OpenRaiser/Intern-Atlas huggingface dataset:
CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Diabetes mellitus poses a significant health risk, as nearly 1 in 9 people are affected by it. Early detection can significantly lower this risk. Despite significant advancements in machine learning for identifying diabetic cases, results can still be influenced by the imbalanced nature of the data. To address this challenge, our study considered copula-based data augmentation, which preserves the dependency structure when generating data for the minority class and integrates it with machine learning (ML) techniques. We selected the Pima Indian dataset and generated data using A2 copula, then applied five machine learning algorithms: logistic regression, random forest, gradient boosting, extreme gradient boosting, and Multilayer Perceptron. Overall, our findings show that Random Forest with A2 copula oversampling (theta = 10) achieved the best performance, with improvements of 5.3% in accuracy, 9.5% in precision, 5.7% in recall, 7.6% in F1-score, and 1.1% in AUC compared to the standard SMOTE method. Furthermore, we statistically validated our results using the McNemar's test. This research represents the first known use of A2 copulas for data augmentation and serves as an alternative to the SMOTE technique, highlighting the efficacy of copulas as a statistical method in machine learning applications.
On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0
Venue: arXiv cs.CR 2025
Year: 2025

Healthcare 5.0 integrates Artificial Intelligence (AI), the Internet of Things (IoT), real-time monitoring, and human-centered design toward personalized medicine and predictive diagnostics. However, the increasing reliance on interconnected medical technologies exposes them to cyber threats. Meanwhile, current AI-driven cybersecurity models often neglect biomedical data, limiting their effectiveness and interpretability. This study addresses this gap by applying eXplainable AI (XAI) to a Healthcare 5.0 dataset that integrates network traffic and biomedical sensor data. Classification outputs indicate that XGBoost achieved 99% F1-score for benign and data alteration, and 81% for spoofing. Explainability findings reveal that network data play a dominant role in intrusion detection whereas biomedical features contributed to spoofing detection, with temperature reaching a Shapley values magnitude of 0.37.
Large Language Models for Spreadsheets: Benchmarking Progress and Evaluating Performance with FLARE
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) have demonstrated some significant capabilities across various domains; however, their effectiveness in spreadsheet related tasks remains underexplored. This study introduces a foundation for a comprehensive benchmark framework to evaluate the performance of leading LLMs in executing spreadsheet functions, formula generation and data manipulation tasks. The benchmark encompasses tasks ranging from basic formula creation to complex, real world spreadsheet scenarios. Our findings reveal that while LLMs exhibit proficiency in straightforward tasks, they often falter in complex, multi step operations, frequently producing plausible yet incorrect outputs. These results underscore the limitations of current LLMs in handling spreadsheet tasks that require precise logical reasoning and highlight the need for integrating symbolic reasoning capabilities into LLM architectures. To support this, we introduce FLARE (Formula Logic, Auditing, Reasoning and Evaluation) a new benchmark for evaluating LLM performance on real-world spreadsheet logic, auditing, and reasoning tasks.
Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

The social metaverse is a growing digital ecosystem that blends virtual and physical worlds. It allows users to interact socially, work, shop, and enjoy entertainment. However, privacy remains a major challenge, as immersive interactions require continuous collection of biometric and behavioral data. At the same time, ensuring high-quality, low-latency streaming is difficult due to the demands of real-time interaction, immersive rendering, and bandwidth optimization. To address these issues, we propose ASMS (Adaptive Social Metaverse Streaming), a novel streaming system based on Federated Multi-Agent Proximal Policy Optimization (F-MAPPO). ASMS leverages F-MAPPO, which integrates federated learning (FL) and deep reinforcement learning (DRL) to dynamically adjust streaming bit rates while preserving user privacy. Experimental results show that ASMS improves user experience by at least 14% compared to existing streaming methods across various network conditions. Therefore, ASMS enhances the social metaverse experience by providing seamless and immersive streaming, even in dynamic and resource-constrained networks, while ensuring that sensitive user data remains on local devices.
A workflow for generating synthetic LiDAR datasets in simulation environments
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a simulation workflow for generating synthetic LiDAR datasets to support autonomous vehicle perception, robotics research, and sensor security analysis. Leveraging the CoppeliaSim simulation environment and its Python API, we integrate time-of-flight LiDAR, image sensors, and two dimensional scanners onto a simulated vehicle platform operating within an urban scenario. The workflow automates data capture, storage, and annotation across multiple formats (PCD, PLY, CSV), producing synchronized multimodal datasets with ground truth pose information. We validate the pipeline by generating large-scale point clouds and corresponding RGB and depth imagery. The study examines potential security vulnerabilities in LiDAR data, such as adversarial point injection and spoofing attacks, and demonstrates how synthetic datasets can facilitate the evaluation of defense strategies. Finally, limitations related to environmental realism, sensor noise modeling, and computational scalability are discussed, and future research directions, such as incorporating weather effects, real-world terrain models, and advanced scanner configurations, are proposed. The workflow provides a versatile, reproducible framework for generating high-fidelity synthetic LiDAR datasets to advance perception research and strengthen sensor security in autonomous systems. Documentation and examples accompany this framework; samples of animated cloud returns and image sensor data can be found at this Link.
Spatial-Temporal Pre-Training for Embryo Viability Prediction Using Time-Lapse Videos
Venue: arXiv cs.CV 2025
Year: 2025

Automating embryo viability prediction for in vitro fertilization (IVF) is important but challenging due to the limited availability of labeled pregnancy outcome data, as only a small fraction of embryos are labeled after transfer. Self-supervised learning (SSL) can leverage both labeled and unlabeled data to improve prediction. However, existing SSL methods for videos are not directly applicable to embryo development videos due to two challenges: (1) embryo time-lapse videos contain hundreds of frames, requiring significant GPU memory for conventional SSL; (2) the dataset contains videos with varying lengths and many outlier frames, causing traditional video alignment methods to struggle with semantic misalignment. We propose Spatial-Temporal Pre-Training (STPT) to address these challenges. STPT includes two stages: spatial and temporal. In each stage, only one encoder is trained while the other is frozen, reducing memory demands. To handle temporal misalignment, STPT avoids frame-by-frame alignment across videos. The spatial stage learns from alignments within each video and its temporally consistent augmentations. The temporal stage then models relationships between video embeddings. Our method efficiently handles long videos and temporal variability. On 23,027 time-lapse videos (3,286 labeled), STPT achieves the highest AUC of 0.635 (95% CI: 0.632-0.638) compared to baselines, with limited computational resources.
UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making
Venue: arXiv cs.CL 2025
Year: 2025

As Large Language Models (LLMs) are integrated into safety-critical applications involving sequential decision-making in the real world, it is essential to know when to trust LLM decisions. Existing LLM Uncertainty Quantification (UQ) methods are primarily designed for single-turn question-answering formats, resulting in multi-step decision-making scenarios, e.g., LLM agentic system, being underexplored. In this paper, we introduce a principled, information-theoretic framework that decomposes LLM sequential decision uncertainty into two parts: (i) internal uncertainty intrinsic to the current decision, which is focused on existing UQ methods, and (ii) extrinsic uncertainty, a Mutual-Information (MI) quantity describing how much uncertainty should be inherited from preceding decisions. We then propose UProp, an efficient and effective extrinsic uncertainty estimator that converts the direct estimation of MI to the estimation of Pointwise Mutual Information (PMI) over multiple Trajectory-Dependent Decision Processes (TDPs). UProp is evaluated over extensive multi-step decision-making benchmarks, e.g., AgentBench and HotpotQA, with state-of-the-art LLMs, e.g., GPT-4.1 and DeepSeek-V3. Experimental results demonstrate that UProp significantly outperforms existing single-turn UQ baselines equipped with thoughtful aggregation strategies. Moreover, we provide a comprehensive analysis of UProp, including sampling efficiency, potential applications, and intermediate uncertainty propagation, to demonstrate its effectiveness. Codes will be available at https://github.com/jinhaoduan/UProp.
Online Adaptation for Flying Quadrotors in Tight Formations
Venue: arXiv cs.RO 2025
Year: 2025

The task of flying in tight formations is challenging for teams of quadrotors because the complex aerodynamic wake interactions can destabilize individual team members as well as the team. Furthermore, these aerodynamic effects are highly nonlinear and fast-paced, making them difficult to model and predict. To overcome these challenges, we present L1 KNODE-DW MPC, an adaptive, mixed expert learning based control framework that allows individual quadrotors to accurately track trajectories while adapting to time-varying aerodynamic interactions during formation flights. We evaluate L1 KNODE-DW MPC in two different three-quadrotor formations and show that it outperforms several MPC baselines. Our results show that the proposed framework is capable of enabling the three-quadrotor team to remain vertically aligned in close proximity throughout the flight. These findings show that the L1 adaptive module compensates for unmodeled disturbances most effectively when paired with an accurate dynamics model. A video showcasing our framework and the physical experiments is available here: https://youtu.be/9QX1Q5Ut9Rs
Learning golf swing signatures from a single wrist-worn inertial sensor
Venue: arXiv cs.CV 2025
Year: 2025

Despite its importance for performance and injury prevention, golf swing analysis is limited by isolated metrics, underrepresentation of professional athletes, and a lack of rich, interpretable movement representations. We address these gaps with a holistic, data-driven framework for personalized golf swing analysis from a single wrist-worn sensor. We build a large dataset of professional swings from publicly available videos, reconstruct full-body 3D kinematics using biologically accurate human mesh recovery, and generate synthetic inertial data to train neural networks that infer motion and segment swing phases from wrist-based input. We learn a compositional, discrete vocabulary of motion primitives that facilitates the detection and visualization of technical flaws, and is expressive enough to predict player identity, club type, sex, and age. Our system accurately estimates full-body kinematics and swing events from wrist data, delivering lab-grade motion analysis on-course and supporting early detection of anomalous movement patterns. Explainability methods reveal subtle, individualized movement signatures, reinforcing the view that variability is a hallmark of skilled performance. Longitudinal tracking demonstrates practical value: as one player's handicap improved from 50 to 2.2 over 1.5 years, our system captured measurable technical progress and provided targeted, actionable feedback. Our findings challenge common assumptions, such as swing consistency across clubs and the existence of a single "ideal" swing, and uncover latent biomarkers shaped by both intrinsic traits and task-specific constraints. This work bridges lab and field-based biomechanics, offering scalable, accessible, high-fidelity motion analysis for research, coaching, and injury prevention, while opening new directions in movement-based phenotyping, personalized equipment design, and motor skill development.
VeriLocc: End-to-End Cross-Architecture Register Allocation via LLM
Venue: arXiv cs.CL 2025
Year: 2025

Modern GPUs evolve rapidly, yet production compilers still rely on hand-crafted register allocation heuristics that require substantial re-tuning for each hardware generation. We introduce VeriLocc, a framework that combines large language models (LLMs) with formal compiler techniques to enable generalizable and verifiable register allocation across GPU architectures. VeriLocc fine-tunes an LLM to translate intermediate representations (MIRs) into target-specific register assignments, aided by static analysis for cross-architecture normalization and generalization and a verifier-guided regeneration loop to ensure correctness. Evaluated on matrix multiplication (GEMM) and multi-head attention (MHA), VeriLocc achieves 85-99% single-shot accuracy and near-100% pass@100. Case study shows that VeriLocc discovers more performant assignments than expert-tuned libraries, outperforming rocBLAS by over 10% in runtime.
Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nyström Method
Venue: arXiv cs.DS 2025
Year: 2025

The Nyström method is a popular low-rank approximation technique for large matrices that arise in kernel methods and convex optimization. Yet, when the data exhibits heavy-tailed spectral decay, the effective dimension of the problem often becomes so large that even the Nyström method may be outside of our computational budget. To address this, we propose Block-Nyström, an algorithm that injects a block-diagonal structure into the Nyström method, thereby significantly reducing its computational cost while recovering strong approximation guarantees. We show that Block-Nyström can be used to construct improved preconditioners for second-order optimization, as well as to efficiently solve kernel ridge regression for statistical learning over Hilbert spaces. Our key technical insight is that, within the same computational budget, combining several smaller Nyström approximations leads to stronger tail estimates of the input spectrum than using one larger approximation. Along the way, we provide a novel recursive preconditioning scheme for efficiently inverting the Block-Nyström matrix, and provide new statistical learning bounds for a broad class of approximate kernel ridge regression solvers.
Signal Recovery on Algebraic Varieties Using Linear Samples
Venue: arXiv cs.IT 2025
Year: 2025

The recovery of an unknown signal from its linear measurements is a fundamental problem spanning numerous scientific and engineering disciplines. Commonly, prior knowledge suggests that the underlying signal resides within a known algebraic variety. This context naturally leads to a question: what is the minimum number of measurements required to uniquely recover any signal belonging to such an algebraic variety? In this survey paper, we introduce a method that leverages tools from algebraic geometry to address this question. We then demonstrate the utility of this approach by applying it to two problems: phase retrieval and low-rank matrix recovery. We also highlight several open problems, which could serve as a basis for future investigations in this field.
Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps
Venue: arXiv cs.CY 2025
Year: 2025

Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundamental challenge for tutoring systems. Previous research has reduced overpractice through the development of better problem selection algorithms and the authoring of focused practice tasks. However, few efforts have concentrated on reducing overpractice through step-level adaptivity, which can avoid resource-intensive curriculum redesign. We propose and evaluate Fast-Forwarding as a technique that enhances existing problem selection algorithms. Based on simulation studies informed by learner models and problem-solving pathways derived from real student data, Fast-Forwarding can reduce overpractice by up to one-third, as it does not require students to complete problem-solving steps if all remaining pathways are fully mastered. Fast-Forwarding is a flexible method that enhances any problem selection algorithm, though its effectiveness is highest for algorithms that preferentially select difficult problems. Therefore, our findings suggest that while Fast-Forwarding may improve student practice efficiency, the size of its practical impact may also depend on students' ability to stay motivated and engaged at higher levels of difficulty.
OpenMAP-BrainAge: Generalizable and Interpretable Brain Age Predictor
Venue: arXiv cs.CV 2025
Year: 2025

Purpose: To develop an age prediction model which is interpretable and robust to demographic and technological variances in brain MRI scans. Materials and Methods: We propose a transformer-based architecture that leverages self-supervised pre-training on large-scale datasets. Our model processes pseudo-3D T1-weighted MRI scans from three anatomical views and incorporates brain volumetric information. By introducing a stem architecture, we reduce the conventional quadratic complexity of transformer models to linear complexity, enabling scalability for high-dimensional MRI data. We trained our model on ADNI2 $\&$ 3 (N=1348) and OASIS3 (N=716) datasets (age range: 42 - 95) from the North America, with an 8:1:1 split for train, validation and test. Then, we validated it on the AIBL dataset (N=768, age range: 60 - 92) from Australia. Results: We achieved an MAE of 3.65 years on ADNI2 $\&$ 3 and OASIS3 test set and a high generalizability of MAE of 3.54 years on AIBL. There was a notable increase in brain age gap (BAG) across cognitive groups, with mean of 0.15 years (95% CI: [-0.22, 0.51]) in CN, 2.55 years ([2.40, 2.70]) in MCI, 6.12 years ([5.82, 6.43]) in AD. Additionally, significant negative correlation between BAG and cognitive scores was observed, with correlation coefficient of -0.185 (p < 0.001) for MoCA and -0.231 (p < 0.001) for MMSE. Gradient-based feature attribution highlighted ventricles and white matter structures as key regions influenced by brain aging. Conclusion: Our model effectively fused information from different views and volumetric information to achieve state-of-the-art brain age prediction accuracy, improved generalizability and interpretability with association to neurodegenerative disorders.
HIRE: Lightweight High-Resolution Image Feature Enrichment for Multimodal LLMs
Venue: arXiv cs.CV 2025
Year: 2025

The integration of high-resolution image features in modern multimodal large language models has demonstrated significant improvements in fine-grained visual understanding tasks, achieving high performance across multiple benchmarks. Since these features are obtained from large image encoders like ViT, they come with a significant increase in computational costs due to multiple calls to these encoders. In this work, we first develop an intuition for feature upsampling as a natural extension of high-resolution feature generation. Through extensive experiments and ablations, we demonstrate how a shallow feature enricher can achieve competitive results with tremendous reductions in training and inference time as well as computational cost, with upto 1.5x saving in FLOPs.
Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

While Large Language Models (LLMs) demonstrate impressive reasoning capabilities, growing evidence suggests much of their success stems from memorized answer-reasoning patterns rather than genuine inference. In this work, we investigate a central question: are LLMs primarily anchored to final answers or to the textual pattern of reasoning chains? We propose a five-level answer-visibility prompt framework that systematically manipulates answer cues and probes model behavior through indirect, behavioral analysis. Experiments across state-of-the-art LLMs reveal a strong and consistent reliance on explicit answers. The performance drops by 26.90\% when answer cues are masked, even with complete reasoning chains. These findings suggest that much of the reasoning exhibited by LLMs may reflect post-hoc rationalization rather than true inference, calling into question their inferential depth. Our study uncovers the answer-anchoring phenomenon with rigorous empirical validation and underscores the need for a more nuanced understanding of what constitutes reasoning in LLMs.
Pixel-Optimization-Free Patch Attack on Stereo Depth Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Stereo Depth Estimation (SDE) is essential for scene perception in vision-based systems such as autonomous driving. Prior work shows SDE is vulnerable to pixel-optimization attacks, but these methods are limited to digital, static, and view-specific settings, making them impractical. This raises a central question: how to design deployable, adaptive, and transferable attacks under realistic constraints? We present two contributions to answer it. First, we build a unified framework that extends pixel-optimization attacks to four stereo-matching stages: feature extraction, cost-volume construction, cost aggregation, and disparity regression. Through systematic evaluation across nine SDE models with realistic constraints like photometric consistency, we show existing attacks suffer from poor transferability. Second, we propose PatchHunter, the first pixel-optimization-free attack. PatchHunter casts patch generation as a search in a structured space of visual patterns that disrupt core SDE assumptions, and uses a reinforcement learning policy to discover effective and transferable patterns efficiently. We evaluate PatchHunter on three levels: autonomous driving dataset, high-fidelity simulator, and real-world deployment. On KITTI, PatchHunter outperforms pixel-level attacks in both effectiveness and black-box transferability. Tests in CARLA and on vehicles with industrial-grade stereo cameras confirm robustness to physical variations. Even under challenging conditions such as low lighting, PatchHunter achieves a D1-all error above 0.4, while pixel-level attacks remain near 0.