OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Prototyping: Autonomous, Enterprise-Grade Frontend Development from Pixel to Production via a Specialized Multi-Agent Framework
Venue: arXiv cs.SE 2025
Year: 2025

We present AI4UI, a framework of autonomous front-end development agents purpose-built to meet the rigorous requirements of enterprise-grade application delivery. Unlike general-purpose code assistants designed for rapid prototyping, AI4UI focuses on production readiness delivering secure, scalable, compliant, and maintainable UI code integrated seamlessly into enterprise workflows. AI4UI operates with targeted human-in-the-loop involvement: at the design stage, developers embed a Gen-AI-friendly grammar into Figma prototypes to encode requirements for precise interpretation; and at the post processing stage, domain experts refine outputs for nuanced design adjustments, domain-specific optimizations, and compliance needs. Between these stages, AI4UI runs fully autonomously, converting designs into engineering-ready UI code. Technical contributions include a Figma grammar for autonomous interpretation, domain-aware knowledge graphs, a secure abstract/package code integration strategy, expertise driven architecture templates, and a change-oriented workflow coordinated by specialized agent roles. In large-scale benchmarks against industry baselines and leading competitor systems, AI4UI achieved 97.24% platform compatibility, 87.10% compilation success, 86.98% security compliance, 78.00% feature implementation success, 73.50% code-review quality, and 73.36% UI/UX consistency. In blind preference studies with 200 expert evaluators, AI4UI emerged as one of the leaders demonstrating strong competitive standing among leading solutions. Operating asynchronously, AI4UI generates thousands of validated UI screens in weeks rather than months, compressing delivery timeline
Measuring the Potential of Scientific Literature: A Network-Based Approach to Identifying Paradigm-Shifting Research
Venue: arXiv cs.DL 2025
Year: 2025

This study introduces the Disruption Index as a superior citation-based metric. This index quantitatively assesses the degree to which a publication redirects subsequent scholarly attention away from its preceding literature, thus measuring its novelty and disruptive impact. We tested the D metric's efficacy using a rigorous dataset comprising seminal publications by Nobel Prize winners across Physics, Chemistry, and Physiology or Medicine, benchmarked against control papers with comparable citation counts but non-transformative influence. Our analysis conclusively demonstrates that the D metric effectively distinguishes these prize-worthy, field-redefining works from highly cited but merely incremental research. Furthermore, we explore two contextual variables associated with high disruptive potential: (i) the scale of collaboration (author team size) and (ii) the linguistic structure of the article's title and summary text. The results reveal a strong positive correlation between larger collaborative teams and elevated average D scores, suggesting that extensive collaboration may be a facilitator for generating paradigm shifts. Additionally, publications with high D values tend to feature more expansive titles and greater density of specialized, technical jargon in their abstracts. These findings validate the D metric as a reliable and scalable instrument for both historical and predictive identification of transformative research. They also furnish empirical evidence concerning the team structures and communication patterns that optimize for the production of groundbreaking scientific knowledge.
Representation Learning for Point Cloud Understanding
Venue: arXiv cs.CV 2025
Year: 2025

With the rapid advancement of technology, 3D data acquisition and utilization have become increasingly prevalent across various fields, including computer vision, robotics, and geospatial analysis. 3D data, captured through methods such as 3D scanners, LiDARs, and RGB-D cameras, provides rich geometric, shape, and scale information. When combined with 2D images, 3D data offers machines a comprehensive understanding of their environment, benefiting applications like autonomous driving, robotics, remote sensing, and medical treatment. This dissertation focuses on three main areas: supervised representation learning for point cloud primitive segmentation, self-supervised learning methods, and transfer learning from 2D to 3D. Our approach, which integrates pre-trained 2D models to support 3D network training, significantly improves 3D understanding without merely transforming 2D data. Extensive experiments validate the effectiveness of our methods, showcasing their potential to advance point cloud representation learning by effectively integrating 2D knowledge.
Deep learning recognition and analysis of Volatile Organic Compounds based on experimental and synthetic infrared absorption spectra
Venue: arXiv cs.LG 2025
Year: 2025

Volatile Organic Compounds (VOCs) are organic molecules that have low boiling points and therefore easily evaporate into the air. They pose significant risks to human health, making their accurate detection the crux of efforts to monitor and minimize exposure. Infrared (IR) spectroscopy enables the ultrasensitive detection at low-concentrations of VOCs in the atmosphere by measuring their IR absorption spectra. However, the complexity of the IR spectra limits the possibility to implement VOC recognition and quantification in real-time. While deep neural networks (NNs) are increasingly used for the recognition of complex data structures, they typically require massive datasets for the training phase. Here, we create an experimental VOC dataset for nine different classes of compounds at various concentrations, using their IR absorption spectra. To further increase the amount of spectra and their diversity in term of VOC concentration, we augment the experimental dataset with synthetic spectra created via conditional generative NNs. This allows us to train robust discriminative NNs, able to reliably identify the nine VOCs, as well as to precisely predict their concentrations. The trained NN is suitable to be incorporated into sensing devices for VOCs recognition and analysis.
Shoot-Bounce-3D: Single-Shot Occlusion-Aware 3D from Lidar by Decomposing Two-Bounce Light
Venue: arXiv cs.CV 2025
Year: 2025

3D scene reconstruction from a single measurement is challenging, especially in the presence of occluded regions and specular materials, such as mirrors. We address these challenges by leveraging single-photon lidars. These lidars estimate depth from light that is emitted into the scene and reflected directly back to the sensor. However, they can also measure light that bounces multiple times in the scene before reaching the sensor. This multi-bounce light contains additional information that can be used to recover dense depth, occluded geometry, and material properties. Prior work with single-photon lidar, however, has only demonstrated these use cases when a laser sequentially illuminates one scene point at a time. We instead focus on the more practical - and challenging - scenario of illuminating multiple scene points simultaneously. The complexity of light transport due to the combined effects of multiplexed illumination, two-bounce light, shadows, and specular reflections is challenging to invert analytically. Instead, we propose a data-driven method to invert light transport in single-photon lidar. To enable this approach, we create the first large-scale simulated dataset of ~100k lidar transients for indoor scenes. We use this dataset to learn a prior on complex light transport, enabling measured two-bounce light to be decomposed into the constituent contributions from each laser spot. Finally, we experimentally demonstrate how this decomposed light can be used to infer 3D geometry in scenes with occlusions and mirrors from a single measurement. Our code and dataset are released at https://shoot-bounce-3d.github.io.
Compass: Mapping Space Exploration for Multi-Chiplet Accelerators Targeting LLM Inference Serving Workloads
Venue: arXiv cs.AR 2025
Year: 2025

Large Language Models (LLMs) impose massive computational demands, driving the need for scalable multi-chiplet accelerators. However, existing mapping space exploration efforts for such accelerators primarily focus on traditional CNN/Transformer workloads and fail to adequately support the dynamic behaviors of mixed request types and variable sequence lengths in real-world LLM inference serving. To bridge this gap, we first propose a computation execution graph-based mapping encoding scheme that decouples micro-batches and layers, enabling fine-grained execution control on heterogeneous chiplets and flexibly representing various parallelism strategies. Second, building upon this scheme, we develop the Compass framework, which integrates an evaluation engine and a genetic algorithm-based mapping generation engine to achieve efficient mapping search. Compared to state-of-the-art works, our solution achieves an average EDP reduction of 63.12%.
Empathy by Design: Aligning Large Language Models for Healthcare Dialogue
Venue: arXiv cs.CL 2025
Year: 2025

General-purpose large language models (LLMs) have demonstrated remarkable generative and reasoning capabilities but remain limited in healthcare and caregiving applications due to two key deficiencies: factual unreliability and a lack of empathetic communication. These shortcomings pose significant risks in sensitive contexts where users, particularly non-professionals and caregivers, seek medically relevant guidance or emotional reassurance. To address these challenges, we introduce a Direct Preference Optimization (DPO)-based alignment framework designed to improve factual correctness, semantic coherence, and human-centric qualities such as empathy, politeness, and simplicity in caregiver-patient dialogues. Our approach fine-tunes domain-adapted LLMs using pairwise preference data, where preferred responses reflect supportive and accessible communication styles while rejected ones represent prescriptive or overly technical tones. This direct optimization method aligns model outputs with human preferences more efficiently than traditional reinforcement-learning-based alignment. Empirical evaluations across multiple open and proprietary LLMs show that our DPO-tuned models achieve higher semantic alignment, improved factual accuracy, and stronger human-centric evaluation scores compared to baseline and commercial alternatives such as Google medical dialogue systems. These improvements demonstrate that preference-based alignment offers a scalable and transparent pathway toward developing trustworthy, empathetic, and clinically informed AI assistants for caregiver and healthcare communication. Our open-source code is available at: https://github.com/LeonG19/Empathy-by-Design
SpectraIrisPAD: Leveraging Vision Foundation Models for Spectrally Conditioned Multispectral Iris Presentation Attack Detection
Venue: arXiv cs.CV 2025
Year: 2025

Iris recognition is widely recognized as one of the most accurate biometric modalities. However, its growing deployment in real-world applications raises significant concerns regarding its vulnerability to Presentation Attacks (PAs). Effective Presentation Attack Detection (PAD) is therefore critical to ensure the integrity and security of iris-based biometric systems. While conventional iris recognition systems predominantly operate in the near-infrared (NIR) spectrum, multispectral imaging across multiple NIR bands provides complementary reflectance information that can enhance the generalizability of PAD methods. In this work, we propose \textbf{SpectraIrisPAD}, a novel deep learning-based framework for robust multispectral iris PAD. The SpectraIrisPAD leverages a DINOv2 Vision Transformer (ViT) backbone equipped with learnable spectral positional encoding, token fusion, and contrastive learning to extract discriminative, band-specific features that effectively distinguish bona fide samples from various spoofing artifacts. Furthermore, we introduce a new comprehensive dataset Multispectral Iris PAD (\textbf{MSIrPAD}) with diverse PAIs, captured using a custom-designed multispectral iris sensor operating at five distinct NIR wavelengths (800\,nm, 830\,nm, 850\,nm, 870\,nm, and 980\,nm). The dataset includes 18,848 iris images encompassing eight diverse PAI categories, including five textured contact lenses, print attacks, and display-based attacks. We conduct comprehensive experiments under unseen attack evaluation protocols to assess the generalization capability of the proposed method. SpectraIrisPAD consistently outperforms several state-of-the-art baselines across all performance metrics, demonstrating superior robustness and generalizability in detecting a wide range of presentation attacks.
Hardware Software Optimizations for Fast Model Recovery on Reconfigurable Architectures
Venue: arXiv cs.AR 2025
Year: 2025

Model Recovery (MR) is a core primitive for physical AI and real-time digital twins, but GPUs often execute MR inefficiently due to iterative dependencies, kernel-launch overheads, underutilized memory bandwidth, and high data-movement latency. We present MERINDA, an FPGA-accelerated MR framework that restructures computation as a streaming dataflow pipeline. MERINDA exploits on-chip locality through BRAM tiling, fixed-point kernels, and the concurrent use of LUT fabric and carry-chain adders to expose fine-grained spatial parallelism while minimizing off-chip traffic. This hardware-aware formulation removes synchronization bottlenecks and sustains high throughput across the iterative updates in MR. On representative MR workloads, MERINDA delivers up to 6.3x fewer cycles than an FPGA-based LTC baseline, enabling real-time performance for time-critical physical systems.
Probabilistic Weapon Engagement Zones for a Turn Constrained Pursuer
Venue: arXiv cs.RO 2025
Year: 2025

Curve-straight probabilistic engagement zones (CSPEZ) quantify the spatial regions an evader should avoid to reduce capture risk from a turn-rate-limited pursuer following a curve-straight path with uncertain parameters including position, heading, velocity, range, and maximum turn rate. This paper presents methods for generating evader trajectories that minimize capture risk under such uncertainty. We first derive an analytic solution for the deterministic curve-straight basic engagement zone (CSBEZ), then extend this formulation to a probabilistic framework using four uncertainty-propagation approaches: Monte Carlo sampling, linearization, quadratic approximation, and neural-network regression. We evaluate the accuracy and computational cost of each approximation method and demonstrate how CSPEZ constraints can be integrated into a trajectory-optimization algorithm to produce safe paths that explicitly account for pursuer uncertainty.
Physics-Informed Neural Koopman Machine for Interpretable Longitudinal Personalized Alzheimer's Disease Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Early forecasting of individual cognitive decline in Alzheimer's disease (AD) is central to disease evaluation and management. Despite advances, it is as of yet challenging for existing methodological frameworks to integrate multimodal data for longitudinal personalized forecasting while maintaining interpretability. To address this gap, we present the Neural Koopman Machine (NKM), a new machine learning architecture inspired by dynamical systems and attention mechanisms, designed to forecast multiple cognitive scores simultaneously using multimodal genetic, neuroimaging, proteomic, and demographic data. NKM integrates analytical ($α$) and biological ($β$) knowledge to guide feature grouping and control the hierarchical attention mechanisms to extract relevant patterns. By implementing Fusion Group-Aware Hierarchical Attention within the Koopman operator framework, NKM transforms complex nonlinear trajectories into interpretable linear representations. To demonstrate NKM's efficacy, we applied it to study the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our results suggest that NKM consistently outperforms both traditional machine learning methods and deep learning models in forecasting trajectories of cognitive decline. Specifically, NKM (1) forecasts changes of multiple cognitive scores simultaneously, (2) quantifies differential biomarker contributions to predicting distinctive cognitive scores, and (3) identifies brain regions most predictive of cognitive deterioration. Together, NKM advances personalized, interpretable forecasting of future cognitive decline in AD using past multimodal data through an explainable, explicit system and reveals potential multimodal biological underpinnings of AD progression.
Entropic Regularization in the Deep Linear Network
Venue: arXiv cs.NE 2025
Year: 2025

We study regularization for the deep linear network (DLN) using the entropy formula introduced in arXiv:2509.09088. The equilibria and gradient flow of the free energy on the Riemannian manifold of end-to-end maps of the DLN are characterized for energies that depend symmetrically on the singular values of the end-to-end matrix. The only equilibria are minimizers and the set of minimizers is an orbit of the orthogonal group. In contrast with random matrix theory there is no singular value repulsion. The corresponding gradient flow reduces to a one-dimensional ordinary differential equation whose solution gives explicit relaxation rates toward the minimizers. We also study the concavity of the entropy in the chamber of singular values. The entropy is shown to be strictly concave in the Euclidean geometry on the chamber but not in the Riemannian geometry defined by the DLN metric.
gp2Scale: A Class of Compactly-Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
Venue: arXiv cs.LG 2025
Year: 2025

Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty quantification accuracy, and customizability persists. This is because the vast majority of existing methodologies exploit various levels of approximations that lower accuracy and limit the flexibility of kernel and noise-model designs -- an unacceptable drawback at a time when expressive non-stationary kernels are on the rise in many fields. Here, we propose a methodology we term \emph{gp2Scale} that scales exact Gaussian processes to more than 10 million data points without relying on inducing points, kernel interpolation, or neighborhood-based approximations, and instead leveraging the existing capabilities of a GP: its kernel design. Highly flexible, compactly supported, and non-stationary kernels lead to the identification of naturally occurring sparse structure in the covariance matrix, which is then exploited for the calculations of the linear system solution and the log-determinant for training. We demonstrate our method's functionality on several real-world datasets and compare it with state-of-the-art approximation algorithms. Although we show superior approximation performance in many cases, the method's real power lies in its agnosticism toward arbitrary GP customizations -- core kernel design, noise, and mean functions -- and the type of input space, making it optimally suited for modern Gaussian process applications.
Sift or Get Off the PoC: Applying Information Retrieval to Vulnerability Research with SiftRank
Venue: arXiv cs.CR 2025
Year: 2025

Security research is fundamentally a problem of resource constraint and consequent prioritization. There is simply too much attack surface and too little time and energy to spend analyzing it all. The most effective security researchers are often those who are most skilled at intuitively deciding which part of an expansive attack surface to investigate. We demonstrate that this problem of selecting the most promising option from among many possibilities can be reframed as an information retrieval problem, and solved using document ranking techniques with LLMs performing the heavy lifting as general-purpose rankers. We present SiftRank, a ranking algorithm achieving O(n) complexity through three key mechanisms: listwise ranking using an LLM to order documents in small batches of approximately 10 items at a time; inflection-based convergence detection that adaptively terminates ranking when score distributions have stabilized; and iterative refinement that progressively focuses ranking effort on the most relevant documents. Unlike existing reranking approaches that require a separate first-stage retrieval step to narrow datasets to approximately 100 candidates, SiftRank operates directly on thousands of items, with each document evaluated across multiple randomized batches to mitigate inconsistent judgments by an LLM. We demonstrate practical effectiveness on N-day vulnerability analysis, successfully identifying a vulnerability-fixing function among 2,197 changed functions in a stripped binary firmware patch within 99 seconds at an inference cost of $0.82. Our approach enables scalable security prioritization for problems that are generally constrained by manual analysis, requiring only standard LLM API access without specialized infrastructure, embedding, or domain-specific fine-tuning. An open-source implementation of SiftRank may be found at https://github.com/noperator/siftrank.
Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection
Venue: arXiv cs.CV 2025
Year: 2025

Shearography is an interferometric technique sensitive to surface displacement gradients, providing high sensitivity for detecting subsurface defects in safety-critical components. A key limitation to industrial adoption is the lack of high-quality annotated datasets, since manual labeling remains labor-intensive, subjective, and difficult to standardize. We introduce an automated workflow that generates defect annotations from shearography measurements using deep learning, producing high-resolution segmentation and bounding-box labels. Evaluation against expert-labeled data demonstrates sufficient accuracy to enable weakly supervised training, reducing manual effort and supporting scalable dataset creation for robust defect detection.
Physics-Grounded Shadow Generation from Monocular 3D Geometry Priors and Approximate Light Direction
Venue: arXiv cs.CV 2025
Year: 2025

Shadow generation aims to produce photorealistic shadows that are visually consistent with object geometry and scene illumination. In the physics of shadow formation, the occluder blocks some light rays casting from the light source that would otherwise arrive at the surface, creating a shadow that follows the silhouette of the occluder. However, such explicit physical modeling has rarely been used in deep-learning-based shadow generation. In this paper, we propose a novel framework that embeds explicit physical modeling - geometry and illumination - into deep-learning-based shadow generation. First, given a monocular RGB image, we obtain approximate 3D geometry in the form of dense point maps and predict a single dominant light direction. These signals allow us to recover fairly accurate shadow location and shape based on the physics of shadow formation. We then integrate this physics-based initial estimate into a diffusion framework that refines the shadow into a realistic, high-fidelity appearance while ensuring consistency with scene geometry and illumination. Trained on DESOBAV2, our model produces shadows that are both visually realistic and physically coherent, outperforming existing approaches, especially in scenes with complex geometry or ambiguous lighting.