OpenRaiser/Intern-Atlas huggingface dataset:
iHHO-SMOTe: A Cleansed Approach for Handling Outliers and Reducing Noise to Improve Imbalanced Data Classification
Venue: arXiv cs.LG 2025
Year: 2025

Classifying imbalanced datasets remains a significant challenge in machine learning, particularly with big data where instances are unevenly distributed among classes, leading to class imbalance issues that impact classifier performance. While Synthetic Minority Over-sampling Technique (SMOTE) addresses this challenge by generating new instances for the under-represented minority class, it faces obstacles in the form of noise and outliers during the creation of new samples. In this paper, a proposed approach, iHHO-SMOTe, which addresses the limitations of SMOTE by first cleansing the data from noise points. This process involves employing feature selection using a random forest to identify the most valuable features, followed by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to detect outliers based on the selected features. The identified outliers from the minority classes are then removed, creating a refined dataset for subsequent oversampling using the hybrid approach called iHHO-SMOTe. The comprehensive experiments across diverse datasets demonstrate the exceptional performance of the proposed model, with an AUC score exceeding 0.99, a high G-means score of 0.99 highlighting its robustness, and an outstanding F1-score consistently exceeding 0.967. These findings collectively establish Cleansed iHHO-SMOTe as a formidable contender in addressing imbalanced datasets, focusing on noise reduction and outlier handling for improved classification models.
Versatile, Robust, and Explosive Locomotion with Rigid and Articulated Compliant Quadrupeds
Venue: arXiv cs.RO 2025
Year: 2025

Achieving versatile and explosive motion with robustness against dynamic uncertainties is a challenging task. Introducing parallel compliance in quadrupedal design is deemed to enhance locomotion performance, which, however, makes the control task even harder. This work aims to address this challenge by proposing a general template model and establishing an efficient motion planning and control pipeline. To start, we propose a reduced-order template model-the dual-legged actuated spring-loaded inverted pendulum with trunk rotation-which explicitly models parallel compliance by decoupling spring effects from active motor actuation. With this template model, versatile acrobatic motions, such as pronking, froggy jumping, and hop-turn, are generated by a dual-layer trajectory optimization, where the singularity-free body rotation representation is taken into consideration. Integrated with a linear singularity-free tracking controller, enhanced quadrupedal locomotion is achieved. Comparisons with the existing template model reveal the improved accuracy and generalization of our model. Hardware experiments with a rigid quadruped and a newly designed compliant quadruped demonstrate that i) the template model enables generating versatile dynamic motion; ii) parallel elasticity enhances explosive motion. For example, the maximal pronking distance, hop-turn yaw angle, and froggy jumping distance increase at least by 25%, 15% and 25%, respectively; iii) parallel elasticity improves the robustness against dynamic uncertainties, including modelling errors and external disturbances. For example, the allowable support surface height variation increases by 100% for robust froggy jumping.
3D-PNAS: 3D Industrial Surface Anomaly Synthesis with Perlin Noise
Venue: arXiv cs.GR 2025
Year: 2025

Large pretrained vision foundation models have shown significant potential in various vision tasks. However, for industrial anomaly detection, the scarcity of real defect samples poses a critical challenge in leveraging these models. While 2D anomaly generation has significantly advanced with established generative models, the adoption of 3D sensors in industrial manufacturing has made leveraging 3D data for surface quality inspection an emerging trend. In contrast to 2D techniques, 3D anomaly generation remains largely unexplored, limiting the potential of 3D data in industrial quality inspection. To address this gap, we propose a novel yet simple 3D anomaly generation method, 3D-PNAS, based on Perlin noise and surface parameterization. Our method generates realistic 3D surface anomalies by projecting the point cloud onto a 2D plane, sampling multi-scale noise values from a Perlin noise field, and perturbing the point cloud along its normal direction. Through comprehensive visualization experiments, we demonstrate how key parameters - including noise scale, perturbation strength, and octaves, provide fine-grained control over the generated anomalies, enabling the creation of diverse defect patterns from pronounced deformations to subtle surface variations. Additionally, our cross-category experiments show that the method produces consistent yet geometrically plausible anomalies across different object types, adapting to their specific surface characteristics. We also provide a comprehensive codebase and visualization toolkit to facilitate future research.
Tree-NeRV: A Tree-Structured Neural Representation for Efficient Non-Uniform Video Encoding
Venue: arXiv cs.CV 2025
Year: 2025

Implicit Neural Representations for Videos (NeRV) have emerged as a powerful paradigm for video representation, enabling direct mappings from frame indices to video frames. However, existing NeRV-based methods do not fully exploit temporal redundancy, as they rely on uniform sampling along the temporal axis, leading to suboptimal rate-distortion (RD) performance. To address this limitation, we propose Tree-NeRV, a novel tree-structured feature representation for efficient and adaptive video encoding. Unlike conventional approaches, Tree-NeRV organizes feature representations within a Binary Search Tree (BST), enabling non-uniform sampling along the temporal axis. Additionally, we introduce an optimization-driven sampling strategy, dynamically allocating higher sampling density to regions with greater temporal variation. Extensive experiments demonstrate that Tree-NeRV achieves superior compression efficiency and reconstruction quality, outperforming prior uniform sampling-based methods. Code will be released.
The Rise of Bluesky
Venue: arXiv cs.SI 2025
Year: 2025

This study investigates the rapid growth and evolving network structure of Bluesky from August 2023 to February 2025. Through multiple waves of user migrations, the platform has reached a stable, persistently active user base. The growth process has given rise to a dense follower network with clustering and hub features that favor viral information diffusion. These developments highlight engagement and structural similarities between Bluesky and established platforms.
QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?
Venue: arXiv cs.MA 2025
Year: 2025

Credit assignment has remained a fundamental challenge in multi-agent reinforcement learning (MARL). Previous studies have primarily addressed this issue through value decomposition methods under the centralized training with decentralized execution paradigm, where neural networks are utilized to approximate the nonlinear relationship between individual Q-values and the global Q-value. Although these approaches have achieved considerable success in various benchmark tasks, they still suffer from several limitations, including imprecise attribution of contributions, limited interpretability, and poor scalability in high-dimensional state spaces. To address these challenges, we propose a novel algorithm, QLLM, which facilitates the automatic construction of credit assignment functions using large language models (LLMs). Specifically, the concept of TFCAF is introduced, wherein the credit allocation process is represented as a direct and expressive nonlinear functional formulation. A custom-designed coder-evaluator framework is further employed to guide the generation and verification of executable code by LLMs, significantly mitigating issues such as hallucination and shallow reasoning during inference. Furthermore, an IGM-Gating Mechanism enables QLLM to flexibly enforce or relax the monotonicity constraint depending on task demands, covering both IGM-compliant and non-monotonic scenarios. Extensive experiments conducted on several standard MARL benchmarks demonstrate that the proposed method consistently outperforms existing state-of-the-art baselines. Moreover, QLLM exhibits strong generalization capability and maintains compatibility with a wide range of MARL algorithms that utilize mixing networks, positioning it as a promising and versatile solution for complex multi-agent scenarios. The code is available at https://github.com/zhouyangjiang71-sys/QLLM.
A Phenomenological Approach to Analyzing User Queries in IT Systems Using Heidegger's Fundamental Ontology
Venue: arXiv cs.SE 2025
Year: 2025

This paper presents a novel research analytical IT system grounded in Martin Heidegger's Fundamental Ontology, distinguishing between beings (das Seiende) and Being (das Sein). The system employs two modally distinct, descriptively complete languages: a categorical language of beings for processing user inputs and an existential language of Being for internal analysis. These languages are bridged via a phenomenological reduction module, enabling the system to analyze user queries (including questions, answers, and dialogues among IT specialists), identify recursive and self-referential structures, and provide actionable insights in categorical terms. Unlike contemporary systems limited to categorical analysis, this approach leverages Heidegger's phenomenological existential analysis to uncover deeper ontological patterns in query processing, aiding in resolving logical traps in complex interactions, such as metaphor usage in IT contexts. The path to full realization involves formalizing the language of Being by a research team based on Heidegger's Fundamental Ontology; given the existing completeness of the language of beings, this reduces the system's computability to completeness, paving the way for a universal query analysis tool. The paper presents the system's architecture, operational principles, technical implementation, use cases--including a case based on real IT specialist dialogues--comparative evaluation with existing tools, and its advantages and limitations.
Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Robust Response Generation in the Wild
Venue: arXiv cs.CL 2025
Year: 2025

The proliferation of large language models (LLMs) has significantly advanced intelligent systems. Unfortunately, LLMs often face knowledge conflicts between internal memory and retrieved external information, arising from misinformation, biases, or outdated knowledge. These conflicts undermine response reliability and introduce uncertainty in decision-making. In this work, we analyze how LLMs navigate knowledge conflicts from an information-theoretic perspective and reveal that when conflicting and supplementary information exhibit significant differences, LLMs confidently resolve their preferences and alleviate the uncertainty during their response generation. When this difference is ambiguous, LLMs experience considerable uncertainty about their generation. Based on this insight, we propose Swin-VIB, a novel framework that integrates a pipeline of variational information bottleneck models to adapt the retrieved information difference, facilitating robust response generation of LLMs even in conflicting contexts. Extensive experiments confirm our theoretical analysis and demonstrate the performance of Swin-VIB. Notably, Swin-VIB outperforms all competitive baselines in terms of the accuracy of the multiple-choice task, while improving the EM values in the open-ended QA task by at least 11.14%.
Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling
Venue: arXiv cs.CV 2025
Year: 2025

This study presents an ensemble-based approach for cocoa pod disease classification by integrating transfer learning with three ensemble learning strategies: Bagging, Boosting, and Stacking. Pre-trained convolutional neural networks, including VGG16, VGG19, ResNet50, ResNet101, InceptionV3, and Xception, were fine-tuned and employed as base learners to detect three disease categories: Black Pod Rot, Pod Borer, and Healthy. A balanced dataset of 6,000 cocoa pod images was curated and augmented to ensure robustness against variations in lighting, orientation, and disease severity. The performance of each ensemble method was evaluated using accuracy, precision, recall, and F1-score. Experimental results show that Bagging consistently achieved superior classification performance with a test accuracy of 100%, outperforming Boosting (97%) and Stacking (92%). The findings confirm that combining transfer learning with ensemble techniques improves model generalization and reliability, making it a promising direction for precision agriculture and automated crop disease management.
SHA256 at SemEval-2025 Task 4: Selective Amnesia -- Constrained Unlearning for Large Language Models via Knowledge Isolation
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) frequently memorize sensitive information during training, posing risks when deploying publicly accessible models. Current machine unlearning methods struggle to selectively remove specific data associations without degrading overall model capabilities. This paper presents our solution to SemEval-2025 Task 4 on targeted unlearning, which introduces a two-stage methodology that combines causal mediation analysis with layer-specific optimization. Through systematic causal tracing experiments on OLMo architectures (1B and 7B parameters), we identify the critical role of the first few transformer layers (layers 0-5) in storing subject-attribute associations within MLP modules. Building on this insight, we develop a constrained optimization approach that freezes upper layers while applying a novel joint loss function to lower layers-simultaneously maximizing forget set loss via output token cross-entropy penalties and minimizing retain set deviation through adaptive regularization. Our method achieves 2nd place in the 1B model track, demonstrating strong task performance while maintaining 88% of baseline MMLU accuracy. These results establish causal-informed layer optimization as a promising paradigm for efficient, precise unlearning in LLMs, offering a significant step forward in addressing data privacy concerns in AI systems.
All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception
Venue: arXiv cs.CV 2025
Year: 2025

Efficiently transferring Learned Image Compression (LIC) model from human perception to machine perception is an emerging challenge in vision-centric representation learning. Existing approaches typically adapt LIC to downstream tasks in a single-task manner, which is inefficient, lacks task interaction, and results in multiple task-specific bitstreams. In this paper, we propose a multi-task adaptation framework that enables transferring a pre-trained base codec to multiple machine vision tasks through a unified model and a single training process. To achieve this, we design an asymmetric adaptation architecture consisting of a task-agnostic encoder adaptation and task-specific decoder adaptation. Furthermore, we introduce two feature propagation modules to facilitate inter-task and inter-scale feature represenation learning. Experiments on PASCAL-Context and NYUD-V2 dataset demonstrate that our method outperforms both Fully Fine-Tuned and other Parameter Efficient Fine-Tuned (PEFT) baselines. Code will be released.
GSAC: Leveraging Gaussian Splatting for Photorealistic Avatar Creation with Unity Integration
Venue: arXiv cs.GR 2025
Year: 2025

Photorealistic avatars have become essential for immersive applications in virtual reality (VR) and augmented reality (AR), enabling lifelike interactions in areas such as training simulations, telemedicine, and virtual collaboration. These avatars bridge the gap between the physical and digital worlds, improving the user experience through realistic human representation. However, existing avatar creation techniques face significant challenges, including high costs, long creation times, and limited utility in virtual applications. Manual methods, such as MetaHuman, require extensive time and expertise, while automatic approaches, such as NeRF-based pipelines often lack efficiency, detailed facial expression fidelity, and are unable to be rendered at a speed sufficent for real-time applications. By involving several cutting-edge modern techniques, we introduce an end-to-end 3D Gaussian Splatting (3DGS) avatar creation pipeline that leverages monocular video input to create a scalable and efficient photorealistic avatar directly compatible with the Unity game engine. Our pipeline incorporates a novel Gaussian splatting technique with customized preprocessing that enables the user of "in the wild" monocular video capture, detailed facial expression reconstruction and embedding within a fully rigged avatar model. Additionally, we present a Unity-integrated Gaussian Splatting Avatar Editor, offering a user-friendly environment for VR/AR application development. Experimental results validate the effectiveness of our preprocessing pipeline in standardizing custom data for 3DGS training and demonstrate the versatility of Gaussian avatars in Unity, highlighting the scalability and practicality of our approach.
The Role of Empathy in Software Engineering -- A Socio-Technical Grounded Theory
Venue: arXiv cs.SE 2025
Year: 2025

Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.
Hierarchical Feature Learning for Medical Point Clouds via State Space Model
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning-based point cloud modeling has been widely investigated as an indispensable component of general shape analysis. Recently, transformer and state space model (SSM) have shown promising capacities in point cloud learning. However, limited research has been conducted on medical point clouds, which have great potential in disease diagnosis and treatment. This paper presents an SSM-based hierarchical feature learning framework for medical point cloud understanding. Specifically, we down-sample input into multiple levels through the farthest point sampling. At each level, we perform a series of k-nearest neighbor (KNN) queries to aggregate multi-scale structural information. To assist SSM in processing point clouds, we introduce coordinate-order and inside-out scanning strategies for efficient serialization of irregular points. Point features are calculated progressively from short neighbor sequences and long point sequences through vanilla and group Point SSM blocks, to capture both local patterns and long-range dependencies. To evaluate the proposed method, we build a large-scale medical point cloud dataset named MedPointS for anatomy classification, completion, and segmentation. Extensive experiments conducted on MedPointS demonstrate that our method achieves superior performance across all tasks. The dataset is available at https://flemme-docs.readthedocs.io/en/latest/medpoints.html. Code is merged to a public medical imaging platform: https://github.com/wlsdzyzl/flemme.
ChatEXAONEPath: An Expert-level Multimodal Large Language Model for Histopathology Using Whole Slide Images
Venue: arXiv cs.CL 2025
Year: 2025

Recent studies have made significant progress in developing large language models (LLMs) in the medical domain, which can answer expert-level questions and demonstrate the potential to assist clinicians in real-world clinical scenarios. Studies have also witnessed the importance of integrating various modalities with the existing LLMs for a better understanding of complex clinical contexts, which are innately multi-faceted by nature. Although studies have demonstrated the ability of multimodal LLMs in histopathology to answer questions from given images, they lack in understanding of thorough clinical context due to the patch-level data with limited information from public datasets. Thus, developing WSI-level MLLMs is significant in terms of the scalability and applicability of MLLMs in histopathology. In this study, we introduce an expert-level MLLM for histopathology using WSIs, dubbed as ChatEXAONEPath. We present a retrieval-based data generation pipeline using 10,094 pairs of WSIs and histopathology reports from The Cancer Genome Atlas (TCGA). We also showcase an AI-based evaluation protocol for a comprehensive understanding of the medical context from given multimodal information and evaluate generated answers compared to the original histopathology reports. We demonstrate the ability of diagnosing the given histopathology images using ChatEXAONEPath with the acceptance rate of 62.9% from 1,134 pairs of WSIs and reports. Our proposed model can understand pan-cancer WSIs and clinical context from various cancer types. We argue that our proposed model has the potential to assist clinicians by comprehensively understanding complex morphology of WSIs for cancer diagnosis through the integration of multiple modalities.
TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

Remote Sensing Image Super-Resolution (RSISR) reconstructs high-resolution (HR) remote sensing images from low-resolution inputs to support fine-grained ground object interpretation. Existing methods face three key challenges: (1) Difficulty in extracting multi-scale features from spatially heterogeneous RS scenes, (2) Limited prior information causing semantic inconsistency in reconstructions, and (3) Trade-off imbalance between geometric accuracy and visual quality. To address these issues, we propose the Texture Transfer Residual Denoising Dual Diffusion Model (TTRD3) with three innovations: First, a Multi-scale Feature Aggregation Block (MFAB) employing parallel heterogeneous convolutional kernels for multi-scale feature extraction. Second, a Sparse Texture Transfer Guidance (STTG) module that transfers HR texture priors from reference images of similar scenes. Third, a Residual Denoising Dual Diffusion Model (RDDM) framework combining residual diffusion for deterministic reconstruction and noise diffusion for diverse generation. Experiments on multi-source RS datasets demonstrate TTRD3's superiority over state-of-the-art methods, achieving 1.43% LPIPS improvement and 3.67% FID enhancement compared to best-performing baselines. Code/model: https://github.com/LED-666/TTRD3.