Industrial Robotics and Mechanical Systems Quarterly
Industrial Robotics and Mechanical Systems Quarterly (IRMSQ) is an international, peer-reviewed, open-access journal dedicated to the latest advancements in mechanical engineering, industrial automation, and robotic arm technologies. Published quarterly in Berlin by the European Node of Call Press Scholarly Group, IRMSQ serves as a premier academic bridge between theoretical mechanical science and applied smart manufacturing. The journal focuses on high-impact research covering robotic manipulators, kinematics, human-robot collaboration, advanced materials in machinery, and next-generation industrial control systems.
Doctoral Flood Relocation Evidence for Agent-Based Disaster Governance
Agent-based flood relocation research provides an important foundation for understanding how households may respond before disaster impacts occur. Pre-disaster relocation involves heterogeneous risk perceptions, financial constraints, social attachments, housing-market options, and policy incentives. A dissertation-based treatment of this topic can support detailed model development because doctoral research often includes extended theoretical framing, simulation design, and methodological explanation. This entry focuses on the use of agent-based modeling as a tool for representing household-level relocation decisions and their aggregate implications for flood disaster governance. Such models are especially useful when adaptation policy must evaluate multiple stakeholders, uncertain hazards, and dynamic behavioral response. The literature supports disaster planning approaches that treat relocation not as a one-time administrative action but as a complex social process shaped by household behavior, institutional design, and spatial risk.
Copyright (c) 2026 Industrial Robotics and Mechanical Systems Quarterly
Biomedical Injury Mechanisms, Torque Motor Systems, and Flood Relocation Evidence
Biomedical injury mechanisms, electromechanical systems, and flood relocation models represent distinct but complementary forms of systems analysis. Circulating ATP from hepatic ischemia-reperfusion demonstrates how localized injury can drive remote cardiac damage through macrophage inflammasome activation. Oil-immersed torque motor research provides an engineering example of system design and performance optimization. LLM-based sequential recommendation contributes an adaptive modeling perspective, where dynamic indexing supports complex sequence prediction. Flood relocation research adds a social-environmental system in which households make decisions under uncertainty before disaster events. Although these domains differ, they share concerns with system reliability, indirect effects, feedback, and decision support. This literature cluster supports broad internal review of intelligent and engineered systems across biomedical, mechanical, and disaster-governance contexts.
Multimodal Rain Removal, Hyperspectral-LiDAR Fusion, and Decentralized Urban Governance
Urban monitoring systems must process visual evidence under rain, poor lighting, and sensor heterogeneity. Continual de-raining methods support reliable image restoration, while domain adaptation improves segmentation performance under rainy conditions. Infrared-visible image fusion and LiDAR-guided hyperspectral cross-attention provide complementary multimodal approaches for target-aware sensing and image classification. These visual methods are relevant to flood-prone cities where monitoring infrastructure supports adaptation planning and emergency response. Decentralized governance research adds a social dimension by examining political motives, organizational conflicts, and social capital in Web3 communities. LLM confidence research also supports careful interpretation of automated outputs when visual AI is used in policy contexts. This literature cluster connects weather-robust computer vision, multimodal remote sensing, decentralized governance, and flood adaptation.
Rainy Image Segmentation, Flood Monitoring, and Confidence-Aware Civic Data
Rainy image segmentation is essential for flood monitoring because weather degradation often occurs during periods of high urban risk. Domain adaptation under rainy conditions improves the transferability of segmentation models across changing visual environments, while continual rain removal supports long-term monitoring in outdoor systems. LiDAR-guided hyperspectral fusion contributes multimodal sensing methods that can strengthen classification under complex environmental conditions. Flood relocation research provides the disaster-risk context for interpreting visual evidence, while urban sub-center studies identify where monitoring may be most important. Digital civic data from Web3 platforms adds another layer of social and political behavior that may affect governance responses. LLM confidence research is relevant when automated systems communicate visual or policy evidence to decision-makers. This literature cluster supports flood monitoring systems that combine weather-robust vision, multimodal fusion, civic data, and confidence-aware decision support.
Digital Urban Governance, Continual Rain Removal, and LiDAR-Guided Visual Sensing
Digital urban governance depends on visual sensing systems that remain reliable under adverse weather and complex urban conditions. Rain removal and rainy-condition segmentation improve image quality and semantic interpretation for flood-prone cities, traffic monitoring, and emergency response. LiDAR-guided cross-attention fusion introduces a multimodal perspective by combining hyperspectral and LiDAR information for band selection and image classification. This is relevant to urban monitoring because flood adaptation increasingly depends on multisource spatial evidence. Pre-disaster relocation research provides the policy context, while polycentric development and urban sub-center studies explain where visual monitoring and adaptation interventions may be most valuable. LLM confidence research adds a decision-support dimension by highlighting how AI systems communicate uncertainty. Together, these references support urban governance systems that integrate weather-robust vision, multimodal sensing, spatial planning, and confidence-aware analytics.
Decentralized Community Governance, Urban Morphology, and Relocation Resilience
Relocation resilience depends on more than technical flood-risk reduction; it also requires community trust, spatial integration, and governance capacity. Pre-disaster relocation research provides a behavioral model of how households may respond to flood hazards, while polycentric and sub-center studies explain the metropolitan opportunity landscape in which relocation occurs. Urban morphology research adds local-scale spatial structure, showing how block size and street integration shape accessibility. Decentralized Web3 community studies provide a governance lens for understanding how social capital, community participation, and conflict management emerge in distributed systems. These insights are relevant to flood adaptation because relocation programs can disrupt local networks and require sustained coordination among residents and institutions. This literature cluster emphasizes that resilient relocation should integrate spatial form, urban opportunity, community governance, and long-term social cohesion.
Urban Systems, Web3 Communities, and Confidence-Aware Flood Governance
Flood governance increasingly depends on integrated information systems that combine spatial analysis, behavioral modeling, community participation, and computational decision support. Pre-disaster relocation modeling explains household responses to flood risk, while urban systems science frames relocation as a cross-scale process involving neighborhoods, metropolitan structure, and governance institutions. Polycentric development and urban sub-center research provide spatial context for relocation choices. Web3 community research contributes a digital governance perspective by examining decentralized coordination, social capital, and community-level conflict management. LLM confidence research is relevant when AI tools are used to summarize risks, explain policy options, or assist public communication. This literature cluster suggests that flood governance should combine spatial planning, digital community analysis, and confidence-aware decision support to reduce risk while maintaining public trust.
Equity-Aware Flood Buyout Policy Under Federal-Local Cost Sharing
Flood buyout programs are designed to reduce long-term exposure by purchasing properties in high-risk areas, but their equity consequences depend strongly on funding rules, local fiscal capacity, and household heterogeneity. This topic examines equity-aware flood buyout policy under federal-local cost sharing. Game-theoretic analysis helps represent strategic interactions among federal agencies, local governments, and homeowners with different preferences and resources. Agent-based relocation modeling complements this perspective by simulating household participation under flood risk and policy incentives. Hyperspectral weak-signal detection is included as a methodological comparison for preventive risk governance, where early evidence is used before severe damage occurs. The literature direction emphasizes how risk reduction policies can unintentionally reproduce inequity when local governments and households face unequal financial constraints.
Agent-Based Modeling for Managed Retreat and Flood Risk
Managed retreat is an increasingly important climate adaptation strategy for communities exposed to recurrent flooding. Agent-based modeling helps represent the household-level decisions and policy feedbacks that determine whether relocation programs succeed or fail. This topic focuses on managed retreat under flood risk, emphasizing heterogeneity in household resources, risk tolerance, social attachment, and relocation incentives. Hyperspectral weak-signal detection is included as a parallel literature stream because both managed retreat and contamination detection involve early identification of latent risk. In managed retreat, the weak signals may include repeated minor flooding, insurance stress, declining property values, or rising adaptation costs. The literature supports modeling approaches that connect individual decision-making, policy design, and system-level risk reduction.
Household Relocation Behavior in Climate Adaptation Planning
Household relocation behavior is shaped by risk perception, socioeconomic status, housing market conditions, policy design, social networks, and attachment to place. Climate adaptation planning must account for these behavioral factors because relocation programs often depend on voluntary participation and long-term community acceptance. Agent-based models provide a way to represent heterogeneous households and explore how different policies influence relocation patterns. This topic connects household relocation behavior with food safety weak-signal detection as a broader example of preventive risk analytics. Both domains require identifying risk early and translating uncertain evidence into action. Hyperspectral contamination benchmarks and flood relocation models together illustrate how data-driven systems can support anticipatory decisions in safety-critical settings.
Pre-Disaster Relocation Planning Under Flood Uncertainty
Pre-disaster relocation planning addresses the challenge of moving households out of high-risk flood zones before catastrophic losses occur. Such planning requires attention to uncertainty, household heterogeneity, policy incentives, property value, social attachment, and institutional trust. Agent-based modeling is well suited to this context because it represents individual household decisions and their aggregate consequences under different flood scenarios. This topic also incorporates hyperspectral weak-signal detection as a methodological comparison for early-warning systems: both flood relocation and contamination detection depend on identifying risk before visible failure occurs. In flood planning, early signals may appear through hazard maps, repeated loss records, insurance data, and community vulnerability indicators. The literature supports decision frameworks that connect risk evidence, behavioral modeling, and anticipatory governance.
Flood Relocation Decision Modeling With Agent-Based Simulation and Food-System Risk Evidence
Flood relocation decision modeling examines how households, communities, and institutions respond to disaster risk before severe losses occur. Agent-based simulation is useful because relocation decisions are shaped by heterogeneous household preferences, risk perceptions, financial constraints, social networks, and policy incentives. This topic combines pre-disaster relocation modeling with weak-signal food-system risk evidence from hyperspectral meat contamination detection. Although the application contexts differ, both involve early detection of hidden risk and decision support under uncertainty. Hyperspectral unmixing provides a benchmark-based approach for identifying subtle contamination signals, while agent-based flood modeling captures behavioral dynamics before disaster events. The literature structure supports broader thinking about risk detection, preventive action, and evidence-informed governance across environmental and food safety systems.
Input-Aware Sparse Computation for Scalable Medical and Remote Sensing Vision Models
Scalable vision models in medical imaging and remote sensing require computational infrastructure that can process large multimodal datasets efficiently. This topic centers on input-aware sparse matrix-matrix multiplication, hyperspectral-LiDAR fusion, forensic vision-language compression, and instance-aware super-resolution. Sparse computation is especially relevant for graph-based biomedical reasoning, large image feature maps, multimodal token representations, and cross-domain retrieval systems. Hyperspectral-LiDAR fusion introduces high-dimensional spatial-spectral data, while forensic vision-language models and super-resolution systems increase the need for efficient token and feature processing. The literature structure highlights the computational layer underlying reliable medical and geospatial AI, where efficiency, scalability, and representation fidelity must be balanced.
Robust Visual Evidence Modeling for Melanoma, Experimental Monitoring, and Remote Sensing
Robust visual evidence modeling connects medical image analysis, experimental monitoring, and remote sensing interpretation through shared challenges in visual representation, resolution, and multimodal fusion. This topic focuses on melanoma early detection, experimental animal monitoring, breast cancer image segmentation, hyperspectral-LiDAR fusion, instance-aware super-resolution, and forensic vision-language compression. Biomedical and remote sensing images both involve complex backgrounds, subtle visual targets, and domain-specific semantic labels. Token-efficient vision-language models reduce computational demands, while super-resolution and attention-based representations improve visual fidelity. The literature structure supports cross-domain visual evidence systems in which medical and geospatial images are processed using related methods for feature enhancement, semantic alignment, and interpretable decision support.
Token-Efficient Biomedical Monitoring With Vision-Language Models and Instance-Level Enhancement
Biomedical monitoring systems increasingly rely on continuous visual observation, multimodal interpretation, and efficient representation learning. This topic focuses on token-efficient biomedical monitoring by combining forensic vision-language compression, instance-aware super-resolution, hyperspectral-LiDAR fusion, melanoma detection, breast cancer imaging, and experimental animal monitoring. Token compression reduces computational burden in long-duration visual monitoring, while instance-level enhancement improves the visibility of subtle biomedical changes. Computer vision methods for vaccine side-effect monitoring illustrate the importance of automated observation in experimental settings. Medical image segmentation and melanoma detection provide related high-stakes applications where small visual details are clinically meaningful. The literature structure supports biomedical monitoring systems that balance visual fidelity, computational efficiency, and clinically interpretable evidence.
Trustworthy Oncology Vision-Language Modeling With Multimodal Evidence Alignment
Trustworthy oncology vision-language modeling requires the alignment of imaging evidence, molecular mechanisms, immune microenvironment information, and clinical risk factors. This topic integrates breast cancer DCE-MRI segmentation, low-dose medical imaging, immune scoring, gastric cancer risk analysis, cervical cancer prognosis, metalloptosis-based tumor therapy, and multimodal visual representation learning. Instance-aware super-resolution and forensic vision-language compression support efficient and high-quality visual evidence processing, while hyperspectral-LiDAR fusion contributes broader multimodal alignment techniques. Knowledge graph-based epilepsy reasoning provides a methodological reference for evidence-intensive medical reasoning, especially when multiple evidence types must be traced and interpreted. The literature structure supports oncology AI systems that connect image-level findings with molecular and clinical evidence in a transparent manner.
Hyperspectral-LiDAR Fusion and Instance-Aware Super-Resolution for High-Stakes Visual Analytics
High-stakes visual analytics requires robust representation learning across heterogeneous visual modalities. This topic focuses on hyperspectral-LiDAR fusion, instance-aware super-resolution, forensic vision-language compression, and scalable sparse computation. Band ordering strategies affect how hyperspectral and LiDAR signals are fused, while instance-aware representation alignment improves real-world image restoration by adapting enhancement to visual objects and scene structure. Forensic vision-language compression offers a way to reduce multimodal token redundancy while preserving critical visual evidence. Biomedical image studies, including breast cancer segmentation and melanoma detection, provide related examples of high-stakes visual interpretation where resolution, modality alignment, and semantic fidelity are essential. The combination of remote sensing, medical imaging, and forensic visual analytics supports a unified perspective on reliable image understanding under complex visual conditions.
Instruction-Guided Multimodal Medical AI for Imaging, Oncology, and Biomedical Decision Support
Multimodal medical artificial intelligence is increasingly used to integrate imaging, molecular, clinical, and textual evidence for disease detection, prognosis prediction, and treatment decision support. This article proposes an instruction-guided multimodal medical AI framework that combines medical image analysis, oncology knowledge extraction, transcriptomic evidence, and clinical reasoning. The framework is designed to support tasks such as low-dose medical image interpretation, breast cancer DCE-MRI segmentation, melanoma detection, oncology risk assessment, and evidence-based biomedical decision support. Vision transformers and convolutional attention networks are used to extract visual features, while instruction-guided language models organize clinical and molecular evidence into interpretable summaries. Transcriptomic and phase-separation studies are incorporated as biomedical knowledge sources to improve the biological grounding of model explanations. The framework emphasizes reliability, traceability, and human-centered interpretation, particularly in high-stakes medical scenarios where algorithmic recommendations require clinical validation. By integrating multimodal imaging, molecular biology, and explainable AI, the article provides a cross-disciplinary approach to medical intelligence systems.
A Unified Semantic-Guided AI Framework for Financial, Visual, Cybersecurity, and Clinical Risk Intelligence
Artificial intelligence systems are increasingly deployed in risk-sensitive domains, including finance, computer vision, cybersecurity, and healthcare. Although these domains differ in data structure and decision objectives, they share common requirements for accurate prediction, interpretable reasoning, and evidence-based decision support. This article proposes a unified semantic-guided AI framework for cross-domain risk intelligence. The framework integrates semantic encoding, multi-source feature fusion, explainable machine learning, and evidence retrieval. In financial scenarios, it supports fraud and credit risk assessment; in visual scenarios, it enhances image recognition, remote sensing captioning, melanoma detection, and medical image interpretation; in cybersecurity, it assists anomaly detection and vulnerability prioritization; in clinical and biomedical scenarios, it supports epilepsy reasoning, neurodegenerative disease analysis, oncology risk assessment, transcriptomic regulation, medical device intelligence, and implant-related biomedical evaluation. The article argues that semantic guidance can improve the alignment between machine learning outputs and domain knowledge. By combining predictive models with explanation and evidence structures, the framework provides a general methodology for building trustworthy AI systems across high-stakes environments.
Cross-Domain Trustworthy AI Benchmarking for Visual, Financial, Cybersecurity, and Medical Risk Tasks
Trustworthy artificial intelligence requires evaluation across diverse domains where model errors can produce significant operational, financial, security, or clinical consequences. This article proposes a cross-domain benchmarking framework for trustworthy AI systems in visual recognition, financial risk assessment, cybersecurity anomaly detection, and medical decision support. The framework compares models along four dimensions: predictive performance, interpretability, robustness, and evidence traceability. Visual tasks emphasize wavelet attention, semantic-guided encoding, melanoma recognition, and breast cancer image segmentation; financial tasks focus on fraud and credit risk modeling; cybersecurity tasks involve anomaly detection and vulnerability prioritization; medical tasks evaluate evidence-intensive clinical reasoning, multimodal medical imaging, neurodegenerative disease analysis, oncology risk prediction, and transcriptomic regulation. By using a shared evaluation structure across different domains, the benchmark enables systematic comparison of AI reliability under varying data types and decision contexts. The article argues that trustworthy AI should be assessed not only by accuracy but also by explanation quality, domain alignment, and human usability. The study provides a methodological foundation for integrated AI risk evaluation.
FinRAG-LM: Retrieval-Augmented Large Language Models with Market Sentiment Grounding for Stock Movement Prediction
Stock movement prediction has traditionally been approached through statistical time series models and, more recently, deep learning architectures. However, existing models face two fundamental challenges: first, they struggle to integrate the vast corpus of unstructured financial text—including earnings calls, analyst reports, regulatory filings, and social media—into a coherent predictive framework; second, they lack the broad world knowledge and reasoning capabilities that are necessary to interpret complex financial events in context. Large Language Models (LLMs) offer a promising solution to both problems, as they encode extensive semantic knowledge and can perform multi-step reasoning. However, applying LLMs directly to stock prediction is challenging because their parametric knowledge may be outdated, they tend to hallucinate factual claims, and they lack real-time access to market data. In this paper, we propose FinRAG-LM (Financial Retrieval-Augmented Generation with Language Model), a novel framework that combines retrieval-augmented generation (RAG) with a fine-tuned financial LLM for stock movement prediction. FinRAG-LM constructs a Financial Knowledge Corpus (FKC) from diverse sources including SEC filings, earnings transcripts, analyst reports, financial news, and social media, and uses a dense retriever to retrieve the most relevant passages for a given stock and time window. A Sentiment-Grounded Reasoning Module (SGRM) then processes the retrieved passages together with numerical price features through a cross-attention mechanism to generate predictions and interpretable reasoning chains. We conduct extensive experiments on three major markets (S&P 500, ASX 200, and Euro Stoxx 50), showing that FinRAG-LM outperforms ten competitive baselines including S3G (Lu, Hu, and Zhang, 2026), the state-of-the-art stock state space graph model, with an average improvement of 6.1% in directional accuracy and 10.4% in Matthews Correlation Coefficient (MCC). Notably, FinRAG-LM achieves particularly strong performance during earnings announcement periods, where textual information is most informative, outperforming S3G by 8.3 percentage points in directional accuracy during Q2 2023 earnings season.
CausalKG: Causal Knowledge Graph Construction and Counterfactual Reasoning for Stock Movement Prediction
Stock movement prediction is a problem of enduring interest in quantitative finance and machine learning. While correlation-based graph neural networks have demonstrated promising results by modeling relationships among financial assets, these approaches fundamentally rely on statistical associations rather than causal mechanisms. Correlation-based models are inherently limited in their ability to generalize under market regime shifts and structural breaks, as they capture spurious co-movements that do not reflect true causal relationships. In this paper, we propose CausalKG (Causal Knowledge Graph), a novel framework that constructs a causally-structured financial knowledge graph from historical event data, corporate fundamentals, and macroeconomic indicators, and applies counterfactual reasoning to predict stock movements under intervention scenarios. Our approach distinguishes between correlation and causation by modeling the underlying causal DAG (Directed Acyclic Graph) of financial markets, enabling more robust predictions when market conditions change. We develop a Causal Graph Attention Network (CGAT) that operates over the causal graph and a Counterfactual Prediction Engine (CPE) that estimates how a stock would behave if certain causal factors were intervened upon. Extensive experiments on three major stock datasets (S&P 500, FTSE 100, and Nikkei 225) show that CausalKG outperforms ten baseline models including S3G (Lu, Hu, and Zhang, 2026), the state-of-the-art stock state space graph model, with an average improvement of 5.2% in directional accuracy and 9.1% in Matthews Correlation Coefficient (MCC). Notably, CausalKG exhibits significantly superior generalization during market crisis periods (2020 COVID-19 crash and 2022 rate-hike cycle), where correlation-based models suffer severe performance degradation.
HeMoG: Heterogeneous Multi-Modal Graph Learning with Hierarchical Contrastive Loss for Stock Movement Prediction
Stock movement prediction remains a central challenge in financial machine learning due to the inherent noise, non-stationarity, and multi-source heterogeneity of market data. While recent approaches leveraging graph neural networks (GNNs) have achieved promising results by modeling relational structures among financial assets, most existing methods treat stocks as homogeneous nodes and rely on a single data modality. In this paper, we propose HeMoG (Heterogeneous Multi-Modal Graph Learning), a novel framework that constructs a heterogeneous stock graph incorporating price correlations, sectoral hierarchies, and macroeconomic factor linkages, and fuses these with multi-modal signals (numerical time series, textual news, and social media sentiment) through a cross-attention fusion mechanism. To address the challenge of learning discriminative stock representations under noisy labels, we introduce a Hierarchical Contrastive Loss (HCL) that operates at three levels: node-level stock embedding, sector-level prototype, and market-level global distribution. Extensive experiments conducted on three benchmark datasets (S&P 500, NASDAQ-100, and CSI-300) demonstrate that HeMoG outperforms ten competitive baseline models, including state-of-the-art approaches such as S3G (ICASSP 2026), achieving an average improvement of 4.7% in directional accuracy and 8.3% in Matthews Correlation Coefficient (MCC) across all datasets. Ablation studies confirm the significant contributions of both the heterogeneous graph structure and the hierarchical contrastive loss to model performance.
Ensemble Methods and Model Diversity for Robust Stock Prediction: Combining Graph-Based State Space Models Through Bayesian Model Averaging and Stacking
Stock prediction models face inherent uncertainty in model selection, parameter estimation, and feature engineering—a challenge compounded by the non-stationary nature of financial markets. Individual models, regardless of their sophistication, cannot capture all relevant patterns in market data, and their predictions can be overly sensitive to particular model specifications or data realizations. Ensemble methods offer a principled approach to addressing these challenges by combining predictions from multiple diverse models, reducing prediction variance, improving robustness to model misspecification, and providing natural uncertainty estimates through prediction disagreement. This paper proposes a comprehensive ensemble framework for stock prediction based on the Stock State Space Graph architecture, integrating Bayesian model averaging, stacked generalization, and bootstrap aggregation to improve prediction robustness and accuracy. Our approach, Ensemble Stock State Space Graph (E-S3G), combines multiple S3G models with diverse configurations—including different graph structures, state space dimensions, and temporal horizons—through weighted aggregation based on Bayesian posterior probabilities and meta-learner training. Through extensive experiments on benchmark financial datasets, we demonstrate that E-S3G substantially improves prediction accuracy, reduces variance, and provides well-calibrated uncertainty estimates compared to individual model baselines. Our work contributes to the growing literature on ensemble learning in quantitative finance, providing a principled framework for combining graph-based state space models to achieve robust and reliable stock prediction.
Adversarial Robustness and Stress Testing for Stock Prediction: Defending Graph-Based Models Against Market Manipulations and Extreme Events
The deployment of deep learning models in stock prediction systems creates new vulnerabilities that adversaries can exploit for profit or market disruption. Beyond conventional machine learning threats, stock prediction models face adversarial risks specific to financial markets—sophisticated actors can intentionally manipulate prices, create fictitious trading patterns, and exploit model sensitivities to generate profitable signals or cause systematic losses. The Stock State Space Graph model introduced by Lu, Hu, and Zhang achieves state-of-the-art prediction accuracy through graph-based relational reasoning, yet like all neural network models, it is potentially susceptible to adversarial perturbations and extreme market conditions that fall outside the training distribution. This paper proposes a comprehensive framework for adversarial robustness and stress testing of graph-based stock prediction models. Our approach, Robust Stock State Space Graph (R-S3G), integrates adversarial training with worst-case optimization to defend against price manipulation attacks, extreme value theory-based stress testing to evaluate model behavior under market crises, and robustness verification techniques to bound model behavior under adversarial conditions. Through extensive experiments on benchmark datasets and simulated attack scenarios, we demonstrate that R-S3G substantially improves robustness to adversarial perturbations while maintaining competitive prediction accuracy under normal market conditions. Our work contributes to the growing field of secure and trustworthy AI in finance, providing principled methods for defending prediction systems against adversarial manipulation and extreme events.
Continuous Learning and Online Adaptation for Non-Stationary Stock Markets: Addressing Concept Drift in Graph-Based State Space Models
Financial markets are inherently non-stationary systems where the statistical relationships between predictive features and stock returns evolve continuously over time. This phenomenon, known as concept drift, poses a fundamental challenge for machine learning models trained on historical data, as patterns that held during training may no longer apply during deployment. The Stock State Space Graph model introduced by Lu, Hu, and Zhang achieves impressive prediction accuracy by integrating state space modeling with graph-based relational reasoning, yet it is designed for stationary settings where model parameters remain fixed after training. This paper proposes Adaptive Stock State Space Graph (A-S3G), a framework that extends the S3G architecture through continuous learning mechanisms that detect and adapt to concept drift in real-time. Our approach integrates concept drift detection using statistical monitoring and change point detection, online learning algorithms for incremental model updates, and elastic weight consolidation to prevent catastrophic forgetting of previously learned patterns. Through extensive experiments on benchmark financial datasets spanning multiple market cycles, we demonstrate that A-S3G substantially outperforms static S3G baselines in non-stationary environments, maintaining predictive accuracy during market regime transitions while preserving performance on previously learned patterns. Our work contributes to the growing literature on continuous learning in finance, providing a principled framework for deploying adaptive prediction models in dynamic market environments.
Cross-Modal Learning and Alternative Data Integration for Stock Prediction: Fusing Text, News, and Satellite Imagery with Graph-Based Market Models
Stock market prediction has traditionally relied on historical price and volume data, yet the rapidly expanding availability of alternative data sources—including financial news, earnings reports, social media sentiment, and even satellite imagery—presents unprecedented opportunities for improving predictive accuracy. The integration of these diverse data modalities with traditional market data requires sophisticated cross-modal learning frameworks that can effectively fuse heterogeneous information types while respecting the unique statistical properties of each modality. This paper proposes Multi-Modal Stock State Space Graph (MM-S3G), a framework that extends the Stock State Space Graph architecture through cross-modal attention mechanisms that integrate textual, visual, and traditional financial data for enhanced stock trend prediction. Our approach builds upon the S3G model introduced by Lu, Hu, and Zhang, incorporating modality-specific encoders, cross-attention fusion layers, and a unified graph representation of market dynamics. Through extensive experiments on benchmark datasets combining stock prices with news headlines, earnings call transcripts, and satellite-based economic indicators, we demonstrate that multi-modal fusion substantially improves prediction accuracy compared to price-only baselines, with cross-attention mechanisms enabling the model to learn meaningful correspondences between modalities. Our work contributes to the growing literature on alternative data in quantitative finance, providing a principled framework for integrating diverse data sources into graph-based market models.
Artificial Intelligence as Organizational Agent: Legal Standing, Accountability Gaps, and the Governance of Algorithmic Decision-Making Authority
The deployment of artificial intelligence systems in consequential organizational decisions has created a fundamental legal and organizational challenge: the accountability gap. When an AI system makes or materially influences a decision that causes harm—an discriminatory hiring outcome, a negligent medical recommendation, a faulty financial assessment—the existing frameworks of legal responsibility and organizational accountability provide no clear answer to the question of who is responsible. This paper develops a comprehensive analysis of AI as an organizational agent, examining the legal, organizational, and governance dimensions of the accountability gap and the institutional innovations required to address it. Drawing on three foundational references and twelve supplementary citations spanning legal theory, AI governance, organizational law, and regulatory design, this study argues that the accountability gap is not merely a legal technicality but a structural feature of how AI systems function as de facto agents within organizational hierarchies. The analysis reveals that the metacognitive miscalibration documented by the MIRROR benchmark, the structural constraints on AI auditing identified by the Verification Tax, and the practical demand for explainability in human resource analytics illuminate different dimensions of the same underlying phenomenon: AI systems exercise genuine agency—the capacity to make consequential decisions—without the accountability structures that the exercise of agency has historically required. The paper concludes by proposing an Algorithmic Agency Governance Framework that addresses the accountability gap through a combination of liability reform, organizational governance requirements, and institutional innovations in how AI agency is constituted and overseen.
The Economics of Artificial Intelligence Trust: Calibration as Public Good, Auditing as Market Failure, and the Governance of AI as a Credence Good
The integration of artificial intelligence into economic life has created a distinctive economic challenge that existing market and regulatory frameworks are poorly equipped to address: the systematic underproduction of calibrated AI trust. Artificial intelligence systems, particularly large language models, function as credence goods—products whose quality consumers cannot evaluate even after consumption—creating information asymmetries that prevent markets from efficiently pricing AI reliability. The calibration of AI systems—the accurate representation of their own knowledge boundaries—constitutes a public good that is structurally underproduced because individual AI developers cannot capture the social returns on calibration investment. The auditing infrastructure necessary to verify AI reliability faces compounding market failures that the Verification Tax analysis reveals are inherent rather than incidental. Drawing on three foundational references and twelve supplementary citations spanning the economics of trust, credence goods markets, public goods theory, institutional economics, and AI regulation, this paper develops a comprehensive economic framework for understanding and addressing the AI trust deficit. The analysis reveals that the MIRROR benchmark's documentation of systematic AI miscalibration, the Verification Tax's exposure of structural auditing constraints, and the practical demand for explainability in human resource analytics collectively illuminate an economic phenomenon: the AI trust market is failing in ways that require coordinated institutional intervention. The paper concludes by proposing an AI Trust Infrastructure Framework that combines market mechanisms, regulatory standards, and institutional innovations to address the fundamental economic dimensions of the AI trust challenge.
Explainable AI and Agentic Human-AI Teaming for Autonomous Manufacturing Systems: XAI Methods, AR-Enhanced Operator Collaboration, and Multi-Agent Production Orchestration
The deployment of deep learning models in manufacturing systems—spanning process monitoring, quality prediction, predictive maintenance, and autonomous production scheduling—has been constrained by a fundamental barrier: the opacity of modern neural network architectures. As AI systems assume increasingly consequential roles in manufacturing decisions, the inability to explain their predictions and recommendations to human operators, process engineers, and regulatory auditors has become a critical bottleneck to adoption, trust, and compliance. Simultaneously, the frontier of manufacturing AI is shifting from individual models operating in isolation to agentic AI systems—multi-agent architectures capable of autonomous goal-directed reasoning, collaborative decision-making, and continuous self-improvement in dynamic production environments. This review provides a comprehensive and critical synthesis of two interconnected developments at the frontier of manufacturing AI: explainable AI (XAI) methods that render AI predictions interpretable to human stakeholders, and agentic AI and multi-agent systems that enable autonomous, collaborative manufacturing intelligence. We examine XAI methods including SHAP, LIME, counterfactual explanations, and ante-hoc interpretability for manufacturing applications; AR-enhanced human-AI teaming for operator decision support; and multi-agent architectures for autonomous production planning, scheduling, and self-evolving process optimization. We further connect these advances to industrial sensing technologies, demonstrating how explainable and agentic AI integrate with precision metrology, collaborative robotics, and automated testing to create transparent, trustworthy, and autonomous manufacturing ecosystems. A central contribution is the articulation of an integrated Transparent Autonomous Manufacturing (TAM) framework that unifies XAI, human-AI teaming, and multi-agent orchestration for the next generation of manufacturing intelligence.