OpenRaiser/Intern-Atlas huggingface dataset:
Cross-Border Data Security and Privacy Risks in Large Language Models and IoT Systems
Venue: arXiv cs.CR 2026
Year: 2026

The reliance of Large Language Models and Internet of Things systems on massive, globally distributed data flows creates systemic security and privacy challenges. When data traverses borders, it becomes subject to conflicting legal regimes, such as the EU's General Data Protection Regulation and China's Personal Information Protection Law, compounded by technical vulnerabilities like model memorization. Current static encryption and data localization methods are fragmented and reactive, failing to provide adequate, policy-aligned safeguards. This research proposes a Jurisdiction-Aware, Privacy-by-Design architecture that dynamically integrates localized encryption, adaptive differential privacy, and real-time compliance assertion via cryptographic proofs. Empirical validation in a multi-jurisdictional simulation demonstrates this architecture reduced unauthorized data exposure to below five percent and achieved zero compliance violations. These security gains were realized while maintaining model utility retention above ninety percent and limiting computational overhead. This establishes that proactive, integrated controls are feasible for secure and globally compliant AI deployment.
A Framework for Kara-Kichwa Data Sovereignty in Latin America and the Caribbean
Venue: arXiv cs.CY 2026
Year: 2026

In the high-altitude territories of the Andean-Amazonian-Atlantic pathway, data is not merely a digital resource but an extension of Khipu Panaka, the genealogical and relational memory of the Kara-Kichwa Republics. This perspective paper introduces the Kara-Kichwa Data Sovereignty Framework, a living instrument designed to counteract the "intellectual gentrification" and systemic invisibility of Andean Indigenous Peoples in global data ecosystems. Grounded in Indigenous legal systems thinking, the framework codifies five customary pillars, Kamachy (Self-determination), Ayllu-llaktapak kamachy (Collective Authority), Tantanakuy (Relational Accountability), Willay-panka-tantay (Ancestral Memory), and Sumak Kawsay (Biocultural Ethics), to govern the lifecycle of data from generation to expiration.
Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency
Venue: arXiv cs.LG 2026
Year: 2026

Research in machine learning has questioned whether increases in training token counts reliably produce proportional performance gains in large language models. Building on prior work introducing an energy-aware parameter efficiency metric, this study empirically examines the effects of increasing training token counts under fixed hardware and training conditions. The significance of this work lies in the explicit integration of power consumption and execution duration, as reflected by the power sampling frequency, into token-scale analysis. This addresses a gap in prior studies emphasizing performance outcomes while underrepresenting computational and energy costs. Using a repeated-measures experimental design on a constant GPU instance with an identical model architecture, optimizer settings, and epoch counts, a 1.1-billion-parameter TinyLlama model was trained at three token counts (500K, 1M, and 2M). While conventional performance metrics exhibited inconsistent or diminishing returns across token scales, the inclusion of power consumption and execution duration revealed a strictly monotonic decline in training efficiency as token count increased. Repeated-measures ANOVA demonstrated a strong effect of token count on parameter efficiency, with all pairwise comparisons remaining significant following Bonferroni correction. These findings indicate that increases in training token counts may be energetically inefficient even when marginal performance improvements are observed, underscoring the importance of efficiency-aware evaluation in large language model training.
Learning Password Best Practices Through In-Task Instruction
Venue: arXiv cs.HC 2026
Year: 2026

Users often make security- and privacy-relevant decisions without a clear understanding of the rules that govern safe behavior. We introduce pedagogical friction, a design approach that introduces brief, instructional interactions at the moment of action. We evaluate this approach in the context of password creation, a task with clear, objective quality criteria and broad familiarity. We conducted a randomized repeated-measures study with 128 participants across four interface conditions that varied the depth and interactivity of guidance. We assessed three outcomes: (1) rule compliance in a subsequent password task without guidance, (2) accuracy on survey questions matched to the rules shown earlier, and (3) behavior-knowledge alignment, which captures whether participants who correctly followed a rule also recognized it on the survey. Across all guided conditions, participants corrected most rule violations in the follow-up task, achieved moderate accuracy on matched rule questions, and showed high behavior-knowledge alignment. These results support pedagogical friction as a lightweight and generalizable intervention for security- and privacy-critical interfaces.
SafePro: Evaluating the Safety of Professional-Level AI Agents
Venue: arXiv cs.AI 2026
Year: 2026

Large language model-based agents are rapidly evolving from simple conversational assistants into autonomous systems capable of performing complex, professional-level tasks in various domains. While these advancements promise significant productivity gains, they also introduce critical safety risks that remain under-explored. Existing safety evaluations primarily focus on simple, daily assistance tasks, failing to capture the intricate decision-making processes and potential consequences of misaligned behaviors in professional settings. To address this gap, we introduce \textbf{SafePro}, a comprehensive benchmark designed to evaluate the safety alignment of AI agents performing professional activities. SafePro features a dataset of high-complexity tasks across diverse professional domains with safety risks, developed through a rigorous iterative creation and review process. Our evaluation of state-of-the-art AI models reveals significant safety vulnerabilities and uncovers new unsafe behaviors in professional contexts. We further show that these models exhibit both insufficient safety judgment and weak safety alignment when executing complex professional tasks. In addition, we investigate safety mitigation strategies for improving agent safety in these scenarios and observe encouraging improvements. Together, our findings highlight the urgent need for robust safety mechanisms tailored to the next generation of professional AI agents.
Reinforcement Learning-Guided Dynamic Multi-Graph Fusion for Evacuation Traffic Prediction
Venue: arXiv cs.LG 2026
Year: 2026

Real-time traffic prediction is critical for managing transportation systems during hurricane evacuations. Although data-driven graph-learning models have demonstrated strong capabilities in capturing the complex spatiotemporal dynamics of evacuation traffic at a network level, they mostly consider a single dimension (e.g., travel-time or distance) to construct the underlying graph. Furthermore, these models often lack interpretability, offering little insight into which input variables contribute most to their predictive performance. To overcome these limitations, we develop a novel Reinforcement Learning-guided Dynamic Multi-Graph Fusion (RL-DMF) framework for evacuation traffic prediction. We construct multiple dynamic graphs at each time step to represent heterogeneous spatiotemporal relationships between traffic detectors. A dynamic multi-graph fusion (DMF) module is employed to adaptively learn and combine information from these graphs. To enhance model interpretability, we introduce RL-based intelligent feature selection and ranking (RL-IFSR) method that learns to mask irrelevant features during model training. The model is evaluated using a real-world dataset of 12 hurricanes affecting Florida from 2016 to 2024. For an unseen hurricane (Milton, 2024), the model achieves a 95% accuracy (RMSE = 293.9) for predicting the next 1-hour traffic flow. Moreover, the model can forecast traffic flow for up to next 6 hours with 90% accuracy (RMSE = 426.4). The RL-DMF framework outperforms several state-of-the-art traffic prediction models. Furthermore, ablation experiments confirm the effectiveness of dynamic multi-graph fusion and RL-IFSR approaches for improving model performance. This research provides a generalized and interpretable model for real-time evacuation traffic forecasting, with significant implications for evacuation traffic management.
Characterising Toxicity in Generative Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

In recent years, the advent of the attention mechanism has significantly advanced the field of natural language processing (NLP), revolutionizing text processing and text generation. This has come about through transformer-based decoder-only architectures, which have become ubiquitous in NLP due to their impressive text processing and generation capabilities. Despite these breakthroughs, language models (LMs) remain susceptible to generating undesired outputs: inappropriate, offensive, or otherwise harmful responses. We will collectively refer to these as ``toxic'' outputs. Although methods like reinforcement learning from human feedback (RLHF) have been developed to align model outputs with human values, these safeguards can often be circumvented through carefully crafted prompts. Therefore, this paper examines the extent to which LLMs generate toxic content when prompted, as well as the linguistic factors -- both lexical and syntactic -- that influence the production of such outputs in generative models.
Evaluating Accounting Reasoning Capabilities of Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Large language models are transforming learning, cognition, and research across many fields. Effectively integrating them into professional domains, such as accounting, is a key challenge for enterprise digital transformation. To address this, we define vertical domain accounting reasoning and propose evaluation criteria derived from an analysis of the training data characteristics of representative GLM models. These criteria support systematic study of accounting reasoning and provide benchmarks for performance improvement. Using this framework, we evaluate GLM-6B, GLM-130B, GLM-4, and OpenAI GPT-4 on accounting reasoning tasks. Results show that prompt design significantly affects performance, with GPT-4 demonstrating the strongest capability. Despite these gains, current models remain insufficient for real-world enterprise accounting, indicating the need for further optimization to unlock their full practical value.
Privacy-Preserving Data Processing in Cloud : From Homomorphic Encryption to Federated Analytics
Venue: arXiv cs.CR 2026
Year: 2026

Privacy-preserving data processing refers to the methods and models that allow computing and analyzing sensitive data with a guarantee of confidentiality. As cloud computing and applications that rely on data continue to expand, there is an increasing need to protect personal, financial and healthcare information. Conventional centralized data processing methods expose sensitive data to risk of breaches, compelling the need to use decentralized and secure data methods. This paper gives a detailed review of privacy-saving mechanisms in the cloud platform, such as statistical approaches like differential privacy and cryptographic solutions like homomorphic encryption. Federated analytics and federated learning, two distributed learning frameworks, are also discussed. Their principles, applications, benefits, and limitations are reviewed, with roles of use in the fields of healthcare, finance, IoT, and industrial cases. Comparative analyses measure trade-offs in security, efficiency, scalability, and accuracy, and investigations are done of emerging hybrid frameworks to provide better privacy protection. Critical issues, including computational overhead, privacy-utility trade-offs, standardization, adversarial threats, and cloud integration are also addressed. This review examines in detail the recent privacy-protecting approaches in cloud computation and offers scholars and practitioners crucial information on secure and effective solutions to data processing.
FO-Complete Program Verification for Heap Logics
Venue: arXiv cs.LO 2026
Year: 2026

We develop the first two heap logics that have implicit heaplets and that admit FO-complete program verification. The notion of FO-completeness is a theoretical guarantee that all theorems that are valid when recursive definitions are interpreted as fixpoint definitions (instead of least fixpoint) are guaranteed to be eventually proven by the system. The logics we develop are a frame logic ($\textit{FL}$) and a separation logic ($\textit{SL-FL}$) that has an alternate semantics inspired by frame logic. We show verification condition generation for FL that is amenable to FO-complete reasoning using quantifier instantiation and SMT solvers. We show $\textit{SL-FL}$ can be translated to FL in order to obtain FO-complete reasoning. We implement tools that realize our technique and show the expressiveness of our logics and the efficacy of the verification technique on a suite of benchmarks that manipulate data structures.
Vextra: A Unified Middleware Abstraction for Heterogeneous Vector Database Systems
Venue: arXiv cs.DB 2026
Year: 2026

The rapid integration of vector search into AI applications, particularly for Retrieval Augmented Generation (RAG), has catalyzed the emergence of a diverse ecosystem of specialized vector databases. While this innovation offers a rich choice of features and performance characteristics, it has simultaneously introduced a significant challenge: severe API fragmentation. Developers face a landscape of disparate, proprietary, and often volatile API contracts, which hinders application portability, increases maintenance overhead, and leads to vendor lock-in. This paper introduces Vextra, a novel middleware abstraction layer designed to address this fragmentation. Vextra presents a unified, high-level API for core database operations, including data upsertion, similarity search, and metadata filtering. It employs a pluggable adapter architecture to translate these unified API calls into the native protocols of various backend databases. We argue that such an abstraction layer is a critical step towards maturing the vector database ecosystem, fostering interoperability, and enabling higher-level query optimization, while imposing minimal performance overhead.
Logic-Driven Semantic Communication for Resilient Multi-Agent Systems
Venue: arXiv cs.MA 2026
Year: 2026

The advent of 6G networks is accelerating autonomy and intelligence in large-scale, decentralized multi-agent systems (MAS). While this evolution enables adaptive behavior, it also heightens vulnerability to stressors such as environmental changes and adversarial behavior. Existing literature on resilience in decentralized MAS largely focuses on isolated aspects, such as fault tolerance, without offering a principled unified definition of multi-agent resilience. This gap limits the ability to design systems that can continuously sense, adapt, and recover under dynamic conditions. This article proposes a formal definition of MAS resilience grounded in two complementary dimensions: epistemic resilience, wherein agents recover and sustain accurate knowledge of the environment, and action resilience, wherein agents leverage that knowledge to coordinate and sustain goals under disruptions. We formalize resilience via temporal epistemic logic and quantify it using recoverability time (how quickly desired properties are re-established after a disturbance) and durability time (how long accurate beliefs and goal-directed behavior are sustained after recovery). We design an agent architecture and develop decentralized algorithms to achieve both epistemic and action resilience. We provide formal verification guarantees, showing that our specifications are sound with respect to the metric bounds and admit finite-horizon verification, enabling design-time certification and lightweight runtime monitoring. Through a case study on distributed multi-agent decision-making under stressors, we show that our approach outperforms baseline methods. Our formal verification analysis and simulation results highlight that the proposed framework enables resilient, knowledge-driven decision-making and sustained operation, laying the groundwork for resilient decentralized MAS in next-generation communication systems.
Deep Recurrent Hidden Markov Learning Framework for Multi-Stage Advanced Persistent Threat Prediction
Venue: arXiv cs.CR 2026
Year: 2026

Advanced Persistent Threats (APTs) represent hidden, multi\-stage cyberattacks whose long term persistence and adaptive behavior challenge conventional intrusion detection systems (IDS). Although recent advances in machine learning and probabilistic modeling have improved APT detection performance, most existing approaches remain reactive and alert\-centric, providing limited capability for stage-aware prediction and principled inference under uncertainty, particularly when observations are sparse or incomplete. This paper proposes E\-HiDNet, a unified hybrid deep probabilistic learning framework that integrates convolutional and recurrent neural networks with a Hidden Markov Model (HMM) to allow accurate prediction of the progression of the APT campaign. The deep learning component extracts hierarchical spatio\-temporal representations from correlated alert sequences, while the HMM models latent attack stages and their stochastic transitions, allowing principled inference under uncertainty and partial observability. A modified Viterbi algorithm is introduced to handle incomplete observations, ensuring robust decoding under uncertainty. The framework is evaluated using a synthetically generated yet structurally realistic APT dataset (S\-DAPT\-2026). Simulation results show that E\-HiDNet achieves up to 98.8\-100\% accuracy in stage prediction and significantly outperforms standalone HMMs when four or more observations are available, even under reduced training data scenarios. These findings highlight that combining deep semantic feature learning with probabilistic state\-space modeling enhances predictive APT stage performance and situational awareness for proactive APT defense.
Comparative Separation: Evaluating Separation on Comparative Judgment Test Data
Venue: arXiv cs.SE 2026
Year: 2026

This research seeks to benefit the software engineering society by proposing comparative separation, a novel group fairness notion to evaluate the fairness of machine learning software on comparative judgment test data. Fairness issues have attracted increasing attention since machine learning software is increasingly used for high-stakes and high-risk decisions. It is the responsibility of all software developers to make their software accountable by ensuring that the machine learning software do not perform differently on different sensitive groups -- satisfying the separation criterion. However, evaluation of separation requires ground truth labels for each test data point. This motivates our work on analyzing whether separation can be evaluated on comparative judgment test data. Instead of asking humans to provide the ratings or categorical labels on each test data point, comparative judgments are made between pairs of data points such as A is better than B. According to the law of comparative judgment, providing such comparative judgments yields a lower cognitive burden for humans than providing ratings or categorical labels. This work first defines the novel fairness notion comparative separation on comparative judgment test data, and the metrics to evaluate comparative separation. Then, both theoretically and empirically, we show that in binary classification problems, comparative separation is equivalent to separation. Lastly, we analyze the number of test data points and test data pairs required to achieve the same level of statistical power in the evaluation of separation and comparative separation, respectively. This work is the first to explore fairness evaluation on comparative judgment test data. It shows the feasibility and the practical benefits of using comparative judgment test data for model evaluations.
ALFA: A Safe-by-Design Approach to Mitigate Quishing Attacks Launched via Fancy QR Codes
Venue: arXiv cs.CR 2026
Year: 2026

Phishing with Quick Response (QR) codes is termed as Quishing. The attackers exploit this method to manipulate individuals into revealing their confidential data. Recently, we see the colorful and fancy representations of QR codes, the 2D matrix of QR codes which does not reflect a typical mixture of black-white modules anymore. Instead, they become more tempting as an attack vector for adversaries which can evade the state-of-the-art deep learning visual-based and other prevailing countermeasures. We introduce "ALFA", a safe-by-design approach, to mitigate Quishing and prevent everyone from accessing the post-scan harmful payload of fancy QR codes. Our method first converts a fancy QR code into the replica of binary grid and then identify the erroneous representation of modules in that grid. Following that, we present "FAST" method which can conveniently recover erroneous modules from that binary grid. Afterwards, using this binary grid, our solution extracts the structural features of fancy QR code and predicts its legitimacy using a pre-trained model. The effectiveness of our proposal is demonstrated by the experimental evaluation on a synthetic dataset (containing diverse variations of fancy QR codes) and achieve a FNR of 0.06% only. We also develop the mobile app to test the practical feasibility of our solution and provide a performance comparison of the app with the real-world QR readers. This comparison further highlights the classification reliability and detection accuracy of this solution in real-world environments.
AutoTour: Automatic Photo Tour Guide with Smartphones and LLMs
Venue: arXiv cs.HC 2026
Year: 2026

We present AutoTour, a system that enhances user exploration by automatically generating fine-grained landmark annotations and descriptive narratives for photos captured by users. The key idea of AutoTour is to fuse visual features extracted from photos with nearby geospatial features queried from open matching databases. Unlike existing tour applications that rely on pre-defined content or proprietary datasets, AutoTour leverages open and extensible data sources to provide scalable and context-aware photo-based guidance. To achieve this, we design a training-free pipeline that first extracts and filters relevant geospatial features around the user's GPS location. It then detects major landmarks in user photos through VLM-based feature detection and projects them into the horizontal spatial plane. A geometric matching algorithm aligns photo features with corresponding geospatial entities based on their estimated distance and direction. The matched features are subsequently grounded and annotated directly on the original photo, accompanied by large language model-generated textual and audio descriptions to provide an informative, tour-like experience. We demonstrate that AutoTour can deliver rich, interpretable annotations for both iconic and lesser-known landmarks, enabling a new form of interactive, context-aware exploration that bridges visual perception and geospatial understanding.