OpenRaiser/Intern-Atlas huggingface dataset:
A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach
Venue: arXiv cs.LG 2025
Year: 2025

Background: Existing robust, pervasive device-based systems developed in recent years to detect depression require data collected over a long period and may not be effective in cases where early detection is crucial. Objective: Our main objective was to develop a minimalistic system to identify depression using data retrieved in the fastest possible time. Methods: We developed a fast tool that retrieves the past 7 days' app usage data in 1 second (mean 0.31, SD 1.10 seconds). A total of 100 students from Bangladesh participated in our study, and our tool collected their app usage data. To identify depressed and nondepressed students, we developed a diverse set of ML models. We selected important features using the stable approach, along with 3 main types of feature selection (FS) approaches. Results: Leveraging only the app usage data retrieved in 1 second, our light gradient boosting machine model used the important features selected by the stable FS approach and correctly identified 82.4% (n=42) of depressed students (precision=75%, F1-score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and showed a maximum precision of 77.4% (balanced accuracy=77.9%). A SHAP analysis of our best models presented behavioral markers that were related to depression. Conclusions: Due to our system's fast and minimalistic nature, it may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of our findings can facilitate the development of less resource-intensive systems to better understand students who are depressed.
Not All Visitors are Bilingual: A Measurement Study of the Multilingual Web from an Accessibility Perspective
Venue: arXiv cs.CL 2025
Year: 2025

English is the predominant language on the web, powering nearly half of the world's top ten million websites. Support for multilingual content is nevertheless growing, with many websites increasingly combining English with regional or native languages in both visible content and hidden metadata. This multilingualism introduces significant barriers for users with visual impairments, as assistive technologies like screen readers frequently lack robust support for non-Latin scripts and misrender or mispronounce non-English text, compounding accessibility challenges across diverse linguistic contexts. Yet, large-scale studies of this issue have been limited by the lack of comprehensive datasets on multilingual web content. To address this gap, we introduce LangCrUX, the first large-scale dataset of 120,000 popular websites across 12 languages that primarily use non-Latin scripts. Leveraging this dataset, we conduct a systematic analysis of multilingual web accessibility and uncover widespread neglect of accessibility hints. We find that these hints often fail to reflect the language diversity of visible content, reducing the effectiveness of screen readers and limiting web accessibility. We finally propose Kizuki, a language-aware automated accessibility testing extension to account for the limited utility of language-inconsistent accessibility hints.
Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large vision-language models (LVLMs) have demonstrated exceptional capabilities in understanding visual information with human languages but also exhibit an imbalance in multilingual capabilities. In this work, we delve into the multilingual working pattern of LVLMs and identify a salient correlation between the multilingual understanding ability of LVLMs and language-specific neuron activations in shallow layers. Building on this insight, we introduce PLAST, a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise LAnguage-Specific layers fine-Tuning. PLAST first identifies layers involved in multilingual understanding by monitoring language-specific neuron activations. These layers are then precisely fine-tuned with question-translation pairs to achieve multilingual alignment. Our empirical results on MM-Bench and MMMB demonstrate that PLAST effectively improves the multilingual capabilities of LVLMs and achieves significant efficiency with only 14% of the parameters tuned. Further analysis reveals that PLAST can be generalized to low-resource and complex visual reasoning tasks, facilitating the language-specific visual information engagement in shallow layers.
Mining the Long Tail: A Comparative Study of Data-Centric Criticality Metrics for Robust Offline Reinforcement Learning in Autonomous Motion Planning
Venue: arXiv cs.RO 2025
Year: 2025

Offline Reinforcement Learning (RL) presents a promising paradigm for training autonomous vehicle (AV) planning policies from large-scale, real-world driving logs. However, the extreme data imbalance in these logs, where mundane scenarios vastly outnumber rare "long-tail" events, leads to brittle and unsafe policies when using standard uniform data sampling. In this work, we address this challenge through a systematic, large-scale comparative study of data curation strategies designed to focus the learning process on information-rich samples. We investigate six distinct criticality weighting schemes which are categorized into three families: heuristic-based, uncertainty-based, and behavior-based. These are evaluated at two temporal scales, the individual timestep and the complete scenario. We train seven goal-conditioned Conservative Q-Learning (CQL) agents with a state-of-the-art, attention-based architecture and evaluate them in the high-fidelity Waymax simulator. Our results demonstrate that all data curation methods significantly outperform the baseline. Notably, data-driven curation using model uncertainty as a signal achieves the most significant safety improvements, reducing the collision rate by nearly three-fold (from 16.0% to 5.5%). Furthermore, we identify a clear trade-off where timestep-level weighting excels at reactive safety while scenario-level weighting improves long-horizon planning. Our work provides a comprehensive framework for data curation in Offline RL and underscores that intelligent, non-uniform sampling is a critical component for building safe and reliable autonomous agents.
Efficient task and path planning for maintenance automation using a robot system
Venue: arXiv cs.RO 2025
Year: 2025

The research and development of intelligent automation solutions is a ground-breaking point for the factory of the future. A promising and challenging mission is the use of autonomous robot systems to automate tasks in the field of maintenance. For this purpose, the robot system must be able to plan autonomously the different manipulation tasks and the corresponding paths. Basic requirements are the development of algorithms with a low computational complexity and the possibility to deal with environmental uncertainties. In this work, an approach is presented, which is especially suited to solve the problem of maintenance automation. For this purpose, offline data from CAD is combined with online data from an RGBD vision system via a probabilistic filter, to compensate uncertainties from offline data. For planning the different tasks, a method is explained, which use a symbolic description, founded on a novel sampling-based method to compute the disassembly space. For path planning we use global state-of-the art algorithms with a method that allows the adaption of the exploration stepsize in order to reduce the planning time. Every method is experimentally validated and discussed.
LPLC: A Dataset for License Plate Legibility Classification
Venue: arXiv cs.CV 2025
Year: 2025

Automatic License Plate Recognition (ALPR) faces a major challenge when dealing with illegible license plates (LPs). While reconstruction methods such as super-resolution (SR) have emerged, the core issue of recognizing these low-quality LPs remains unresolved. To optimize model performance and computational efficiency, image pre-processing should be applied selectively to cases that require enhanced legibility. To support research in this area, we introduce a novel dataset comprising 10,210 images of vehicles with 12,687 annotated LPs for legibility classification (the LPLC dataset). The images span a wide range of vehicle types, lighting conditions, and camera/image quality levels. We adopt a fine-grained annotation strategy that includes vehicle- and LP-level occlusions, four legibility categories (perfect, good, poor, and illegible), and character labels for three categories (excluding illegible LPs). As a benchmark, we propose a classification task using three image recognition networks to determine whether an LP image is good enough, requires super-resolution, or is completely unrecoverable. The overall F1 score, which remained below 80% for all three baseline models (ViT, ResNet, and YOLO), together with the analyses of SR and LP recognition methods, highlights the difficulty of the task and reinforces the need for further research. The proposed dataset is publicly available at https://github.com/lmlwojcik/lplc-dataset.
Privacy-Preserving Federated Learning Framework for Risk-Based Adaptive Authentication
Venue: arXiv cs.CR 2025
Year: 2025

Balancing robust security with strong privacy guarantees is critical for Risk-Based Adaptive Authentication (RBA), particularly in decentralized settings. Federated Learning (FL) offers a promising solution by enabling collaborative risk assessment without centralizing user data. However, existing FL approaches struggle with Non-Independent and Identically Distributed (Non-IID) user features, resulting in biased, unstable, and poorly generalized global models. This paper introduces FL-RBA2, a novel Federated Learning framework for Risk-Based Adaptive Authentication that addresses Non-IID challenges through a mathematically grounded similarity transformation. By converting heterogeneous user features (including behavioral, biometric, contextual, interaction-based, and knowledge-based modalities) into IID similarity vectors, FL-RBA2 supports unbiased aggregation and personalized risk modeling across distributed clients. The framework mitigates cold-start limitations via clustering-based risk labeling, incorporates Differential Privacy (DP) to safeguard sensitive information, and employs Message Authentication Codes (MACs) to ensure model integrity and authenticity. Federated updates are securely aggregated into a global model, achieving strong balance between user privacy, scalability, and adaptive authentication robustness. Rigorous game-based security proofs in the Random Oracle Model formally establish privacy, correctness, and adaptive security guarantees. Extensive experiments on keystroke, mouse, and contextual datasets validate FL-RBA2's effectiveness in high-risk user detection and its resilience to model inversion and inference attacks, even under strong DP constraints.
VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in code generation have shown remarkable success across software domains, yet hardware description languages (HDLs) such as Verilog remain underexplored due to their concurrency semantics, syntactic rigidity, and simulation complexity. In this work, we address these challenges by introducing a reinforcement learning (RL) framework tailored for Verilog code generation. We first construct Veribench-53K, a high-quality dataset curated from over 700K Verilog problems, enriched with structured prompts, complexity labels, and diverse testbenches. To tackle the problem of sparse and noisy reward signals, we propose a Trace-back based Rescore mechanism that leverages reasoning paths and iterative refinement to enhance feedback reliability and support reward model training. Furthermore, to mitigate catastrophic forgetting and overfitting during RL fine-tuning, we introduce a sample-balanced weighting strategy that adaptively balances learning dynamics based on reward-probability distributions. These innovations are integrated into an iterative RL pipeline that co-evolves the policy and reward models. In contrast to recent work such as CraftRTL, which relies on large-scale closed-source model distillation, and DeepSeek-style approaches that struggle with sparse feedback, our method demonstrates superior performance using a smaller but high-quality dataset combined with RL optimization. Experiments on Verilog generation tasks demonstrate state-of-the-art performance, with substantial gains in test pass rate, functional correctness, and compilation robustness. Our findings highlight the potential of RL-driven approaches for structured code generation in hardware-centric domains. VERIRL is publicly available at https://github.com/omniAI-Lab/VeriRL.
A Praxis of Influence: Framing the Observation and Measurement of Information Power
Venue: arXiv cs.CY 2025
Year: 2025

Information power is the capacity to convert data flows into durable shifts in attention, belief, and behavior. We argue that this power has migrated from broadcast persuasion to platform-ized, data-driven operations that fuse computational delivery with cognitive effects. In this context, we define and bound information power within international relations and the information environment while demonstrating why observing and measuring it demands an integrated lens that combines politics (goals and governance), computing (data movement and algorithmic delivery), and psychology (attention, affect, memory, and belief). The article contributes three elements: (1) a triadic analytical framework that specifies the minimum variables and instrumentation needed for study; (2) two crosswalks that map common objectives (persuade, disrupt, shape) and target classes (leaders, elites, publics) to political, computational, and psychological tactics, yielding practical coding heuristics and testable hypotheses; and (3) a McCumber-style cube for information influence that integrates targets, operations, as well as machines (automation and AI) into a single space. The space provides for comparative analysis, data fusion, and effect measurement. Using recent cases across state and commercial platforms, we illustrate how virality, stickiness, and denial of logic exploit fast cognition, why conventional reach metrics understate impact, and where instrumentation should focus. We conclude with a mixed-methods research program coupling computational sensing including large-language-model text mining with experiments and polling. The intention is to move from detecting activity to estimating belief change and decision effects.
Collaborative Intelligence: Topic Modelling of Large Language Model use in Live Cybersecurity Operations
Venue: arXiv cs.CR 2025
Year: 2025

Objective: This work describes the topic modelling of Security Operations Centre (SOC) use of a large language model (LLM), during live security operations. The goal is to better understand how these specialists voluntarily use this tool. Background: Human-automation teams have been extensively studied, but transformer-based language models have sparked a new wave of collaboration. SOC personnel at a major cybersecurity provider used an LLM to support live security operations. This study examines how these specialists incorporated the LLM into their work. Method: Our data set is the result of 10 months of SOC operators accessing GPT-4 over an internally deployed HTTP-based chat application. We performed two topic modelling exercises, first using the established BERTopic model (Grootendorst, 2022), and second, using a novel topic modeling workflow. Results: Both the BERTopic analysis and novel modelling approach revealed that SOC operators primarily used the LLM to facilitate their understanding of complex text strings. Variations on this use-case accounted for ~40% of SOC LLM usage. Conclusion: SOC operators are required to rapidly interpret complex commands and similar information. Their natural tendency to leverage LLMs to support this activity indicates that their workflow can be supported and augmented by designing collaborative LLM tools for use in the SOC. Application: This work can aid in creating next-generation tools for Security Operations Centres. By understanding common use-cases, we can develop workflows supporting SOC task flow. One example is a right-click context menu for executing a command line analysis LLM call directly in the SOC environment.
DiskJoin: Large-scale Vector Similarity Join with SSD
Venue: arXiv cs.DB 2025
Year: 2025

Similarity join--a widely used operation in data science--finds all pairs of items that have distance smaller than a threshold. Prior work has explored distributed computation methods to scale similarity join to large data volumes but these methods require a cluster deployment, and efficiency suffers from expensive inter-machine communication. On the other hand, disk-based solutions are more cost-effective by using a single machine and storing the large dataset on high-performance external storage, such as NVMe SSDs, but in these methods the disk I/O time is a serious bottleneck. In this paper, we propose DiskJoin, the first disk-based similarity join algorithm that can process billion-scale vector datasets efficiently on a single machine. DiskJoin improves disk I/O by tailoring the data access patterns to avoid repetitive accesses and read amplification. It also uses main memory as a dynamic cache and carefully manages cache eviction to improve cache hit rate and reduce disk retrieval time. For further acceleration, we adopt a probabilistic pruning technique that can effectively prune a large number of vector pairs from computation. Our evaluation on real-world, large-scale datasets shows that DiskJoin significantly outperforms alternatives, achieving speedups from 50x to 1000x.
BTW: A Non-Parametric Variance Stabilization Framework for Multimodal Model Integration
Venue: arXiv cs.LG 2025
Year: 2025

Mixture-of-Experts (MoE) models have become increasingly powerful in multimodal learning by enabling modular specialization across modalities. However, their effectiveness remains unclear when additional modalities introduce more noise than complementary information. Existing approaches, such as the Partial Information Decomposition, struggle to scale beyond two modalities and lack the resolution needed for instance-level control. We propose Beyond Two-modality Weighting (BTW), a bi-level, non-parametric weighting framework that combines instance-level Kullback-Leibler (KL) divergence and modality-level mutual information (MI) to dynamically adjust modality importance during training. Our method does not require additional parameters and can be applied to an arbitrary number of modalities. Specifically, BTW computes per-example KL weights by measuring the divergence between each unimodal and the current multimodal prediction, and modality-wide MI weights by estimating global alignment between unimodal and multimodal outputs. Extensive experiments on sentiment regression and clinical classification demonstrate that our method significantly improves regression performance and multiclass classification accuracy.
A Note on Graphon-Signal Analysis of Graph Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

A recent paper, ``A Graphon-Signal Analysis of Graph Neural Networks'', by Levie, analyzed message passing graph neural networks (MPNNs) by embedding the input space of MPNNs, i.e., attributed graphs (graph-signals), to a space of attributed graphons (graphon-signals). Based on extensions of standard results in graphon analysis to graphon-signals, the paper proved a generalization bound and a sampling lemma for MPNNs. However, there are some missing ingredients in that paper, limiting its applicability in practical settings of graph machine learning. In the current paper, we introduce several refinements and extensions to existing results that address these shortcomings. In detail, 1) we extend the main results in the paper to graphon-signals with multidimensional signals (rather than 1D signals), 2) we extend the Lipschitz continuity to MPNNs with readout with respect to cut distance (rather than MPNNs without readout with respect to cut metric), 3) we improve the generalization bound by utilizing robustness-type generalization bounds, and 4) we extend the analysis to non-symmetric graphons and kernels.
Strata: Hierarchical Context Caching for Long Context Language Model Serving
Venue: arXiv cs.DC 2025
Year: 2025

Large Language Models (LLMs) with expanding context windows face significant performance hurdles. While caching key-value (KV) states is critical for avoiding redundant computation, the storage footprint of long-context caches quickly exceeds GPU memory capacity, forcing production systems to adopt hierarchical caching across memory hierarchies. However, transferring large cached contexts back to the GPU introduces severe performance bottlenecks: fragmented I/O from paged layouts prevents full bandwidth utilization, and existing schedulers fail to account for cache-loading delays, leaving systems loading-bound rather than compute-bound. We present Strata, a hierarchical context caching framework designed for efficient long context LLM serving. Strata introduces GPU-assisted I/O to combat KV cache fragmentation, decoupling GPU and CPU memory layouts and employs cache-aware request scheduling to balance compute with I/O latency and overlapping unavoidable stalls with complementary tasks. Built on SGLang and deployed in production, Strata achieves up to 5x lower Time-To-First-Token (TTFT) compared to vLLM + LMCache and 3.75x speedup over NVIDIA TensorRT-LLM on long-context benchmarks, without degrading short-context performance.
Multi-Resolution Codebook Design and Multiuser Interference Management for Discrete XL-RIS-Aided Near-Field MIMO Systems
Venue: arXiv cs.IT 2025
Year: 2025

Extremely large-scale reconfigurable intelligent surface (XL-RIS) can effectively overcome severe fading and provide higher communication performance. However, current research on XL-RIS overlooks the discrete phase-shift characteristics of RIS in practical systems, which will result in significant performance degradation.In this paper, we investigate near-field communication schemes assisted by XL-RIS with discrete phase shifts.Specifically, we propose a hierarchical beam training method to obtain the user channel state information (CSI), and develop the jointly optimized codebook construction (JOCC) method and separately optimized codebook construction (SOCC) method for base station (BS) precoding and XL-RIS phase shifts, respectively. With JOCC, the most superior beam training performance can be obtained.With SOCC, higher performance than the single-antenna BS codebook can be obtained at a similar complexity.Further, we propose a flexible multiuser interference management (IM) method that is simple to solve. The IM method uses adaptive gain matrix approximation to take into account user fairness and can be solved in closed-form iterations. In addition, we extend the proposed method to a hybrid precoding design. Simulation results demonstrate that the proposed multi-resolution codebook construction method can obtain more accurate beam patterns and user CSI, and the proposed IM method obtains superior performance over the benchmark methods.