OpenRaiser/Intern-Atlas huggingface dataset:
Repositioning, Ride-matching, and Abandonment in On-demand Ride-hailing Platforms: A Mean Field Game Approach
Venue: arXiv cs.GT 2025
Year: 2025

The on-demand ride-hailing industry has experienced rapid growth, transforming transportation norms worldwide. Despite improvements in efficiency over traditional taxi services, significant challenges remain, including drivers' strategic repositioning behavior, customer abandonment, and inefficiencies in dispatch algorithms. To address these issues, we introduce a comprehensive mean field game model that systematically analyzes the dynamics of ride-hailing platforms by incorporating driver repositioning across multiple regions, customer abandonment behavior, and platform dispatch algorithms. Using this framework, we identify all possible mean field equilibria as the Karush-Kuhn-Tucker (KKT) points of an associated optimization problem. Our analysis reveals the emergence of multiple equilibria, including the inefficient "Wild Goose Chase" one, characterized by drivers pursuing distant requests, leading to suboptimal system performance. To mitigate these inefficiencies, we propose a novel two-matching-radius nearest-neighbor dispatch algorithm that eliminates undesirable equilibria and ensures a unique mean field equilibrium for multi-region systems. The algorithm dynamically adjusts matching radii based on driver supply rates, optimizing pick-up times and waiting times for drivers while maximizing request completion rates. Numerical experiments and simulation results show that our proposed algorithm reduces customer abandonment, minimizes waiting times for both customers and drivers, and improves overall platform efficiency.
Sublinear Time Algorithms for Abelian Group Isomorphism and Basis Construction
Venue: arXiv cs.CC 2025
Year: 2025

In this paper, we study the problems of abelian group isomorphism and basis construction in two models. In the {\it partially specified model} (PS-model), the algorithm does not know the group size but can access randomly chosen elements of the group along with the Cayley table of those elements, which provides the result of the binary operation for every pair of selected elements. In the stronger {\it fully specified model} (FS-model), the algorithm knows the size of the group and has access to its elements and Cayley table. Given two abelian groups, $G$, and $H$, we present an algorithm in the PS-model (and hence in the FS-model) that runs in time $\tilde O(\sqrt{|G|})$ and decides if they are isomorphic. This improves on Kavitha's linear-time algorithm and gives the first sublinear-time solution for this problem. We then prove the lower bound $Ω(|G|^{1/4})$ for the FS-model and the tight bound $Ω(\sqrt{|G|})$ for the PS-model. This is the first known lower bound for this problem. We obtain similar results for finding a basis for abelian groups. For deterministic algorithms, a simple $Ω(|G|)$ lower bound is given.
Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillation
Venue: arXiv cs.CV 2025
Year: 2025

Marine Saliency Segmentation (MSS) plays a pivotal role in various vision-based marine exploration tasks. However, existing marine segmentation techniques face the dilemma of object mislocalization and imprecise boundaries due to the complex underwater environment. Meanwhile, despite the impressive performance of diffusion models in visual segmentation, there remains potential to further leverage contextual semantics to enhance feature learning of region-level salient objects, thereby improving segmentation outcomes. Building on this insight, we propose DiffMSS, a novel marine saliency segmenter based on the diffusion model, which utilizes semantic knowledge distillation to guide the segmentation of marine salient objects. Specifically, we design a region-word similarity matching mechanism to identify salient terms at the word level from the text descriptions. These high-level semantic features guide the conditional feature learning network in generating salient and accurate diffusion conditions with semantic knowledge distillation. To further refine the segmentation of fine-grained structures in unique marine organisms, we develop the dedicated consensus deterministic sampling to suppress overconfident missegmentations. Comprehensive experiments demonstrate the superior performance of DiffMSS over state-of-the-art methods in both quantitative and qualitative evaluations.
The quasi-semantic competence of LLMs: a case study on the part-whole relation
Venue: arXiv cs.CL 2025
Year: 2025

Understanding the extent and depth of the semantic competence of \emph{Large Language Models} (LLMs) is at the center of the current scientific agenda in Artificial Intelligence (AI) and Computational Linguistics (CL). We contribute to this endeavor by investigating their knowledge of the \emph{part-whole} relation, a.k.a. \emph{meronymy}, which plays a crucial role in lexical organization, but it is significantly understudied. We used data from ConceptNet relations \citep{speer2016conceptnet} and human-generated semantic feature norms \citep{McRae:2005} to explore the abilities of LLMs to deal with \textit{part-whole} relations. We employed several methods based on three levels of analysis: i.) \textbf{behavioral} testing via prompting, where we directly queried the models on their knowledge of meronymy, ii.) sentence \textbf{probability} scoring, where we tested models' abilities to discriminate correct (real) and incorrect (asymmetric counterfactual) \textit{part-whole} relations, and iii.) \textbf{concept representation} analysis in vector space, where we proved the linear organization of the \textit{part-whole} concept in the embedding and unembedding spaces. These analyses present a complex picture that reveals that the LLMs' knowledge of this relation is only partial. They have just a ``\emph{quasi}-semantic'' competence and still fall short of capturing deep inferential properties.
MonoGS++: Fast and Accurate Monocular RGB Gaussian SLAM
Venue: arXiv cs.CV 2025
Year: 2025

We present MonoGS++, a novel fast and accurate Simultaneous Localization and Mapping (SLAM) method that leverages 3D Gaussian representations and operates solely on RGB inputs. While previous 3D Gaussian Splatting (GS)-based methods largely depended on depth sensors, our approach reduces the hardware dependency and only requires RGB input, leveraging online visual odometry (VO) to generate sparse point clouds in real-time. To reduce redundancy and enhance the quality of 3D scene reconstruction, we implemented a series of methodological enhancements in 3D Gaussian mapping. Firstly, we introduced dynamic 3D Gaussian insertion to avoid adding redundant Gaussians in previously well-reconstructed areas. Secondly, we introduced clarity-enhancing Gaussian densification module and planar regularization to handle texture-less areas and flat surfaces better. We achieved precise camera tracking results both on the synthetic Replica and real-world TUM-RGBD datasets, comparable to those of the state-of-the-art. Additionally, our method realized a significant 5.57x improvement in frames per second (fps) over the previous state-of-the-art, MonoGS.
Towards Learning-Augmented Peer-to-Peer Networks: Self-Stabilizing Graph Linearization with Untrusted Advice
Venue: arXiv cs.DC 2025
Year: 2025

Distributed peer-to-peer systems are widely popular due to their decentralized nature, which ensures that no peer is critical for the functionality of the system. However, fully decentralized solutions are usually much harder to design, and tend to have a much higher overhead compared to centralized approaches, where the peers are connected to a powerful server. On the other hand, centralized approaches have a single point of failure. Thus, is there some way to combine their advantages without inheriting their disadvantages? To that end, we consider a supervised peer-to-peer approach where the peers can ask a potentially unreliable supervisor for advice. This is in line with the increasingly popular algorithmic paradigm called algorithms with predictions or learning-augmented algorithms, but we are the first to consider it in the context of peer-to-peer networks. Specifically, we design self-stabilizing algorithms for the fundamental problem of distributed graph linearization, where peers are supposed to recover the "sorted line" network from any initial network after a transient fault. With the help of the supervisor, peers can recover the sorted line network in $O(\log n)$ time, if the advice is correct; otherwise, the algorithm retains its original recovery time (i.e., without any supervisor). A crucial challenge that we overcome is to correctly compose multiple self-stabilizing algorithms, that is, one that processes and exploits the advice, and another that does not rely on the advice at all. Our key technical contributions combine ideas from the fields of overlay networks and proof-labeling schemes. Finally, we give a matching lower bound of $Ω(\log n)$ for the recovery time of any algorithm if the advice can be corrupted, where $n$ is the network size.
Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness
Venue: arXiv cs.CY 2025
Year: 2025

Current fairness metrics and mitigation techniques provide tools for practitioners to asses how non-discriminatory Automatic Decision Making (ADM) systems are. What if I, as an individual facing a decision taken by an ADM system, would like to know: Am I being treated fairly? We explore how to create the affordance for users to be able to ask this question of ADM. In this paper, we argue for the reification of fairness not only as a property of ADM, but also as an epistemic right of an individual to acquire information about the decisions that affect them and use that information to contest and seek effective redress against those decisions, in case they are proven to be discriminatory. We examine key concepts from existing research not only in algorithmic fairness but also in explainable artificial intelligence, accountability, and contestability. Integrating notions from these domains, we propose a conceptual framework to ascertain fairness by combining different tools that empower the end-users of ADM systems. Our framework shifts the focus from technical solutions aimed at practitioners to mechanisms that enable individuals to understand, challenge, and verify the fairness of decisions, and also serves as a blueprint for organizations and policymakers, bridging the gap between technical requirements and practical, user-centered accountability.
Semiconductor Wafer Map Defect Classification with Tiny Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Semiconductor wafer defect classification is critical for ensuring high precision and yield in manufacturing. Traditional CNN-based models often struggle with class imbalances and recognition of the multiple overlapping defect types in wafer maps. To address these challenges, we propose ViT-Tiny, a lightweight Vision Transformer (ViT) framework optimized for wafer defect classification. Trained on the WM-38k dataset. ViT-Tiny outperforms its ViT-Base counterpart and state-of-the-art (SOTA) models, such as MSF-Trans and CNN-based architectures. Through extensive ablation studies, we determine that a patch size of 16 provides optimal performance. ViT-Tiny achieves an F1-score of 98.4%, surpassing MSF-Trans by 2.94% in four-defect classification, improving recall by 2.86% in two-defect classification, and increasing precision by 3.13% in three-defect classification. Additionally, it demonstrates enhanced robustness under limited labeled data conditions, making it a computationally efficient and reliable solution for real-world semiconductor defect detection.
Blockchain and Distributed Ledger Technologies for Cyberthreat Intelligence Sharing
Venue: arXiv cs.CR 2025
Year: 2025

Cyberthreat intelligence sharing is a critical aspect of cybersecurity, and it is essential to understand its definition, objectives, benefits, and impact on society. Blockchain and Distributed Ledger Technology (DLT) are emerging technologies that have the potential to transform intelligence sharing. This paper aims to provide a comprehensive understanding of intelligence sharing and the role of blockchain and DLT in enhancing it. The paper addresses questions related to the definition, objectives, benefits, and impact of intelligence sharing and provides a review of the existing literature. Additionally, the paper explores the challenges associated with blockchain and DLT and their potential impact on security and privacy. The paper also discusses the use of DLT and blockchain in security and intelligence sharing and highlights the associated challenges and risks. Furthermore, the paper examines the potential impact of a National Cybersecurity Strategy on addressing cybersecurity risks. Finally, the paper explores the experimental set up required for implementing blockchain and DLT for intelligence sharing and discusses the curricular ramifications of intelligence sharing.
Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results
Venue: arXiv cs.CV 2025
Year: 2025

Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water currents. In this paper, we address a novel task: rip current instance segmentation. We introduce a comprehensive dataset containing $2,466$ images with newly created polygonal annotations for instance segmentation, used for training and validation. Additionally, we present a novel dataset comprising $17$ drone videos (comprising about $24K$ frames) captured at $30 FPS$, annotated with both polygons for instance segmentation and bounding boxes for object detection, employed for testing purposes. We train various versions of YOLOv8 for instance segmentation on static images and assess their performance on the test dataset (videos). The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of $88.94%$ on the validation dataset and $81.21%$ macro average on the test dataset. The results provide a baseline for future research in rip current segmentation. Our work contributes to the existing literature by introducing a detailed, annotated dataset, and training a deep learning model for instance segmentation of rip currents. The code, training details and the annotated dataset are made publicly available at https://github.com/Irikos/rip_currents.
Leveraging LLM For Synchronizing Information Across Multilingual Tables
Venue: arXiv cs.CL 2025
Year: 2025

The vast amount of online information today poses challenges for non-English speakers, as much of it is concentrated in high-resource languages such as English and French. Wikipedia reflects this imbalance, with content in low-resource languages frequently outdated or incomplete. Recent research has sought to improve cross-language synchronization of Wikipedia tables using rule-based methods. These approaches can be effective, but they struggle with complexity and generalization. This paper explores large language models (LLMs) for multilingual information synchronization, using zero-shot prompting as a scalable solution. We introduce the Information Updation dataset, simulating the real-world process of updating outdated Wikipedia tables, and evaluate LLM performance. Our findings reveal that single-prompt approaches often produce suboptimal results, prompting us to introduce a task decomposition strategy that enhances coherence and accuracy. Our proposed method outperforms existing baselines, particularly in Information Updation (1.79%) and Information Addition (20.58%), highlighting the model strength in dynamically updating and enriching data across architectures.
"I Feel Like I'm Teaching in a Gladiator Ring": Barriers and Benefits of Live Coding in Classroom Settings
Venue: arXiv cs.HC 2025
Year: 2025

Live coding for teaching-synchronously writing software in front of students-can be an effective method for engaging students and instilling practical programming skills. However, not all settings are conducive to live coding and not all instructors are successful in this challenging task. We present results from a study involving university instructors, teaching assistants, and students identifying both barriers and benefits of live coding. Physical infrastructure, a positive classroom community with psychological safety, and opportunities for teacher development are practical considerations for live coding. In order for live coding to be an active learning experience, we recommend that tools support multiple mechanisms for engaging students, directing audience attention, and encouraging student-led live coding.
Reservoir Computing: A New Paradigm for Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

A Literature Review of Reservoir Computing. Even before Artificial Intelligence was its own field of computational science, humanity has tried to mimic the activity of the human brain. In the early 1940s the first artificial neuron models were created as purely mathematical concepts. Over the years, ideas from neuroscience and computer science were used to develop the modern Neural Network. The interest in these models rose quickly but fell when they failed to be successfully applied to practical applications, and rose again in the late 2000s with the drastic increase in computing power, notably in the field of natural language processing, for example with the state-of-the-art speech recognizer making heavy use of deep neural networks. Recurrent Neural Networks (RNNs), a class of neural networks with cycles in the network, exacerbates the difficulties of traditional neural nets. Slow convergence limiting the use to small networks, and difficulty to train through gradient-descent methods because of the recurrent dynamics have hindered research on RNNs, yet their biological plausibility and their capability to model dynamical systems over simple functions makes then interesting for computational researchers. Reservoir Computing emerges as a solution to these problems that RNNs traditionally face. Promising to be both theoretically sound and computationally fast, Reservoir Computing has already been applied successfully to numerous fields: natural language processing, computational biology and neuroscience, robotics, even physics. This survey will explore the history and appeal of both traditional feed-forward and recurrent neural networks, before describing the theory and models of this new reservoir computing paradigm. Finally recent papers using reservoir computing in a variety of scientific fields will be reviewed.
Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) provides a powerful method to address problems in operations research. However, its real-world application often fails due to a lack of user acceptance and trust. A possible remedy is to provide managers with the possibility of altering the RL policy by incorporating human expert knowledge. In this study, we analyze the benefits and caveats of including human knowledge via action masking. While action masking has so far been used to exclude invalid actions, its ability to integrate human expertise remains underexplored. Human knowledge is often encapsulated in heuristics, which suggest reasonable, near-optimal actions in certain situations. Enforcing such actions should hence increase trust among the human workforce to rely on the model's decisions. Yet, a strict enforcement of heuristic actions may also restrict the policy from exploring superior actions, thereby leading to overall lower performance. We analyze the effects of action masking based on three problems with different characteristics, namely, paint shop scheduling, peak load management, and inventory management. Our findings demonstrate that incorporating human knowledge through action masking can achieve substantial improvements over policies trained without action masking. In addition, we find that action masking is crucial for learning effective policies in constrained action spaces, where certain actions can only be performed a limited number of times. Finally, we highlight the potential for suboptimal outcomes when action masks are overly restrictive.
HQViT: Hybrid Quantum Vision Transformer for Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

Transformer-based architectures have revolutionized the landscape of deep learning. In computer vision domain, Vision Transformer demonstrates remarkable performance on par with or even surpassing that of convolutional neural networks. However, the quadratic computational complexity of its self-attention mechanism poses challenges for classical computing, making model training with high-dimensional input data, e.g., images, particularly expensive. To address such limitations, we propose a Hybrid Quantum Vision Transformer (HQViT), that leverages the principles of quantum computing to accelerate model training while enhancing model performance. HQViT introduces whole-image processing with amplitude encoding to better preserve global image information without additional positional encoding. By leveraging quantum computation on the most critical steps and selectively handling other components in a classical way, we lower the cost of quantum resources for HQViT. The qubit requirement is minimized to $O(log_2N)$ and the number of parameterized quantum gates is only $O(log_2d)$, making it well-suited for Noisy Intermediate-Scale Quantum devices. By offloading the computationally intensive attention coefficient matrix calculation to the quantum framework, HQViT reduces the classical computational load by $O(T^2d)$. Extensive experiments across various computer vision datasets demonstrate that HQViT outperforms existing models, achieving a maximum improvement of up to $10.9\%$ (on the MNIST 10-classification task) over the state of the art. This work highlights the great potential to combine quantum and classical computing to cope with complex image classification tasks.
MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs
Venue: arXiv cs.CL 2025
Year: 2025

We introduce MultiBLiMP 1.0, a massively multilingual benchmark of linguistic minimal pairs, covering 101 languages and 2 types of subject-verb agreement, containing more than 128,000 minimal pairs. Our minimal pairs are created using a fully automated pipeline, leveraging the large-scale linguistic resources of Universal Dependencies and UniMorph. MultiBLiMP 1.0 evaluates abilities of LLMs at an unprecedented multilingual scale, and highlights the shortcomings of the current state-of-the-art in modelling low-resource languages.