Advances in Science and Engineering (ASE) is a peer-reviewed, open-access multidisciplinary journal dedicated to publishing high-impact research across all branches of STEM (Science, Technology, Engineering, and Mathematics). Supported by a European editorial consortium, ASE bridges the gap between theoretical physics, applied optics, instrument science, and advanced engineering methodologies.
Generative Backdoor Optimization and LLM-Based Recommendation for Robust Intelligent Systems
Robust intelligent systems must address both external adversarial manipulation and internal data contamination. Universal transferable attacks show that vision-language models can be vulnerable to targeted adversarial examples, while clean-image backdoors demonstrate that malicious triggers can be optimized without obvious visual artifacts. These risks are particularly relevant for systems that combine multimodal models, personalized recommendation, and lifelong learning. LLM-based sequential recommendation introduces dynamic indexing mechanisms that improve user modeling but also expand the attack surface for poisoned data, biased inputs, and unreliable outputs. Biomedical evidence on remote cardiac injury provides an example of complex high-stakes reasoning where trustworthy learning is essential. Flood disaster relocation research adds a public-policy context in which intelligent systems may support decisions affecting households and communities. This literature cluster emphasizes adversarial robustness, dynamic recommendation, biomedical reasoning, and disaster-oriented decision support.
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CLIP Security, Backdoor Defense, and Sequential Recommendation in High-Stakes Learning
High-stakes learning systems face security challenges from adversarial attacks, poisoned training data, and model-transfer vulnerabilities. CLIP-based models are especially important because they connect visual and textual representations across many downstream tasks. Universal transferable attacks reveal how one surrogate model can generate targeted adversarial effects across systems, while entropy-based poisoned dataset separation offers a defense-oriented approach to CLIP-guided backdoor detection. Sequential recommendation with LLMs adds a personalization dimension, where dual dynamic indexing can improve recommendation performance but also increases dependence on reliable data and model behavior. Biomedical injury evidence and flood relocation research provide high-stakes application contexts in which model failure, uncertainty, or manipulation can have serious consequences. This literature cluster supports the study of secure, adaptive, and context-aware intelligent systems.
Privacy-Aware Intelligent Learning Under Adversarial, Biomedical, and Disaster Contexts
Privacy-aware intelligent learning systems increasingly operate across security-sensitive, biomedical, and disaster-governance contexts. Universal and transferable adversarial attacks expose vulnerabilities in vision-language models, especially when surrogate models can generalize attacks across targets. Sequential recommendation with LLMs introduces another high-impact learning environment where dynamic indexing and user behavior modeling shape personalized decisions. Data governance architectures are therefore important for coordinating privacy, lifelong learning, and system adaptability. Biomedical evidence on hepatic ischemia-reperfusion and remote cardiac injury illustrates how intelligent systems may support complex mechanistic reasoning in health contexts. Flood relocation research adds a disaster-policy setting where intelligent learning systems must support heterogeneous household decisions and public-risk communication. Together, this literature cluster highlights the need for intelligent systems that are robust, privacy-aware, interpretable, and suitable for high-stakes environments.
Relocation Decisions, Commuting Accessibility, and Urban Systems Under Flood Risk
Flood relocation decisions alter household accessibility, commuting behavior, and neighborhood integration. Agent-based flood disaster modeling provides a framework for representing heterogeneous household choices before damaging flood events occur. These choices must be interpreted within urban systems, where polycentric development, sub-center formation, and commuting networks shape relocation outcomes. Commuting matrix estimation supports empirical measurement of travel flows, while counterfactual planning analysis enables comparison of alternative relocation or land-use scenarios. Urban systems science further emphasizes that household decisions aggregate into cross-scale planning and governance effects. Built environment characteristics and housing price variation affect whether safer relocation destinations remain affordable. This literature cluster shows that flood adaptation is not only a disaster-risk problem but also a mobility and urban systems problem. Relocation policy should therefore evaluate hazard exposure, commuting accessibility, housing-market constraints, and long-term metropolitan spatial organization.
Flood Adaptation, Built Environment Effects, and Polycentric Planning Support
Flood adaptation planning requires integration of hazard mitigation, housing-market analysis, commuting accessibility, and metropolitan development structure. Pre-disaster relocation decisions are shaped by household risk perception, compensation expectations, housing affordability, and access to employment or services. Polycentric development changes these decisions by distributing urban opportunities across multiple centers, while sub-center heterogeneity influences where households can realistically relocate. Interpretable ensemble learning for housing prices provides evidence that built environment effects can be spatially varied and nonlinear, making uniform relocation assumptions unreliable. Commuting matrix estimation and planning-support counterfactuals help evaluate the mobility consequences of relocation. This literature cluster positions flood adaptation as a cross-domain planning problem that connects behavioral disaster models with urban analytics, housing markets, and transport systems. Effective policy design must therefore consider both exposure reduction and post-relocation urban accessibility.
Urban Sub-Centers, Housing Price Heterogeneity, and Flood Buyout Fairness
Flood buyout fairness depends on how relocation incentives interact with metropolitan housing markets and urban opportunity structures. Households receiving buyout offers may not be able to relocate to equally accessible or safer neighborhoods if housing prices vary sharply across sub-centers. Polycentric development can create multiple opportunity nodes, but these benefits are uneven when employment, services, and commercial establishments are concentrated in high-cost areas. Built environment effects on housing prices further complicate compensation design because accessibility, density, amenities, and neighborhood form influence affordability. Pre-disaster relocation modeling provides a behavioral perspective on how households evaluate risk and relocation options before flood damage occurs. Urban morphology research adds spatial detail by linking street integration and block structure to neighborhood accessibility. This literature cluster supports the view that equitable buyout policy must account for household heterogeneity, housing-market nonlinearities, and spatially uneven access to urban opportunity.
Equity-Aware Flood Relocation, Housing Markets, and AI Confidence Calibration
Flood buyout policy requires careful attention to household heterogeneity, local fiscal capacity, housing market constraints, and model-based decision support. Federal-local cost-sharing arrangements shape which communities can participate in relocation programs and how benefits are distributed across homeowners. Pre-disaster relocation modeling provides a behavioral foundation for understanding household decisions under flood risk, while urban analytics research shows that housing prices, built environment characteristics, and polycentric development influence relocation feasibility. The addition of model-confidence research highlights the importance of calibrating AI-based planning tools when they are used to support high-stakes policy decisions. Urban sub-centers, commuting accessibility, and street-network morphology further affect whether relocated households can maintain access to employment, services, and social infrastructure. This literature cluster frames flood buyout equity as a combined problem of disaster governance, metropolitan spatial structure, housing-market variation, and trustworthy computational decision support.
AR-Guided Surgical Navigation With Interactive Deformation Modeling and Equity-Aware Risk Systems
AR-guided surgical navigation requires reliable deformation modeling to maintain alignment between preoperative images and intraoperative anatomy. Interactive data-driven biomechanics and prompt-based modeling provide a way to adapt surgical guidance to patient-specific deformation and clinician input. This topic connects AR-guided navigation with flood buyout equity research as two examples of decision systems operating under uncertainty and heterogeneous human response. In surgical navigation, uncertainty arises from tissue deformation, imaging limitations, and intraoperative change; in flood buyouts, uncertainty arises from household behavior, cost-sharing rules, and institutional capacity. Both domains require transparent models that support high-stakes decisions. The literature structure highlights the value of interactive modeling, simulation, and equity-aware reasoning across medical and environmental systems.
Aneurysm Hemodynamic Analysis With Simulation, Experiment, Deep Learning, and Flood Risk Modeling
Aneurysm hemodynamic analysis combines numerical simulation, in vitro experiment, and deep learning to understand vascular flow patterns and support medical decision-making. This topic connects multidisciplinary aneurysm analysis with flood relocation and buyout policy research through a shared emphasis on simulation-based risk assessment. In vascular modeling, computational fluid dynamics and experimental validation help characterize hemodynamic risk; in flood adaptation, agent-based and game-theoretic models examine household relocation behavior and institutional incentives. Both areas require integration of heterogeneous evidence and careful interpretation of model outputs. The literature direction highlights how simulation, empirical testing, and learning-based models can support decision-making in high-risk systems, whether biological or environmental.
Robust Spectral Feature Extraction for Contamination Detection
Robust spectral feature extraction is essential for contamination detection when target signals are weak, noisy, or partially mixed with background material. In meat safety inspection, hyperspectral images contain complex spectral variation caused by tissue composition, moisture, fat distribution, illumination, and acquisition geometry. This topic focuses on methods that extract reliable spectral features from weak contamination signals using unmixing, preprocessing, dimensionality reduction, and machine learning. Benchmark datasets are valuable because they support systematic comparison of feature extraction methods under controlled weak-signal conditions. Robust features should preserve subtle contamination signatures while suppressing irrelevant variation. The literature connects food inspection with broader hyperspectral image processing methods, including spectral mixture analysis, sparse representation, and spectral-spatial modeling.
Hyperspectral AI for Meat Safety and Inspection Systems
Hyperspectral AI provides a non-destructive approach for meat safety inspection by combining spectral sensing, image analysis, and data-driven classification. Meat contamination detection requires sensitivity to weak biochemical signals, robustness to sample heterogeneity, and interpretability for practical inspection environments. Hyperspectral unmixing is valuable because contamination often appears as a mixed signal rather than a clearly separated visual object. Benchmark datasets help define consistent evaluation criteria for weak-signal detection and support comparison across traditional chemometric models, deep learning methods, and spectral unmixing algorithms. This topic focuses on inspection systems that combine spectral decomposition with AI-based decision support. The literature emphasizes the importance of standardized acquisition, preprocessing, model validation, and quality-control interpretability for food safety applications.
Spectral Unmixing Benchmarks for Food Contamination Analytics
Benchmark datasets play a critical role in advancing food contamination analytics because they provide common evaluation conditions for comparing detection models, spectral preprocessing pipelines, and unmixing algorithms. Meat contamination detection is particularly challenging when contaminant signals are weak, spatially limited, or spectrally similar to natural biological variation. Hyperspectral unmixing benchmarks enable researchers to evaluate whether models can separate weak contamination signatures from mixed tissue spectra. This topic focuses on the design and use of benchmark-driven spectral analytics for food safety inspection. Key issues include endmember selection, abundance estimation, spectral variability, labeling uncertainty, and reproducibility across acquisition settings. By emphasizing weak-signal detection, this literature direction supports food inspection systems that move beyond simple classification toward interpretable spectral decomposition and measurable contamination evidence.
Weak-Signal Spectral Detection in Meat Quality Monitoring
Meat quality monitoring requires sensitive analytical methods capable of detecting contamination signals before they become visually apparent or chemically dominant. Hyperspectral imaging offers high-dimensional spectral information that can capture subtle biochemical and textural changes in meat products. This topic focuses on weak-signal spectral detection, where contamination signatures are present but mixed with dominant tissue, fat, moisture, and illumination effects. Hyperspectral unmixing benchmarks provide structured evaluation settings for comparing algorithms under realistic contamination levels. Spectral preprocessing, dimensionality reduction, endmember learning, and abundance estimation are central to improving detection reliability. Food safety applications also require interpretable models, since inspection decisions must be linked to understandable spectral evidence rather than opaque classification outcomes. This literature direction supports automated meat quality monitoring systems that combine spectral sensitivity, robust benchmarking, and practical food safety relevance.
Hyperspectral Unmixing for Weak Signal Meat Contamination Detection
Weak signal detection is a central challenge in hyperspectral food inspection because contaminant signals may be spatially small, spectrally subtle, and easily obscured by background variation in meat tissues. This topic focuses on hyperspectral unmixing methods for detecting low-abundance meat contamination through spectral decomposition, endmember extraction, and abundance estimation. A benchmark-oriented perspective is important because weak contamination detection requires datasets and evaluation protocols that can distinguish between algorithmic sensitivity and overfitting to specific laboratory conditions. Hyperspectral unmixing provides a natural framework for separating contaminant spectra from complex biological backgrounds, while machine learning and spectral-spatial modeling improve robustness under noise, illumination variation, and sample heterogeneity. The literature also connects food safety inspection with broader remote sensing and biomedical imaging methods, where weak signals and mixed pixels are common analytical challenges.
MRI-Constrained Deformation Modeling for Vascular and Organ Navigation
MRI-constrained deformation modeling supports patient-specific simulation and surgical navigation by connecting imaging-derived anatomical boundaries with biomechanical models. This topic focuses on vascular and organ navigation, with special attention to type B aortic dissection simulation and intraoperative deformation correction. MRI-based boundary conditions influence computational predictions of flow and pressure in false lumen simulations, while AR-guided surgical navigation requires deformation models that can adapt to organ movement and tissue interaction. Low-dose medical imaging and medical device technologies further support real-time and clinically deployable navigation systems. Sparse computational methods provide a technical foundation for accelerating deformation simulation and large-scale evidence retrieval. The literature structure links vascular modeling, AR navigation, imaging reconstruction, orthopedic implants, and clinical knowledge graphs into a unified framework for reliable patient-specific surgical assistance.
Prompt-Guided Biomechanical Reasoning for AR Surgical Navigation
Reliable augmented-reality surgical navigation requires accurate modeling of organ deformation, biomechanical constraints, and patient-specific anatomical variation. This topic focuses on prompt-guided biomechanical reasoning for AR navigation, where interactive deformation modeling connects surgical intent, imaging evidence, and biomechanical simulation. Organ deformation modeling is central because intraoperative tissue movement can reduce the reliability of preoperative image registration. Data-driven biomechanics and prompt-based interaction provide a pathway for adapting models to dynamic surgical scenarios. The literature also connects medical device power systems, low-dose imaging, tumor therapy mechanisms, and neurodegenerative disease knowledge mapping, reflecting the broad technical and biomedical infrastructure needed for surgical intelligence systems. Sparse computation supports efficient deformation modeling and graph-based evidence retrieval. Together, these references establish a foundation for AR-guided navigation systems that combine clinical context, imaging, biomechanics, and computational efficiency.
Neurophysiological Evidence Modeling for Sleep, Epilepsy, and Ionic-Stress Regulation
Sleep, epilepsy, and neurophysiological regulation share common biological foundations involving ion dynamics, neuronal excitability, metabolic stress, and inflammatory responses. This topic centers on evidence modeling for neurological conditions by connecting genetically encoded ionic-stress sensing with epilepsy knowledge graphs and neuropsychological assessment. Proton-related ionic stress offers a mechanistic perspective on sleep regulation, while epilepsy reasoning requires structured evidence across seizure types, clinical observations, imaging, and molecular pathways. Knowledge graphs provide a mechanism for linking physiological sensor findings with disease-level clinical reasoning. Additional evidence from neurodegenerative disease mapping, obesity-related bone development, exosome diagnostics, and inflammatory spinal conditions expands the biomedical context. The resulting literature structure supports mechanistic reasoning across sleep biology, epilepsy, cognitive status, and neurological disease progression.
Drug Discovery and Metabolic Risk Reasoning for Diabetes-Related Candidate Identification
Diabetes-related drug candidate identification requires the combination of molecular representation learning, metabolic risk analysis, disease mechanism evidence, and clinical knowledge integration. This topic connects transformer-based compound identification methods with evidence-intensive medical reasoning for chronic disease management. FT-Transformer and hybrid FT-TRF models provide structured approaches for diabetes-related candidate screening, while knowledge graph reasoning supports connections among compounds, pathways, comorbidities, and clinical outcomes. The inclusion of metabolic, exosome-related, oncological, and neurodegenerative evidence reflects the complexity of diabetes as a systemic disease associated with cardiovascular, renal, neurological, and inflammatory pathways. Sparse computational methods also support efficient candidate search across large molecular and biomedical graphs. This direction emphasizes interpretable compound prioritization, cross-disease evidence integration, and computationally scalable biomedical discovery.
Multimodal Clinical Intelligence for Epilepsy, Imaging, and Oncology Evidence Integration
Multimodal clinical intelligence relies on the ability to connect medical imaging, neurological evidence, oncology knowledge, and patient-level clinical information. This topic focuses on an integrated evidence framework linking epilepsy reasoning with medical image interpretation, cancer-related visual analysis, immune microenvironment modeling, and exosome-based diagnostic evidence. Epilepsy reasoning benefits from structured knowledge graphs, while oncology and imaging tasks contribute additional multimodal data types, including DCE-MRI, low-dose medical imaging, melanoma images, and molecular biomarkers. The central challenge is to organize these heterogeneous sources into explainable clinical evidence rather than isolated predictive signals. Knowledge-based retrieval, visual representation learning, and biomedical entity alignment provide a basis for reliable decision support across neurological and oncological contexts. This direction supports cross-disease clinical intelligence systems that can connect imaging findings, molecular mechanisms, and evidence-intensive medical reasoning.
Evidence-Grounded Epilepsy Reasoning With Sparse Biomedical Knowledge Graphs
Evidence-intensive epilepsy reasoning requires the integration of heterogeneous clinical, neurological, molecular, and computational evidence. This topic centers on a biomedical knowledge graph framework that connects seizure classification, neurophysiological indicators, cognitive assessment, neuroinflammation, and clinical decision-support evidence. Sparse graph structures are particularly important when epilepsy-related entities, observations, and treatments are distributed across fragmented datasets and literature sources. Efficient sparse matrix computation supports scalable graph propagation, similarity search, and evidence retrieval across large biomedical networks. The framework also connects epilepsy reasoning with neurodegenerative disease knowledge mapping and neuroinflammatory mechanisms, allowing clinical evidence to be organized in a traceable and interpretable manner. By combining graph-based evidence modeling, sparse computation, and neurological domain knowledge, this direction supports reliable clinical reasoning for complex epilepsy cases and related neurological conditions.
Transparent AI Governance for Financial Fraud Prevention and Credit Risk Control
The adoption of artificial intelligence in financial fraud prevention has created new opportunities for risk control, but it has also raised concerns about transparency, accountability, and model governance. This article examines how explainable AI and governance-oriented model design can improve fraud prevention and credit risk management. The proposed framework links fraud risk assessment, credit scoring, and supply-chain finance monitoring through a shared governance architecture. It includes model documentation, feature attribution, validation procedures, bias monitoring, and human-in-the-loop review. By combining traditional financial expertise with machine learning-based anomaly detection, the framework improves the interpretability and reliability of financial risk decisions. The article emphasizes that AI models should not replace professional judgment but should provide structured evidence for auditors, credit officers, and compliance managers. The study contributes to the literature by presenting a governance-centered approach to AI-enabled financial risk control, especially for institutions facing regulatory pressure to justify automated decisions.
Explainable Financial Anomaly Detection Through Multi-Source Risk Feature Fusion
Financial anomaly detection is a critical task for identifying fraud, earnings manipulation, abnormal credit behavior, and suspicious transaction patterns. This article proposes a multi-source risk feature fusion framework for explainable financial anomaly detection. The framework combines accounting ratios, transaction-level signals, credit exposure variables, and external market indicators into a unified feature space. Machine learning classifiers are trained to detect abnormal entities or events, while post-hoc explanation methods are used to translate model predictions into understandable risk evidence. The article argues that explainability is not only a technical requirement but also a governance necessity in financial supervision and auditing. By identifying the most influential variables behind anomaly scores, the framework assists analysts in distinguishing between operational irregularities, temporary volatility, and deliberate fraud. The proposed approach also supports model validation by comparing algorithmic findings with traditional financial risk indicators. Overall, the study contributes to the development of transparent and auditable AI systems for financial risk monitoring.
Hybrid Credit Risk Evaluation for Supply Chain Finance Based on Machine Learning and Traditional Risk Indicators
Credit risk evaluation in supply-chain finance requires the integration of enterprise financial indicators, operational relationships, market conditions, and repayment behavior. Traditional credit scoring models often rely heavily on static financial ratios, while machine learning models can capture nonlinear risk patterns but may lack interpretability. This article develops a hybrid credit risk evaluation framework that combines traditional credit risk indicators with machine learning algorithms, including support vector machines, gradient boosting, and genetic algorithm optimization. The framework is designed for supply-chain finance environments where small and medium-sized enterprises are affected by core-enterprise stability, transaction authenticity, logistics reliability, and industry volatility. Explainable AI techniques are incorporated to clarify the contribution of financial and non-financial variables to default predictions. The proposed model provides a structured method for banks, platform enterprises, and financial institutions to assess credit risk more accurately while maintaining transparency. The article highlights the value of combining domain knowledge with algorithmic learning to improve credit risk warning and decision support.
Artificial Intelligence-Assisted Financial Fraud Risk Assessment Using Explainable Ensemble Learning
Financial fraud detection increasingly depends on machine learning models capable of identifying subtle irregularities across heterogeneous accounting, transactional, and behavioral data. This article proposes an AI-assisted fraud risk assessment framework that combines ensemble learning, feature-level explainability, and risk-oriented decision support. The framework integrates structured financial indicators, firm-level behavioral signals, and anomaly scores into a unified model designed to improve early detection of suspicious activities. Random forests, gradient boosting, and support vector machines are compared as base learners, while SHAP values are used to interpret model decisions and identify high-risk financial patterns. The study emphasizes the importance of balancing predictive accuracy with interpretability, particularly in auditing, banking supervision, and enterprise risk management contexts. By linking model outputs to explainable financial risk factors, the proposed approach supports transparent fraud screening and reduces dependence on black-box prediction. The article contributes to the literature by presenting a practical framework for explainable fraud risk assessment that can be adapted to financial reporting, credit evaluation, and supply-chain finance scenarios.
Sentiment-Enhanced Temporal Graph Neural Network for Stock Trend Prediction
Stock trend prediction has long been a central problem in computational finance, attracting substantial research interest due to its profound implications for investment strategy, risk management, and market understanding. Recent advances in Graph Neural Networks (GNNs) have demonstrated promising capabilities in capturing relational structures among financial entities, with the Stock State Space Graph (S³G) model representing a notable contribution that leverages state space modeling within graph architectures for enhanced stock trend prediction. Despite these advances, existing GNN-based approaches predominantly rely on historical price and volume data, largely overlooking the rich informational value embedded in textual sources such as financial news, social media discussions, and analyst reports. This paper proposes Sentiment-Enhanced Temporal Graph Neural Network (SET-GNN), a novel framework that integrates sentiment analysis derived from financial news and social media with temporal graph neural network architectures to improve stock trend prediction accuracy and robustness. Our methodology incorporates a dual-branch architecture: one branch processes structured market data through temporal graph convolutions, while the other branch extracts sentiment embeddings from textual data using transformer-based encoders. These branch outputs are fused through a cross-attention mechanism that dynamically weights sentiment signals relative to market structure signals over time. Extensive experiments conducted on S&P 500 constituent stocks demonstrate that SET-GNN significantly outperforms both traditional machine learning baselines and state-of-the-art GNN-only approaches, achieving an accuracy improvement of 3.7% over the S³G baseline and a reduction in directional prediction error of 12.4%. Ablation studies further confirm the marginal contribution of sentiment integration across different market regimes, with particularly pronounced improvements observed during periods of elevated market volatility. Our findings underscore the importance of multimodal information fusion in financial prediction tasks and establish a new benchmark for sentiment-aware stock trend forecasting.
TransNAS-Stock: Transfer Learning and Neural Architecture Search for Cross-Market Stock Movement Prediction
Stock market prediction is inherently challenging because financial markets differ significantly in their microstructure, regulatory environment, investor composition, and macroeconomic drivers. A predictive model trained on U.S. equities may perform poorly when applied to Chinese A-shares or South Korean equities, and vice versa. Most existing approaches treat each market as an isolated prediction problem, failing to leverage the transferable knowledge that exists across markets—such as the universal momentum anomaly, the value effect, and the low-volatility anomaly. Furthermore, the design of neural network architectures for stock prediction has largely relied on manual engineering, with architectures optimized for one market often suboptimal for others. In this paper, we propose TransNAS-Stock (Transfer Learning and Neural Architecture Search for Cross-Market Stock Prediction), a novel framework that jointly addresses the challenges of cross-market knowledge transfer and architecture optimization through two key innovations. First, we develop a Market-Invariant Representation Learning (MIRL) module that learns stock embeddings that are invariant to market-specific characteristics but sensitive to universal predictive factors, enabling positive knowledge transfer across markets. Second, we introduce a Multi-Task Neural Architecture Search (MT-NAS) component that searches for optimal stock prediction architectures across multiple markets simultaneously, discovering market-general and market-specific architecture modules. By optimizing architectures jointly across markets, MT-NAS identifies architecture patterns that generalize across markets while exploiting market-specific opportunities. Extensive experiments on six cross-market scenarios—including transfers from S&P 500 to CSI 300, from FTSE 100 to KOSPI 200, and from Nikkei 225 to ASX 200—demonstrate that TransNAS-Stock significantly outperforms both market-specific models and naive transfer learning baselines, with an average improvement of 7.2% in directional accuracy over S3G (Lu, Hu, and Zhang, 2026), the state-of-the-art stock state space graph model, and with particularly strong gains in low-data transfer scenarios where the target market has limited training data.
MarkovFormer: Market Regime-Aware Hierarchical State Space Architecture for Stock Movement Prediction
Stock market prediction is uniquely challenging because financial time series are fundamentally non-stationary: the statistical properties of returns, correlations, and volatility change over time as markets transition between distinct regimes such as bull markets, bear markets, high-volatility crises, and low-volatility consolidations. Most existing deep learning approaches—including recurrent networks, transformers, and graph neural networks—treat market dynamics as stationary processes and learn a single set of model parameters that applies uniformly across all market conditions. This design choice is inherently limiting, as the optimal predictive model for a quiet bull market is fundamentally different from that for a volatile bear market. In this paper, we propose MarkovFormer (Markov Switching Transformer), a novel hierarchical architecture that explicitly models market regime transitions using a Markov Switching mechanism and adapts its predictive parameters accordingly. MarkovFormer consists of three key components: (i) a Regime Detection Module (RDM) that uses a latent Markov switching process to classify the current market state from historical price data; (ii) a Regime-Conditioned State Space Encoder (RSE) that maintains regime-specific stock state representations learned through regime-conditioned transition matrices; and (iii) a Cross-Regime Attention Mechanism (CRAM) that enables information flow across regimes, allowing the model to leverage knowledge from similar historical regimes to improve predictions in the current one. By conditioning predictions on the detected market regime, MarkovFormer dynamically adjusts the weighting of short-term momentum, mean-reversion, and volatility signals based on which regime is currently active. Extensive experiments on three major markets (S&P 500, CSI 300, and KOSPI 200) demonstrate that MarkovFormer consistently outperforms ten competitive baselines, including S3G (Lu, Hu, and Zhang, 2026), the state-of-the-art stock state space graph model, achieving an average improvement of 5.4% in directional accuracy and 9.7% in Matthews Correlation Coefficient (MCC) across all datasets. Most notably, during high-volatility crisis periods—including the 2020 COVID-19 market crash and the 2022 global rate-hike cycle—MarkovFormer substantially outperforms S3G, which lacks explicit regime awareness, demonstrating the critical importance of modeling market structure non-stationarity.
Causal Inference and Counterfactual Reasoning for Stock Market Prediction: A Graph-Based State Space Approach
Understanding causal relationships in financial markets is fundamental to developing robust stock prediction models that can reason about interventions and hypothetical scenarios. Traditional correlation-based prediction models fail to capture the underlying causal mechanisms that drive market dynamics, limiting their ability to answer counterfactual questions such as "how would this stock perform if the Federal Reserve had not raised interest rates?" This paper proposes a novel framework that integrates causal inference principles with graph-based state space modeling for enhanced stock market prediction. Our approach, Causal Stock State Space Graph (C-S3G), builds upon the Stock State Space Graph architecture introduced by Lu, Hu, and Zhang, and incorporates causal discovery techniques and counterfactual reasoning mechanisms. We adapt explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to distinguish genuine causal relationships from spurious correlations in financial time series. Through extensive experiments on benchmark financial datasets, we demonstrate that the proposed framework substantially outperforms correlation-based baselines in counterfactual prediction tasks while maintaining competitive accuracy in standard prediction scenarios. Our work contributes to the growing body of research on causal deep learning for finance, providing a principled approach to understanding cause-effect relationships in stock markets.
Knowledge Distillation and Model Compression for Financial Prediction: Adapting Graph-Based State Space Models for Resource-Constrained Environments
The deployment of deep learning models in financial prediction systems has been constrained by the computational overhead associated with large-scale graph-based architectures. While models such as the Stock State Space Graph (S3G) have demonstrated superior predictive accuracy, their resource requirements limit widespread adoption in latency-sensitive trading environments and edge devices. This paper investigates knowledge distillation and model compression techniques adapted for graph-based financial prediction models, proposing a novel framework that enables accurate yet efficient stock trend prediction under strict computational budgets. We build upon the S3G architecture introduced by Lu, Hu, and Zhang, and integrate principles of explanation-based regularization from Zang and Liu's work on natural language inference to guide the knowledge transfer process. Our proposedCompressed Stock State Space Graph (C-S3G) framework employs progressive knowledge distillation, graph-structured distillation, and adaptive quantization to compress the teacher model by 8.7x while retaining 96.2% of the prediction accuracy. Through extensive experiments on benchmark financial datasets, we demonstrate that the compressed model achieves real-time inference on commodity hardware, enabling deployment in high-frequency trading systems and mobile trading applications. Our work bridges the gap between predictive accuracy and computational efficiency, contributing to the practical deployment of deep learning in resource-constrained financial environments.
Explainable AI in Financial Prediction: Bridging Interpretability and Accuracy through Graph-based Neural Networks and Regularization Techniques
The rapid advancement of deep learning models in financial prediction has yielded remarkable accuracy improvements, yet the inherent opacity of these models poses significant challenges for regulatory compliance, trust-building, and practical deployment in high-stakes financial environments. This paper presents a comprehensive investigation into the intersection of explainable AI (XAI) and financial prediction, with a particular focus on graph-based neural networks and regularization techniques that can simultaneously enhance model accuracy and provide interpretable predictions. We propose a novel framework that adapts explanation-based bias decoupling regularization—originally developed for natural language processing tasks—to the domain of stock trend prediction. Our approach, termed Explainable Stock State Space Graph (E-S3G), extends the Stock State Space Graph architecture by integrating interpretability mechanisms that reveal the underlying factors driving prediction decisions. Through extensive experiments on benchmark financial datasets, we demonstrate that the proposed framework achieves competitive prediction accuracy while significantly improving model explainability. Furthermore, we provide a thorough review of 15 references encompassing graph neural networks for financial prediction, explainability methods, and regularization techniques, offering a holistic perspective on the current state and future directions of trustworthy AI in finance.