational cost quantified by average token consumption, and difficulty-stratified performance analysis on contamination-free competitive programming tasks. Results indicate that structured chain-of-thought prompting yields the most consistent improvements across all models, achieving 6.7–8.5 percentage point gains over zero-shot baselines on HumanEval+. Multi-turn strategies such as self-debugging deliver the highest absolute performance on frontier models (80.5% pass@1 for GPT-4o on HumanEval+) while offering diminished returns on smaller open-source models. Cost-effectiveness analysis reveals that single-turn structured prompting achieves 77–93% of multi-turn performance at 72% lower token cost. These findings provide empirically grounded guidance for practitioners selecting prompting strategies under varying model capability levels and computational resource constraints.
Evaluating the Quality of Large Language Model-Generated Explanations in Recommendation Tasks: A Multi-Dimensional Comparative Analysis
The integration of large language models (LLMs) into recommendation systems has introduced new possibilities for generating natural language explanations that accompany recommended items. While prior research has explored methods of leveraging LLMs for explanation generation, limited attention has been given to systematically evaluating the quality of these explanations across different models, domains, and prompting strategies. This paper presents a multi-dimensional comparative analysis of LLM-generated recommendation explanations, examining four commercially available and open-source LLMs across three public recommendation datasets. A structured evaluation framework is proposed that encompasses four quality dimensions: faithfulness, informativeness, persuasiveness, and personalization. The evaluation employs both automatic metrics (BLEU-4, ROUGE-L, BERTScore) and human annotation protocols involving 12 trained evaluators. The experimental results indicate that larger-parameter models produce more informative and faithful explanations, though the gap narrows substantially when context-enhanced prompting strategies are applied. Automatic metrics show moderate correlation with human judgments on informativeness and faithfulness but limited alignment on persuasiveness and personalization. These findings offer practical guidance for selecting appropriate LLMs and prompting strategies in explanation-augmented recommendation applications.
Multi-Metric Trustworthiness Evaluation of AI-Assisted Medical Imaging Diagnosis: Integrating Confidence Calibration and Distribution Shift Detection
The rapid proliferation of artificial intelligence in medical imaging diagnosis has created an urgent need for comprehensive trustworthiness evaluation frameworks. This study proposes a multi-metric evaluation methodology that integrates confidence calibration assessment with distribution shift detection to address the critical challenge of silent failures in deployed AI diagnostic systems. The proposed framework encompasses Expected Calibration Error measurement across demographic subgroups, statistical distance-based distribution shift detection in deep feature spaces, and a novel trustworthiness scorecard that synthesizes prediction confidence, distributional similarity, and historical accuracy metrics. Experimental validation across three chest radiograph datasets demonstrates that the integrated evaluation approach identifies 23.7% more potentially unreliable predictions compared to single-metric methods while maintaining clinical utility. The framework provides actionable insights for FDA postmarket surveillance requirements and supports evidence-based clinical AI governance strategies.
Graph Neural Network-Based Cascading Disruption Path Identification in Multi-Tier Rare Earth Processing Networks
The United States confronts acute strategic vulnerability across rare earth supply chains, with import dependency on China exceeding 80% at most processing stages. This concentration risk is amplified by the sequential multi-tier structure of rare earth processing, in which primary mining, oxide separation, metal refining, alloy manufacturing, and permanent magnet production form a strictly ordered transformation hierarchy. A disruption at any single tier propagates to adjacent stages in nonlinear and often asymmetric ways, yet existing quantitative frameworks remain insufficient for tracing these cascading pathways at the tier level. This paper develops a graph neural network (GNN) framework applied to a heterogeneous multi-tier network representing rare earth processing flows across major producing and consuming nations. The framework integrates bilateral trade data from the BACI International Trade Database, processing capacity statistics from the U.S. Geological Survey (USGS) and the International Energy Agency (IEA), and the Caldara-Iacoviello Geopolitical Risk Index as node-level features. A relational graph convolutional network (R-GCN) simulates the propagation of disruption signals across processing tiers, and a graph attention module ranks cascading pathways by cumulative vulnerability scores. Experiments conducted on 34 documented disruption events from 2000 to 2024 identify oxide separation as the dominant structural bottleneck tier, and ablation analysis confirms that heterogeneous edge treatment and geopolitical risk features each contribute independently to model performance. The framework quantifies how alternative sourcing corridors alter network-level vulnerability across four geopolitical disruption scenarios.
Effectiveness Evaluation of Attention Mechanism Strategies in Deep Learning-Based Single Image Super-Resolution
Single image super-resolution (SISR) has experienced rapid advancements through the integration of attention mechanisms into deep neural networks. Diverse attention strategies, including channel attention, spatial attention, second-order attention, and self-attention, have been incorporated into SISR architectures with varying degrees of success. A systematic and fair comparison of these attention strategies under unified experimental conditions remains absent from the current literature. This study conducts a controlled benchmark evaluation of five representative SISR algorithms—EDSR, RCAN, SAN, SwinIR, and HAT—each embodying a distinct attention paradigm, across four standard datasets (Set5, Set14, BSD100, Urban100) at ×2 and ×4 scaling factors. The evaluation encompasses distortion-oriented metrics (PSNR, SSIM), perceptual quality metrics (LPIPS), and computational efficiency indicators (parameters, FLOPs, inference time). Experimental results reveal that hybrid attention mechanisms in HAT achieve the highest reconstruction fidelity, with 1.25 dB PSNR gain over the non-attention baseline EDSR on Urban100 at ×2 scale, while window-based self-attention in SwinIR offers the most favorable accuracy-efficiency trade-off. The findings provide practical guidance for selecting attention strategies in SISR applications under different resource constraints.
Artificial Intelligence Applications Across Interdisciplinary Domains: A Comprehensive Survey of Methods, Challenges, and Future Directions
The rapid advancement of artificial intelligence (AI) has catalyzed transformative innovations across a multitude of disciplines, ranging from healthcare and biomedicine to finance, cybersecurity, energy sustainability, and beyond. This comprehensive survey systematically reviews 133 recent studies published between 2023 and 2026 that collectively represent the state of the art in AI-driven methodologies and their real-world applications. We categorize the reviewed literature into nine principal domains: (1) healthcare and biomedical sciences, (2) finance and risk management, (3) privacy-preserving computing and data security, (4) natural language processing and communication, (5) transportation and logistics, (6) energy and environmental sustainability, (7) education and social impact, (8) media and advertising technology, and (9) emerging cross-domain applications. For each domain, we analyze the predominant AI techniques employed, including deep learning, federated learning, reinforcement learning, natural language processing, and graph-based methods, while highlighting their respective strengths and limitations. Furthermore, we present a taxonomic framework that elucidates the interconnections among these application domains and identify critical research gaps that warrant future investigation. Our analysis reveals several overarching trends, notably the increasing adoption of privacy-preserving paradigms, the integration of multimodal data fusion, and the growing emphasis on explainability and fairness in AI systems. This survey provides researchers and practitioners with a holistic reference for understanding the current landscape and future trajectory of applied AI research.
A Comprehensive Survey on Artificial Intelligence Applications Across Interdisciplinary Domains: Techniques, Challenges, and Future Directions
Artificial Intelligence (AI) has rapidly evolved into a transformative technology with far-reaching applications across healthcare, finance, cybersecurity, natural language processing, computer vision, and beyond. This survey systematically reviews 120 recent studies published between 2023 and 2026, offering a comprehensive analysis of AI-driven methodologies including deep learning, federated learning, reinforcement learning, differential privacy, and multimodal data fusion. The reviewed works are organized thematically and cited in sequential order to facilitate a coherent narrative of the current research landscape. In addition, we present comparative tables summarizing key techniques, application domains, and open challenges. Our analysis reveals that while AI has achieved significant breakthroughs in domain-specific applications, persistent challenges related to data privacy, model interpretability, algorithmic fairness, and cross-domain transferability remain. This survey aims to provide researchers and practitioners with a holistic overview and to inspire future interdisciplinary research endeavors.
Diagnosing CI/CD Failures and Recommending Repair Diff Footprints with Lightweight LLM-Assisted Models: Full Experimental Evaluation on BugSwarm CI Fail–Pass Pairs
CI/CD pipelines execute builds and tests on every change, generating high-volume failure signals and log artifacts that require rapid diagnosis and repair. This paper studies two operational tasks that appear in modern DevOps automation: (i) failure diagnosis and (ii) repair recommendation in a Top-k setting. We conducted full experimental evaluations on a public BugSwarm-derived artifact list containing 325 Java fail–pass CI pairs (SHA-256: 267fdfc1ee603af3613db96ec79230c7c2e856fa5b4594ffda6ec51f38809df6). We defined three failure types from CI outcome metadata—TEST_FAIL, BUILD_OR_SETUP_FAIL, and NONTEST_FAIL—and defined repair targets as diff-footprint patterns A{a}C{c}D{d} based on the numbers of added/changed/deleted files, mapped to 24 classes (23 frequent patterns with support ≥3 plus OTHER). Using only information available at failure time (commit message, repository metadata, build system, test framework, and test counters), we compared six baselines and a proposed probability-averaging ensemble of text-only and numeric-only multinomial logistic regression. On failure diagnosis (5-fold stratified CV), the proposed ensemble achieved macro-F1=0.881 and accuracy=0.926. On repair recommendation (3-fold stratified CV), the proposed ensemble achieved Hit@1=0.511, Hit@3=0.754, Hit@5=0.852, Hit@10=0.920, and MRR=0.657. The manuscript reports detailed tables and figures and specifies all hyperparameters to make the reported empirical findings reproducible.
Deep Learning-Based Real-Time Fraud Detection in Digital Payment Systems: A Multi-Feature Behavioural Analysis Framework
Digital payment fraud continues to escalate with the rapid expansion of e-commerce and mobile transactions, posing significant financial risks to both consumers and enterprises. This research presents a comprehensive deep learning framework for real-time fraud detection in digital payment systems through multi-dimensional behavioral analysis. The proposed framework integrates temporal transaction patterns, user behavioral sequences, device fingerprinting, and network topology features to construct a robust fraud identification mechanism. Utilizing convolutional neural networks (CNN) for spatial feature extraction and long short-term memory (LSTM) networks for temporal sequence modeling, the system achieves superior detection accuracy while maintaining low false-positive rates. Experimental evaluation on a dataset comprising 2.3 million transactions demonstrates that the proposed framework attains 96.8% detection accuracy with 2.1% false-positive rate, outperforming traditional rule-based systems by 23.4%. The research provides empirical evidence for the effectiveness of multi-feature fusion in enhancing fraud detection capabilities across diverse transaction scenarios. Implementation results indicate substantial improvements in real-time processing efficiency, with average detection latency reduced to 127 milliseconds, making the framework suitable for high-volume production environments.
AI-Driven Cross-Modal Feature Alignment for Multimodal Fraud Detection in Financial Transaction Systems
Financial fraud detection systems increasingly encounter sophisticated attack patterns that exploit multiple data modalities simultaneously. Traditional single-modal detection approaches demonstrate limited effectiveness when adversaries coordinate deceptive behaviors across transaction records, user behavioral sequences, and communication metadata. This research proposes an AI-driven cross-modal feature alignment framework that integrates heterogeneous data streams through attention-based fusion mechanisms and contrastive learning strategies. The methodology employs deep neural architectures to extract discriminative representations from structured transaction data, unstructured behavioral logs, and temporal interaction patterns, subsequently aligning these features in a unified embedding space. Experimental validation on real-world financial datasets demonstrates that the proposed framework achieves superior detection performance compared to baseline methods, with particular effectiveness in identifying coordinated fraud schemes that span multiple channels. The framework maintains computational efficiency suitable for real-time deployment while providing interpretable explanations for detection decisions through attention weight visualization and feature attribution analysis.
Adaptive Feature Selection Optimization for High-Dimensional Data Classification: A Comparative Machine Learning Approach
High-dimensional data classification remains a critical challenge across numerous domains including finance, healthcare, and network security. Traditional feature selection methods struggle with computational complexity and suboptimal performance when handling massive datasets. This research investigates adaptive feature selection optimization techniques through comprehensive comparative analysis of machine learning algorithms. The study implements filter-based, wrapper-based, and hybrid approaches to evaluate classification accuracy improvements while reducing dimensionality. Experimental results demonstrate that adaptive threshold mechanisms combined with temporal feature extraction achieve superior performance metrics compared to conventional methods. The proposed framework reduces feature space by 67.3% while maintaining 94.8% classification accuracy across multiple benchmark datasets. Performance evaluations reveal significant computational efficiency gains, with processing time reductions of 58.2% for real-time applications. This work contributes empirical evidence supporting adaptive optimization strategies for feature selection in high-dimensional classification tasks.
Multi-Dimensional Feature Selection and Optimization Algorithms for Financial Fraud Detection: A Comparative Study
Financial fraud detection has become increasingly critical as digital transactions proliferate across global markets. This research presents a comprehensive comparative analysis of multi-dimensional feature selection and optimization algorithms applied to fraud detection scenarios. The study evaluates various algorithmic approaches including filter-based methods, wrapper-based techniques, and hybrid optimization strategies across diverse financial transaction datasets. Through systematic experimentation involving over 500,000 transaction records, this research quantifies the performance trade-offs between detection accuracy, computational efficiency, and false positive rates. Results demonstrate that adaptive feature selection methods achieve detection rates exceeding 94.7% while maintaining false positive rates below 2.3%, representing substantial improvements over traditional baseline approaches. The comparative analysis reveals distinct performance characteristics across different algorithm categories, with wrapper-based methods showing superior accuracy at the cost of computational overhead, while filter-based approaches offer faster processing with slightly reduced precision. This work provides empirical evidence to guide practitioners in selecting appropriate feature engineering strategies based on specific operational requirements, system constraints, and risk tolerance thresholds. The findings contribute to the growing body of knowledge in financial security analytics and establish benchmarks for algorithmic performance in fraud detection applications.
Feature Engineering Optimization Methods for Multi-Domain Predictive Analytics: A Comprehensive Evaluation Study
Feature engineering remains a critical component in predictive analytics across diverse application domains. This study presents a comprehensive evaluation of feature engineering optimization methods applied to multi-domain predictive tasks including financial risk assessment, healthcare analytics, and digital advertising. The research examines various feature selection techniques, transformation approaches, and their impact on model performance across different data characteristics. Through systematic experimentation on six real-world datasets, we analyze the effectiveness of filter-based, wrapper-based, and embedded feature selection methods. Our experimental results reveal domain-specific patterns in feature engineering effectiveness, with wrapper methods achieving 8.3% improvement in financial datasets while filter approaches demonstrate superior computational efficiency with only 2.1% performance trade-off. The study provides empirical evidence for selecting appropriate feature engineering strategies based on dataset properties and application requirements. Our findings contribute to establishing practical guidelines for practitioners implementing predictive analytics solutions across varied domains.
Attention-Enhanced Time Series Anomaly Detection for Financial Risk Early Warning: A Deep Learning Approach
Financial market volatility and risk propagation present significant challenges for timely intervention and loss prevention. This paper proposes an attention-enhanced deep learning framework for detecting anomalous patterns in financial time series data, enabling early warning of potential risks. The framework integrates temporal convolutional networks with multi-head self-attention mechanisms to capture both short-term fluctuations and long-range dependencies in transaction sequences. Comprehensive experiments on real-world financial datasets demonstrate substantial improvements in detection accuracy, with F1-scores reaching 94.3% for fraud identification and 91.7% for market manipulation detection. The proposed approach achieves a 37% reduction in false positive rates compared to traditional statistical methods while maintaining computational efficiency suitable for real-time deployment. Performance analysis across multiple financial instruments reveals consistent effectiveness in identifying emerging risk patterns 48-72 hours before significant market events. The findings provide valuable insights for financial institutions seeking to enhance their risk management capabilities through advanced analytics.