OpenRaiser/Intern-Atlas huggingface dataset:
Deep Learning in Mild Cognitive Impairment Diagnosis using Eye Movements and Image Content in Visual Memory Tasks
Venue: arXiv cs.HC 2025
Year: 2025

The global prevalence of dementia is projected to double by 2050, highlighting the urgent need for scalable diagnostic tools. This study utilizes digital cognitive tasks with eye-tracking data correlated with memory processes to distinguish between Healthy Controls (HC) and Mild Cognitive Impairment (MCI), a precursor to dementia. A deep learning model based on VTNet was trained using eye-tracking data from 44 participants (24 MCI, 20 HCs) who performed a visual memory task. The model utilizes both time series and spatial data derived from eye-tracking. It was modified to incorporate scan paths, heat maps, and image content. These modifications also enabled testing parameters such as image resolution and task performance, analyzing their impact on model performance. The best model, utilizing $700\times700px$ resolution heatmaps, achieved 68% sensitivity and 76% specificity. Despite operating under more challenging conditions (e.g., smaller dataset size, shorter task duration, or a less standardized task), the model's performance is comparable to an Alzheimer's study using similar methods (70% sensitivity and 73% specificity). These findings contribute to the development of automated diagnostic tools for MCI. Future work should focus on refining the model and using a standardized long-term visual memory task.
Mind the Dark: A Gamified Exploration of Deceptive Design Awareness for Children in the Digital Age
Venue: arXiv cs.HC 2025
Year: 2025

This paper addresses the critical issue of deceptive design elements prevalent in technology, and their potential impact on children. Recent research highlights the impact of dark patterns on adults and adolescents, while studies involving children are scarce. In an era where children wield greater independence with digital devices, their vulnerability to dark patterns amplifies without early education. Our findings show a significant positive impact of dark pattern education on children's awareness, revealing that heightened awareness considerably alters children's navigation of social media, video games, and streaming platforms. To this end, we developed a gamified application aimed at instructing children on identifying and responding to various dark patterns. Our evaluation results emphasize the critical role of early education in empowering children to recognize and counter deceptive design, thereby cultivating a digitally literate generation capable of making informed choices in the complex landscape of digital technology.
Scenario-Based Hierarchical Reinforcement Learning for Automated Driving Decision Making
Venue: arXiv cs.RO 2025
Year: 2025

Developing decision-making algorithms for highly automated driving systems remains challenging, since these systems have to operate safely in an open and complex environments. Reinforcement Learning (RL) approaches can learn comprehensive decision policies directly from experience and already show promising results in simple driving tasks. However, current approaches fail to achieve generalizability for more complex driving tasks and lack learning efficiency. Therefore, we present Scenario-based Automated Driving Reinforcement Learning (SAD-RL), the first framework that integrates Reinforcement Learning (RL) of hierarchical policy in a scenario-based environment. A high-level policy selects maneuver templates that are evaluated and executed by a low-level control logic. The scenario-based environment allows to control the training experience for the agent and to explicitly introduce challenging, but rate situations into the training process. Our experiments show that an agent trained using the SAD-RL framework can achieve safe behaviour in easy as well as challenging situations efficiently. Our ablation studies confirmed that both HRL and scenario diversity are essential for achieving these results.
BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs
Venue: arXiv cs.LG 2025
Year: 2025

Physics-informed neural networks (PINNs) offer a flexible way to solve partial differential equations (PDEs) with machine learning, yet they still fall well short of the machine-precision accuracy many scientific tasks demand. In this work, we investigate whether the precision ceiling comes from the ill-conditioning of the PDEs or from the typical multi-layer perceptron (MLP) architecture. We introduce the Barycentric Weight Layer (BWLer), which models the PDE solution through barycentric polynomial interpolation. A BWLer can be added on top of an existing MLP (a BWLer-hat) or replace it completely (explicit BWLer), cleanly separating how we represent the solution from how we take derivatives for the PDE loss. Using BWLer, we identify fundamental precision limitations within the MLP: on a simple 1-D interpolation task, even MLPs with O(1e5) parameters stall around 1e-8 RMSE -- about eight orders above float64 machine precision -- before any PDE terms are added. In PDE learning, adding a BWLer lifts this ceiling and exposes a tradeoff between achievable accuracy and the conditioning of the PDE loss. For linear PDEs we fully characterize this tradeoff with an explicit error decomposition and navigate it during training with spectral derivatives and preconditioning. Across five benchmark PDEs, adding a BWLer on top of an MLP improves RMSE by up to 30x for convection, 10x for reaction, and 1800x for wave equations while remaining compatible with first-order optimizers. Replacing the MLP entirely lets an explicit BWLer reach near-machine-precision on convection, reaction, and wave problems (up to 10 billion times better than prior results) and match the performance of standard PINNs on stiff Burgers' and irregular-geometry Poisson problems. Together, these findings point to a practical path for combining the flexibility of PINNs with the precision of classical spectral solvers.
Towards a better approach to the Vehicle Routing Problem
Venue: arXiv cs.CE 2025
Year: 2025

The Vehicle Routing Problem (VRP) is a fundamental challenge in logistics management research, given its substantial influence on transportation efficiency, cost minimization, and service quality. As a combinatorial optimization problem, VRP plays a crucial role in a wide range of real world applications, particularly in transportation, logistics, and delivery systems, due to its diverse formulations and numerous extensions. Over the years, researchers have introduced various VRP variants to address specific operational constraints, emerging industry requirements and optimize specific objectives, making it one of the most extensively studied problems in operations research. This article provides a comprehensive overview of VRP by exploring its theoretical foundations, discussing the limitations of its classical model, and introducing its key extensions. By systematically reviewing the diverse constraints, objectives, and variants examined in recent literature, this study aims to contribute to a deeper understanding of VRP while highlighting its ongoing evolution and relevance in modern optimization and decision making processes.
Feature-Wise Mixing for Mitigating Contextual Bias in Predictive Supervised Learning
Venue: arXiv cs.LG 2025
Year: 2025

Bias in predictive machine learning (ML) models is a fundamental challenge due to the skewed or unfair outcomes produced by biased models. Existing mitigation strategies rely on either post-hoc corrections or rigid constraints. However, emerging research claims that these techniques can limit scalability and reduce generalizability. To address this, this paper introduces a feature-wise mixing framework to mitigate contextual bias. This was done by redistributing feature representations across multiple contextual datasets. To assess feature-wise mixing's effectiveness, four ML classifiers were trained using cross-validation and evaluated with bias-sensitive loss functions, including disparity metrics and mean squared error (MSE), which served as a standard measure of predictive performance. The proposed method achieved an average bias reduction of 43.35% and a statistically significant decrease in MSE across all classifiers trained on mixed datasets. Additionally, benchmarking against established bias mitigation techniques found that feature-wise mixing consistently outperformed SMOTE oversampling and demonstrated competitive effectiveness without requiring explicit bias attribute identification. Feature-wise mixing efficiently avoids the computational overhead typically associated with fairness-aware learning algorithms. Future work could explore applying feature-wise mixing for real-world fields where accurate predictions are necessary.
Double-Diffusion: Diffusion Conditioned Diffusion Probabilistic Model For Air Quality Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Air quality prediction is a challenging forecasting task due to its spatio-temporal complexity and the inherent dynamics as well as uncertainty. Most of the current models handle these two challenges by applying Graph Neural Networks or known physics principles, and quantifying stochasticity through probabilistic networks like Diffusion models. Nevertheless, finding the right balancing point between the certainties and uncertainties remains an open question. Therefore, we propose Double-Diffusion, a novel diffusion probabilistic model that harnesses the power of known physics to guide air quality forecasting with stochasticity. To the best of our knowledge, while precedents have been made of using conditional diffusion models to predict air pollution, this is the first attempt to use physics as a conditional generative approach for air quality prediction. Along with a sampling strategy adopted from image restoration and a new denoiser architecture, Double-Diffusion ranks first in most evaluation scenarios across two real-life datasets compared with other probabilistic models, it also cuts inference time by 50% to 30% while enjoying an increase between 3-12% in Continuous Ranked Probabilistic Score (CRPS).
Verifying Properties of Index Arrays in a Purely-Functional Data-Parallel Language
Venue: arXiv cs.PL 2025
Year: 2025

This paper presents a novel approach to automatically verify properties of pure data-parallel programs with non-linear indexing -- expressed as pre- and post-conditions on functions. Programs consist of nests of second-order array combinators (e.g., map, scan, and scatter) and loops. The key idea is to represent arrays as index functions: programs are index function transformations over which properties are propagated and inferred. Our framework proves properties on index functions by distilling them into algebraic (in)equalities and discharging them to a Fourier-Motzkin-based solver. The framework is practical and accessible: properties are not restricted to a decidable logic, but instead are carefully selected to express practically useful guarantees that can be automatically reasoned about and inferred. These guarantees extend beyond program correctness and can be exploited by the entire compiler pipeline for optimization. We implement our system in the pure data-parallel language Futhark and demonstrate its practicality on seven applications, reporting an average verification time of 1 second. Two case studies show how eliminating dynamic verification in GPU programs results in significant speedups.
Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest
Venue: arXiv cs.IR 2025
Year: 2025

Industrial recommendation systems are typically composed of multiple stages, including retrieval, ranking, and blending. The retrieval stage plays a critical role in generating a high-recall set of candidate items that covers a wide range of diverse user interests. Effectively covering the diverse and long-tail user interests within this stage poses a significant challenge: traditional two-tower models struggle in this regard due to limited user-item feature interaction and often bias towards top use cases. To address these issues, we propose a novel multi-embedding retrieval framework designed to enhance user interest representation by generating multiple user embeddings conditioned on both implicit and explicit user interests. Implicit interests are captured from user history through a Differentiable Clustering Module (DCM), whereas explicit interests, such as topics that the user has followed, are modeled via Conditional Retrieval (CR). These methodologies represent a form of conditioned user representation learning that involves condition representation construction and associating the target item with the relevant conditions. Synergizing implicit and explicit user interests serves as a complementary approach to achieve more effective and comprehensive candidate retrieval as they benefit on different user segments and extract conditions from different but supplementary sources. Extensive experiments and A/B testing reveal significant improvements in user engagements and feed diversity metrics. Our proposed framework has been successfully deployed on Pinterest home feed.
Frequency-enhanced Multi-granularity Context Network for Efficient Vertebrae Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Automated and accurate segmentation of individual vertebra in 3D CT and MRI images is essential for various clinical applications. Due to the limitations of current imaging techniques and the complexity of spinal structures, existing methods still struggle with reducing the impact of image blurring and distinguishing similar vertebrae. To alleviate these issues, we introduce a Frequency-enhanced Multi-granularity Context Network (FMC-Net) to improve the accuracy of vertebrae segmentation. Specifically, we first apply wavelet transform for lossless downsampling to reduce the feature distortion in blurred images. The decomposed high and low-frequency components are then processed separately. For the high-frequency components, we apply a High-frequency Feature Refinement (HFR) to amplify the prominence of key features and filter out noises, restoring fine-grained details in blurred images. For the low-frequency components, we use a Multi-granularity State Space Model (MG-SSM) to aggregate feature representations with different receptive fields, extracting spatially-varying contexts while capturing long-range dependencies with linear complexity. The utilization of multi-granularity contexts is essential for distinguishing similar vertebrae and improving segmentation accuracy. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on both CT and MRI vertebrae segmentation datasets. The source code is publicly available at https://github.com/anaanaa/FMCNet.
Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
Venue: arXiv cs.IR 2025
Year: 2025

Online advertising in recommendation platforms has gained significant attention, with a predominant focus on channel recommendation and budget allocation strategies. However, current offline reinforcement learning (RL) methods face substantial challenges when applied to sparse advertising scenarios, primarily due to severe overestimation, distributional shifts, and overlooking budget constraints. To address these issues, we propose MTORL, a novel multi-task offline RL model that targets two key objectives. First, we establish a Markov Decision Process (MDP) framework specific to the nuances of advertising. Then, we develop a causal state encoder to capture dynamic user interests and temporal dependencies, facilitating offline RL through conditional sequence modeling. Causal attention mechanisms are introduced to enhance user sequence representations by identifying correlations among causal states. We employ multi-task learning to decode actions and rewards, simultaneously addressing channel recommendation and budget allocation. Notably, our framework includes an automated system for integrating these tasks into online advertising. Extensive experiments on offline and online environments demonstrate MTORL's superiority over state-of-the-art methods.
Computer-Aided Multi-Stroke Character Simplification by Stroke Removal
Venue: arXiv cs.CV 2025
Year: 2025

Multi-stroke characters in scripts such as Chinese and Japanese can be highly complex, posing significant challenges for both native speakers and, especially, non-native learners. If these characters can be simplified without degrading their legibility, it could reduce learning barriers for non-native speakers, facilitate simpler and legible font designs, and contribute to efficient character-based communication systems. In this paper, we propose a framework to systematically simplify multi-stroke characters by selectively removing strokes while preserving their overall legibility. More specifically, we use a highly accurate character recognition model to assess legibility and remove those strokes that minimally impact it. Experimental results on 1,256 character classes with 5, 10, 15, and 20 strokes reveal several key findings, including the observation that even after removing multiple strokes, many characters remain distinguishable. These findings suggest the potential for more formalized simplification strategies.
MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three key limitations: the use of causal attention in VLM backbones is suboptimal for embedding tasks; scalability issues due to reliance on high-quality labeled paired data for contrastive learning; and limited diversity in training objectives and data. To address these issues, we propose MoCa, a two-stage framework for transforming pre-trained VLMs into effective bidirectional multimodal embedding models. The first stage, Modality-aware Continual Pre-training, introduces a joint reconstruction objective that simultaneously denoises interleaved text and image inputs, enhancing bidirectional context-aware reasoning. The second stage, Heterogeneous Contrastive Fine-tuning, leverages diverse, semantically rich multimodal data beyond simple image-caption pairs to enhance generalization and alignment. Our method addresses the stated limitations by introducing bidirectional attention through continual pre-training, scaling effectively with massive unlabeled datasets via joint reconstruction objectives, and utilizing diverse multimodal data for enhanced representation robustness. Experiments demonstrate that MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results, and exhibits strong scalability with both model size and training data on MMEB.
The Societal Impact of Foundation Models: Advancing Evidence-based AI Policy
Venue: arXiv cs.AI 2025
Year: 2025

Artificial intelligence is humanity's most promising technology because of the remarkable capabilities offered by foundation models. Yet, the same technology brings confusion and consternation: foundation models are poorly understood and they may precipitate a wide array of harms. This dissertation explains how technology and society coevolve in the age of AI, organized around three themes. First, the conceptual framing: the capabilities, risks, and the supply chain that grounds foundation models in the broader economy. Second, the empirical insights that enrich the conceptual foundations: transparency created via evaluations at the model level and indexes at the organization level. Finally, the transition from understanding to action: superior understanding of the societal impact of foundation models advances evidence-based AI policy. View together, this dissertation makes inroads into achieving better societal outcomes in the age of AI by building the scientific foundations and research-policy interface required for better AI governance.
Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons
Venue: arXiv cs.AI 2025
Year: 2025

How far are Large Language Models (LLMs) in performing deep relational reasoning? In this paper, we evaluate and compare the reasoning capabilities of three cutting-edge LLMs, namely, DeepSeek-R1, DeepSeek-V3 and GPT-4o, through a suite of carefully designed benchmark tasks in family tree and general graph reasoning. Our experiments reveal that DeepSeek-R1 consistently achieves the highest F1-scores across multiple tasks and problem sizes, demonstrating strong aptitude in logical deduction and relational inference. However, all evaluated models, including DeepSeek-R1, struggle significantly as problem complexity increases, largely due to token length limitations and incomplete output structures. A detailed analysis of DeepSeek-R1's long Chain-of-Thought responses uncovers its unique planning and verification strategies, but also highlights instances of incoherent or incomplete reasoning, calling attention to the need for deeper scrutiny into LLMs' internal inference dynamics. We further discuss key directions for future work, including the role of multimodal reasoning and the systematic examination of reasoning failures. Our findings provide both empirical insights and theoretical implications for advancing LLMs' reasoning abilities, particularly in tasks that demand structured, multi-step logical inference. Our code repository will be publicly available at https://github.com/kelvinhkcs/Deep-Relational-Reasoning.
maneuverRecognition -- A Python package for Timeseries Classification in the domain of Vehicle Telematics
Venue: arXiv cs.LG 2025
Year: 2025

In the domain of vehicle telematics the automated recognition of driving maneuvers is used to classify and evaluate driving behaviour. This not only serves as a component to enhance the personalization of insurance policies, but also to increase road safety, reduce accidents and the associated costs as well as to reduce fuel consumption and support environmentally friendly driving. In this context maneuver recognition technically requires a continuous application of time series classification which poses special challenges to the transfer, preprocessing and storage of telematic sensor data, the training of predictive models, and the prediction itself. Although much research has been done in the field of gathering relevant data or regarding the methods to build predictive models for the task of maneuver recognition, there is a practical need for python packages and functions that allow to quickly transform data into the required structure as well as to build and evaluate such models. The maneuverRecognition package was therefore developed to provide the necessary functions for preprocessing, modelling and evaluation and also includes a ready to use LSTM based network structure that can be modified. The implementation of the package is demonstrated using real driving data of three different persons recorded via smartphone sensors.
V-SYNTHESIS: Task-Agnostic Synthesis of Consistent and Diverse In-Context Demonstrations from Scratch via V-Entropy
Venue: arXiv cs.CL 2025
Year: 2025

High labeling cost for in-context learning (ICL) demonstrations motivates using large language models (LLMs) for synthesis to reduce overhead. However, existing synthesis methods are mainly task-specific or rely on pre-existing demonstrations. So this paper focuses on synthesizing demonstrations from scratch for arbitrary tasks. A major challenge in synthesizing from scratch is ensuring consistency with the target task, as the lack of labeling guidance could lead to synthesis bias. We first propose a consistency metric called V-Score, which has higher performance and lower computation cost compared with the metrics based on grams or embedding vectors. Furthermore, we introduce V-Synthesis, which leverages V-Score for proportional sampling to ensure both high consistency and diversity of synthesized demonstrations. Experimental results demonstrate that V-Synthesis yields an average performance improvement of 2.0% compared to existing synthesis methods confirming the effectiveness of V-Synthesis.