OpenRaiser/Intern-Atlas huggingface dataset:
Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study
Venue: arXiv cs.IR 2025
Year: 2025

Children are often exposed to items curated by recommendation algorithms. Yet, research seldom considers children as a user group, and when it does, it is anchored on datasets where children are underrepresented, risking overlooking their interests, favoring those of the majority, i.e., mainstream users. Recently, Ungruh et al. demonstrated that children's consumption patterns and preferences differ from those of mainstream users, resulting in inconsistent recommendation algorithm performance and behavior for this user group. These findings, however, are based on two datasets with a limited child user sample. We reproduce and replicate this study on a wider range of datasets in the movie, music, and book domains, uncovering interaction patterns and aspects of child-recommender interactions consistent across domains, as well as those specific to some user samples in the data. We also extend insights from the original study with popularity bias metrics, given the interpretation of results from the original study. With this reproduction and extension, we uncover consumption patterns and differences between age groups stemming from intrinsic differences between children and others, and those unique to specific datasets or domains.
Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000
Venue: arXiv cs.LG 2025
Year: 2025

When applying machine learning to medical image classification, data leakage is a critical issue. Previous methods, such as adding noise to gradients for differential privacy, work well on large datasets like MNIST and CIFAR-100, but fail on small, imbalanced medical datasets like HAM10000. This is because the imbalanced distribution causes gradients from minority classes to be clipped and lose crucial information, while majority classes dominate. This leads the model to fall into suboptimal solutions early. To address this, we propose SAD-DPSGD, which uses a linear decaying mechanism for noise and clipping thresholds. By allocating more privacy budget and using higher clipping thresholds in the initial training phases, the model avoids suboptimal solutions and enhances performance. Experiments show that SAD-DPSGD outperforms Auto-DPSGD on HAM10000, improving accuracy by 2.15% under $ε= 3.0$ , $δ= 10^{-3}$.
Expediting data extraction using a large language model (LLM) and scoping review protocol: a methodological study within a complex scoping review
Venue: arXiv cs.CL 2025
Year: 2025

The data extraction stages of reviews are resource-intensive, and researchers may seek to expediate data extraction using online (large language models) LLMs and review protocols. Claude 3.5 Sonnet was used to trial two approaches that used a review protocol to prompt data extraction from 10 evidence sources included in a case study scoping review. A protocol-based approach was also used to review extracted data. Limited performance evaluation was undertaken which found high accuracy for the two extraction approaches (83.3% and 100%) when extracting simple, well-defined citation details; accuracy was lower (9.6% and 15.8%) when extracting more complex, subjective data items. Considering all data items, both approaches had precision >90% but low recall (<25%) and F1 scores (<40%). The context of a complex scoping review, open response types and methodological approach likely impacted performance due to missed and misattributed data. LLM feedback considered the baseline extraction accurate and suggested minor amendments: four of 15 (26.7%) to citation details and 8 of 38 (21.1%) to key findings data items were considered to potentially add value. However, when repeating the process with a dataset featuring deliberate errors, only 2 of 39 (5%) errors were detected. Review-protocol-based methods used for expediency require more robust performance evaluation across a range of LLMs and review contexts with comparison to conventional prompt engineering approaches. We recommend researchers evaluate and report LLM performance if using them similarly to conduct data extraction or review extracted data. LLM feedback contributed to protocol adaptation and may assist future review protocol drafting.
Diff$^2$I2P: Differentiable Image-to-Point Cloud Registration with Diffusion Prior
Venue: arXiv cs.CV 2025
Year: 2025

Learning cross-modal correspondences is essential for image-to-point cloud (I2P) registration. Existing methods achieve this mostly by utilizing metric learning to enforce feature alignment across modalities, disregarding the inherent modality gap between image and point data. Consequently, this paradigm struggles to ensure accurate cross-modal correspondences. To this end, inspired by the cross-modal generation success of recent large diffusion models, we propose Diff$^2$I2P, a fully Differentiable I2P registration framework, leveraging a novel and effective Diffusion prior for bridging the modality gap. Specifically, we propose a Control-Side Score Distillation (CSD) technique to distill knowledge from a depth-conditioned diffusion model to directly optimize the predicted transformation. However, the gradients on the transformation fail to backpropagate onto the cross-modal features due to the non-differentiability of correspondence retrieval and PnP solver. To this end, we further propose a Deformable Correspondence Tuning (DCT) module to estimate the correspondences in a differentiable way, followed by the transformation estimation using a differentiable PnP solver. With these two designs, the Diffusion model serves as a strong prior to guide the cross-modal feature learning of image and point cloud for forming robust correspondences, which significantly improves the registration. Extensive experimental results demonstrate that Diff$^2$I2P consistently outperforms SoTA I2P registration methods, achieving over 7% improvement in registration recall on the 7-Scenes benchmark.
Federated Learning Inspired Fuzzy Systems: Decentralized Rule Updating for Privacy and Scalable Decision Making
Venue: arXiv cs.LG 2025
Year: 2025

Fuzzy systems are a way to allow machines, systems and frameworks to deal with uncertainty, which is not possible in binary systems that most computers use. These systems have already been deployed for certain use cases, and fuzzy systems could be further improved as proposed in this paper. Such technologies to draw inspiration from include machine learning and federated learning. Machine learning is one of the recent breakthroughs of technology and could be applied to fuzzy systems to further improve the results it produces. Federated learning is also one of the recent technologies that have huge potential, which allows machine learning training to improve by reducing privacy risk, reducing burden on networking infrastructure, and reducing latency of the latest model. Aspects from federated learning could be used to improve federated learning, such as applying the idea of updating the fuzzy rules that make up a key part of fuzzy systems, to further improve it over time. This paper discusses how these improvements would be implemented in fuzzy systems, and how it would improve fuzzy systems. It also discusses certain limitations on the potential improvements. It concludes that these proposed ideas and improvements require further investigation to see how far the improvements are, but the potential is there to improve fuzzy systems.
FlexGaussian: Flexible and Cost-Effective Training-Free Compression for 3D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

3D Gaussian splatting has become a prominent technique for representing and rendering complex 3D scenes, due to its high fidelity and speed advantages. However, the growing demand for large-scale models calls for effective compression to reduce memory and computation costs, especially on mobile and edge devices with limited resources. Existing compression methods effectively reduce 3D Gaussian parameters but often require extensive retraining or fine-tuning, lacking flexibility under varying compression constraints. In this paper, we introduce FlexGaussian, a flexible and cost-effective method that combines mixed-precision quantization with attribute-discriminative pruning for training-free 3D Gaussian compression. FlexGaussian eliminates the need for retraining and adapts easily to diverse compression targets. Evaluation results show that FlexGaussian achieves up to 96.4% compression while maintaining high rendering quality (<1 dB drop in PSNR), and is deployable on mobile devices. FlexGaussian delivers high compression ratios within seconds, being 1.7-2.1x faster than state-of-the-art training-free methods and 10-100x faster than training-involved approaches. The code is being prepared and will be released soon at: https://github.com/Supercomputing-System-AI-Lab/FlexGaussian
Effects of task difficulty and music expertise in virtual reality: Observations of cognitive load and task accuracy in a rhythm exergame
Venue: arXiv cs.HC 2025
Year: 2025

This study explores the relationship between musical training, cognitive load (CL), and task accuracy within the virtual reality (VR) exergame Beat Saber across increasing levels of difficulty. Participants (N=32) completed a series of post-task questionnaires after playing the game under three task difficulty levels while having their physiological data measured by an Emotibit. Using regression analyses, we found that task difficulty and gaming experience significantly predicted subjective CL, whereas musical training did not. However, musical training significantly predicted higher task accuracy, along with lower subjective CL, increased gaming experience, and greater physiological arousal. These results suggest that musical training enhances task-specific performance but does not directly reduce subjective CL. Future research should consider alternative methods of grouping musical expertise and the additional predictability of flow and self-efficacy.
Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement
Venue: arXiv cs.LG 2025
Year: 2025

Imitation Learning from Observation (IfO) offers a powerful way to learn behaviors at large-scale: Unlike behavior cloning or offline reinforcement learning, IfO can leverage action-free demonstrations and thus circumvents the need for costly action-labeled demonstrations or reward functions. However, current IfO research focuses on idealized scenarios with mostly bimodal-quality data distributions, restricting the meaningfulness of the results. In contrast, this paper investigates more nuanced distributions and introduces a method to learn from such data, moving closer to a paradigm in which imitation learning can be performed iteratively via self-improvement. Our method adapts RL-based imitation learning to action-free demonstrations, using a value function to transfer information between expert and non-expert data. Through comprehensive evaluation, we delineate the relation between different data distributions and the applicability of algorithms and highlight the limitations of established methods. Our findings provide valuable insights for developing more robust and practical IfO techniques on a path to scalable behaviour learning.
PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems
Venue: arXiv cs.LG 2025
Year: 2025

State estimation for nonlinear dynamical systems is a critical challenge in control and engineering applications, particularly when only partial and noisy measurements are available. This paper introduces a novel Adaptive Physics-Informed Neural Network-based Observer (PINN-Obs) for accurate state estimation in nonlinear systems. Unlike traditional model-based observers, which require explicit system transformations or linearization, the proposed framework directly integrates system dynamics and sensor data into a physics-informed learning process. The observer adaptively learns an optimal gain matrix, ensuring convergence of the estimated states to the true system states. A rigorous theoretical analysis establishes formal convergence guarantees, demonstrating that the proposed approach achieves uniform error minimization under mild observability conditions. The effectiveness of PINN-Obs is validated through extensive numerical simulations on diverse nonlinear systems, including an induction motor model, a satellite motion system, and benchmark academic examples. Comparative experimental studies against existing observer designs highlight its superior accuracy, robustness, and adaptability.
Combining Human-centred Explainability and Explainable AI
Venue: arXiv cs.HC 2025
Year: 2025

This position paper looks at differences between the current understandings of human-centered explainability and explainability AI. We discuss current ideas in both fields, as well as the differences and opportunities we discovered. As an example of combining both, we will present preliminary work on a new algebraic machine learning approach. We are excited to continue discussing design opportunities for human-centered explainability (HCx) and xAI with the broader HCxAI community.
KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents the first application of Kolmogorov-Arnold Convolution for Text (KAConvText) in sentence classification, addressing three tasks: imbalanced binary hate speech detection, balanced multiclass news classification, and imbalanced multiclass ethnic language identification. We investigate various embedding configurations, comparing random to fastText embeddings in both static and fine-tuned settings, with embedding dimensions of 100 and 300 using CBOW and Skip-gram models. Baselines include standard CNNs and CNNs augmented with a Kolmogorov-Arnold Network (CNN-KAN). In addition, we investigated KAConvText with different classification heads - MLP and KAN, where using KAN head supports enhanced interpretability. Results show that KAConvText-MLP with fine-tuned fastText embeddings achieves the best performance of 91.23% accuracy (F1-score = 0.9109) for hate speech detection, 92.66% accuracy (F1-score = 0.9267) for news classification, and 99.82% accuracy (F1-score = 0.9982) for language identification.
Finetuning Vision-Language Models as OCR Systems for Low-Resource Languages: A Case Study of Manchu
Venue: arXiv cs.CV 2025
Year: 2025

Manchu, a critically endangered language essential for understanding early modern Eastern Eurasian history, lacks effective OCR systems that can handle real-world historical documents. This study develops high-performing OCR systems by fine-tuning three open-source vision-language models (LLaMA-3.2-11B, Qwen2.5-VL-7B, Qwen2.5-VL-3B) on 60,000 synthetic Manchu word images using parameter-efficient training. LLaMA-3.2-11B achieved exceptional performance with 98.3\% word accuracy and 0.0024 character error rate on synthetic data, while crucially maintaining 93.1\% accuracy on real-world handwritten documents. Comparative evaluation reveals substantial advantages over traditional approaches: while a CRNN baseline achieved 99.8\% synthetic accuracy, it suffered severe degradation to 72.5\% on real documents. Our approach demonstrates effective synthetic-to-real domain transfer, providing a cost-effective solution deployable on accessible infrastructure. This work establishes a transferable framework for endangered language OCR that removes technical and financial barriers in digital humanities, enabling historians and linguists to process historical archives without specialized computing resources. Code and model weights are available at https://github.com/mic7ch1/ManchuAI-OCR.
Detecção de Conflitos Semânticos com Testes Gerados por LLM
Venue: arXiv cs.SE 2025
Year: 2025

Semantic conflicts arise when a developer introduces changes to a codebase that unintentionally affect the behavior of changes integrated in parallel by other developers. Traditional merge tools are unable to detect such conflicts, so complementary tools like SMAT have been proposed. SMAT relies on generating and executing unit tests: if a test fails on the base version, passes on a developer's modified version, but fails again after merging with another developer's changes, a semantic conflict is indicated. While SMAT is effective at detecting conflicts, it suffers from a high rate of false negatives, partly due to the limitations of unit test generation tools such as Randoop and Evosuite. To investigate whether large language models (LLMs) can overcome these limitations, we propose and integrate a new test generation tool based on Code Llama 70B into SMAT. We explore the model's ability to generate tests using different interaction strategies, prompt contents, and parameter configurations. Our evaluation uses two samples: a benchmark with simpler systems from related work, and a more significant sample based on complex, real-world systems. We assess the effectiveness of the new SMAT extension in detecting conflicts. Results indicate that, although LLM-based test generation remains challenging and computationally expensive in complex scenarios, there is promising potential for improving semantic conflict detection.
FOLC-Net: A Federated-Optimized Lightweight Architecture for Enhanced MRI Disease Diagnosis across Axial, Coronal, and Sagittal Views
Venue: arXiv cs.CV 2025
Year: 2025

The framework is designed to improve performance in the analysis of combined as well as single anatomical perspectives for MRI disease diagnosis. It specifically addresses the performance degradation observed in state-of-the-art (SOTA) models, particularly when processing axial, coronal, and sagittal anatomical planes. The paper introduces the FOLC-Net framework, which incorporates a novel federated-optimized lightweight architecture with approximately 1.217 million parameters and a storage requirement of only 0.9 MB. FOLC-Net integrates Manta-ray foraging optimization (MRFO) mechanisms for efficient model structure generation, global model cloning for scalable training, and ConvNeXt for enhanced client adaptability. The model was evaluated on combined multi-view data as well as individual views, such as axial, coronal, and sagittal, to assess its robustness in various medical imaging scenarios. Moreover, FOLC-Net tests a ShallowFed model on different data to evaluate its ability to generalize beyond the training dataset. The results show that FOLC-Net outperforms existing models, particularly in the challenging sagittal view. For instance, FOLC-Net achieved an accuracy of 92.44% on the sagittal view, significantly higher than the 88.37% accuracy of study method (DL + Residual Learning) and 88.95% of DL models. Additionally, FOLC-Net demonstrated improved accuracy across all individual views, providing a more reliable and robust solution for medical image analysis in decentralized environments. FOLC-Net addresses the limitations of existing SOTA models by providing a framework that ensures better adaptability to individual views while maintaining strong performance in multi-view settings. The incorporation of MRFO, global model cloning, and ConvNeXt ensures that FOLC-Net performs better in real-world medical applications.
Constraint Optimized Multichannel Mixer-limiter Design
Venue: arXiv cs.SD 2025
Year: 2025

Multichannel audio mixer and limiter designs are conventionally decoupled for content reproduction over loudspeaker arrays due to high computational complexity and run-time costs. We propose a coupled mixer-limiter-envelope design formulated as an efficient linear-constrained quadratic program that minimizes a distortion objective over multichannel gain variables subject to sample mixture constraints. Novel methods for asymmetric constant overlap-add window optimization, objective function approximation, variable and constraint reduction are presented. Experiments demonstrate distortion reduction of the coupled design, and computational trade-offs required for efficient real-time processing.
Temporal Information Retrieval via Time-Specifier Model Merging
Venue: arXiv cs.IR 2025
Year: 2025

The rapid expansion of digital information and knowledge across structured and unstructured sources has heightened the importance of Information Retrieval (IR). While dense retrieval methods have substantially improved semantic matching for general queries, they consistently underperform on queries with explicit temporal constraints--often those containing numerical expressions and time specifiers such as ``in 2015.'' Existing approaches to Temporal Information Retrieval (TIR) improve temporal reasoning but often suffer from catastrophic forgetting, leading to reduced performance on non-temporal queries. To address this, we propose Time-Specifier Model Merging (TSM), a novel method that enhances temporal retrieval while preserving accuracy on non-temporal queries. TSM trains specialized retrievers for individual time specifiers and merges them in to a unified model, enabling precise handling of temporal constraints without compromising non-temporal retrieval. Extensive experiments on both temporal and non-temporal datasets demonstrate that TSM significantly improves performance on temporally constrained queries while maintaining strong results on non-temporal queries, consistently outperforming other baseline methods. Our code is available at https://github.com/seungyoonee/TSM .
Stream Function-Based Navigation for Complex Quadcopter Obstacle Avoidance
Venue: arXiv cs.RO 2025
Year: 2025

This article presents a novel stream function-based navigational control system for obstacle avoidance, where obstacles are represented as two-dimensional (2D) rigid surfaces in inviscid, incompressible flows. The approach leverages the vortex panel method (VPM) and incorporates safety margins to control the stream function and flow properties around virtual surfaces, enabling navigation in complex, partially observed environments using real-time sensing. To address the limitations of the VPM in managing relative distance and avoiding rapidly accelerating obstacles at close proximity, the system integrates a model predictive controller (MPC) based on higher-order control barrier functions (HOCBF). This integration incorporates VPM trajectory generation, state estimation, and constraint handling into a receding-horizon optimization problem. The 2D rigid surfaces are enclosed using minimum bounding ellipses (MBEs), while an adaptive Kalman filter (AKF) captures and predicts obstacle dynamics, propagating these estimates into the MPC-HOCBF for rapid avoidance maneuvers. Evaluation is conducted using a PX4-powered Clover drone Gazebo simulator and real-time experiments involving a COEX Clover quadcopter equipped with a 360 degree LiDAR sensor.
Comparing Dialectical Systems: Contradiction and Counterexample in Belief Change (Extended Version)
Venue: arXiv cs.AI 2025
Year: 2025

Dialectical systems are a mathematical formalism for modeling an agent updating a knowledge base seeking consistency. Introduced in the 1970s by Roberto Magari, they were originally conceived to capture how a working mathematician or a research community refines beliefs in the pursuit of truth. Dialectical systems also serve as natural models for the belief change of an automated agent, offering a unifying, computable framework for dynamic belief management. The literature distinguishes three main models of dialectical systems: (d-)dialectical systems based on revising beliefs when they are seen to be inconsistent, p-dialectical systems based on revising beliefs based on finding a counterexample, and q-dialectical systems which can do both. We answer an open problem in the literature by proving that q-dialectical systems are strictly more powerful than p-dialectical systems, which are themselves known to be strictly stronger than (d-)dialectical systems. This result highlights the complementary roles of counterexample and contradiction in automated belief revision, and thus also in the reasoning processes of mathematicians and research communities.