OpenRaiser/Intern-Atlas huggingface dataset:
BikeBench: A Bicycle Design Benchmark for Generative Models with Objectives and Constraints
Venue: arXiv cs.CE 2025
Year: 2025

We introduce BikeBench, an engineering design benchmark for evaluating generative models on problems with multiple real-world objectives and constraints. As generative AI's reach continues to grow, evaluating its capability to understand physical laws, human guidelines, and hard constraints grows increasingly important. Engineering product design lies at the intersection of these difficult tasks, providing new challenges for AI capabilities. BikeBench evaluates AI models' capabilities to generate bicycle designs that not only resemble the dataset, but meet specific performance objectives and constraints. To do so, BikeBench quantifies a variety of human-centered and multiphysics performance characteristics, such as aerodynamics, ergonomics, structural mechanics, human-rated usability, and similarity to subjective text or image prompts. Supporting the benchmark are several datasets of simulation results, a dataset of 10,000 human-rated bicycle assessments, and a synthetically generated dataset of 1.6M designs, each with a parametric, CAD/XML, SVG, and PNG representation. BikeBench is uniquely configured to evaluate tabular generative models, large language models (LLMs), design optimization, and hybrid algorithms side-by-side. Our experiments indicate that LLMs and tabular generative models fall short of hybrid GenAI+optimization algorithms in design quality, constraint satisfaction, and similarity scores, suggesting significant room for improvement. We hope that BikeBench, a first-of-its-kind benchmark, will help catalyze progress in generative AI for constrained multi-objective engineering design problems. We provide code, data, an interactive leaderboard, and other resources at https://github.com/Lyleregenwetter/BikeBench.
A Comparative Study of Classical and Post-Quantum Cryptographic Algorithms in the Era of Quantum Computing
Venue: arXiv cs.ET 2025
Year: 2025

The advent of quantum computing poses a significant threat to the foundational cryptographic algorithms that secure modern digital communications. Protocols such as HTTPS, digital certificates, and public key infrastructures (PKIs) heavily rely on cryptographic primitives like RSA, ECC, and Diffie-Hellman, which are vulnerable to quantum attacks -- most notably Shor's algorithm. This paper presents a comprehensive comparative analysis between classical cryptographic algorithms currently in widespread use and emerging post-quantum cryptographic schemes designed to withstand quantum adversaries. We review the cryptographic mechanisms underpinning modern internet security, outline the mathematical foundations of quantum attacks, and evaluate the security, performance, and implementation feasibility of quantum-resistant alternatives such as Kyber, Dilithium, and Falcon. Additionally, we assess the hybrid approaches currently being explored by institutions and tech companies to enable a smooth transition to post-quantum cryptography. By providing an in-depth comparison, this study aims to guide researchers, developers, and policymakers in understanding the critical implications of quantum computing on cryptographic infrastructures and the necessary steps for securing communications in the quantum era.
Team PA-VCG's Solution for Competition on Understanding Chinese College Entrance Exam Papers in ICDAR'25
Venue: arXiv cs.CV 2025
Year: 2025

This report presents Team PA-VGG's solution for the ICDAR'25 Competition on Understanding Chinese College Entrance Exam Papers. In addition to leveraging high-resolution image processing and a multi-image end-to-end input strategy to address the challenges of dense OCR extraction and complex document layouts in Gaokao papers, our approach introduces domain-specific post-training strategies. Experimental results demonstrate that our post-training approach achieves the most outstanding performance, securing first place with an accuracy rate of 89.6%.
Rxiv-Maker: an automated template engine for streamlined scientific publications
Venue: arXiv cs.DL 2025
Year: 2025

The rapid growth of preprint servers has accelerated scientific dissemination but has also shifted the technical burden of manuscript preparation to authors. This challenge is particularly acute in computational research, where manuscripts must remain synchronised with evolving data and code. We present Rxiv-Maker, a framework that resolves this by converting simple Markdown files into professionally typeset, publication-ready PDFs. Its core feature is the ability to execute embedded code, creating a self-updating manuscript where figures and statistical values are generated directly from source data during compilation. This ensures that the final document is always current and fully reproducible. By integrating with standard tools like Git and Visual Studio (VS) Code, Rxiv-Maker provides an efficient, transparent, and collaborative authoring experience, applying principles of software engineering to academic writing to foster open and verifiable science.
QDockBank: A Dataset for Ligand Docking on Protein Fragments Predicted on Utility-Level Quantum Computers
Venue: arXiv cs.ET 2025
Year: 2025

Protein structure prediction is a core challenge in computational biology, particularly for fragments within ligand-binding regions, where accurate modeling is still difficult. Quantum computing offers a novel first-principles modeling paradigm, but its application is currently limited by hardware constraints, high computational cost, and the lack of a standardized benchmarking dataset. In this work, we present QDockBank-the first large-scale protein fragment structure dataset generated entirely using utility-level quantum computers, specifically designed for protein-ligand docking tasks. QDockBank comprises 55 protein fragments extracted from ligand-binding pockets. The dataset was generated through tens of hours of execution on superconducting quantum processors, making it the first quantum-based protein structure dataset with a total computational cost exceeding one million USD. Experimental evaluations demonstrate that structures predicted by QDockBank outperform those predicted by AlphaFold2 and AlphaFold3 in terms of both RMSD and docking affinity scores. QDockBank serves as a new benchmark for evaluating quantum-based protein structure prediction.
GPT Chatbots for Alleviating Anxiety and Depression: A Pilot Randomized Controlled Trial with Afghan Women
Venue: arXiv cs.HC 2025
Year: 2025

In this study, we investigated the effects of GPT-4, with and without specific conversational instructions, on the mental health of Afghan women. These women face multifaceted challenges, including Taliban-imposed restrictions, societal inequalities, and domestic violence, adversely affecting their well-being. We conducted a randomized controlled trial with 60 participants, dividing them into three groups: GPT-4, a supportive listener (GPT-4 with empathetic engagement instructions), and a waiting list. The Hospital Anxiety and Depression Scale (HADS) was used to measure anxiety and depression before and after the intervention. Linguistic analysis of chat data examined personal pronouns, tones, emotions, and Language Style Matching (LSM). The supportive listener group showed a significant reduction in HADS scores compared to the other groups. Linguistic analysis revealed a more positive tone and higher LSM in the supportive listener group, with a significant negative correlation between LSM and changes in HADS scores, indicating greater linguistic alignment was linked to reductions in anxiety and depression. Perceived empathy ratings were also significantly higher in the supportive listener group. These findings highlight the potential of AI-driven interventions, like GPT-4, in providing accessible mental health support. However, such interventions should complement traditional psychotherapy, ensuring a collaborative approach to optimize therapeutic outcomes.
RestAware: Non-Invasive Sleep Monitoring Using FMCW Radar and AI-Generated Summaries
Venue: arXiv cs.HC 2025
Year: 2025

Monitoring sleep posture and behavior is critical for diagnosing sleep disorders and improving overall sleep quality. However, traditional approaches, such as wearable devices, cameras, and pressure sensors, often compromise user comfort, fail under obstructions like blankets, and raise privacy concerns. To overcome these limitations, we present RestAware, a non-invasive, contactless sleep monitoring system based on a 24GHz frequency-modulated continuous wave (FMCW) radar. Our system is evaluated on 25 participants across eight common sleep postures, achieving 92% classification accuracy and an F1-score of 0.91 using a K-Nearest Neighbors (KNN) classifier. In addition, we integrate instruction-tuned large language models (Mistral, Llama, and Falcon) to generate personalized, human-readable sleep summaries from radar-derived posture data. This low-cost ($ 35), privacy-preserving solution offers a practical alternative for real-time deployment in smart homes and clinical environments.
A survey on proximity monitoring and warning in construction
Venue: arXiv cs.DL 2025
Year: 2025

Various technologies have been applied to monitor the proximity between two construction entities, preventing struck-by accidents and thereby enhancing onsite safety. This study comprehensively reviews related efforts dedicated to proximity monitoring and warning (PMW) based on 97 relevant articles published between 2010 and 2024. The bibliometric analysis reveals the technical roadmap over time, as well as the five most influential leaders and the two largest research networks they have established. The qualitative review is then conducted from four perspectives: influencing factor study, hazard level definition and determination, proximity perception, and alarm issuing and receiving. Finally, the limitations and challenges of current proximity perception are discussed, along with corresponding future research directions, including end-to-end three-dimensional (3D) object detection, real-time 3D reconstruction and updating for dynamic construction scenes, and multimodal fusion. This review presents the current research status, limitations, and future directions of PMW, guiding the future development of PMW systems.
A Relational (Re)Turn: Revisit Interactive Art through Interaction and Aesthetics
Venue: arXiv cs.CY 2025
Year: 2025

This paper revisits the concept of interaction in interactive art, tracing its evolution from sociocultural origins to its narrowing within human-computer paradigms. It critiques this reduction and proposes a relational (re)turn through reclaiming interaction as intersubjective and relational. Through a synthesis of aesthetic theories and case studies from Ars Electronica, the paper introduces Techno Relational Aesthetics, a new conceptual lens that emphasizes technologically mediated relationality. This approach expands interactive art beyond audience-artwork interaction and opens the possibility to broader relational practices.
Stochastic Optimal Control via Measure Relaxations
Venue: arXiv cs.LG 2025
Year: 2025

The optimal control problem of stochastic systems is commonly solved via robust or scenario-based optimization methods, which are both challenging to scale to long optimization horizons. We cast the optimal control problem of a stochastic system as a convex optimization problem over occupation measures. We demonstrate our method on a set of synthetic and real-world scenarios, learning cost functions from data via Christoffel polynomials. The code for our experiments is available at https://github.com/ebuehrle/dpoc.
Maximize margins for robust splicing detection
Venue: arXiv cs.LG 2025
Year: 2025

Despite recent progress in splicing detection, deep learning-based forensic tools remain difficult to deploy in practice due to their high sensitivity to training conditions. Even mild post-processing applied to evaluation images can significantly degrade detector performance, raising concerns about their reliability in operational contexts. In this work, we show that the same deep architecture can react very differently to unseen post-processing depending on the learned weights, despite achieving similar accuracy on in-distribution test data. This variability stems from differences in the latent spaces induced by training, which affect how samples are separated internally. Our experiments reveal a strong correlation between the distribution of latent margins and a detector's ability to generalize to post-processed images. Based on this observation, we propose a practical strategy for building more robust detectors: train several variants of the same model under different conditions, and select the one that maximizes latent margins.
An analysis of AI Decision under Risk: Prospect theory emerges in Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Judgment of risk is key to decision-making under uncertainty. As Daniel Kahneman and Amos Tversky famously discovered, humans do so in a distinctive way that departs from mathematical rationalism. Specifically, they demonstrated experimentally that humans accept more risk when they feel themselves at risk of losing something than when they might gain. I report the first tests of Kahneman and Tversky's landmark 'prospect theory' with Large Language Models, including today's state of the art chain-of-thought 'reasoners'. In common with humans, I find that prospect theory often anticipates how these models approach risky decisions across a range of scenarios. I also demonstrate that context is key to explaining much of the variance in risk appetite. The 'frame' through which risk is apprehended appears to be embedded within the language of the scenarios tackled by the models. Specifically, I find that military scenarios generate far larger 'framing effects' than do civilian settings, ceteris paribus. My research suggests, therefore, that language models the world, capturing our human heuristics and biases. But also that these biases are uneven - the idea of a 'frame' is richer than simple gains and losses. Wittgenstein's notion of 'language games' explains the contingent, localised biases activated by these scenarios. Finally, I use my findings to reframe the ongoing debate about reasoning and memorisation in LLMs.
Translating Machine Learning Interpretability into Clinical Insights for ICU Mortality Prediction
Venue: arXiv cs.CY 2025
Year: 2025

Current research efforts largely focus on employing at most one interpretable method to elucidate machine learning (ML) model performance. However, significant barriers remain in translating these interpretability techniques into actionable insights for clinicians, notably due to complexities such as variability across clinical settings and the Rashomon effect. In this study, we developed and rigorously evaluated two ML models along with interpretation mechanisms, utilizing data from 131,051 ICU admissions across 208 hospitals in the United States, sourced from the eICU Collaborative Research Database. We examined two datasets: one with imputed missing values (130,810 patients, 5.58% ICU mortality) and another excluding patients with missing data (5,661 patients, 23.65% ICU mortality). The random forest (RF) model demonstrated an AUROC of 0.912 with the first dataset and 0.839 with the second dataset, while the XGBoost model achieved an AUROC of 0.924 with the first dataset and 0.834 with the second dataset. Consistently identified predictors of ICU mortality across datasets, cross-validation folds, models, and explanation mechanisms included lactate levels, arterial pH, body temperature, and others. By aligning with routinely collected clinical variables, this study aims to enhance ML model interpretability for clinical use, promote greater understanding and adoption among clinicians, and ultimately contribute to improved patient outcomes.
WOCD: A Semi-Supervised Method for Overlapping Community Detection Using Weak Cliques
Venue: arXiv cs.SI 2025
Year: 2025

Overlapping community detection (OCD) is a fundamental graph data analysis task for extracting graph patterns. Traditional OCD methods can be broadly divided into node clustering and link clustering approaches, both of which rely solely on link information to identify overlapping communities. In recent years, deep learning-based methods have made significant advancements for this task. However, existing GNN-based approaches often face difficulties in effectively integrating link, attribute, and prior information, along with challenges like limited receptive fields and over-smoothing, which hinder their performance on complex overlapping community detection. In this paper, we propose a Weak-clique based Overlapping Community Detection method, namely WOCD, which incorporates prior information and optimizes the use of link information to improve detection accuracy. Specifically, we introduce pseudo-labels within a semi-supervised framework to strengthen the generalization ability, making WOCD more versatile. Furthermore, we initialize pseudo-labels using weak cliques to fully leverage link and prior information, leading to better detection accuracy. Additionally, we employ a single-layer Graph Transformer combined with GNN, which achieves significant performance improvements while maintaining efficiency. We evaluate WOCD on eight real-world attributed datasets, and the results demonstrate that it outperforms the state-of-the-art semi-supervised OCD method by a significant margin in terms of accuracy.
Cooperative effects in feature importance of individual patterns: application to air pollutants and Alzheimer disease
Venue: arXiv cs.LG 2025
Year: 2025

Leveraging recent advances in the analysis of synergy and redundancy in systems of random variables, an adaptive version of the widely used metric Leave One Covariate Out (LOCO) has been recently proposed to quantify cooperative effects in feature importance (Hi-Fi), a key technique in explainable artificial intelligence (XAI), so as to disentangle high-order effects involving a particular input feature in regression problems. Differently from standard feature importance tools, where a single score measures the relevance of each feature, each feature is here characterized by three scores, a two-body (unique) score and higher-order scores (redundant and synergistic). This paper presents a framework to assign those three scores (unique, redundant, and synergistic) to each individual pattern of the data set, while comparing it with the well-known measure of feature importance named {\it Shapley effect}. To illustrate the potential of the proposed framework, we focus on a One-Health application: the relation between air pollutants and Alzheimer's disease mortality rate. Our main result is the synergistic association between features related to $O_3$ and $NO_2$ with mortality, especially in the provinces of Bergamo e Brescia; notably also the density of urban green areas displays synergistic influence with pollutants for the prediction of AD mortality. Our results place local Hi-Fi as a promising tool of wide applicability, which opens new perspectives for XAI as well as to analyze high-order relationships in complex systems.
How Sovereign Is Sovereign Compute? A Review of 775 Non-U.S. Data Centers
Venue: arXiv cs.CY 2025
Year: 2025

Previous literature has proposed that the companies operating data centers enforce government regulations on AI companies. Using a new dataset of 775 non-U.S. data center projects, this paper estimates how often data centers could be subject to foreign legal authorities due to the nationality of the data center operators. We find that U.S. companies operate 48% of all non-U.S. data center projects in our dataset when weighted by investment value - a proxy for compute capacity. This is an approximation based on public data and should be interpreted as an initial estimate. For the United States, our findings suggest that data center operators offer a lever for internationally governing AI that complements traditional export controls, since operators can be used to regulate computing resources already deployed in non-U.S. data centers. For other countries, our results show that building data centers locally does not guarantee digital sovereignty if those facilities are run by foreign entities. To support future research, we release our dataset, which documents over 20 variables relating to each data center, including the year it was announced, the investment value, and its operator's national affiliation. The dataset also includes over 1,000 quotes describing these data centers' strategic motivations, operational challenges, and engagement with U.S. and Chinese entities.
LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring
Venue: arXiv cs.CR 2025
Year: 2025

Trustworthy evaluations of dangerous capabilities are increasingly crucial for determining whether an AI system is safe to deploy. One empirically demonstrated threat is sandbagging - the strategic underperformance on evaluations by AI models or their developers. A promising defense is to monitor a model's chain-of-thought (CoT) reasoning, as this could reveal its intentions and plans. In this work, we measure the ability of models to sandbag on dangerous capability evaluations against a CoT monitor by prompting them to sandbag while being either monitor-oblivious or monitor-aware. We show that both frontier models and small open-sourced models can covertly sandbag against CoT monitoring 0-shot without hints. However, they cannot yet do so reliably: they bypass the monitor 16-36% of the time when monitor-aware, conditioned on sandbagging successfully. We qualitatively analyzed the uncaught CoTs to understand why the monitor failed. We reveal a rich attack surface for CoT monitoring and contribute five covert sandbagging policies generated by models. These results inform potential failure modes of CoT monitoring and may help build more diverse sandbagging model organisms.
Investigating Crossing Perception in 3D Graph Visualisation
Venue: arXiv cs.GR 2025
Year: 2025

Human perception of graph drawings is influenced by a variety of impact factors for which quality measures are used as a proxy indicator. The investigation of those impact factors and their effects is important to evaluate and improve quality measures and drawing algorithms. The number of edge crossings in a 2D graph drawing has long been a main quality measure for drawing evaluation. The use of stereoscopic 3D graph visualisations has gained attraction over the last years, and results from several studies indicate that they can improve analysis efficiency for a range of analysis scenarios. While edge crossings can also occur in 3D, there are edge configurations in space that are not crossings but might be perceived as such from a specific viewpoint. Such configurations create crossings when projected on the corresponding 2D image plane and could impact readability similar to 2D crossings. In 3D drawings, the additional depth aspect and the subsequent impact factors of edge distance and relative edge direction in space might further influence the importance of those configurations for readability. We investigate the impact of such factors in an empirical study and report on findings of difference between major factor categories.