OpenRaiser/Intern-Atlas huggingface dataset:
Global Perspectives of AI Risks and Harms: Analyzing the Negative Impacts of AI Technologies as Prioritized by News Media
Venue: arXiv cs.CY 2025
Year: 2025

Emerging AI technologies have the potential to drive economic growth and innovation but can also pose significant risks to society. To mitigate these risks, governments, companies, and researchers have contributed regulatory frameworks, risk assessment approaches, and safety benchmarks, but these can lack nuance when considered in global deployment contexts. One way to understand these nuances is by looking at how the media reports on AI, as news media has a substantial influence on what negative impacts of AI are discussed in the public sphere and which impacts are deemed important. In this work, we analyze a broad and diverse sample of global news media spanning 27 countries across Asia, Africa, Europe, Middle East, North America, and Oceania to gain valuable insights into the risks and harms of AI technologies as reported and prioritized across media outlets in different countries. This approach reveals a skewed prioritization of Societal Risks followed by Legal & Rights-related Risks, Content Safety Risks, Cognitive Risks, Existential Risks, and Environmental Risks, as reflected in the prevalence of these risk categories in the news coverage of different nations. Furthermore, it highlights how the distribution of such concerns varies based on the political bias of news outlets, underscoring the political nature of AI risk assessment processes and public opinion. By incorporating views from various regions and political orientations for assessing the risks and harms of AI, this work presents stakeholders, such as AI developers and policy makers, with insights into the AI risks categories prioritized in the public sphere. These insights may guide the development of more inclusive, safe, and responsible AI technologies that address the diverse concerns and needs across the world.
Flexible 3D Cage-based Deformation via Green Coordinates on Bézier Patches
Venue: arXiv cs.GR 2025
Year: 2025

Cage-based deformation is a fundamental problem in geometry processing, where a cage, a user-specified boundary of a region, is used to deform the ambient space of a given mesh. Traditional 3D cages are typically composed of triangles and quads. While quads can represent non-planar regions when their four corners are not coplanar, they form ruled surfaces with straight isoparametric curves, which limits their ability to handle curved and high-curvature deformations. In this work, we extend the cage for curved boundaries using Bézier patches, enabling flexible and high-curvature deformations with only a few control points. The higher-order structure of the Bézier patch also allows for the creation of a more compact and precise curved cage for the input model. Based on Green's third identity, we derive the Green coordinates for the Bézier cage, achieving shape-preserving deformation with smooth surface boundaries. These coordinates are defined based on the vertex positions and normals of the Bézier control net. Given that the coordinates are approximately calculated through the Riemann summation, we propose a global projection technique to ensure that the coordinates accurately conform to the linear reproduction property. Experimental results show that our method achieves high performance in handling curved and high-curvature deformations.
StreamingRAG: Real-time Contextual Retrieval and Generation Framework
Venue: arXiv cs.CV 2025
Year: 2025

Extracting real-time insights from multi-modal data streams from various domains such as healthcare, intelligent transportation, and satellite remote sensing remains a challenge. High computational demands and limited knowledge scope restrict the applicability of Multi-Modal Large Language Models (MM-LLMs) on these data streams. Traditional Retrieval-Augmented Generation (RAG) systems address knowledge limitations of these models, but suffer from slow preprocessing, making them unsuitable for real-time analysis. We propose StreamingRAG, a novel RAG framework designed for streaming data. StreamingRAG constructs evolving knowledge graphs capturing scene-object-entity relationships in real-time. The knowledge graph achieves temporal-aware scene representations using MM-LLMs and enables timely responses for specific events or user queries. StreamingRAG addresses limitations in existing methods, achieving significant improvements in real-time analysis (5-6x faster throughput), contextual accuracy (through a temporal knowledge graph), and reduced resource consumption (using lightweight models by 2-3x).
5G LDPC Linear Transformer for Channel Decoding
Venue: arXiv cs.LG 2025
Year: 2025

This work introduces a novel, fully differentiable linear-time complexity transformer decoder and a transformer decoder to correct 5G New Radio (NR) LDPC. We propose a scalable approach to decode linear block codes with $O(n)$ complexity rather than $O(n^2)$ for regular transformers. The architectures' performances are compared to Belief Propagation (BP), the production-level decoding algorithm used for 5G New Radio (NR) LDPC codes. We achieve bit error rate performance that matches a regular Transformer decoder and surpases one iteration BP, also achieving competitive time performance against BP, even for larger block codes. We utilize Sionna, Nvidia's 5G & 6G physical layer research software, for reproducible results.
Collaborating in a competitive world: Heterogeneous Multi-Agent Decision Making in Symbiotic Supply Chain Environments
Venue: arXiv cs.MA 2025
Year: 2025

Supply networks require collaboration in a competitive environment. To achieve this, nodes in the network often form symbiotic relationships as they can be adversely effected by the closure of companies in the network, especially where products are niche. However, balancing support for other nodes in the network against profit is challenging. Agents are increasingly being explored to define optimal strategies in these complex networks. However, to date much of the literature focuses on homogeneous agents where a single policy controls all of the nodes. This isn't realistic for many supply chains as this level of information sharing would require an exceptionally close relationship. This paper therefore compares the behaviour of this type of agent to a heterogeneous structure, where the agents each have separate polices, to solve the product ordering and pricing problem. An approach to reward sharing is developed that doesn't require sharing profit. The homogenous and heterogeneous agents exhibit different behaviours, with the homogenous retailer retaining high inventories and witnessing high levels of backlog while the heterogeneous agents show a typical order strategy. This leads to the heterogeneous agents mitigating the bullwhip effect whereas the homogenous agents do not. In the high demand environment, the agent architecture dominates performance with the Soft Actor-Critic (SAC) agents outperforming the Proximal Policy Optimisation (PPO) agents. Here, the factory controls the supply chain. In the low demand environment the homogenous agents outperform the heterogeneous agents. Control of the supply chain shifts significantly, with the retailer outperforming the factory by a significant margin.
CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization
Venue: arXiv cs.CL 2025
Year: 2025

Legal professionals often struggle with lengthy judgments and require efficient summarization for quick comprehension. To address this challenge, we investigate the need for structured planning in legal case summarization, particularly through event-centric representations that reflect the narrative nature of legal case documents. We propose our framework, CoPERLex, which operates in three stages: first, it performs content selection to identify crucial information from the judgment; second, the selected content is utilized to generate intermediate plans through event-centric representations modeled as Subject-Verb-Object tuples; and finally, it generates coherent summaries based on both the content and the structured plan. Our experiments on four legal summarization datasets demonstrate the effectiveness of integrating content selection and planning components, highlighting the advantages of event-centric plans over traditional entity-centric approaches in the context of legal judgements.
Refactoring for Dockerfile Quality: A Dive into Developer Practices and Automation Potential
Venue: arXiv cs.SE 2025
Year: 2025

Docker, the industry standard for packaging and deploying applications, leverages Infrastructure as Code (IaC) principles to facilitate the creation of images through Dockerfiles. However, maintaining Dockerfiles presents significant challenges. Refactoring, in particular, is often a manual and complex process. This paper explores the utility and practicality of automating Dockerfile refactoring using 600 Dockerfiles from 358 open-source projects. Our study reveals that Dockerfile image size and build duration tend to increase as projects evolve, with developers often postponing refactoring efforts until later stages in the development cycle. This trend motivates the automation of refactoring. To achieve this, we leverage In Context Learning (ICL) along with a score-based demonstration selection strategy. Our approach leads to an average reduction of 32% in image size and a 6% decrease in build duration, with improvements in understandability and maintainability observed in 77% and 91% of cases, respectively. Additionally, our analysis shows that automated refactoring reduces Dockerfile image size by 2x compared to manual refactoring and 10x compared to smell-fixing tools like PARFUM. This work establishes a foundation for automating Dockerfile refactoring, indicating that such automation could become a standard practice within CI/CD pipelines to enhance Dockerfile quality throughout every step of the software development lifecycle.
Development of a Validation and Inspection Tool for Armband-based Lifelog Data (VITAL) to Facilitate the Clinical Use of Wearable Data: A Prototype and Usability Evaluation
Venue: arXiv cs.HC 2025
Year: 2025

Background: The rise of mobile technology and health apps has increased the use of person-generated health data (PGHD). PGHD holds significant potential for clinical decision-making but remains challenging to manage. Objective: This study aimed to enhance the clinical utilization of wearable health data by developing the Validation and Inspection Tool for Armband-Based Lifelog Data (VITAL), a pipeline for data integration, visualization, and quality management, and evaluating its usability. Methods: The study followed a structured process of requirement gathering, tool implementation, and usability evaluation. Requirements were identified through input from four clinicians. Wearable health data from Samsung, Apple, Fitbit, and Xiaomi devices were integrated into a standardized dataframe at 10-minute intervals, focusing on biometrics, activity, and sleep. Features of VITAL support data integration, visualization, and quality management. Usability evaluation involved seven clinicians performing tasks, completing the Unified Theory of Acceptance and Use of Technology (UTAUT) survey, and participating in interviews to identify usability issues. Results: VITAL successfully integrated wearable data, thus enabling all participants to complete tasks with minimal errors without prior participant training. UTAUT survey results were positive, with average scores of 4.2 for performance expectancy, 3.96 for effort expectancy, and 4.14 for intention to use, indicating high user satisfaction and intent to adopt the tool. Conclusions: By enhancing wearable data integration, visualization, and quality management, the VITAL prototype shows significant potential for clinical application. Positive feedback highlights its promise, while emphasizing the need for further studies to confirm its real-world effectiveness.
Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions
Venue: arXiv cs.LG 2025
Year: 2025

Recent work uncovered potential flaws in \eg attribution or heatmap based saliency methods. A typical flaw is a confirmations bias, where the scores are compared to human expectation. Since measuring the quality of saliency methods is hard due to missing ground truth model reasoning, finding general limitations is also hard. This is further complicated, because masking-based evaluation on complex data can easily introduce a bias, as most methods cannot fully ignore inputs. In this work, we extend our previous analysis on the logical dataset framework ANDOR, where we showed that all analysed saliency methods fail to grasp all needed classification information for all possible scenarios. Specifically, this paper extends our previous work using analysis on more datasets, in order to better understand in which scenarios the saliency methods fail. Further, we apply the Global Coherence Representation as an additional evaluation method in order to enable actual input omission.
UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices
Venue: arXiv cs.LG 2025
Year: 2025

Lightweight deep learning approaches for malaria detection have gained attention for their potential to enhance diagnostics in resource constrained environments. For our study, we selected SqueezeNet1.1 as it is one of the most popular lightweight architectures. SqueezeNet1.1 is a later version of SqueezeNet1.0 and is 2.4 times more computationally efficient than the original model. We proposed and implemented three ultra-lightweight architecture variants to SqueezeNet1.1 architecture, namely Variant 1 (one fire module), Variant 2 (two fire modules), and Variant 3 (four fire modules), which are even more compact than SqueezeNetV1.1 (eight fire modules). These models were implemented to evaluate the best performing variant that achieves superior computational efficiency without sacrificing accuracy in malaria blood cell classification. The models were trained and evaluated using the NIH Malaria dataset. We assessed each model's performance based on metrics including accuracy, recall, precision, F1-score, and Area Under the Curve (AUC). The results show that the SqueezeNet1.1 model achieves the highest performance across all metrics, with a classification accuracy of 97.12%. Variant 3 (four fire modules) offers a competitive alternative, delivering almost identical results (accuracy 96.55%) with a 6x reduction in computational overhead compared to SqueezeNet1.1. Variant 2 and Variant 1 perform slightly lower than Variant 3, with Variant 2 (two fire modules) reducing computational overhead by 28x, and Variant 1 (one fire module) achieving a 54x reduction in trainable parameters compared to SqueezeNet1.1. These findings demonstrate that our SqueezeNet1.1 architecture variants provide a flexible approach to malaria detection, enabling the selection of a variant that balances resource constraints and performance.
Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity
Venue: arXiv cs.LG 2025
Year: 2025

Graph anomaly detection (GAD) aims to identify nodes from a graph that are significantly different from normal patterns. Most previous studies are model-driven, focusing on enhancing the detection effect by improving the model structure. However, these approaches often treat all nodes equally, neglecting the different contributions of various nodes to the training. Therefore, we introduce graph curriculum learning as a simple and effective plug-and-play module to optimize GAD methods. The existing graph curriculum learning mainly focuses on the homogeneity of graphs and treats nodes with high homogeneity as easy nodes. In fact, GAD models can handle not only graph homogeneity but also heterogeneity, which leads to the unsuitability of these existing methods. To address this problem, we propose an innovative Bi-directional Curriculum Learning strategy (BCL), which considers nodes with higher and lower similarity to neighbor nodes as simple nodes in the direction of focusing on homogeneity and focusing on heterogeneity, respectively, and prioritizes their training. Extensive experiments show that BCL can be quickly integrated into existing detection processes and significantly improves the performance of ten GAD anomaly detection models on seven commonly used datasets.
Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning
Venue: arXiv cs.CV 2025
Year: 2025

A mutation in the DNA of a single cell that compromises its function initiates leukemia,leading to the overproduction of immature white blood cells that encroach upon the space required for the generation of healthy blood cells.Leukemia is treatable if identified in its initial stages. However,its diagnosis is both arduous and time consuming. This study proposes a novel approach for diagnosing leukemia across four stages Benign,Early,Pre,and Pro using deep learning techniques.We employed two Convolutional Neural Network (CNN) models as MobileNetV2 with an altered head and a custom model. The custom model consists of multiple convolutional layers,each paired with corresponding max pooling layers.We utilized MobileNetV2 with ImageNet weights,adjusting the head to integrate the final results.The dataset used is the publicly available "Acute Lymphoblastic Leukemia (ALL) Image Dataset", and we applied the Synthetic Minority Oversampling Technique (SMOTE) to augment and balance the training dataset.The custom model achieved an accuracy of 98.6%, while MobileNetV2 attained a superior accuracy of 99.69%. The pretrained model showed promising results,indicating an increased likelihood of real-world application.
Cascaded Large-Scale TSP Solving with Unified Neural Guidance: Bridging Local and Population-based Search
Venue: arXiv cs.NE 2025
Year: 2025

The traveling salesman problem (TSP) is a fundamental NP-hard optimization problem. Over the past decades, traditional heuristic methods have achieved substantial success in solving TSP, yet their performance, particularly for large-scale instances, remains to be further improved. The advancement of deep learning technologies over the past decade has driven a growing number of attempts to solve TSP by leveraging neural guidance. However, these efforts predominantly focus on small-scale TSP instances, with limited improvements in solving performance for large-scale instances, revealing persistent scalability challenges. This work presents UNiCS, a novel unified neural-guided cascaded solver for solving large-scale TSP instances. UNiCS comprises a local search (LS) phase and a population-based search (PBS) phase, both guided by a learning component called unified neural guidance (UNG). Specifically, UNG guides solution generation across both phases and determines appropriate phase transition timing to effectively combine the complementary strengths of LS and PBS. While trained only on simple distributions with relatively small-scale TSP instances, UNiCS generalizes effectively to challenging TSP benchmarks containing much larger instances (10,000-71,009 nodes) with diverse node distributions entirely unseen during training. Experimental results on the large-scale TSP instances demonstrate that UNiCS consistently outperforms state-of-the-art methods, with its advantage remaining consistent across various runtime budgets.
A Comprehensive Framework for Semantic Similarity Analysis of Human and AI-Generated Text Using Transformer Architectures and Ensemble Techniques
Venue: arXiv cs.CL 2025
Year: 2025

The rapid advancement of large language models (LLMs) has made detecting AI-generated text an increasingly critical challenge. Traditional methods often fail to capture the nuanced semantic differences between human and machine-generated content. We therefore propose a novel approach based on semantic similarity analysis, leveraging a multi-layered architecture that combines a pre-trained DeBERTa-v3-large model, Bi-directional LSTMs, and linear attention pooling to capture both local and global semantic patterns. To enhance performance, we employ advanced input and output augmentation techniques such as sector-level context integration and wide output configurations. These techniques enable the model to learn more discriminative features and generalize across diverse domains. Experimental results show that this approach works better than traditional methods, proving its usefulness for AI-generated text detection and other text comparison tasks.
TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection
Venue: arXiv cs.CV 2025
Year: 2025

Object detection has witnessed remarkable advancements over the past decade, largely driven by breakthroughs in deep learning and the proliferation of large scale datasets. However, the domain of road damage detection remains relatively under explored, despite its critical significance for applications such as infrastructure maintenance and road safety. This paper addresses this gap by introducing a novel top down benchmark that offers a complementary perspective to existing datasets, specifically tailored for road damage detection. Our proposed Top Down Road Damage Detection Dataset (TDRD) includes three primary categories of road damage cracks, potholes, and patches captured from a top down viewpoint. The dataset consists of 7,088 high resolution images, encompassing 12,882 annotated instances of road damage. Additionally, we present a novel real time object detection framework, TDYOLOV10, designed to handle the unique challenges posed by the TDRD dataset. Comparative studies with state of the art models demonstrate competitive baseline results. By releasing TDRD, we aim to accelerate research in this crucial area. A sample of the dataset will be made publicly available upon the paper's acceptance.
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education
Venue: arXiv cs.CY 2025
Year: 2025

Automated grading has become an essential tool in education technology due to its ability to efficiently assess large volumes of student work, provide consistent and unbiased evaluations, and deliver immediate feedback to enhance learning. However, current systems face significant limitations, including the need for large datasets in few-shot learning methods, a lack of personalized and actionable feedback, and an overemphasis on benchmark performance rather than student experience. To address these challenges, we propose a Zero-Shot Large Language Model (LLM)-Based Automated Assignment Grading (AAG) system. This framework leverages prompt engineering to evaluate both computational and explanatory student responses without requiring additional training or fine-tuning. The AAG system delivers tailored feedback that highlights individual strengths and areas for improvement, thereby enhancing student learning outcomes. Our study demonstrates the system's effectiveness through comprehensive evaluations, including survey responses from higher education students that indicate significant improvements in motivation, understanding, and preparedness compared to traditional grading methods. The results validate the AAG system's potential to transform educational assessment by prioritizing learning experiences and providing scalable, high-quality feedback.