OpenRaiser/Intern-Atlas huggingface dataset:
Feature Relevancy, Necessity and Usefulness: Complexity and Algorithms
Venue: arXiv cs.AI 2025
Year: 2025

Given a classification model and a prediction for some input, there are heuristic strategies for ranking features according to their importance in regard to the prediction. One common approach to this task is rooted in propositional logic and the notion of \textit{sufficient reason}. Through this concept, the categories of relevant and necessary features were proposed in order to identify the crucial aspects of the input. This paper improves the existing techniques and algorithms for deciding which are the relevant and/or necessary features, showing in particular that necessity can be detected efficiently in complex models such as neural networks. We also generalize the notion of relevancy and study associated problems. Moreover, we present a new global notion (i.e. that intends to explain whether a feature is important for the behavior of the model in general, not depending on a particular input) of \textit{usefulness} and prove that it is related to relevancy and necessity. Furthermore, we develop efficient algorithms for detecting it in decision trees and other more complex models, and experiment on three datasets to analyze its practical utility.
EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models
Venue: arXiv cs.RO 2025
Year: 2025

Recent advances in creative AI have enabled the synthesis of high-fidelity images and videos conditioned on language instructions. Building on these developments, text-to-video diffusion models have evolved into embodied world models (EWMs) capable of generating physically plausible scenes from language commands, effectively bridging vision and action in embodied AI applications. This work addresses the critical challenge of evaluating EWMs beyond general perceptual metrics to ensure the generation of physically grounded and action-consistent behaviors. We propose the Embodied World Model Benchmark (EWMBench), a dedicated framework designed to evaluate EWMs based on three key aspects: visual scene consistency, motion correctness, and semantic alignment. Our approach leverages a meticulously curated dataset encompassing diverse scenes and motion patterns, alongside a comprehensive multi-dimensional evaluation toolkit, to assess and compare candidate models. The proposed benchmark not only identifies the limitations of existing video generation models in meeting the unique requirements of embodied tasks but also provides valuable insights to guide future advancements in the field. The dataset and evaluation tools are publicly available at https://github.com/AgibotTech/EWMBench.
Training Deep Morphological Neural Networks as Universal Approximators
Venue: arXiv cs.LG 2025
Year: 2025

We investigate deep morphological neural networks (DMNNs). We demonstrate that despite their inherent non-linearity, "linear" activations are essential for DMNNs. To preserve their inherent sparsity, we propose architectures that constraint the parameters of the "linear" activations: For the first (resp. second) architecture, we work under the constraint that the majority of parameters (resp. learnable parameters) should be part of morphological operations. We improve the generalization ability of our networks via residual connections and weight dropout. Our proposed networks can be successfully trained, and are more prunable than linear networks. To the best of our knowledge, we are the first to successfully train DMNNs under such constraints. Finally, we propose a hybrid network architecture combining linear and morphological layers, showing empirically that the inclusion of morphological layers significantly accelerates the convergence of gradient descent with large batches.
A Generative Neural Annealer for Black-Box Combinatorial Optimization
Venue: arXiv cs.LG 2025
Year: 2025

We propose a generative, end-to-end solver for black-box combinatorial optimization that emphasizes both sample efficiency and solution quality on NP problems. Drawing inspiration from annealing-based algorithms, we treat the black-box objective as an energy function and train a neural network to model the associated Boltzmann distribution. By conditioning on temperature, the network captures a continuum of distributions--from near-uniform at high temperatures to sharply peaked around global optima at low temperatures--thereby learning the structure of the energy landscape and facilitating global optimization. When queries are expensive, the temperature-dependent distributions naturally enable data augmentation and improve sample efficiency. When queries are cheap but the problem remains hard, the model learns implicit variable interactions, effectively "opening" the black box. We validate our approach on challenging combinatorial tasks under both limited and unlimited query budgets, showing competitive performance against state-of-the-art black-box optimizers.
Theoretical Model of Acoustic Power Transfer Through Solids
Venue: arXiv cs.SD 2025
Year: 2025

Acoustic Power Transfer is a relatively new technology. It is a modern type of a wireless interface, where data signals and supply voltages are transmitted, with the use of mechanical waves, through a medium. The simplest application of such systems is the measurement of frequency response for audio speakers. It consists of a variable signal generator, a measuring amplifier which drives an acoustic source and the loudspeaker driver. The receiver contains a microphone circuit with a level recorder. Acoustic Power Transfer could have many applications, such as: Cochlear Implants, Sonar Systems and Wireless Charging. However, it is a new technology, thus it needs further investigation.
Ontology-Based Structuring and Analysis of North Macedonian Public Procurement Contracts
Venue: arXiv cs.DB 2025
Year: 2025

Public procurement plays a critical role in government operations, ensuring the efficient allocation of resources and fostering economic growth. However, traditional procurement data is often stored in rigid, tabular formats, limiting its analytical potential and hindering transparency. This research presents a methodological framework for transforming structured procurement data into a semantic knowledge graph, leveraging ontological modeling and automated data transformation techniques. By integrating RDF and SPARQL-based querying, the system enhances the accessibility and interpretability of procurement records, enabling complex semantic queries and advanced analytics. Furthermore, by incorporating machine learning-driven predictive modeling, the system extends beyond conventional data analysis, offering insights into procurement trends and risk assessment. This work contributes to the broader field of public procurement intelligence by improving data transparency, supporting evidence-based decision-making, and enabling in-depth analysis of procurement activities in North Macedonia.
On Signed Network Coordination Games
Venue: arXiv cs.GT 2025
Year: 2025

We study binary-action pairwise-separable network games that encompass both coordinating and anti-coordinating behaviors. Our model is grounded in an underlying directed signed graph, where each link is associated with a weight that describes the strenght and nature of the interaction. The utility for each agent is an aggregation of pairwise terms determined by the weights of the signed graph in addition to an individual bias term. We consider a scenario that assumes the presence of a prominent cohesive subset of players, who are either connected exclusively by positive weights, or forms a structurally balanced subset that can be bipartitioned into two adversarial subcommunities with positive intra-community and negative inter-community edges. Given the properties of the game restricted to the remaining players, our results guarantee the existence of Nash equilibria characterized by a consensus or, respectively, a polarization within the first group, as well as their stability under best response transitions. Our results can be interpreted as robustness results, building on the supermodular properties of coordination games and on a novel use of the concept of graph cohesiveness.
Lossless Compression for LLM Tensor Incremental Snapshots
Venue: arXiv cs.LG 2025
Year: 2025

During the training of Large Language Models (LLMs), tensor data is periodically "checkpointed" to persistent storage to allow recovery of work done in the event of failure. The volume of data that must be copied during each checkpoint, even when using reduced-precision representations such as bfloat16, often reaches hundreds of gigabytes. Furthermore, the data must be moved across a network and written to a storage system before the next epoch occurs. With a view to ultimately building an optimized checkpointing solution, this paper presents experimental analysis of checkpoint data used to derive a design that maximizes the use of lossless compression to reduce the volume of data. We examine how tensor data and its compressibility evolve during model training and evaluate the efficacy of existing common off-the-shelf general purpose compression engines combined with known data optimization techniques such as byte-grouping and incremental delta compression. Leveraging our analysis we have built an effective compression solution, known as Language Model Compressor (LMC), which is based on byte-grouping and Huffman encoding. LMC offers more compression performance than the best alternative (BZ2) but with an order-of-magnitude reduction in the time needed to perform the compression. We show that a 16-core parallel implementation of LMC can attain compression and decompression throughput of 2.78 GiB/s and 3.76 GiB/s respectively. This increase in performance ultimately reduces the CPU resources needed and provides more time to copy the data to the storage system before the next epoch thus allowing for higher-frequency checkpoints.
$XX^{t}$ Can Be Faster
Venue: arXiv cs.DS 2025
Year: 2025

We present RXTX, a new algorithm for computing the product of matrix by its transpose $XX^{t}$ for $X\in \mathbb{R}^{n\times m}$. RXTX uses $5\%$ fewer multiplications and $5\%$ fewer operations (additions and multiplications) than State-of-the-Art algorithms. Note that the accelerations not only holds asymptotically for large matrices with $n \rightarrow \infty$, but also for small matrices including $n = 4$. The algorithm was discovered by combining Machine Learning-based search methods with Combinatorial Optimization.
BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes
Venue: arXiv cs.CV 2025
Year: 2025

Obtaining large-scale medical data, annotated or unannotated, is challenging due to stringent privacy regulations and data protection policies. In addition, annotating medical images requires that domain experts manually delineate anatomical structures, making the process both time-consuming and costly. As a result, semi-supervised methods have gained popularity for reducing annotation costs. However, the performance of semi-supervised methods is heavily dependent on the availability of unannotated data, and their effectiveness declines when such data are scarce or absent. To overcome this limitation, we propose a simple, yet effective and computationally efficient approach for medical image segmentation that leverages only existing annotations. We propose BoundarySeg , a multi-task framework that incorporates organ boundary prediction as an auxiliary task to full organ segmentation, leveraging consistency between the two task predictions to provide additional supervision. This strategy improves segmentation accuracy, especially in low data regimes, allowing our method to achieve performance comparable to or exceeding state-of-the-art semi supervised approaches all without relying on unannotated data or increasing computational demands. Code will be released upon acceptance.
Few-Shot Learning of Visual Compositional Concepts through Probabilistic Schema Induction
Venue: arXiv cs.CV 2025
Year: 2025

The ability to learn new visual concepts from limited examples is a hallmark of human cognition. While traditional category learning models represent each example as an unstructured feature vector, compositional concept learning is thought to depend on (1) structured representations of examples (e.g., directed graphs consisting of objects and their relations) and (2) the identification of shared relational structure across examples through analogical mapping. Here, we introduce Probabilistic Schema Induction (PSI), a prototype model that employs deep learning to perform analogical mapping over structured representations of only a handful of examples, forming a compositional concept called a schema. In doing so, PSI relies on a novel conception of similarity that weighs object-level similarity and relational similarity, as well as a mechanism for amplifying relations relevant to classification, analogous to selective attention parameters in traditional models. We show that PSI produces human-like learning performance and outperforms two controls: a prototype model that uses unstructured feature vectors extracted from a deep learning model, and a variant of PSI with weaker structured representations. Notably, we find that PSI's human-like performance is driven by an adaptive strategy that increases relational similarity over object-level similarity and upweights the contribution of relations that distinguish classes. These findings suggest that structured representations and analogical mapping are critical to modeling rapid human-like learning of compositional visual concepts, and demonstrate how deep learning can be leveraged to create psychological models.
Electrodermal Insights into Stress Dynamics of AR-Assisted Safety Warnings in Virtual Roadway Work Zone Environments
Venue: arXiv cs.HC 2025
Year: 2025

This study examines stress levels in roadway workers utilizing AR-assisted multi-sensory warning systems under varying work intensities. A high-fidelity Virtual Reality environment was used to replicate real-world scenarios, allowing safe exploration of high-risk situations while focusing on the physiological impacts of work conditions. Wearable sensors were used to continuously and non-invasively collect physiological data, including electrodermal activity to monitor stress responses. Analysis of data from 18 participants revealed notable differences in EDR between light- and medium-intensity activities, reflecting variations in autonomic nervous system activity under stress. Also, a feature importance analysis revealed that peak and central tendency metrics of EDR were robust indicators of physiological responses, between light- and medium-intensity activities. The findings emphasize the relationship between AR-enabled warnings, work intensity, and worker stress, offering an approach to active stress monitoring and improved safety practices. By leveraging real-time physiological insights, this methodology has the potential to support better stress management and the development of more effective safety warning systems for roadway work zones. This research also provides valuable guidance for designing interventions to enhance worker safety, productivity, and well-being in high-risk settings.
Context-AI Tunes: Context-Aware AI-Generated Music for Stress Reduction
Venue: arXiv cs.HC 2025
Year: 2025

Music plays a critical role in emotional regulation and stress relief; however, individuals often need different types of music tailored to their unique stress levels or surrounding environment. Choosing the right music can be challenging due to the overwhelming number of options and the time-consuming trial-and-error process. To address this, we propose Context-AI Tune (CAT), a system that generates personalized music based on environmental inputs and the user's self-assessed stress level. A 2x2 within-subject experiment (N=26) was conducted with two independent variables: AI (AI, NoAI) and Environment (Busy Hub, Quiet Library). CAT's effectiveness in reducing stress was evaluated using the Visual Analog Scale for Stress (VAS-S). Results show that CAT is more effective than manually chosen music in reducing stress by adapting to user context.
Characterizing Unintended Consequences in Human-GUI Agent Collaboration for Web Browsing
Venue: arXiv cs.HC 2025
Year: 2025

The proliferation of Large Language Model (LLM)-based Graphical User Interface (GUI) agents in web browsing scenarios present complex unintended consequences (UCs). This paper characterizes three UCs from three perspectives: phenomena, influence and mitigation, drawing on social media analysis (N=221 posts) and semi-structured interviews (N=14). Key phenomenon for UCs include agents' deficiencies in comprehending instructions and planning tasks, challenges in executing accurate GUI interactions and adapting to dynamic interfaces, the generation of unreliable or misaligned outputs, and shortcomings in error handling and feedback processing. These phenomena manifest as influences from unanticipated actions and user frustration, to privacy violations and security vulnerabilities, and further to eroded trust and wider ethical concerns. Our analysis also identifies user-initiated mitigation, such as technical adjustments and manual oversight, and provides implications for designing future LLM-based GUI agents that are robust, user-centric, and transparent, fostering a crucial balance between automation and human oversight.
Post-Post-API Age: Studying Digital Platforms in Scant Data Access Times
Venue: arXiv cs.HC 2025
Year: 2025

Over the past decade, data provided by digital platforms has informed substantial research in HCI to understand online human interaction and communication. Following the closure of major social media APIs that previously provided free access to large-scale data (the "post-API age"), emerging data access programs required by the European Union's Digital Services Act (DSA) have sparked optimism about increased platform transparency and renewed opportunities for comprehensive research on digital platforms, leading to the "post-post-API age." However, it remains unclear whether platforms provide adequate data access in practice. To assess how platforms make data available under the DSA, we conducted a comprehensive survey followed by in-depth interviews with 19 researchers to understand their experiences with data access in this new era. Our findings reveal significant challenges in accessing social media data, with researchers facing multiple barriers including complex API application processes, difficulties obtaining credentials, and limited API usability. These challenges have exacerbated existing institutional, regional, and financial inequities in data access. Based on these insights, we provide actionable recommendations for platforms, researchers, and policymakers to foster more equitable and effective data access, while encouraging broader dialogue within the CSCW community around interdisciplinary and multi-stakeholder solutions.
Determining Absence of Unreasonable Risk: Approval Guidelines for an Automated Driving System Deployment
Venue: arXiv cs.SE 2025
Year: 2025

This paper provides an overview of how the determination of absence of unreasonable risk can be operationalized. It complements previous theoretical work published by existing developers of Automated Driving Systems (ADS) on the overall engineering practices and methodologies for readiness determination. Readiness determination is, at its core, a risk assessment process. It is aimed at evaluating the residual risk associated with a new deployment. The paper proposes methodological criteria to ground the readiness review process for an ADS release. While informed by Waymo's experience in this domain, the criteria presented are agnostic of any specific ADS technological solution and/or architectural choice, to support broad implementation by others in the industry. The paper continues with a discussion on governance and decision-making toward approval of a new release candidate for the ADS. The implementation of the presented criteria requires the existence of appropriate safety management practices in addition to many other cultural, procedural, and operational considerations. As such, the paper is concluded by a statement of limitations for those wishing to replicate part or all of its content.
Offline Reinforcement Learning for Microgrid Voltage Regulation
Venue: arXiv cs.AI 2025
Year: 2025

This paper presents a study on using different offline reinforcement learning algorithms for microgrid voltage regulation with solar power penetration. When environment interaction is unviable due to technical or safety reasons, the proposed approach can still obtain an applicable model through offline-style training on a previously collected dataset, lowering the negative impact of lacking online environment interactions. Experiment results on the IEEE 33-bus system demonstrate the feasibility and effectiveness of the proposed approach on different offline datasets, including the one with merely low-quality experience.
Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback
Venue: arXiv cs.LG 2025
Year: 2025

This paper introduces an interactive continual learning paradigm where AI models dynamically learn new skills from real-time human feedback while retaining prior knowledge. This paradigm distinctively addresses two major limitations of traditional continual learning: (1) dynamic model updates using streaming, real-time human-annotated data, rather than static datasets with fixed labels, and (2) the assumption of clean labels, by explicitly handling the noisy feedback common in real-world interactions. To tackle these problems, we propose RiCL, a Reinforced interactive Continual Learning framework leveraging Large Language Models (LLMs) to learn new skills effectively from dynamic feedback. RiCL incorporates three key components: a temporal consistency-aware purifier to automatically discern clean from noisy samples in data streams; an interaction-aware direct preference optimization strategy to align model behavior with human intent by reconciling AI-generated and human-provided feedback; and a noise-resistant contrastive learning module that captures robust representations by exploiting inherent data relationships, thus avoiding reliance on potentially unreliable labels. Extensive experiments on two benchmark datasets (FewRel and TACRED), contaminated with realistic noise patterns, demonstrate that our RiCL approach substantially outperforms existing combinations of state-of-the-art online continual learning and noisy-label learning methods.
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
Venue: arXiv cs.CV 2025
Year: 2025

Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle with designing prompt templates, complex token interactions, or requiring additional fine-tuning, resulting in limited flexibility. In this work, we present a simple yet effective method called AdaptCLIP based on two key insights. First, adaptive visual and textual representations should be learned alternately rather than jointly. Second, comparative learning between query and normal image prompt should incorporate both contextual and aligned residual features, rather than relying solely on residual features. AdaptCLIP treats CLIP models as a foundational service, adding only three simple adapters, visual adapter, textual adapter, and prompt-query adapter, at its input or output ends. AdaptCLIP supports zero-/few-shot generalization across domains and possesses a training-free manner on target domains once trained on a base dataset. AdaptCLIP achieves state-of-the-art performance on 12 anomaly detection benchmarks from industrial and medical domains, significantly outperforming existing competitive methods. We will make the code and model of AdaptCLIP available at https://github.com/gaobb/AdaptCLIP.