OpenRaiser/Intern-Atlas huggingface dataset:
Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding
Venue: arXiv cs.RO 2025
Year: 2025

This manuscript investigates the integration of positional encoding -- a technique widely used in computer graphics -- into the input vector of a binary classification model for self-collision detection. The results demonstrate the benefits of incorporating positional encoding, which enhances classification accuracy by enabling the model to better capture high-frequency variations, leading to a more detailed and precise representation of complex collision patterns. The manuscript shows that machine learning-based techniques, such as lightweight multilayer perceptrons (MLPs) operating in a low-dimensional feature space, offer a faster alternative for collision checking than traditional methods that rely on geometric approaches, such as triangle-to-triangle intersection tests and Bounding Volume Hierarchies (BVH) for mesh-based models.
The General Expiration Streaming Model: Diameter, $k$-Center, Counting, Sampling, and Friends
Venue: arXiv cs.DS 2025
Year: 2025

An important thread in the study of data-stream algorithms focuses on settings where stream items are active only for a limited time. We introduce a new expiration model, where each item arrives with its own expiration time. The special case where items expire in the order that they arrive, which we call consistent expirations, contains the classical sliding-window model of Datar, Gionis, Indyk, and Motwani [SICOMP 2002] and its timestamp-based variant of Braverman and Ostrovsky [FOCS 2007]. Our first set of results presents algorithms (in the expiration streaming model) for several fundamental problems, including approximate counting, uniform sampling, and weighted sampling by efficiently tracking active items without explicitly storing them all. Naturally, these algorithms have many immediate applications to other problems. Our second and main set of results designs algorithms (in the expiration streaming model) for the diameter and $k$-center problems, where items are points in a metric space. Our results significantly extend those known for the special case of sliding-window streams by Cohen-Addad, Schwiegelshohn, and Sohler [ICALP 2016], including also a strictly better approximation factor for the diameter in the important special case of high-dimensional Euclidean space. We develop new decomposition and coordination techniques along with a geometric dominance framework, to filter out redundant points based on both temporal and spatial proximity.
Transformer-Based Approach to Optimal Sensor Placement for Structural Health Monitoring of Probe Cards
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents an innovative Transformer-based deep learning strategy for optimizing the placement of sensors aiming at structural health monitoring of semiconductor probe cards. Failures in probe cards, including substrate cracks and loosened screws, would critically affect semiconductor manufacturing yield and reliability. Some failure modes could be detected by equipping a probe card with adequate sensors. Frequency response functions from simulated failure scenarios are adopted within a finite element model of a probe card. A comprehensive dataset, enriched by physics-informed scenario expansion and physics-aware statistical data augmentation, is exploited to train a hybrid Convolutional Neural Network and Transformer model. The model achieves high accuracy (99.83%) in classifying the probe card health states (baseline, loose screw, crack) and an excellent crack detection recall (99.73%). Model robustness is confirmed through a rigorous framework of 3 repetitions of 10-fold stratified cross-validation. The attention mechanism also pinpoints critical sensor locations: an analysis of the attention weights offers actionable insights for designing efficient, cost-effective monitoring systems by optimizing sensor configurations. This research highlights the capability of attention-based deep learning to advance proactive maintenance, enhancing operational reliability and yield in semiconductor manufacturing.
Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
Venue: arXiv cs.LG 2025
Year: 2025

Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare. While numerous studies have explored rebalancing techniques to address this issue, less attention has been given to evaluating the performance of binary classifiers under imbalance when no such techniques are applied. Therefore, the goal of this study is to assess the performance of binary classifiers "as-is", without performing any explicit rebalancing. Specifically, we systematically evaluate the robustness of a diverse set of binary classifiers across both real-world and synthetic datasets, under progressively reduced minority class sizes, using one-shot and few-shot scenarios as baselines. Our approach also explores varying data complexities through synthetic decision boundary generation to simulate real-world conditions. In addition to standard classifiers, we include experiments using undersampling, oversampling strategies, and one-class classification (OCC) methods to examine their behavior under severe imbalance. The results confirm that classification becomes more difficult as data complexity increases and the minority class size decreases. While traditional classifiers deteriorate under extreme imbalance, advanced models like TabPFN and boosting-based ensembles retain relatively higher performance and better generalization compared to traditional classifiers. Visual interpretability and evaluation metrics further validate these findings. Our work offers valuable guidance on model selection for imbalanced learning, providing insights into classifier robustness without dependence on explicit rebalancing techniques.
What's in the Box: Ergonomic and Expressive Capture Tracking over Generic Data Structures (Extended Version)
Venue: arXiv cs.PL 2025
Year: 2025

Capturing types in Scala unify static effect and resource tracking with object capabilities, enabling lightweight effect polymorphism with minimal notational overhead. However, their expressiveness has been insufficient for tracking capabilities embedded in generic data structures, preventing them from scaling to the standard collections library -- an essential prerequisite for broader adoption. This limitation stems from the inability to name capabilities within the system's notion of box types. This paper develops System Capless, a new foundation for capturing types that provides the theoretical basis for reach capabilities (rcaps), a novel mechanism for naming "what's in the box." The calculus refines the universal capability notion into a new scheme with existential and universal capture set quantification. Intuitively, rcaps witness existentially quantified capture sets inside the boxes of generic types in a way that does not require exposing existential capture types in the surface language. We have fully mechanized the formal metatheory of System Capless in Lean, including proofs of type soundness and scope safety. System Capless supports the same lightweight notation of capturing types plus rcaps, as certified by a type-preserving translation, and also enables fully optional explicit capture-set quantification to increase expressiveness. Finally, we present a full reimplementation of capture checking in Scala 3 based on System Capless and migrate the entire Scala collections library and an asynchronous programming library to evaluate its practicality and ergonomics. Our results demonstrate that reach capabilities enable the adoption of capture checking in production code with minimal changes and minimal-to-zero notational overhead in a vast majority of cases.
Data-Efficient Fine-Tuning of Vision-Language Models for Diagnosis of Alzheimer's Disease
Venue: arXiv cs.CV 2025
Year: 2025

Medical vision-language models (Med-VLMs) have shown impressive results in tasks such as report generation and visual question answering, but they still face several limitations. Most notably, they underutilize patient metadata and lack integration of clinical diagnostic knowledge. Moreover, most existing models are typically trained from scratch or fine-tuned on large-scale 2D image-text pairs, requiring extensive computational resources, and their effectiveness on 3D medical imaging is often limited due to the absence of structural information. To address these gaps, we propose a data-efficient fine-tuning pipeline to adapt 3D CT-based Med-VLMs for 3D MRI and demonstrate its application in Alzheimer's disease (AD) diagnosis. Our system introduces two key innovations. First, we convert structured metadata into synthetic reports, enriching textual input for improved image-text alignment. Second, we add an auxiliary token trained to predict the mini-mental state examination (MMSE) score, a widely used clinical measure of cognitive function that correlates with AD severity. This provides additional supervision for fine-tuning. Applying lightweight prompt tuning to both image and text modalities, our approach achieves state-of-the-art performance on ADNI with only 1,504 training MRIs, outperforming methods trained on 27,161 MRIs, and shows strong zero-shot generalization on OASIS-2 and AIBL. Code is available at https://github.com/CFQ666312/DEFT-VLM-AD.
Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling
Venue: arXiv cs.AI 2025
Year: 2025

Direct Prompt Injection (DPI) attacks pose a critical security threat to Large Language Models (LLMs) due to their low barrier of execution and high potential damage. To address the impracticality of existing white-box/gray-box methods and the poor transferability of black-box methods, we propose an activations-guided prompt injection attack framework. We first construct an Energy-based Model (EBM) using activations from a surrogate model to evaluate the quality of adversarial prompts. Guided by the trained EBM, we employ the token-level Markov Chain Monte Carlo (MCMC) sampling to adaptively optimize adversarial prompts, thereby enabling gradient-free black-box attacks. Experimental results demonstrate our superior cross-model transferability, achieving 49.6% attack success rate (ASR) across five mainstream LLMs and 34.6% improvement over human-crafted prompts, and maintaining 36.6% ASR on unseen task scenarios. Interpretability analysis reveals a correlation between activations and attack effectiveness, highlighting the critical role of semantic patterns in transferable vulnerability exploitation.
Towards End-to-End Model-Agnostic Explanations for RAG Systems
Venue: arXiv cs.IR 2025
Year: 2025

Retrieval Augmented Generation (RAG) systems, despite their growing popularity for enhancing model response reliability, often struggle with trustworthiness and explainability. In this work, we present a novel, holistic, model-agnostic, post-hoc explanation framework leveraging perturbation-based techniques to explain the retrieval and generation processes in a RAG system. We propose different strategies to evaluate these explanations and discuss the sufficiency of model-agnostic explanations in RAG systems. With this work, we further aim to catalyze a collaborative effort to build reliable and explainable RAG systems.
Neural Proxies for Sound Synthesizers: Learning Perceptually Informed Preset Representations
Venue: arXiv cs.SD 2025
Year: 2025

Deep learning appears as an appealing solution for Automatic Synthesizer Programming (ASP), which aims to assist musicians and sound designers in programming sound synthesizers. However, integrating software synthesizers into training pipelines is challenging due to their potential non-differentiability. This work tackles this challenge by introducing a method to approximate arbitrary synthesizers. Specifically, we train a neural network to map synthesizer presets onto an audio embedding space derived from a pretrained model. This facilitates the definition of a neural proxy that produces compact yet effective representations, thereby enabling the integration of audio embedding loss into neural-based ASP systems for black-box synthesizers. We evaluate the representations derived by various pretrained audio models in the context of neural-based nASP and assess the effectiveness of several neural network architectures, including feedforward, recurrent, and transformer-based models, in defining neural proxies. We evaluate the proposed method using both synthetic and hand-crafted presets from three popular software synthesizers and assess its performance in a synthesizer sound matching downstream task. While the benefits of the learned representation are nuanced by resource requirements, encouraging results were obtained for all synthesizers, paving the way for future research into the application of synthesizer proxies for neural-based ASP systems.
Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network
Venue: arXiv cs.RO 2025
Year: 2025

In robots task and motion planning (TAMP), it is crucial to sample within the robot's configuration space to meet task-level global constraints and enhance the efficiency of subsequent motion planning. Due to the complexity of joint configuration sampling under multi-level constraints, traditional methods often lack efficiency. This paper introduces the principle of RobKiNet, a kinematics-informed neural network, for end-to-end sampling within the Continuous Feasible Set (CFS) under multiple constraints in configuration space, establishing its Optimization Expectation Model. Comparisons with traditional sampling and learning-based approaches reveal that RobKiNet's kinematic knowledge infusion enhances training efficiency by ensuring stable and accurate gradient optimization.Visualizations and quantitative analyses in a 2-DOF space validate its theoretical efficiency, while its application on a 9-DOF autonomous mobile manipulator robot(AMMR) demonstrates superior whole-body and decoupled control, excelling in battery disassembly tasks. RobKiNet outperforms deep reinforcement learning with a training speed 74.29 times faster and a sampling accuracy of up to 99.25%, achieving a 97.33% task completion rate in real-world scenarios.
Graph-based Integrated Gradients for Explaining Graph Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Integrated Gradients (IG) is a common explainability technique to address the black-box problem of neural networks. Integrated gradients assumes continuous data. Graphs are discrete structures making IG ill-suited to graphs. In this work, we introduce graph-based integrated gradients (GB-IG); an extension of IG to graphs. We demonstrate on four synthetic datasets that GB-IG accurately identifies crucial structural components of the graph used in classification tasks. We further demonstrate on three prevalent real-world graph datasets that GB-IG outperforms IG in highlighting important features for node classification tasks.
Inference of Altruism and Intrinsic Rewards in Multi-Agent Systems
Venue: arXiv cs.GT 2025
Year: 2025

Human interactions are influenced by emotions, temperament, and affection, often conflicting with individuals' underlying preferences. Without explicit knowledge of those preferences, judging whether behaviour is appropriate becomes guesswork, leaving us highly prone to misinterpretation. Yet, such understanding is critical if autonomous agents are to collaborate effectively with humans. We frame the problem with multi-agent inverse reinforcement learning and show that even a simple model, where agents weigh their own welfare against that of others, can cover a wide range of social behaviours. Using novel Bayesian techniques, we find that intrinsic rewards and altruistic tendencies can be reliably identified by placing agents in different groups. Crucially, this disentanglement of intrinsic motivation from altruism enables the synthesis of new behaviours aligned with any desired level of altruism, even when demonstrations are drawn from restricted behaviour profiles.
Nearest Neighbor Projection Removal Adversarial Training
Venue: arXiv cs.CV 2025
Year: 2025

Deep neural networks have exhibited impressive performance in image classification tasks but remain vulnerable to adversarial examples. Standard adversarial training enhances robustness but typically fails to explicitly address inter-class feature overlap, a significant contributor to adversarial susceptibility. In this work, we introduce a novel adversarial training framework that actively mitigates inter-class proximity by projecting out inter-class dependencies from adversarial and clean samples in the feature space. Specifically, our approach first identifies the nearest inter-class neighbors for each adversarial sample and subsequently removes projections onto these neighbors to enforce stronger feature separability. Theoretically, we demonstrate that our proposed logits correction reduces the Lipschitz constant of neural networks, thereby lowering the Rademacher complexity, which directly contributes to improved generalization and robustness. Extensive experiments across standard benchmarks including CIFAR-10, CIFAR-100, and SVHN show that our method demonstrates strong performance that is competitive with leading adversarial training techniques, highlighting significant achievements in both robust and clean accuracy. Our findings reveal the importance of addressing inter-class feature proximity explicitly to bolster adversarial robustness in DNNs.
HYLU: Hybrid Parallel Sparse LU Factorization
Venue: arXiv cs.AR 2025
Year: 2025

This article introduces HYLU, a hybrid parallel LU factorization-based general-purpose solver designed for efficiently solving sparse linear systems (Ax=b) on multi-core shared-memory architectures. The key technical feature of HYLU is the integration of hybrid numerical kernels so that it can adapt to various sparsity patterns of coefficient matrices. Tests on 34 sparse matrices from SuiteSparse Matrix Collection reveal that HYLU outperforms Intel MKL PARDISO in the numerical factorization phase by geometric means of 2.04X (for one-time solving) and 2.58X (for repeated solving). HYLU can be downloaded from https://github.com/chenxm1986/hylu.
Bridging the Gap Between Binary and Source Based Package Management in Spack
Venue: arXiv cs.SE 2025
Year: 2025

Binary package managers install software quickly but they limit configurability due to rigid ABI requirements that ensure compatibility between binaries. Source package managers provide flexibility in building software, but compilation can be slow. For example, installing an HPC code with a new MPI implementation may result in a full rebuild. Spack, a widely deployed, HPC-focused package manager, can use source and pre-compiled binaries, but lacks a binary compatibility model, so it cannot mix binaries not built together. We present splicing, an extension to Spack that models binary compatibility between packages and allows seamless mixing of source and binary distributions. Splicing augments Spack's packaging language and dependency resolution engine to reuse compatible binaries but maintains the flexibility of source builds. It incurs minimal installation-time overhead and allows rapid installation from binaries, even for ABI-sensitive dependencies like MPI that would otherwise require many rebuilds.
Digital Twins for Extended Reality Tourism: User Experience Evaluation Across User Groups
Venue: arXiv cs.HC 2025
Year: 2025

This study evaluates the user experience (UX) in extended reality (XR) tourism of two digital twin-based applications: an Augmented Reality Virtual Tour (AR-VT) for enhanced on-site visits and a Virtual Reality Virtual Tour (VR-VT) for remote exploration. Using a quantitative exploratory approach, 84 participants from Spain and Germany, divided into three sample groups, assessed UX, task load, presence, cybersickness, and emotional response through standardized questionnaires. Findings indicate that both applications provided a low task load and high enjoyment. The VR-based tour enhanced presence but posed usability and cybersickness challenges, while the AR-based tour achieved high UX ratings, with qualitative feedback suggesting areas for refinement. Correlation analysis revealed significant relationships between age, prior XR experience, and technological affinity with the measured metrics for both applications. These results highlight the importance of well-designed experiences tailored to XR novices, reinforcing the critical role of UX in digital twin-based XR tourism.
What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects
Venue: arXiv cs.SE 2025
Year: 2025

Context. Code refactoring improves software quality without changing external behavior. Despite its advantages, its benefits are hindered by the considerable cost of time, resources, and continuous effort it demands. Aim. Understanding why developers refactor, and which metrics capture these motivations, may support wider and more effective use of refactoring in practice. Method. We performed a large-scale empirical study to analyze developers refactoring activity, leveraging Large Language Models (LLMs) to identify underlying motivations from version control data, comparing our findings with previous motivations reported in the literature. Results. LLMs matched human judgment in 80% of cases, but aligned with literature-based motivations in only 47%. They enriched 22% of motivations with more detailed rationale, often highlighting readability, clarity, and structural improvements. Most motivations were pragmatic, focused on simplification and maintainability. While metrics related to developer experience and code readability ranked highest, their correlation with motivation categories was weak. Conclusions. We conclude that LLMs effectively capture surface-level motivations but struggle with architectural reasoning. Their value lies in providing localized explanations, which, when combined with software metrics, can form hybrid approaches. Such integration offers a promising path toward prioritizing refactoring more systematically and balancing short-term improvements with long-term architectural goals.
Scaling atomic ordering in shared memory
Venue: arXiv cs.DC 2025
Year: 2025

Atomic multicast is a communication primitive used in dependable systems to ensure consistent ordering of messages delivered to a set of replica groups. This primitive enables critical services to integrate replication and sharding (i.e., state partitioning) to achieve fault tolerance and scalability. While several atomic multicast protocols have been developed for message-passing systems, only a few are designed for the shared memory system model. This paper introduces TRAM, an atomic multicast protocol specifically designed for shared memory systems, leveraging an overlay tree architecture. Due to its simple and practical design, TRAM delivers exceptional performance, increasing throughput by more than 3$\times$ and reducing latency by more than 2.3$\times$ compared to state-of-the-art shared memory-based protocols. Additionally, it significantly outperforms message-passing-based protocols, boosting throughput by up to 5.9$\times$ and reducing latency by up to 106$\times$.