https://arxiv.org/api/g7ilTNKoAipP7/BY693jYl9sijY 2026-09-28T21:56:58Z 15 488 0 http://arxiv.org/abs/2609.23421v1 2026-09-20T07:37:30Z

Standard data center sustainability metrics, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Carbon Usage Effectiveness (CUE), measure a facility's resource use and emissions intensity, normalized to IT energy use, without directly representing local resource scarcity, infrastructure capacity, or social footprint. This gap has become politically consequential. In the first quarter of 2026 alone, local opposition delayed or canceled roughly $130 billion in projects across the United States, driven overwhelmingly by recurring concerns over water use, power demand, infrastructure capacity, and transparency rather than internal efficiency, matching the total for all of 2025 [11]. We propose a five-category local impact audit framework covering efficiency, water stewardship, carbon and renewables, regulatory compliance, and local disclosure. The framework is designed for recurring quarterly assessment and independent verification against public records. We illustrate its application using publicly available data from three Illinois facilities that are currently at the center of local policy disputes, and we examine the data-access barriers that constrain independent verification. We position this framework as both a research contribution and a practical instrument for county-level policymakers evaluating data center permitting and moratorium decisions.

2026-09-20T07:37:30Z Sharifa Sultana Syed Ishtiaque Ahmed http://arxiv.org/abs/2609.22712v1 2026-09-19T02:47:37Z

Agentic AI systems built on large language models can plan over multiple steps, use external tools, retain information in memory, and coordinate with other agents. These capabilities make them more useful than static language models, but they also introduce new security and operational risks. Untrusted content from websites, emails, documents, and databases can enter the same context as system instructions; persistent memory can carry compromised information across sessions; and access to external tools can turn an incorrect model response into a consequential real-world action. This article reviews the trustworthiness of agentic AI across five interconnected dimensions: safety and robustness, alignment and human oversight, transparency and auditability, privacy and data governance, and regulatory compliance. It organizes key failure modes, including indirect prompt injection, backdoor triggers, goal misgeneralization, memory contamination, and cross-session data leakage, into a unified taxonomy. It also examines major mitigation approaches, such as instruction hierarchies, context isolation, spotlighting, process-based supervision, constrained tool use, and privacy-preserving memory, while distinguishing techniques supported by empirical evidence from those that remain largely conceptual. Building on this analysis, we introduce the Trustworthy Agent Development Lifecycle (TADL), a six-phase framework covering specification, design, training, evaluation, deployment, and monitoring. For each phase, TADL identifies relevant trust activities, expected evidence, and risk-based decision gates. Although TADL has not yet been empirically validated, it provides a structured foundation for developing and evaluating more secure and accountable agentic systems. The article concludes by identifying gaps in current benchmarks and outlining priorities for future research.

2026-09-19T02:47:37Z Fayeq Jeelani Syed Rehan Ahmad Ali Al Bataineh Aakriti Adhikari http://arxiv.org/abs/2609.13642v1 2026-09-12T01:31:35Z

We argue that a recurring failure in the evaluation of deployed AI systems occurs when data collected for operational monitoring or regulatory compliance are interpreted as if they were designed for comparative evaluation. Automated driving provides a concrete example of this problem. U.S. disengagement and crash-reporting regimes produce valuable operational evidence, but differences in reporting scope, exposure, deployment domain, event capture, and comparator construction limit the safety claims that can be supported from these measurements alone. We frame this issue as a measurement-validity problem in AI evaluation rather than as a transportation-specific data limitation. We argue that comparative claims about deployed AI systems require alignment between the intended capability, measured outcome, exposure opportunity, deployment domain, data-generation process, and evaluation comparator. Using automated-driving safety evaluation as a case study, we propose an evaluation contract that makes these assumptions explicit before operational data are interpreted as evidence of comparative performance. The broader implication is that data useful for monitoring deployed AI systems are not automatically valid benchmarks for evaluating them.

2026-09-12T01:31:35Z Hung-Yu Lin Xingran Huang Qiming Guo Jinwen Tang http://arxiv.org/abs/2609.10430v1 2026-09-09T16:43:26Z

Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present Glyph, a production system that frames two coupled problems, column description generation and column type annotation for data classification, as cooperating LLM agents orchestrated as stateful graphs. The Descriptor grounds generation in the pipeline source code that produces each column, retrieved on demand from an enterprise GitHub via a reasoning--acting tool loop (active Retrieval-Augmented Generation). The Tagger assigns labels from a governed 275-leaf Data Classification Ontology by running three complementary strategies in parallel (a description tagger, a line-of-business regex tagger, and a metadata tagger backed by a fine-tuned contrastive encoder over a vector database), then fuses their ranked outputs with Reciprocal Rank Fusion (RRF). We fine-tune a 6-layer MiniLM metadata encoder with an in-batch contrastive objective, lifting same-tag retrieval on an in-distribution held-out split from NDCG@10 0.55 to 0.92 (MAP@100 $0.19 \rightarrow 0.90$) relative to the stock base encoder. We report end-to-end multi-label tagging quality under a recall-weighted F2 objective across three evaluation groups, an ablation isolating each strategy and the RRF fusion, and the engineering decisions that distinguish Glyph from prior column-type-annotation work and from commercial value/regex sensitivity scanners: value-free and code-grounded design, per-tag provenance, and graceful degradation. Together these make multi-agent LLM cataloging auditable and operable as a production service.

2026-09-09T16:43:26Z Kostia Kudriavtsev Parvez Rafi Sha Sundaram http://arxiv.org/abs/2609.00171v1 2026-08-31T18:02:10Z

The increasing digitalization of industrial infrastructure and the convergence of information technology (IT) and operational technology (OT) have expanded the cyberattack surface of industrial systems. To address the growing complexity of cyber threats, artificial intelligence (AI) and machine learning (ML) techniques are increasingly deployed within industrial cybersecurity operations, particularly in Security Operations Centers (SOCs). While these approaches improve anomaly detection, threat analysis, and automated response, their opaque decision-making presents challenges for operational trust, regulatory compliance, and incident response. EXplainable Artificial Intelligence (XAI) has emerged as a promising paradigm to improve the transparency and interpretability of AI-driven cybersecurity systems and decisions. This paper provides a comprehensive review of XAI techniques in industrial cybersecurity, focusing on industrial SOC environments and operational security workflows. We examine the role of AI in industrial SOC workflows, the types of operational data leveraged in industrial environments, and the benefits and limitations of AI-based threat detection. We then review major families of XAI approaches, including feature attribution methods, surrogate models, rule-based explanations, and visualization techniques, and analyze their applicability to industrial use cases. We further discuss the operational, regulatory, and safety requirements that distinguish industrial systems from traditional IT environments. Key challenges are examined, including limited labeled datasets, model reliability, explainability-performance tradeoffs, and the integration of XAI tools into SOC workflows. Finally, we identify open research directions and opportunities for developing trustworthy, operationally viable, and domain-specific XAI-enabled cybersecurity solutions for industrial environments.

2026-08-31T18:02:10Z 31 pages, 6 figures Amr S. Mohamed Charlotte Fritz Ahmad Mohammad Saber Yiqun Ma Ratinder Kaur Mohammed Al-Darwbi Daniela Friedrich Deepa Kundur http://arxiv.org/abs/2608.30419v1 2026-08-31T08:17:01Z

Healthcare workforce scheduling is an NP-hard optimization problem requiring simultaneous satisfaction of labor regulations, coverage requirements, employee preferences and cost objectives. Existing approaches (genetic algorithms, integer programming, constraint programming) model 6-12 constraints at shift-level granularity and cannot guarantee regulatory compliance. They also lack support for multi-role, multi-skill heterogeneity, mandatory break scheduling with midpoint control, acuity-weighted workload equity, sub-shift granularity, inter-week stability, and cross-midnight shifts. This paper presents CP-SAT: a Constraint Programming formulation for multi-role, multi-skill healthcare scheduling. CP-SAT enforces 14 hard constraints guaranteeing zero regulatory violations, while optimizing 15 soft objectives via a unified weighted penalty function. Contributions include a shift-window decomposition enabling break scheduling with centrality control, acuity-weighted workload equity, multi-granularity resolution from 15 minutes to 1 day, inter-week stability, and grid-offset preprocessing mapping cross-midnight shifts into a single scheduling day without solver changes. CP-SAT is evaluated on 18 instances: five synthetic hospital units (10-33 nurses), 10 INRC-II benchmarks (5-80 nurses, up to 8-week horizons) and 3 NRP-23 compatible instances (10-25 nurses) with cross-midnight Night shifts. Results: zero hard-constraint violations across all 18 instances by construction; proven optimality on INRC-II n005w4 (objective 118, gap 0.0%, 104s); feasible schedules scaling to 179,800 variables and 351,425 constraints (80 nurses); service quality improved 50-67% over MOGA; and model size scaling near-linearly at approximately 4,400 variables per employee. The formulation enforces 29 total constraints (14 hard, 15 soft), nearly three times the industry average.

2026-08-31T08:17:01Z 13 pages, 10 tables. Accepted at MODeM Workshop, IJCAI-ECAI 2026. Extends arXiv:2607.05177 Vipul Patel Anirudh Deodhar Dagnachew Birru http://arxiv.org/abs/2608.26706v2 2026-09-23T15:10:11Z

Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance, attributes that standard single-pass RAG systems lack. We take a step toward this goal with Self-Improving RAG, a framework that decomposes document QA into three specialized agents (Retrieval, Reasoning, and Judge) coordinated by an orchestrator with feedback-driven self-correction. When the Judge Agent scores an answer below a dynamic threshold, the system triggers retry with escalated strategies: broader retrieval, more careful prompting, and relaxed acceptance criteria. We evaluate on FinanceBench (SEC filing QA), where Self-Improving RAG achieves 86% oracle-guided accuracy (measuring agreement with gold answers) with a 36.4% Lazarus Rate, recovering nearly 4 in 10 initially incorrect answers through targeted retry. A key finding is that a fixed retrieval pipeline with judge-driven retry achieves strong results without dynamic routing, providing full interpretability. Every decision is logged with confidence scores, enabling the audit trails required for regulated financial applications.

2026-08-27T07:01:41Z 17 pages, 2 figures. Accepted at the ICLR 2026 Workshop on Advances in Financial AI Junjie Xiong Shawheen Ghezavat Aum Hirpara http://arxiv.org/abs/2608.21803v1 2026-08-22T06:52:09Z

As machine learning (ML) models are increasingly deployed in high-stakes environments, explainable AI (XAI) methods like SHAP and LIME have become essential for regulatory compliance and trust. However, the current auditing paradigm relies on an implicit "chain of trust" where third-party auditors are assumed to be trusted. Recent research demonstrates that this assumption is flawed and adversarial auditors can manipulate XAI explanations through manipulation attacks such as output shuffling or scaffolding out-of-distribution (OOD) to conceal model biases while maintaining high prediction accuracy aiming for fairwashed explanation. In this paper, we introduce a novel defense framework, ExplainGuard, that leverages a Zero-Trust architecture (ZTA) design to be incorporated within the XAI explanation supply chain and ensures the integrity of the generated explanation. This framework would help us to replace the ambiguous default assumption of "auditor is trustworthy," with a continuous "verify-then-trust" approach. Our design architecture establishes a Policy Decision Point (PDP) that enforces three distinct pillars of verification before any explanation is released to the user: (1) asset integrity via behavioral fingerprint to detect model substitution, (2) semantic validity using axiomatic consistency checks to reject mathematically impossible explanations, and (3) feature faithfulness verification utilizing a ranking stability approach with minimal computational overhead. Finally, we evaluate how ExplainGuard can effectively neutralize state- of-the-art explanation manipulation attacks while transforming the auditing process into a verifiable operation.

2026-08-22T06:52:09Z 9 pages, 2 figures. Accepted at the IEEE Cybersecurity Awareness and Research Symposium 2026 (IEEE CARS 2026) Maraz Mia Shovan Roy Mir Mehedi A. Pritom Maanak Gupta http://arxiv.org/abs/2608.20874v1 2026-08-21T08:51:39Z

Reliable traffic sign detection is a prerequisite for the global deployment of autonomous driving systems, where regulatory compliance and road safety depend on perceiving signs correctly across regions, ranges, and weather conditions. Despite recent progress, vision-based methods continue to face three fundamental limitations: poor cross-regional generalization due to high diversity across countries, degraded performance on small-object detection at long ranges (traffic signs occupy as little as $10{\times}10$ pixels at 200m), and fragile temporal tracking under the strongly non-linear perspective distortion that occurs as a vehicle approaches a sign. In this paper, we address the problem of robust, long-range, region-agnostic traffic sign perception by combining camera and Light Detection and Ranging (LiDAR) sensing. We present a multi-modal detection framework whose Intensity-Aware Deformable Fusion module aligns retro-reflective LiDAR cues with camera features, anchoring detection on geometric invariants rather than region-specific visual appearance. We further introduce a dual motion-model tracker that explicitly accounts for non-linear perspective transformations during vehicle approach, substantially improving temporal consistency over linear motion assumptions. Additionally, we develop a semantic attribute classification pipeline that estimates occlusion level, readability, sign embeddedness, and road relevance, providing actionable context to downstream planning. Extensive evaluation on our dataset, spanning 60+ countries and 2,500+ hours of driving data, shows that the proposed pipeline achieves an Object Miss Ratio (OMR) of 0.49% across 221,068 evaluation sequences, demonstrating globally generalizable traffic sign perception in commercial-grade autonomous driving systems.

2026-08-21T08:51:39Z Meda Lazar Sourab Sridhar Shashwata Gupta Alexandra Tripcea Varun Ravi Senthil Yogamani http://arxiv.org/abs/2608.20440v2 2026-09-07T18:39:21Z

Classification of edible oils in processed foods is important for food quality, fraud prevention, and regulatory compliance. This study develops a Mutually Exclusive, Collectively Exhaustive framework integrating spectral organization, interpretable classification, Physics-Informed Artificial Intelligence (PI-AI), and Frugal AI-based feature reduction. Five edible oils were analyzed in pure form and within a fried-potato-chip matrix using t-SNE, K-means clustering, Decision Trees, and Non-Negative Least Squares (NNLS)-based spectral decomposition. Unsupervised analyses showed stronger class organization and separability in pure oils, while food-matrix effects caused substantial spectral overlap. Decision Trees achieved 100% classification accuracy for pure oils using only four Raman variables from 1866 spectral features. These variables represented only 0.21% of the available spectral information while retaining perfect test-set performance. Two variables associated with lipid unsaturation (about 1650 cm-1) and hydrocarbon-chain organization (about 1127 cm-1) remained important after NNLS matrix correction. Their combined contribution increased from 50% in pure oils to about 62% and 89% in paper-subtracted and paper-plus-potato-subtracted datasets, respectively. NNLS-based PI-AI improved food-matrix classification by separating oil signatures from paper and potato contributions. Optimized post-pruned models achieved nearly 80% test accuracy using only five and four Raman variables, respectively. The four-feature representation reduced the data footprint by 99.44% without loss of accuracy. These findings demonstrate that Raman-based oil identification can use compact, physically meaningful, and interpretable spectral representations, supporting Frugal AI, Edge AI, portable sensing, and embedded food-quality monitoring.

2026-08-20T14:07:32Z 46 pages, 11 figures, 2 tables, 1 supplementary table, 9 supplementary figures Amrita Shaw Chandrasekar S. N. Sai Muthukumar V. Jhinuk Gupta Deepak L. N. Kallepalli http://arxiv.org/abs/2608.19509v1 2026-08-19T23:54:30Z

Complying with the EU Cyber Resilience Act (CRA) is a resource-intensive challenge for SMEs due to the complexity of cybersecurity conformity assessment. Yet, it is essential for demonstrating regulatory compliance and ensuring product security and resilience. To address this, we introduce an automated framework for generating Assurance Cases (ACs) using an agentic Retrieval-Augmented Generation grounded in a formal Claim-Argument-Evidence logic. By systematically mapping technical documentation requirements, the framework streamlines the generation of certification evidence. We validate our approach on a case study of Catalink's PATROLIoT wildfire monitoring system, where the agentic RAG generated 70 ACs with high grounding density (~4.4 artefacts per AC). The proposed Natural Language Inference (NLI) evaluator achieves 0.88 accuracy, which provides robust evidence grounding and traceability, while expert-validated plausibility (3.06) supports interpretable justifications. For practitioners, this work provides a scalable, interpretable approach for automating mandatory CRA conformity assessments, reducing manual effort while maintaining transparent decision support.

2026-08-19T23:54:30Z 6 pages, accepted at ISSRE 2026 (Industry track) Fariz Ikhwantri Iker Lasa Ojanguren Dusica Marijan Maria I. Maslioukova José Arias Marin Pavlos Kosmides http://arxiv.org/abs/2608.17145v1 2026-08-17T21:29:22Z

Received wisdom on payments infrastructure strongly supports the custodial, account-based model as a necessity for transaction integrity, auditability and verification; the set of fundamental primitives for regulated digital money exchange, the argument goes, necessitates designated identifiable entities that store and process credentials, perform KYC, and ultimately act as the 'single version of the truth' for compliance remediation and, most important, AML. In this paper, we propose this is not the case, by arguing that non-custodial, cash-like digital assets can embody such capabilities, in an arguably more secure manner. To that end, we present a reference architecture and core protocol rules for digital-value-exchange systems that preserve meaningful user privacy while enabling strong auditability. The protocol defines the conditions under which digital asset creation, transfer, and redemption are valid. The architecture specifies the allocation of actors, roles and components through which these rules operate, enabling independent verification of transaction compliance with applicable norms. Building upon the Unforgeable, Stateful, Oblivious (USO) asset model of Goodell et al., regulatory compliance data are embedded directly into the asset state as cryptographically signed attestations issued by independent entities. A transfer is valid only upon satisfaction of applicable compliance predicates and inclusion of the resulting signature within the asset state. Compliance enforcement is thus performed at the protocol level rather than through institutional custody or identity-based account control. We conclude that our proposed model can successfully interface with existing payment systems, making it possible to integrate non-custodial, compliance-verified transactions with legacy financial infrastructure.

2026-08-17T21:29:22Z 32 pages, 4 figures Santiago De Simone Geoffrey Goodell Georgios Samakovitis http://arxiv.org/abs/2608.16852v1 2026-08-17T17:37:07Z

Regulatory compliance monitoring in deployed language models is increasingly implemented as a legal and audit control, checking model outputs against written rules spanning data protection, healthcare, financial regulation, and platform policy. Such monitoring is meaningful only if a detector's verdict depends on the stated rule rather than on surface features of the scenario. We show this condition fails across the current class of compliance detectors, a failure we call rule blindness. Deleting, permuting, or substituting the governing rule leaves detection accuracy unchanged for every guard and activation probe we test, including a policy-conditioned guard that correctly cites the governing clause yet barely changes its verdict when that clause is swapped for its permissive counterpart. A purpose-built benchmark crossing two rules with two scenarios, so that neither alone predicts the label, confirms the failure under a design no prior benchmark rules out, and shows that step by step reasoning, not any fast detector we test, is what escapes it. Auditing at scale requires a retraining-free detector, so we introduce the Internal Compliance Score (ICS): a training-free activation readout calibrated from ten labelled pairs and scored by a single projection. We hold ICS to the same scrutiny as the guards it audits: a pre-registered criterion for beating trivial baselines is not met, and a bag-of-words model matches its pooled generalisation exactly. It remains useful because it is inexpensive, letting us audit four deployed guard models, an 8B zero-shot judge, and thirteen benchmarks, and it raises the mechanically verified pass rate when used to rank candidate responses, though an adaptive white-box attack removes this gain. We release the counterfactual protocol and crossed-rule benchmark so rule blindness can be tested in future probe and guard claims.

2026-08-17T17:37:07Z Saisab Sadhu Aadit Sengupta Vinay Kumar Sankarapu Pratinav Seth http://arxiv.org/abs/2608.15276v1 2026-08-15T15:19:10Z

With the rise of decentralized finance (DeFi), cross-chain transactions, transfers of assets across different blockchain networks, face a fundamental conflict between user privacy and regulatory compliance. Unlike single-chain systems, cross-chain environments must balance privacy and auditability across heterogeneous architectures. Existing solutions, from transparent ledgers to anonymous cryptocurrencies, fail to reconcile these two requirements, hindering regulatory adoption. This research proposes an auditable cross-chain framework that integrates three building blocks. First, zero-knowledge proofs (ZKPs) verify transaction compliance (e.g., amount non-negativity, signature validity) without revealing transaction details. Second, a light-client mechanism enables trust-minimized cross-chain verification without relying on third-party relayers. Third, a threshold view-key mechanism based on distributed key generation (DKG) ensures that audit access is granted only to authorized entities under legal triggers such as the FATF Travel Rule and MiCA Regulation. For cross-border investigations, the framework adheres to national laws and the EU Directive on Mutual Legal Assistance. This work systematically combines ZKPs, threshold cryptography, and light-client verification into an auditable, privacy-preserving cross-chain protocol. It contributes to Regulatory Technology (RegTech) and provides a viable path toward compliant, interoperable decentralized finance.

2026-08-15T15:19:10Z 18 pages, 1 figure Huiheng Li Kainuo Feng Jiahao Ding Ziqi Ma http://arxiv.org/abs/2608.12424v2 2026-08-14T14:56:40Z

This study focuses on developing an AI-supported prototype for multiperspective interest rate forecasting that combines classical econometric models with modern artificial intel-ligence methods. Tested in a major European bank, the system enables more precise and flexible prediction of interest rate developments, supporting strategic decision-making in Asset-Liability Management (ALM). It integrates topic modeling, sentiment analysis, econometric forecasting, and market-based analyses within an interactive platform. Leveraging AI to analyze large volumes of financial documents and market data enables the identification of monetary policy trends and sentiment signals at an early stage. The core econometric model is a Bayesian vector autoregression (BVAR) that enables simulation-based scenario analyses to evaluate economic developments from multiple perspectives. The system's innovation lies in its integration of several forecasting approaches that consolidate previously separate information sources and present them transparently and interpretably. Financial analysts and risk managers thus gain a better basis for making decisions, allowing them to assess interest rate risks more accurately and manage market movements more proactively. While the prototype demonstrates how AI can transform interest rate management in banking, further development is required to optimize real-time data integration and regulatory compliance. Even at this stage, the study shows that multi-perspective, AI-driven forecasting provides substantial added value for banks by increasing transparency, strengthening evidence-based decision-making, and improving risk management.

2026-08-12T10:29:38Z Ekkehardt Bauer Dirk Holländer David Scholz Linus Wolff Christoph Ostermair Kyrillus Aiad Joachim Hasebrook