OpenRaiser/Intern-Atlas huggingface dataset:
W3ID: A Quantum Computing-Secure Digital Identity System Redefining Standards for Web3 and Digital Twins
Venue: arXiv cs.CR 2025
Year: 2025

The rapid advancements in quantum computing present significant threats to existing encryption standards and internet security. Simultaneously, the advent of Web 3.0 marks a transformative era in internet history, emphasizing enhanced data security, decentralization, and user ownership. This white paper introduces the W3ID, an abbreviation of Web3 standard meeting universal digital ID, which is a Universal Digital Identity (UDI) model designed to meet Web3 standards while addressing vulnerabilities posed by quantum computing. W3ID innovatively generates secure Digital Object Identifiers (DOIs) tailored for the decentralized Web 3.0 ecosystem. Additionally, W3ID employs a dual-key system for secure authentication, enhancing both public and private verification mechanisms. To further enhance encryption strength and authentication integrity in the quantum computing era, W3ID incorporates an advanced security mechanism. By requiring quadruple application of SHA-256, with consecutive matches for validation, the system expands the number of possibilities to 256^4, which is approximately 4.3 billion times the current SHA-256 capacity. This dramatic increase in computational complexity ensures that even advanced quantum computing systems would face significant challenges in executing brute-force attacks. W3ID redefines digital identity standards for Web 3.0 and the quantum computing era, setting a new benchmark for security, scalability, and decentralization in the global digital twin ecosystem.
Qwen it detect machine-generated text?
Venue: arXiv cs.CL 2025
Year: 2025

This paper describes the approach of the Unibuc - NLP team in tackling the Coling 2025 GenAI Workshop, Task 1: Binary Multilingual Machine-Generated Text Detection. We explored both masked language models and causal models. For Subtask A, our best model achieved first-place out of 36 teams when looking at F1 Micro (Auxiliary Score) of 0.8333, and second-place when looking at F1 Macro (Main Score) of 0.8301
BN-Pool: a Bayesian Nonparametric Approach to Graph Pooling
Venue: arXiv cs.LG 2025
Year: 2025

We introduce BN-Pool, the first clustering-based pooling method for Graph Neural Networks (GNNs) that adaptively determines the number of supernodes in a coarsened graph. By leveraging a Bayesian non-parametric framework, BN-Pool employs a generative model capable of partitioning graph nodes into an unbounded number of clusters. During training, we learn the node-to-cluster assignments by combining the supervised loss of the downstream task with an unsupervised auxiliary term, which encourages the reconstruction of the original graph topology while penalizing unnecessary proliferation of clusters. This adaptive strategy allows BN-Pool to automatically discover an optimal coarsening level, offering enhanced flexibility and removing the need to specify sensitive pooling ratios. We show that BN-Pool achieves superior performance across diverse benchmarks.
Efficient Sampling of Temporal Networks with Preserved Causality Structure
Venue: arXiv cs.SI 2025
Year: 2025

In this paper, we extend the classical Color Refinement algorithm for static networks to temporal (undirected and directed) networks. This enables us to design an algorithm to sample synthetic networks that preserves the $d$-hop neighborhood structure of a given temporal network. The higher $d$ is chosen, the better the temporal neighborhood structure of the original network is preserved. Specifically, we provide efficient algorithms that preserve time-respecting ("causal") paths in the networks up to length $d$, and scale to real-world network sizes. We validate our approach theoretically (for Degree and Katz centrality) and experimentally (for edge persistence, causal triangles, and burstiness). An experimental comparison shows that our method retains these key temporal characteristics more effectively than existing randomization methods.
From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation
Venue: arXiv cs.LG 2025
Year: 2025

Deep reinforcement learning (RL) has shown remarkable success in complex domains, however, the inherent black box nature of deep neural network policies raises significant challenges in understanding and trusting the decision-making processes. While existing explainable RL methods provide local insights, they fail to deliver a global understanding of the model, particularly in high-stakes applications. To overcome this limitation, we propose a novel model-agnostic approach that bridges the gap between explainability and interpretability by leveraging Shapley values to transform complex deep RL policies into transparent representations. The proposed approach offers two key contributions: a novel approach employing Shapley values to policy interpretation beyond local explanations and a general framework applicable to off-policy and on-policy algorithms. We evaluate our approach with three existing deep RL algorithms and validate its performance in two classic control environments. The results demonstrate that our approach not only preserves the original models' performance but also generates more stable interpretable policies.
Empirical Evaluation of Embedding Models in the Context of Text Classification in Document Review in Construction Delay Disputes
Venue: arXiv cs.IR 2025
Year: 2025

Text embeddings are numerical representations of text data, where words, phrases, or entire documents are converted into vectors of real numbers. These embeddings capture semantic meanings and relationships between text elements in a continuous vector space. The primary goal of text embeddings is to enable the processing of text data by machine learning models, which require numerical input. Numerous embedding models have been developed for various applications. This paper presents our work in evaluating different embeddings through a comprehensive comparative analysis of four distinct models, focusing on their text classification efficacy. We employ both K-Nearest Neighbors (KNN) and Logistic Regression (LR) to perform binary classification tasks, specifically determining whether a text snippet is associated with 'delay' or 'not delay' within a labeled dataset. Our research explores the use of text snippet embeddings for training supervised text classification models to identify delay-related statements during the document review process of construction delay disputes. The results of this study highlight the potential of embedding models to enhance the efficiency and accuracy of document analysis in legal contexts, paving the way for more informed decision-making in complex investigative scenarios.
Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing
Venue: arXiv cs.SE 2025
Year: 2025

Testing autonomous driving systems (ADS) is critical to ensuring their reliability and safety. Existing ADS testing works focuses on designing scenarios to evaluate system-level behaviors, while fine-grained testing of ADS source code has received comparatively little attention. To address this gap, we present the first study on testing, specifically unit testing, for ADS source code. Our study focuses on an industrial ADS framework, Autoware. We analyze both human-written test cases and those generated by large language models (LLMs). Our findings reveal that human-written test cases in Autoware exhibit limited test coverage, and significant challenges remain in applying LLM-generated tests for Autoware unit testing. To overcome these challenges, we propose AwTest-LLM, a novel approach to enhance test coverage and improve test case pass rates across Autoware packages.
Understanding the Effectiveness of LLMs in Automated Self-Admitted Technical Debt Repayment
Venue: arXiv cs.SE 2025
Year: 2025

Self-Admitted Technical Debt (SATD), cases where developers intentionally acknowledge suboptimal solutions in code through comments, poses a significant challenge to software maintainability. Left unresolved, SATD can degrade code quality and increase maintenance costs. While Large Language Models (LLMs) have shown promise in tasks like code generation and program repair, their potential in automated SATD repayment remains underexplored. In this paper, we identify three key challenges in training and evaluating LLMs for SATD repayment: (1) dataset representativeness and scalability, (2) removal of irrelevant SATD repayments, and (3) limitations of existing evaluation metrics. To address the first two dataset-related challenges, we adopt a language-independent SATD tracing tool and design a 10-step filtering pipeline to extract SATD repayments from repositories, resulting two large-scale datasets: 58,722 items for Python and 97,347 items for Java. To improve evaluation, we introduce two diff-based metrics, BLEU-diff and CrystalBLEU-diff, which measure code changes rather than whole code. Additionally, we propose another new metric, LEMOD, which is both interpretable and informative. Using our new benchmarks and evaluation metrics, we evaluate two types of automated SATD repayment methods: fine-tuning smaller models, and prompt engineering with five large-scale models. Our results reveal that fine-tuned small models achieve comparable Exact Match (EM) scores to prompt-based approaches but underperform on BLEU-based metrics and LEMOD. Notably, Gemma-2-9B leads in EM, addressing 10.1% of Python and 8.1% of Java SATDs, while Llama-3.1-70B-Instruct and GPT-4o-mini excel on BLEU-diff, CrystalBLEU-diff, and LEMOD metrics. Our work contributes a robust benchmark, improved evaluation metrics, and a comprehensive evaluation of LLMs, advancing research on automated SATD repayment.
Optimizing Secure Quantum Information Transmission in Entanglement-Assisted Quantum Networks
Venue: arXiv cs.CR 2025
Year: 2025

Quantum security improves cryptographic protocols by applying quantum mechanics principles, assuring resistance to both quantum and conventional computer attacks. This work addresses these issues by integrating Quantum Key Distribution (QKD) utilizing the E91 method with Multi-Layer Chaotic Encryption, which employs a variety of patterns to detect eavesdropping, resulting in a highly secure image-transmission architecture. The method leverages entropy calculations to determine the unpredictability and integrity of encrypted and decrypted pictures, guaranteeing strong security. Extensive statistical scenarios illustrate the framework's effectiveness in image encryption while preserving high entropy and sensitivity to the original visuals. The findings indicate significant improvement in encryption and decryption performance, demonstrating the framework's potential as a robust response to weaknesses introduced by advances in quantum computing. Several metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Normalized Cross-Correlation (NCC), Bit Error Rate (BER), entropy values for original, encrypted, and decrypted images, and the correlation between original and decrypted images, validate the framework's effectiveness. The combination of QKD with Multi-Layer Chaotic Encryption provides a scalable and resilient technique to secure image communication. As quantum computing advances, this framework offers a future-proof approach for defining secure communication protocols in crucial sectors such as medical treatment, forensic computing, and national security, where information confidentiality is valuable.
SLIM: Sim-to-Real Legged Instructive Manipulation via Long-Horizon Visuomotor Learning
Venue: arXiv cs.RO 2025
Year: 2025

We present a low-cost legged mobile manipulation system that solves long-horizon real-world tasks, trained by reinforcement learning purely in simulation. This system is made possible by 1) a hierarchical design of a high-level policy for visual-mobile manipulation following task instructions, and a low-level quadruped locomotion policy, 2) a teacher and student training pipeline for the high level, which trains a teacher to tackle long-horizon tasks using privileged task decomposition and target object information, and further trains a student for visual-mobile manipulation via RL guided by the teacher's behavior, and 3) a suite of techniques for minimizing the sim-to-real gap. In contrast to many previous works that use high-end equipments, our system demonstrates effective performance with more accessible hardware -- specifically, a Unitree Go1 quadruped, a WidowX-250S arm, and a single wrist-mounted RGB camera -- despite the increased challenges of sim-to-real transfer. Trained fully in simulation, a single policy autonomously solves long-horizon tasks involving search, move to, grasp, transport, and drop into, achieving nearly 80% real-world success. This performance is comparable to that of expert human teleoperation on the same tasks while the robot is more efficient, operating at about 1.5x the speed of the teleoperation. Finally, we perform extensive ablations on key techniques for efficient RL training and effective sim-to-real transfer, and demonstrate effective deployment across diverse indoor and outdoor scenes under various lighting conditions.
Towards A Litmus Test for Common Sense
Venue: arXiv cs.AI 2025
Year: 2025

This paper is the second in a planned series aimed at envisioning a path to safe and beneficial artificial intelligence. Building on the conceptual insights of "Common Sense Is All You Need," we propose a more formal litmus test for common sense, adopting an axiomatic approach that combines minimal prior knowledge (MPK) constraints with diagonal or Godel-style arguments to create tasks beyond the agent's known concept set. We discuss how this approach applies to the Abstraction and Reasoning Corpus (ARC), acknowledging training/test data constraints, physical or virtual embodiment, and large language models (LLMs). We also integrate observations regarding emergent deceptive hallucinations, in which more capable AI systems may intentionally fabricate plausible yet misleading outputs to disguise knowledge gaps. The overarching theme is that scaling AI without ensuring common sense risks intensifying such deceptive tendencies, thereby undermining safety and trust. Aligning with the broader goal of developing beneficial AI without causing harm, our axiomatic litmus test not only diagnoses whether an AI can handle truly novel concepts but also provides a stepping stone toward an ethical, reliable foundation for future safe, beneficial, and aligned artificial intelligence.
Adaptive Twisting Sliding Control for Integrated Attack UAV's Autopilot and Guidance
Venue: arXiv cs.RO 2025
Year: 2025

This paper investigates an adaptive sliding-mode control for an integrated UAV autopilot and guidance system. First, a two-dimensional mathematical model of the system is derived by considering the incorporated lateral dynamics and relative kinematics of the UAV and its potential target of attack. Then, a sliding surface is derived utilizing the zero-effort miss distance. An adaptive twisting sliding mode (ATSMC) algorithm is applied to the integrated system. Simulation and comparisons have been accomplished. The results show our proposed design performs well in interception precision, even with high nonlinearity, uncertainties, disturbances, and abrupt changes in the target's movement, thanks to the adaptation strategy.
Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the July 2024 Trump Assassination Attempt
Venue: arXiv cs.SI 2025
Year: 2025

On July 13, 2024, at the Trump rally in Pennsylvania, someone attempted to assassinate Republican Presidential Candidate Donald Trump. This attempt sparked a large-scale discussion on social media. We collected posts from X (formerly known as Twitter) one week before and after the assassination attempt and aimed to model the short-term effects of such a ``shock'' on public opinions and discussion topics. Specifically, our study addresses three key questions: first, we investigate how public sentiment toward Donald Trump shifts over time and across regions (RQ1) and examine whether the assassination attempt itself significantly affects public attitudes, independent of the existing political alignments (RQ2). Finally, we explore the major themes in online conversations before and after the crisis, illustrating how discussion topics evolved in response to this politically charged event (RQ3). By integrating large language model-based sentiment analysis, difference-in-differences modeling, and topic modeling techniques, we find that following the attempt the public response was broadly sympathetic to Trump rather than polarizing, despite baseline ideological and regional disparities.
Explainable artificial intelligence (XAI): from inherent explainability to large language models
Venue: arXiv cs.LG 2025
Year: 2025

Artificial Intelligence (AI) has continued to achieve tremendous success in recent times. However, the decision logic of these frameworks is often not transparent, making it difficult for stakeholders to understand, interpret or explain their behavior. This limitation hinders trust in machine learning systems and causes a general reluctance towards their adoption in practical applications, particularly in mission-critical domains like healthcare and autonomous driving. Explainable AI (XAI) techniques facilitate the explainability or interpretability of machine learning models, enabling users to discern the basis of the decision and possibly avert undesirable behavior. This comprehensive survey details the advancements of explainable AI methods, from inherently interpretable models to modern approaches for achieving interpretability of various black box models, including large language models (LLMs). Additionally, we review explainable AI techniques that leverage LLM and vision-language model (VLM) frameworks to automate or improve the explainability of other machine learning models. The use of LLM and VLM as interpretability methods particularly enables high-level, semantically meaningful explanations of model decisions and behavior. Throughout the paper, we highlight the scientific principles, strengths and weaknesses of state-of-the-art methods and outline different areas of improvement. Where appropriate, we also present qualitative and quantitative comparison results of various methods to show how they compare. Finally, we discuss the key challenges of XAI and directions for future research.
GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar Editor
Venue: arXiv cs.CV 2025
Year: 2025

We introduce GaussianAvatar-Editor, an innovative framework for text-driven editing of animatable Gaussian head avatars that can be fully controlled in expression, pose, and viewpoint. Unlike static 3D Gaussian editing, editing animatable 4D Gaussian avatars presents challenges related to motion occlusion and spatial-temporal inconsistency. To address these issues, we propose the Weighted Alpha Blending Equation (WABE). This function enhances the blending weight of visible Gaussians while suppressing the influence on non-visible Gaussians, effectively handling motion occlusion during editing. Furthermore, to improve editing quality and ensure 4D consistency, we incorporate conditional adversarial learning into the editing process. This strategy helps to refine the edited results and maintain consistency throughout the animation. By integrating these methods, our GaussianAvatar-Editor achieves photorealistic and consistent results in animatable 4D Gaussian editing. We conduct comprehensive experiments across various subjects to validate the effectiveness of our proposed techniques, which demonstrates the superiority of our approach over existing methods. More results and code are available at: [Project Link](https://xiangyueliu.github.io/GaussianAvatar-Editor/).
Automatic Speech Recognition for Sanskrit with Transfer Learning
Venue: arXiv cs.CL 2025
Year: 2025

Sanskrit, one of humanity's most ancient languages, has a vast collection of books and manuscripts on diverse topics that have been accumulated over millennia. However, its digital content (audio and text), which is vital for the training of AI systems, is profoundly limited. Furthermore, its intricate linguistics make it hard to develop robust NLP tools for wider accessibility. Given these constraints, we have developed an automatic speech recognition model for Sanskrit by employing transfer learning mechanism on OpenAI's Whisper model. After carefully optimising the hyper-parameters, we obtained promising results with our transfer-learned model achieving a word error rate of 15.42% on Vaksancayah dataset. An online demo of our model is made available for the use of public and to evaluate its performance firsthand thereby paving the way for improved accessibility and technological support for Sanskrit learning in the modern era.
Mapping scientific communities at scale
Venue: arXiv cs.DL 2025
Year: 2025

This study introduces a novel methodology for mapping scientific communities at scale, addressing challenges associated with network analysis in large bibliometric datasets. By leveraging enriched publication metadata from the French research portal scanR and applying advanced filtering techniques to prioritize the strongest interactions between entities, we construct detailed, scalable network maps. These maps are enhanced through systematic disambiguation of authors, affiliations, and topics using persistent identifiers and specialized algorithms. The proposed framework integrates Elasticsearch for efficient data aggregation, Graphology for network spatialization (Force Atltas2) and community detection (Louvain algorithm) and VOSviewer for network vizualization. A Large Language Model (Mistral Nemo) is used to label the communities detected and OpenAlex data helps to enrich the results with citation counts estimation to detect hot topics. This scalable approach enables insightful exploration of research collaborations and thematic structures, with potential applications for strategic decision-making in science policy and funding. These web tools are effective at the global (national) scale but are also available (and can be integrated via iframes) on the perimeter of any French research institution (from large research organisms to any laboratory). The scanR community analysis tool is available online [https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started](https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started). All tools and methodologies are open-source on the repo [https://github.com/dataesr/scanr-ui](https://github.com/dataesr/scanr-ui)
LWGANet: Addressing Spatial and Channel Redundancy in Remote Sensing Visual Tasks with Light-Weight Grouped Attention
Venue: arXiv cs.CV 2025
Year: 2025

Light-weight neural networks for remote sensing (RS) visual analysis must overcome two inherent redundancies: spatial redundancy from vast, homogeneous backgrounds, and channel redundancy, where extreme scale variations render a single feature space inefficient. Existing models, often designed for natural images, fail to address this dual challenge in RS scenarios. To bridge this gap, we propose LWGANet, a light-weight backbone engineered for RS-specific properties. LWGANet introduces two core innovations: a Top-K Global Feature Interaction (TGFI) module that mitigates spatial redundancy by focusing computation on salient regions, and a Light-Weight Grouped Attention (LWGA) module that resolves channel redundancy by partitioning channels into specialized, scale-specific pathways. By synergistically resolving these core inefficiencies, LWGANet achieves a superior trade-off between feature representation quality and computational cost. Extensive experiments on twelve diverse datasets across four major RS tasks--scene classification, oriented object detection, semantic segmentation, and change detection--demonstrate that LWGANet consistently outperforms state-of-the-art light-weight backbones in both accuracy and efficiency. Our work establishes a new, robust baseline for efficient visual analysis in RS images.