OpenRaiser/Intern-Atlas huggingface dataset:
Protect: Towards Robust Guardrailing Stack for Trustworthy Enterprise LLM Systems
Venue: arXiv cs.CL 2025
Year: 2025

The increasing deployment of Large Language Models (LLMs) across enterprise and mission-critical domains has underscored the urgent need for robust guardrailing systems that ensure safety, reliability, and compliance. Existing solutions often struggle with real-time oversight, multi-modal data handling, and explainability -- limitations that hinder their adoption in regulated environments. Existing guardrails largely operate in isolation, focused on text alone making them inadequate for multi-modal, production-scale environments. We introduce Protect, natively multi-modal guardrailing model designed to operate seamlessly across text, image, and audio inputs, designed for enterprise-grade deployment. Protect integrates fine-tuned, category-specific adapters trained via Low-Rank Adaptation (LoRA) on an extensive, multi-modal dataset covering four safety dimensions: toxicity, sexism, data privacy, and prompt injection. Our teacher-assisted annotation pipeline leverages reasoning and explanation traces to generate high-fidelity, context-aware labels across modalities. Experimental results demonstrate state-of-the-art performance across all safety dimensions, surpassing existing open and proprietary models such as WildGuard, LlamaGuard-4, and GPT-4.1. Protect establishes a strong foundation for trustworthy, auditable, and production-ready safety systems capable of operating across text, image, and audio modalities.
Kernel Representation and Similarity Measure for Incomplete Data
Venue: arXiv cs.LG 2025
Year: 2025

Measuring similarity between incomplete data is a fundamental challenge in web mining, recommendation systems, and user behavior analysis. Traditional approaches either discard incomplete data or perform imputation as a preprocessing step, leading to information loss and biased similarity estimates. This paper presents the proximity kernel, a new similarity measure that directly computes similarity between incomplete data in kernel feature space without explicit imputation in the original space. The proposed method introduces data-dependent binning combined with proximity assignment to project data into a high-dimensional sparse representation that adapts to local density variations. For missing value handling, we propose a cascading fallback strategy to estimate missing feature distributions. We conduct clustering tasks on the proposed kernel representation across 12 real world incomplete datasets, demonstrating superior performance compared to existing methods while maintaining linear time complexity. All the code are available at https://anonymous.4open.science/r/proximity-kernel-2289.
Language as a Label: Zero-Shot Multimodal Classification of Everyday Postures under Data Scarcity
Venue: arXiv cs.CV 2025
Year: 2025

Recent Vision-Language Models (VLMs) enable zero-shot classification by aligning images and text in a shared space, a promising approach for data-scarce conditions. However, the influence of prompt design on recognizing visually similar categories, such as human postures, is not well understood. This study investigates how prompt specificity affects the zero-shot classification of sitting, standing, and walking/running on a small, 285-image COCO-derived dataset. A suite of modern VLMs, including OpenCLIP, MetaCLIP 2, and SigLip, were evaluated using a three-tiered prompt design that systematically increases linguistic detail. Our findings reveal a compelling, counter-intuitive trend: for the highest-performing models (MetaCLIP 2 and OpenCLIP), the simplest, most basic prompts consistently achieve the best results. Adding descriptive detail significantly degrades performance for instance, MetaCLIP 2's multi-class accuracy drops from 68.8\% to 55.1\% a phenomenon we term "prompt overfitting". Conversely, the lower-performing SigLip model shows improved classification on ambiguous classes when given more descriptive, body-cue-based prompts.
Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services
Venue: arXiv cs.LG 2025
Year: 2025

This paper addresses the challenges of complex dependencies and diverse anomaly patterns in cloud service environments by proposing a dependency modeling and anomaly detection method that integrates contrastive learning. The method abstracts service interactions into a dependency graph, extracts temporal and structural features through embedding functions, and employs a graph convolution mechanism to aggregate neighborhood information for context-aware service representations. A contrastive learning framework is then introduced, constructing positive and negative sample pairs to enhance the separability of normal and abnormal patterns in the representation space. Furthermore, a temporal consistency constraint is designed to maintain representation stability across time steps and reduce the impact of short-term fluctuations and noise. The overall optimization combines contrastive loss and temporal consistency loss to ensure stable and reliable detection across multi-dimensional features. Experiments on public datasets systematically evaluate the method from hyperparameter, environmental, and data sensitivity perspectives. Results show that the proposed approach significantly outperforms existing methods on key metrics such as Precision, Recall, F1-Score, and AUC, while maintaining robustness under conditions of sparse labeling, monitoring noise, and traffic fluctuations. This study verifies the effectiveness of integrating dependency modeling with contrastive learning, provides a complete technical solution for cloud service anomaly detection, and demonstrates strong adaptability and stability in complex environments.
Semi-sparsity Generalization for Variational Mesh Denoising
Venue: arXiv cs.CG 2025
Year: 2025

In this paper, we propose a new variational framework for 3D surface denoising over triangulated meshes, which is inspired by the success of semi-sparse regularization in image processing. Differing from the uniformly sampled image data, mesh surfaces are typically represented by irregular, non-uniform structures, which thus complicate the direct application of the standard formulation and pose challenges in both model design and numerical implementation. To bridge this gap, we first introduce the discrete approximations of higher-order differential operators over triangle meshes and then develop a semi-sparsity regularized minimization model for mesh denoising. This new model is efficiently solved by using a multi-block alternating direction method of multipliers (ADMM) and achieves high-quality simultaneous fitting performance -- preserving sharp features while promoting piecewise-polynomial smoothing surfaces. To verify its effectiveness, we also present a series of experimental results on both synthetic and real scanning data, showcasing the competitive and superior results compared to state-of-the-art methods, both visually and quantitatively.
Towards Richer Challenge Problems for Scientific Computing Correctness
Venue: arXiv cs.SE 2025
Year: 2025

Correctness in scientific computing (SC) is gaining increasing attention in the formal methods (FM) and programming languages (PL) community. Existing PL/FM verification techniques struggle with the complexities of realistic SC applications. Part of the problem is a lack of a common understanding between the SC and PL/FM communities of machine-verifiable correctness challenges and dimensions of correctness in SC applications. To address this gap, we call for specialized challenge problems to inform the development and evaluation of FM/PL verification techniques for correctness in SC. These specialized challenges are intended to augment existing problems studied by FM/PL researchers for general programs to ensure the needs of SC applications can be met. We propose several dimensions of correctness relevant to scientific computing, and discuss some guidelines and criteria for designing challenge problems to evaluate correctness in scientific computing.
Through the Lens of Doubt: Robust and Efficient Uncertainty Estimation for Visual Place Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Visual Place Recognition (VPR) enables robots and autonomous vehicles to identify previously visited locations by matching current observations against a database of known places. However, VPR systems face significant challenges when deployed across varying visual environments, lighting conditions, seasonal changes, and viewpoints changes. Failure-critical VPR applications, such as loop closure detection in simultaneous localization and mapping (SLAM) pipelines, require robust estimation of place matching uncertainty. We propose three training-free uncertainty metrics that estimate prediction confidence by analyzing inherent statistical patterns in similarity scores from any existing VPR method. Similarity Distribution (SD) quantifies match distinctiveness by measuring score separation between candidates; Ratio Spread (RS) evaluates competitive ambiguity among top-scoring locations; and Statistical Uncertainty (SU) is a combination of SD and RS that provides a unified metric that generalizes across datasets and VPR methods without requiring validation data to select the optimal metric. All three metrics operate without additional model training, architectural modifications, or computationally expensive geometric verification. Comprehensive evaluation across nine state-of-the-art VPR methods and six benchmark datasets confirms that our metrics excel at discriminating between correct and incorrect VPR matches, and consistently outperform existing approaches while maintaining negligible computational overhead, making it deployable for real-time robotic applications across varied environmental conditions with improved precision-recall performance.
Discrimination, artificial intelligence, and algorithmic decision-making
Venue: arXiv cs.CY 2025
Year: 2025

Artificial intelligence (AI) has a huge impact on our personal lives and also on our democratic society as a whole. While AI offers vast opportunities for the benefit of people, its potential to embed and perpetuate bias and discrimination remains one of the most pressing challenges deriving from its increasing use. This new study, which was prepared by Prof. Frederik Zuiderveen Borgesius for the Anti-discrimination Department of the Council of Europe, elaborates on the risks of discrimination caused by algorithmic decision-making and other types of artificial intelligence (AI).
Privacy, freedom of expression, and the right to be forgotten in Europe
Venue: arXiv cs.CY 2025
Year: 2025

In this chapter we discuss the relation between privacy and freedom of expression in Europe. In principle, the two rights have equal weight in Europe - which right prevails depends on the circumstances of a case. We use the Google Spain judgment of the Court of Justice of the European Union, sometimes called the 'right to be forgotten' judgment, to illustrate the difficulties when balancing the two rights. The court decided in Google Spain that people have, under certain conditions, the right to have search results for their name delisted. We discuss how Google and Data Protection Authorities deal with such delisting requests in practice. Delisting requests illustrate that balancing privacy and freedom of expression interests will always remain difficult.
ExpressNet-MoE: A Hybrid Deep Neural Network for Emotion Recognition
Venue: arXiv cs.CV 2025
Year: 2025

In many domains, including online education, healthcare, security, and human-computer interaction, facial emotion recognition (FER) is essential. Real-world FER is still difficult despite its significance because of some factors such as variable head positions, occlusions, illumination shifts, and demographic diversity. Engagement detection, which is essential for applications like virtual learning and customer services, is frequently challenging due to FER limitations by many current models. In this article, we propose ExpressNet-MoE, a novel hybrid deep learning model that blends both Convolution Neural Networks (CNNs) and Mixture of Experts (MoE) framework, to overcome the difficulties. Our model dynamically chooses the most pertinent expert networks, thus it aids in the generalization and providing flexibility to model across a wide variety of datasets. Our model improves on the accuracy of emotion recognition by utilizing multi-scale feature extraction to collect both global and local facial features. ExpressNet-MoE includes numerous CNN-based feature extractors, a MoE module for adaptive feature selection, and finally a residual network backbone for deep feature learning. To demonstrate efficacy of our proposed model we evaluated on several datasets, and compared with current state-of-the-art methods. Our model achieves accuracies of 74.77% on AffectNet (v7), 72.55% on AffectNet (v8), 84.29% on RAF-DB, and 64.66% on FER-2013. The results show how adaptive our model is and how it may be used to develop end-to-end emotion recognition systems in practical settings. Reproducible codes and results are made publicly accessible at https://github.com/DeeptimaanB/ExpressNet-MoE.
LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA
Venue: arXiv cs.CL 2025
Year: 2025

Question Answering (QA) on narrative text poses a unique challenge to current systems, requiring a deep understanding of long, complex documents. However, the reliability of NarrativeQA, the most widely used benchmark in this domain, is hindered by noisy documents and flawed QA pairs. In this work, we introduce LiteraryQA, a high-quality subset of NarrativeQA focused on literary works. Using a human- and LLM-validated pipeline, we identify and correct low-quality QA samples while removing extraneous text from source documents. We then carry out a meta-evaluation of automatic metrics to clarify how systems should be evaluated on LiteraryQA. This analysis reveals that all n-gram-based metrics have a low system-level correlation to human judgment, while LLM-as-a-Judge evaluations, even with small open-weight models, can strongly agree with the ranking identified by humans. Finally, we benchmark a set of long-context LLMs on LiteraryQA. We release our code and data at https://github.com/SapienzaNLP/LiteraryQA.
A Methodology for Assessing the Risk of Metric Failure in LLMs Within the Financial Domain
Venue: arXiv cs.AI 2025
Year: 2025

As Generative Artificial Intelligence is adopted across the financial services industry, a significant barrier to adoption and usage is measuring model performance. Historical machine learning metrics can oftentimes fail to generalize to GenAI workloads and are often supplemented using Subject Matter Expert (SME) Evaluation. Even in this combination, many projects fail to account for various unique risks present in choosing specific metrics. Additionally, many widespread benchmarks created by foundational research labs and educational institutions fail to generalize to industrial use. This paper explains these challenges and provides a Risk Assessment Framework to allow for better application of SME and machine learning Metrics
Experiments \& Analysis of Privacy-Preserving SQL Query Sanitization Systems
Venue: arXiv cs.DB 2025
Year: 2025

Analytical SQL queries are essential for extracting insights from relational databases but concurrently introduce significant privacy risks by potentially exposing sensitive information. To mitigate these risks, numerous query sanitization systems have been developed, employing diverse approaches that create a complex landscape for both researchers and practitioners. These systems vary fundamentally in their design, including the underlying privacy model, such as k-anonymity or Differential Privacy; the protected privacy unit, whether at the tuple- or user-level; and the software architecture, which can be proxy-based or integrated. This paper provides a systematic classification of state-of-the-art SQL sanitization systems based on these qualitative criteria and the scope of queries they support. Furthermore, we present a quantitative analysis of leading systems, empirically measuring the trade-offs between data utility, query execution overhead, and privacy guarantees across a range of analytical queries. This work offers a structured overview and performance assessment intended to clarify the capabilities and limitations of current privacy-preserving database technologies.
Simulating Mediumband Wireless Communication Systems: A Concise Description
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we describe the necessary procedures for accurately simulating digital wireless communication systems operating in the mediumband, aimed at both beginners and experts. In the research literature, digital wireless communication systems are typically simulated in the discrete-time complex baseband domain, where pulse shaping, upconversion, mixing, carrier synchronization, and symbol timing synchronization are often ignored. These assumptions are indeed sufficient in most cases, but to capture the essence of communication in the mediumband, certain physical layer (PHY) operations should be simulated in detail. In this paper, we concisely describe how to simulate a mediumband wireless communication scenario from a single transmitter (TX) to a single receiver (RX) in MATLAB, elaborating the operation of key PHY subsystems. The approach described here ensures that the simulated system captures the delicate dynamics of mediumband wireless communication, including the effect of deep fading avoidance.
Smart UX-design for Rescue Operations Wearable - A Knowledge Graph Informed Visualization Approach for Information Retrieval in Emergency Situations
Venue: arXiv cs.HC 2025
Year: 2025

This paper presents a knowledge graph-informed smart UX-design approach for supporting information retrieval for a wearable, providing treatment recommendations during emergency situations to health professionals. This paper describes requirements that are unique to knowledge graph-based solutions, as well as the direct requirements of health professionals. The resulting implementation is provided for the project, which main goal is to improve first-aid rescue operations by supporting artificial intelligence in situation detection and knowledge graph representation via a contextual-based recommendation for treatment assistance.
In-Browser LLM-Guided Fuzzing for Real-Time Prompt Injection Testing in Agentic AI Browsers
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Model (LLM) based agents integrated into web browsers (often called agentic AI browsers) offer powerful automation of web tasks. However, they are vulnerable to indirect prompt injection attacks, where malicious instructions hidden in a webpage deceive the agent into unwanted actions. These attacks can bypass traditional web security boundaries, as the AI agent operates with the user privileges across sites. In this paper, we present a novel fuzzing framework that runs entirely in the browser and is guided by an LLM to automatically discover such prompt injection vulnerabilities in real time.
Accelerated Feature Detectors for Visual SLAM: A Comparative Study of FPGA vs GPU
Venue: arXiv cs.CV 2025
Year: 2025

Feature detection is a common yet time-consuming module in Simultaneous Localization and Mapping (SLAM) implementations, which are increasingly deployed on power-constrained platforms, such as drones. Graphics Processing Units (GPUs) have been a popular accelerator for computer vision in general, and feature detection and SLAM in particular. On the other hand, System-on-Chips (SoCs) with integrated Field Programmable Gate Array (FPGA) are also widely available. This paper presents the first study of hardware-accelerated feature detectors considering a Visual SLAM (V-SLAM) pipeline. We offer new insights by comparing the best GPU-accelerated FAST, Harris, and SuperPoint implementations against the FPGA-accelerated counterparts on modern SoCs (Nvidia Jetson Orin and AMD Versal). The evaluation shows that when using a non-learning-based feature detector such as FAST and Harris, their GPU implementations, and the GPU-accelerated V-SLAM can achieve better run-time performance and energy efficiency than the FAST and Harris FPGA implementations as well as the FPGA-accelerated V-SLAM. However, when considering a learning-based detector such as SuperPoint, its FPGA implementation can achieve better run-time performance and energy efficiency (up to 3.1$\times$ and 1.4$\times$ improvements, respectively) than the GPU implementation. The FPGA-accelerated V-SLAM can also achieve comparable run-time performance compared to the GPU-accelerated V-SLAM, with better FPS in 2 out of 5 dataset sequences. When considering the accuracy, the results show that the GPU-accelerated V-SLAM is more accurate than the FPGA-accelerated V-SLAM in general. Last but not least, the use of hardware acceleration for feature detection could further improve the performance of the V-SLAM pipeline by having the global bundle adjustment module invoked less frequently without sacrificing accuracy.
Efficient Force and Stiffness Prediction in Robotic Produce Handling with a Piezoresistive Pressure Sensor
Venue: arXiv cs.RO 2025
Year: 2025

Properly handling delicate produce with robotic manipulators is a major part of the future role of automation in agricultural harvesting and processing. Grasping with the correct amount of force is crucial in not only ensuring proper grip on the object, but also to avoid damaging or bruising the product. In this work, a flexible pressure sensor that is both low cost and easy to fabricate is integrated with robotic grippers for working with produce of varying shapes, sizes, and stiffnesses. The sensor is successfully integrated with both a rigid robotic gripper, as well as a pneumatically actuated soft finger. Furthermore, an algorithm is proposed for accelerated estimation of the steady-state value of the sensor output based on the transient response data, to enable real-time applications. The sensor is shown to be effective in incorporating feedback to correctly grasp objects of unknown sizes and stiffnesses. At the same time, the sensor provides estimates for these values which can be utilized for identification of qualities such as ripeness levels and bruising. It is also shown to be able to provide force feedback for objects of variable stiffnesses. This enables future use not only for produce identification, but also for tasks such as quality control and selective distribution based on ripeness levels.
LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
Venue: arXiv cs.RO 2025
Year: 2025

Visual-Language-Action (VLA) models report impressive success rates on robotic manipulation benchmarks, yet these results may mask fundamental weaknesses in robustness. We perform a systematic vulnerability analysis by introducing controlled perturbations across seven dimensions: objects layout, camera viewpoints, robot initial states, language instructions, light conditions, background textures and sensor noise. We comprehensively analyzed multiple state-of-the-art models and revealed consistent brittleness beneath apparent competence. Our analysis exposes critical weaknesses: models exhibit extreme sensitivity to perturbation factors, including camera viewpoints and robot initial states, with performance dropping from 95% to below 30% under modest perturbations. Surprisingly, models are largely insensitive to language variations, with further experiments revealing that models tend to ignore language instructions completely. Our findings challenge the assumption that high benchmark scores equate to true competency and highlight the need for evaluation practices that assess reliability under realistic variation.