OpenRaiser/Intern-Atlas huggingface dataset:
The ProLiFIC dataset: Leveraging LLMs to Unveil the Italian Lawmaking Process
Venue: arXiv cs.CL 2025
Year: 2025

Process Mining (PM), initially developed for industrial and business contexts, has recently been applied to social systems, including legal ones. However, PM's efficacy in the legal domain is limited by the accessibility and quality of datasets. We introduce ProLiFIC (Procedural Lawmaking Flow in Italian Chambers), a comprehensive event log of the Italian lawmaking process from 1987 to 2022. Created from unstructured data from the Normattiva portal and structured using large language models (LLMs), ProLiFIC aligns with recent efforts in integrating PM with LLMs. We exemplify preliminary analyses and propose ProLiFIC as a benchmark for legal PM, fostering new developments.
Topic Identification in LLM Input-Output Pairs through the Lens of Information Bottleneck
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are prone to critical failure modes, including \textit{intrinsic faithfulness hallucinations} (also known as confabulations), where a response deviates semantically from the provided context. Frameworks designed to detect this, such as Semantic Divergence Metrics (SDM), rely on identifying latent topics shared between prompts and responses, typically by applying geometric clustering to their sentence embeddings. This creates a disconnect, as the topics are optimized for spatial proximity, not for the downstream information-theoretic analysis. In this paper, we bridge this gap by developing a principled topic identification method grounded in the Deterministic Information Bottleneck (DIB) for geometric clustering. Our key contribution is to transform the DIB method into a practical algorithm for high-dimensional data by substituting its intractable KL divergence term with a computationally efficient upper bound. The resulting method, which we dub UDIB, can be interpreted as an entropy-regularized and robustified version of K-means that inherently favors a parsimonious number of informative clusters. By applying UDIB to the joint clustering of LLM prompt and response embeddings, we generate a shared topic representation that is not merely spatially coherent but is fundamentally structured to be maximally informative about the prompt-response relationship. This provides a superior foundation for the SDM framework and offers a novel, more sensitive tool for detecting confabulations.
Towards the Datasets Used in Requirements Engineering of Mobile Apps: Preliminary Findings from a Systematic Mapping Study
Venue: arXiv cs.SE 2025
Year: 2025

[Background] Research on requirements engineering (RE) for mobile apps employs datasets formed by app users, developers or vendors. However, little is known about the sources of these datasets in terms of platforms and the RE activities that were researched with the help of the respective datasets. [Aims] The goal of this paper is to investigate the state-of-the-art of the datasets of mobile apps used in existing RE research. [Method] We carried out a systematic mapping study by following the guidelines of Kitchenham et al. [Results] Based on 43 selected papers, we found that Google Play and Apple App Store provide the datasets for more than 90% of published research in RE for mobile apps. We also found that the most investigated RE activities - based on datasets, are requirements elicitation and requirements analysis. [Conclusions] Our most important conclusions are: (1) there is a growth in the use of datasets for RE research of mobile apps since 2012, (2) the RE knowledge for mobile apps might be skewed due to the overuse of Google Play and Apple App Store, (3) there are attempts to supplement reviews of apps from repositories with other data sources, (4) there is a need to expand the alternative sources and experiments with complimentary use of multiple sources, if the community wants more generalizable results. Plus, it is expected to expand the research on other RE activities, beyond elicitation and analysis.
E-ARMOR: Edge case Assessment and Review of Multilingual Optical Character Recognition
Venue: arXiv cs.CL 2025
Year: 2025

Optical Character Recognition (OCR) in multilingual, noisy, and diverse real-world images remains a significant challenge for optical character recognition systems. With the rise of Large Vision-Language Models (LVLMs), there is growing interest in their ability to generalize and reason beyond fixed OCR pipelines. In this work, we introduce Sprinklr-Edge-OCR, a novel OCR system built specifically optimized for edge deployment in resource-constrained environments. We present a large-scale comparative evaluation of five state-of-the-art LVLMs (InternVL, Qwen, GOT OCR, LLaMA, MiniCPM) and two traditional OCR systems (Sprinklr-Edge-OCR, SuryaOCR) on a proprietary, doubly hand annotated dataset of multilingual (54 languages) images. Our benchmark covers a broad range of metrics including accuracy, semantic consistency, language coverage, computational efficiency (latency, memory, GPU usage), and deployment cost. To better reflect real-world applicability, we also conducted edge case deployment analysis, evaluating model performance on CPU only environments. Among the results, Qwen achieved the highest precision (0.54), while Sprinklr-Edge-OCR delivered the best overall F1 score (0.46) and outperformed others in efficiency, processing images 35 faster (0.17 seconds per image on average) and at less than 0.01 of the cost (0.006 USD per 1,000 images) compared to LVLM. Our findings demonstrate that the most optimal OCR systems for edge deployment are the traditional ones even in the era of LLMs due to their low compute requirements, low latency, and very high affordability.
A Machine Learning-Based Study on the Synergistic Optimization of Supply Chain Management and Financial Supply Chains from an Economic Perspective
Venue: arXiv cs.LG 2025
Year: 2025

Based on economic theories and integrated with machine learning technology, this study explores a collaborative Supply Chain Management and Financial Supply Chain Management (SCM - FSCM) model to solve issues like efficiency loss, financing constraints, and risk transmission. We combine Transaction Cost and Information Asymmetry theories and use algorithms such as random forests to process multi-dimensional data and build a data-driven, three-dimensional (cost-efficiency-risk) analysis framework. We then apply an FSCM model of "core enterprise credit empowerment plus dynamic pledge financing." We use Long Short-Term Memory (LSTM) networks for demand forecasting and clustering/regression algorithms for benefit allocation. The study also combines Game Theory and reinforcement learning to optimize the inventory-procurement mechanism and uses eXtreme Gradient Boosting (XGBoost) for credit assessment to enable rapid monetization of inventory. Verified with 20 core and 100 supporting enterprises, the results show a 30\% increase in inventory turnover, an 18\%-22\% decrease in SME financing costs, a stable order fulfillment rate above 95\%, and excellent model performance (demand forecasting error <= 8\%, credit assessment accuracy >= 90\%). This SCM-FSCM model effectively reduces operating costs, alleviates financing constraints, and supports high-quality supply chain development.
Fast Evaluation of Derivatives of Green's Functions Using Recurrences
Venue: arXiv cs.CE 2025
Year: 2025

High-order derivatives of Green's functions are a key ingredient in Taylor-based fast multipole methods, Barnes-Hut $n$-body algorithms, and quadrature by expansion (QBX). In these settings, derivatives underpin either the formation, evaluation, and/or translation of Taylor expansions. In this article, we provide hybrid symbolic-numerical procedures that generate recurrences to attain an $O(n)$ cost for the the computation of $n$ derivatives (i.e. $O(1)$ per derivative) for arbitrary radially symmetric Green's functions. These procedures are general--only requiring knowledge of the PDE that the Green's function solves. We show that the algorithm has controlled, theoretically-understood error. We apply these methods to the method of quadrature by expansion, a method for the evaluation of singular layer potentials, which requires higher-order derivatives of Green's functions. In doing so, we contribute a new rotation-based method for target-specific QBX evaluation in the Cartesian setting that attains dramatically lower cost than existing symbolic approaches. Numerical experiments support our claims of accuracy and cost.
QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception
Venue: arXiv cs.CV 2025
Year: 2025

Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the field of view. However, past research has predominantly focused on improving accuracy metrics without addressing the crucial system-level considerations of efficiency, latency, and real-world deployability. Noticeably, most existing systems rely on full-precision models, which incur high computational and transmission costs, making them impractical for real-time operation in resource-constrained environments. In this paper, we introduce \textbf{QuantV2X}, the first fully quantized multi-agent system designed specifically for efficient and scalable deployment of multi-modal, multi-agent V2X cooperative perception. QuantV2X introduces a unified end-to-end quantization strategy across both neural network models and transmitted message representations that simultaneously reduces computational load and transmission bandwidth. Remarkably, despite operating under low-bit constraints, QuantV2X achieves accuracy comparable to full-precision systems. More importantly, when evaluated under deployment-oriented metrics, QuantV2X reduces system-level latency by 3.2$\times$ and achieves a +9.5 improvement in mAP30 over full-precision baselines. Furthermore, QuantV2X scales more effectively, enabling larger and more capable models to fit within strict memory budgets. These results highlight the viability of a fully quantized multi-agent intermediate fusion system for real-world deployment. The system will be publicly released to promote research in this field: https://github.com/ucla-mobility/QuantV2X.
Distributed MIMO With Over-the-Air Phase Calibration Integrated Into the TDD Flow
Venue: arXiv cs.IT 2025
Year: 2025

Reciprocity-based, joint coherent downlink beamforming from multiple access points (APs) in distributed multiple-input multiple-output (MIMO) with independent local oscillators (LOs) requires the APs to be periodically phase-calibrated (a.k.a. phase-synchronized or phase-aligned). Such phase alignment can be accomplished by bidirectional over-the-air measurements between the APs. In this paper, we show how such over-the-air measurements can be integrated into the time-division duplexing (TDD) flow by appropriately shifting the uplink/downlink switching points of the TDD slot structure, creating short time segments during which APs can measure on one another. We also show how this technique scales to large networks. Furthermore, we analytically characterize the tradeoff between the amount of resources spent on calibration measurements and the resulting spectral efficiency of the system, when conjugate beamforming or zero-forcing beamforming is used. The results demonstrate the feasibility of distributed MIMO with phase-calibration through over-the-air inter-AP measurements integrated into the TDD flow.
MLSD: A Novel Few-Shot Learning Approach to Enhance Cross-Target and Cross-Domain Stance Detection
Venue: arXiv cs.CL 2025
Year: 2025

We present the novel approach for stance detection across domains and targets, Metric Learning-Based Few-Shot Learning for Cross-Target and Cross-Domain Stance Detection (MLSD). MLSD utilizes metric learning with triplet loss to capture semantic similarities and differences between stance targets, enhancing domain adaptation. By constructing a discriminative embedding space, MLSD allows a cross-target or cross-domain stance detection model to acquire useful examples from new target domains. We evaluate MLSD in multiple cross-target and cross-domain scenarios across two datasets, showing statistically significant improvement in stance detection performance across six widely used stance detection models.
Transfer Learning-Based CNN Models for Plant Species Identification Using Leaf Venation Patterns
Venue: arXiv cs.CV 2025
Year: 2025

This study evaluates the efficacy of three deep learning architectures: ResNet50, MobileNetV2, and EfficientNetB0 for automated plant species classification based on leaf venation patterns, a critical morphological feature with high taxonomic relevance. Using the Swedish Leaf Dataset comprising images from 15 distinct species (75 images per species, totalling 1,125 images), the models were demonstrated using standard performance metrics during training and testing phases. ResNet50 achieved a training accuracy of 94.11% but exhibited overfitting, reflected by a reduced testing accuracy of 88.45% and an F1 score of 87.82%. MobileNetV2 demonstrated better generalization capabilities, attaining a testing accuracy of 93.34% and an F1 score of 93.23%, indicating its suitability for lightweight, real-time applications. EfficientNetB0 outperformed both models, achieving a testing accuracy of 94.67% with precision, recall, and F1 scores exceeding 94.6%, highlighting its robustness in venation-based classification. The findings underscore the potential of deep learning, particularly EfficientNetB0, in developing scalable and accurate tools for automated plant taxonomy using venation traits.
The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs
Venue: arXiv cs.AI 2025
Year: 2025

Personality traits have long been studied as predictors of human behavior. Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems, with advanced LLMs displaying consistent behavioral tendencies resembling human traits like agreeableness and self-regulation. Understanding these patterns is crucial, yet prior work primarily relied on simplified self-reports and heuristic prompting, with little behavioral validation. In this study, we systematically characterize LLM personality across three dimensions: (1) the dynamic emergence and evolution of trait profiles throughout training stages; (2) the predictive validity of self-reported traits in behavioral tasks; and (3) the impact of targeted interventions, such as persona injection, on both self-reports and behavior. Our findings reveal that instructional alignment (e.g., RLHF, instruction tuning) significantly stabilizes trait expression and strengthens trait correlations in ways that mirror human data. However, these self-reported traits do not reliably predict behavior, and observed associations often diverge from human patterns. While persona injection successfully steers self-reports in the intended direction, it exerts little or inconsistent effect on actual behavior. By distinguishing surface-level trait expression from behavioral consistency, our findings challenge assumptions about LLM personality and underscore the need for deeper evaluation in alignment and interpretability.
A Quantum Genetic Algorithm-Enhanced Self-Supervised Intrusion Detection System for Wireless Sensor Networks in the Internet of Things
Venue: arXiv cs.CR 2025
Year: 2025

The rapid expansion of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has significantly increased the attack surface of such systems, making them vulnerable to a wide range of cyber threats. Traditional Intrusion Detection Systems (IDS) often fail to meet the stringent requirements of resource-constrained IoT environments due to their high computational cost and reliance on large labeled datasets. To address these challenges, this paper proposes a novel hybrid Intrusion Detection System that integrates a Quantum Genetic Algorithm (QGA) with Self-Supervised Learning (SSL). The QGA leverages quantum-inspired evolutionary operators to optimize feature selection and fine-tune model parameters, ensuring lightweight yet efficient detection in resource-limited networks. Meanwhile, SSL enables the system to learn robust representations from unlabeled data, thereby reducing dependency on manually labeled training sets. The proposed framework is evaluated on benchmark IoT intrusion datasets, demonstrating superior performance in terms of detection accuracy, false positive rate, and computational efficiency compared to conventional evolutionary and deep learning-based IDS models. The results highlight the potential of combining quantum-inspired optimization with self-supervised paradigms to design next-generation intrusion detection solutions for IoT and WSN environments.
Drift Plus Optimistic Penalty: A Learning Framework for Stochastic Network Optimization with Improved Regret Bounds
Venue: arXiv cs.NI 2025
Year: 2025

We consider the problem of joint routing and scheduling in queueing networks, where the edge transmission costs are unknown. At each time-slot, the network controller receives noisy observations of transmission costs only for those edges it selects for transmission. The network controller's objective is to make routing and scheduling decisions so that the total expected cost is minimized. This problem exhibits an exploration-exploitation trade-off, however, previous bandit-style solutions cannot be directly applied to this problem due to the queueing dynamics. In order to ensure network stability, the network controller needs to optimize throughput and cost simultaneously. We show that the best achievable cost is lower bounded by the solution to a static optimization problem, and develop a network control policy using techniques from Lyapunov drift-plus-penalty optimization and multi-arm bandits. We show that the policy achieves a sub-linear regret of order $O(\sqrt{T}\log T)$, as compared to the best policy that has complete knowledge of arrivals and costs. Finally, we evaluate the proposed policy using simulations and show that its regret is indeed sub-linear.
MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting
Venue: arXiv cs.CV 2025
Year: 2025

Radiologic diagnostic errors-under-reading errors, inattentional blindness, and communication failures-remain prevalent in clinical practice. These issues often stem from missed localized abnormalities, limited global context, and variability in report language. These challenges are amplified in 3D imaging, where clinicians must examine hundreds of slices per scan. Addressing them requires systems with precise localized detection, global volume-level reasoning, and semantically consistent natural language reporting. However, existing 3D vision-language models are unable to meet all three needs jointly, lacking local-global understanding for spatial reasoning and struggling with the variability and noise of uncurated radiology reports. We present MedVista3D, a multi-scale semantic-enriched vision-language pretraining framework for 3D CT analysis. To enable joint disease detection and holistic interpretation, MedVista3D performs local and global image-text alignment for fine-grained representation learning within full-volume context. To address report variability, we apply language model rewrites and introduce a Radiology Semantic Matching Bank for semantics-aware alignment. MedVista3D achieves state-of-the-art performance on zero-shot disease classification, report retrieval, and medical visual question answering, while transferring well to organ segmentation and prognosis prediction. Code and datasets will be released.
EGTM: Event-guided Efficient Turbulence Mitigation
Venue: arXiv cs.CV 2025
Year: 2025

Turbulence mitigation (TM) aims to remove the stochastic distortions and blurs introduced by atmospheric turbulence into frame cameras. Existing state-of-the-art deep-learning TM methods extract turbulence cues from multiple degraded frames to find the so-called "lucky'', not distorted patch, for "lucky fusion''. However, it requires high-capacity network to learn from coarse-grained turbulence dynamics between synchronous frames with limited frame-rate, thus fall short in computational and storage efficiency. Event cameras, with microsecond-level temporal resolution, have the potential to fundamentally address this bottleneck with efficient sparse and asynchronous imaging mechanism. In light of this, we (i) present the fundamental \textbf{``event-lucky insight''} to reveal the correlation between turbulence distortions and inverse spatiotemporal distribution of event streams. Then, build upon this insight, we (ii) propose a novel EGTM framework that extracts pixel-level reliable turbulence-free guidance from the explicit but noisy turbulent events for temporal lucky fusion. Moreover, we (iii) build the first turbulence data acquisition system to contribute the first real-world event-driven TM dataset. Extensive experimental results demonstrate that our approach significantly surpass the existing SOTA TM method by 710 times, 214 times and 224 times in model size, inference latency and model complexity respectively, while achieving the state-of-the-art in restoration quality (+0.94 PSNR and +0.08 SSIM) on our real-world EGTM dataset. This demonstrating the great efficiency merit of introducing event modality into TM task. Demo code and data have been uploaded in supplementary material and will be released once accepted.
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Venue: arXiv cs.CL 2025
Year: 2025

We introduce Drivelology, a unique linguistic phenomenon characterised as "nonsense with depth" - utterances that are syntactically coherent yet pragmatically paradoxical, emotionally loaded, or rhetorically subversive. While such expressions may resemble surface-level nonsense, they encode implicit meaning requiring contextual inference, moral reasoning, or emotional interpretation. We find that current large language models (LLMs), despite excelling at many natural language processing (NLP) tasks, consistently fail to grasp the layered semantics of Drivelological text. To investigate this, we construct a benchmark dataset of over 1,200+ meticulously curated and diverse examples across English, Mandarin, Spanish, French, Japanese, and Korean. Each example underwent careful expert review to verify its Drivelological characteristics, involving multiple rounds of discussion and adjudication to address disagreements. Using this dataset, we evaluate a range of LLMs on classification, generation, and reasoning tasks. Our results reveal clear limitations of LLMs: models often confuse Drivelology with shallow nonsense, produce incoherent justifications, or miss implied rhetorical functions altogether. These findings highlight a deep representational gap in LLMs' pragmatic understanding and challenge the assumption that statistical fluency implies cognitive comprehension. We release our dataset and code to facilitate further research in modelling linguistic depth beyond surface-level coherence.