OpenRaiser/Intern-Atlas huggingface dataset:
SO-PIFRNN: Self-optimization physics-informed Fourier-features randomized neural network for solving partial differential equations
Venue: arXiv cs.NE 2025
Year: 2025

This study proposes a self-optimization physics-informed Fourier-features randomized neural network (SO-PIFRNN) framework, which significantly improves the numerical solving accuracy of PDEs through hyperparameter optimization mechanism. The framework employs a bi-level optimization architecture: the outer-level optimization utilizes a multi-strategy collaborated particle swarm optimization (MSC-PSO) algorithm to search for optimal hyperparameters of physics-informed Fourier-features randomized neural network, while the inner-level optimization determines the output layer weights of the neural network via the least squares method. The core innovation of this study is embodied in the following three aspects: First, the Fourier basis function activation mechanism is introduced in the hidden layer of neural network, which significantly enhances the ability of the network to capture multi-frequency components of the solution. Secondly, a novel derivative neural network method is proposed, which improves the calculation accuracy and efficiency of PIFRNN method. Finally, the MSC-PSO algorithm of the hybrid optimization strategy is designed to improve the global search ability and convergence accuracy through the synergistic effect of dynamic parameter adjustment, elitist and mutation strategies. Through a series of numerical experiments, including multiscale equations in complex regions, high-order equations, high-dimensional equations and nonlinear equations, the validity of SO-PIFRNN is verified. The experimental results affirm that SO-PIFRNN exhibits superior approximation accuracy and frequency capture capability.
A Cooperative Game-Based Multi-Criteria Weighted Ensemble Approach for Multi-Class Classification
Venue: arXiv cs.LG 2025
Year: 2025

Since the Fourth Industrial Revolution, AI technology has been widely used in many fields, but there are several limitations that need to be overcome, including overfitting/underfitting, class imbalance, and the limitations of representation (hypothesis space) due to the characteristics of different models. As a method to overcome these problems, ensemble, commonly known as model combining, is being extensively used in the field of machine learning. Among ensemble learning methods, voting ensembles have been studied with various weighting methods, showing performance improvements. However, the existing methods that reflect the pre-information of classifiers in weights consider only one evaluation criterion, which limits the reflection of various information that should be considered in a model realistically. Therefore, this paper proposes a method of making decisions considering various information through cooperative games in multi-criteria situations. Using this method, various types of information known beforehand in classifiers can be simultaneously considered and reflected, leading to appropriate weight distribution and performance improvement. The machine learning algorithms were applied to the Open-ML-CC18 dataset and compared with existing ensemble weighting methods. The experimental results showed superior performance compared to other weighting methods.
HQ-OV3D: A High Box Quality Open-World 3D Detection Framework based on Diffision Model
Venue: arXiv cs.CV 2025
Year: 2025

Traditional closed-set 3D detection frameworks fail to meet the demands of open-world applications like autonomous driving. Existing open-vocabulary 3D detection methods typically adopt a two-stage pipeline consisting of pseudo-label generation followed by semantic alignment. While vision-language models (VLMs) recently have dramatically improved the semantic accuracy of pseudo-labels, their geometric quality, particularly bounding box precision, remains commonly neglected. To address this issue, we propose a High Box Quality Open-Vocabulary 3D Detection (HQ-OV3D) framework, dedicated to generate and refine high-quality pseudo-labels for open-vocabulary classes. The framework comprises two key components: an Intra-Modality Cross-Validated (IMCV) Proposal Generator that utilizes cross-modality geometric consistency to generate high-quality initial 3D proposals, and an Annotated-Class Assisted (ACA) Denoiser that progressively refines 3D proposals by leveraging geometric priors from annotated categories through a DDIM-based denoising mechanism. Compared to the state-of-the-art method, training with pseudo-labels generated by our approach achieves a 7.37% improvement in mAP on novel classes, demonstrating the superior quality of the pseudo-labels produced by our framework. HQ-OV3D can serve not only as a strong standalone open-vocabulary 3D detector but also as a plug-in high-quality pseudo-label generator for existing open-vocabulary detection or annotation pipelines.
Personalized Face Super-Resolution with Identity Decoupling and Fitting
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, face super-resolution (FSR) methods have achieved remarkable progress, generally maintaining high image fidelity and identity (ID) consistency under standard settings. However, in extreme degradation scenarios (e.g., scale $> 8\times$), critical attributes and ID information are often severely lost in the input image, making it difficult for conventional models to reconstruct realistic and ID-consistent faces. Existing methods tend to generate hallucinated faces under such conditions, producing restored images lacking authentic ID constraints. To address this challenge, we propose a novel FSR method with Identity Decoupling and Fitting (IDFSR), designed to enhance ID restoration under large scaling factors while mitigating hallucination effects. Our approach involves three key designs: 1) \textbf{Masking} the facial region in the low-resolution (LR) image to eliminate unreliable ID cues; 2) \textbf{Warping} a reference image to align with the LR input, providing style guidance; 3) Leveraging \textbf{ID embeddings} extracted from ground truth (GT) images for fine-grained ID modeling and personalized adaptation. We first pretrain a diffusion-based model to explicitly decouple style and ID by forcing it to reconstruct masked LR face regions using both style and identity embeddings. Subsequently, we freeze most network parameters and perform lightweight fine-tuning of the ID embedding using a small set of target ID images. This embedding encodes fine-grained facial attributes and precise ID information, significantly improving both ID consistency and perceptual quality. Extensive quantitative evaluations and visual comparisons demonstrate that the proposed IDFSR substantially outperforms existing approaches under extreme degradation, particularly achieving superior performance on ID consistency.
NIRMAL Pooling: An Adaptive Max Pooling Approach with Non-linear Activation for Enhanced Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents NIRMAL Pooling, a novel pooling layer for Convolutional Neural Networks (CNNs) that integrates adaptive max pooling with non-linear activation function for image classification tasks. The acronym NIRMAL stands for Non-linear Activation, Intermediate Aggregation, Reduction, Maximum, Adaptive, and Localized. By dynamically adjusting pooling parameters based on desired output dimensions and applying a Rectified Linear Unit (ReLU) activation post-pooling, NIRMAL Pooling improves robustness and feature expressiveness. We evaluated its performance against standard Max Pooling on three benchmark datasets: MNIST Digits, MNIST Fashion, and CIFAR-10. NIRMAL Pooling achieves test accuracies of 99.25% (vs. 99.12% for Max Pooling) on MNIST Digits, 91.59% (vs. 91.44%) on MNIST Fashion, and 70.49% (vs. 68.87%) on CIFAR-10, demonstrating consistent improvements, particularly on complex datasets. This work highlights the potential of NIRMAL Pooling to enhance CNN performance in diverse image recognition tasks, offering a flexible and reliable alternative to traditional pooling methods.
Analysis of the Compaction Behavior of Textile Reinforcements in Low-Resolution In-Situ CT Scans via Machine-Learning and Descriptor-Based Methods
Venue: arXiv cs.CV 2025
Year: 2025

A detailed understanding of material structure across multiple scales is essential for predictive modeling of textile-reinforced composites. Nesting -- characterized by the interlocking of adjacent fabric layers through local interpenetration and misalignment of yarns -- plays a critical role in defining mechanical properties such as stiffness, permeability, and damage tolerance. This study presents a framework to quantify nesting behavior in dry textile reinforcements under compaction using low-resolution computed tomography (CT). In-situ compaction experiments were conducted on various stacking configurations, with CT scans acquired at 20.22 $μ$m per voxel resolution. A tailored 3D{-}UNet enabled semantic segmentation of matrix, weft, and fill phases across compaction stages corresponding to fiber volume contents of 50--60 %. The model achieved a minimum mean Intersection-over-Union of 0.822 and an $F1$ score of 0.902. Spatial structure was subsequently analyzed using the two-point correlation function $S_2$, allowing for probabilistic extraction of average layer thickness and nesting degree. The results show strong agreement with micrograph-based validation. This methodology provides a robust approach for extracting key geometrical features from industrially relevant CT data and establishes a foundation for reverse modeling and descriptor-based structural analysis of composite preforms.
MedAtlas: Evaluating LLMs for Multi-Round, Multi-Task Medical Reasoning Across Diverse Imaging Modalities and Clinical Text
Venue: arXiv cs.CV 2025
Year: 2025

Artificial intelligence has demonstrated significant potential in clinical decision-making; however, developing models capable of adapting to diverse real-world scenarios and performing complex diagnostic reasoning remains a major challenge. Existing medical multi-modal benchmarks are typically limited to single-image, single-turn tasks, lacking multi-modal medical image integration and failing to capture the longitudinal and multi-modal interactive nature inherent to clinical practice. To address this gap, we introduce MedAtlas, a novel benchmark framework designed to evaluate large language models on realistic medical reasoning tasks. MedAtlas is characterized by four key features: multi-turn dialogue, multi-modal medical image interaction, multi-task integration, and high clinical fidelity. It supports four core tasks: open-ended multi-turn question answering, closed-ended multi-turn question answering, multi-image joint reasoning, and comprehensive disease diagnosis. Each case is derived from real diagnostic workflows and incorporates temporal interactions between textual medical histories and multiple imaging modalities, including CT, MRI, PET, ultrasound, and X-ray, requiring models to perform deep integrative reasoning across images and clinical texts. MedAtlas provides expert-annotated gold standards for all tasks. Furthermore, we propose two novel evaluation metrics: Round Chain Accuracy and Error Propagation Resistance. Benchmark results with existing multi-modal models reveal substantial performance gaps in multi-stage clinical reasoning. MedAtlas establishes a challenging evaluation platform to advance the development of robust and trustworthy medical AI.
Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules
Venue: arXiv cs.CL 2025
Year: 2025

Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs. This work presents Rule2Text, a comprehensive framework that leverages large language models (LLMs) to generate natural language explanations for mined logical rules, thereby improving KG accessibility and usability. We conduct extensive experiments using multiple datasets, including Freebase variants (FB-CVT-REV, FB+CVT-REV, and FB15k-237) as well as the ogbl-biokg dataset, with rules mined using AMIE 3.5.1. We systematically evaluate several LLMs across a comprehensive range of prompting strategies, including zero-shot, few-shot, variable type incorporation, and Chain-of-Thought reasoning. To systematically assess models' performance, we conduct a human evaluation of generated explanations on correctness and clarity. To address evaluation scalability, we develop and validate an LLM-as-a-judge framework that demonstrates strong agreement with human evaluators. Leveraging the best-performing model (Gemini 2.0 Flash), LLM judge, and human-in-the-loop feedback, we construct high-quality ground truth datasets, which we use to fine-tune the open-source Zephyr model. Our results demonstrate significant improvements in explanation quality after fine-tuning, with particularly strong gains in the domain-specific dataset. Additionally, we integrate a type inference module to support KGs lacking explicit type information. All code and data are publicly available at https://github.com/idirlab/KGRule2NL.
Developing and Validating a High-Throughput Robotic System for the Accelerated Development of Porous Membranes
Venue: arXiv cs.RO 2025
Year: 2025

The development of porous polymeric membranes remains a labor-intensive process, often requiring extensive trial and error to identify optimal fabrication parameters. In this study, we present a fully automated platform for membrane fabrication and characterization via nonsolvent-induced phase separation (NIPS). The system integrates automated solution preparation, blade casting, controlled immersion, and compression testing, allowing precise control over fabrication parameters such as polymer concentration and ambient humidity. The modular design allows parallel processing and reproducible handling of samples, reducing experimental time and increasing consistency. Compression testing is introduced as a sensitive mechanical characterization method for estimating membrane stiffness and as a proxy to infer porosity and intra-sample uniformity through automated analysis of stress-strain curves. As a proof of concept to demonstrate the effectiveness of the system, NIPS was carried out with polysulfone, the green solvent PolarClean, and water as the polymer, solvent, and nonsolvent, respectively. Experiments conducted with the automated system reproduced expected effects of polymer concentration and ambient humidity on membrane properties, namely increased stiffness and uniformity with increasing polymer concentration and humidity variations in pore morphology and mechanical response. The developed automated platform supports high-throughput experimentation and is well-suited for integration into self-driving laboratory workflows, offering a scalable and reproducible foundation for data-driven optimization of porous polymeric membranes through NIPS.
JobPulse: A Big Data Approach to Real-Time Engineering Workforce Analysis and National Industrial Policy
Venue: arXiv cs.CY 2025
Year: 2025

Employment on a societal scale contributes heavily to national and global affairs; consequently, job openings and unemployment estimates provide important information to financial markets and governments alike. However, such reports often describe only the supply (employee job seeker) side of the job market, and skill mismatches are poorly understood. Job postings aggregated on recruiting platforms illuminate marketplace demand, but to date have primarily focused on candidate skills described in their personal profiles. In this paper, we report on a big data approach to estimating job market mismatches by focusing on demand, as represented in publicly available job postings. We use commercially available web scraping tools and a new data processing scheme to build a job posting data set for the semiconductor industry, a strategically critical sector of the United States economy; we focus on Southern California as a central hub of advanced technologies. We report on the employer base and relative needs of various job functions. Our work contributes on three fronts: First, we provide nearly real-time insight into workforce demand; second, we discuss disambiguation and semantic challenges in analysis of employer data bases at scale; and third, we report on the Southern California semiconductor engineering ecosystem.
Hell or High Water: Evaluating Agentic Recovery from External Failures
Venue: arXiv cs.CL 2025
Year: 2025

As language model agents are applied to real world problems of increasing complexity, they will be expected to formulate plans across large search spaces. If those plans fail for reasons beyond their control, how well do language agents search for alternative ways to achieve their goals? We devise a specialized agentic planning benchmark to study this question. Each planning problem is solved via combinations of function calls. The agent searches for relevant functions from a set of over four thousand possibilities, and observes environmental feedback in the form of function outputs or error messages. Our benchmark confronts the agent with external failures in its workflow, such as functions that suddenly become unavailable. At the same time, even with the introduction of these failures, we guarantee that the task remains solvable. Ideally, an agent's performance on the planning task should not be affected by the presence of external failures. Overall, we find that language agents struggle to formulate and execute backup plans in response to environment feedback. While state-of-the-art models are often able to identify the correct function to use in the right context, they struggle to adapt to feedback from the environment and often fail to pursue alternate courses of action, even when the search space is artificially restricted. We provide a systematic analysis of the failures of both open-source and commercial models, examining the effects of search space size, as well as the benefits of scaling model size in our setting. Our analysis identifies key challenges for current generative models as well as promising directions for future work.
Advancing 3D Scene Understanding with MV-ScanQA Multi-View Reasoning Evaluation and TripAlign Pre-training Dataset
Venue: arXiv cs.CV 2025
Year: 2025

The advancement of 3D vision-language (3D VL) learning is hindered by several limitations in existing 3D VL datasets: they rarely necessitate reasoning beyond a close range of objects in single viewpoint, and annotations often link instructions to single objects, missing richer contextual alignments between multiple objects. This significantly curtails the development of models capable of deep, multi-view 3D scene understanding over distant objects. To address these challenges, we introduce MV-ScanQA, a novel 3D question answering dataset where 68% of questions explicitly require integrating information from multiple views (compared to less than 7% in existing datasets), thereby rigorously testing multi-view compositional reasoning. To facilitate the training of models for such demanding scenarios, we present TripAlign dataset, a large-scale and low-cost 2D-3D-language pre-training corpus containing 1M <2D view, set of 3D objects, text> triplets that explicitly aligns groups of contextually related objects with text, providing richer, view-grounded multi-object multimodal alignment signals than previous single-object annotations. We further develop LEGO, a baseline method for the multi-view reasoning challenge in MV-ScanQA, transferring knowledge from pre-trained 2D LVLMs to 3D domain with TripAlign. Empirically, LEGO pre-trained on TripAlign achieves state-of-the-art performance not only on the proposed MV-ScanQA, but also on existing benchmarks for 3D dense captioning and question answering. Datasets and code are available at https://matthewdm0816.github.io/tripalign-mvscanqa.
Stories and Systems: Educational Interactive Storytelling to Teach Media Literacy and Systemic Thinking
Venue: arXiv cs.HC 2025
Year: 2025

This paper explores how Interactive Digital Narratives (IDNs) can support learners in developing the critical literacies needed to address complex societal challenges, so-called wicked problems, such as climate change, pandemics, and social inequality. While digital technologies offer broad access to narratives and data, they also contribute to misinformation and the oversimplification of interconnected issues. IDNs enable learners to navigate nonlinear, interactive stories, fostering deeper understanding and engagement. We introduce Systemic Learning IDNs: interactive narrative experiences explicitly designed to help learners explore and reflect on complex systems and interdependencies. To guide their creation and use, we propose the CLASS framework, a structured model that integrates systems thinking, design thinking, and storytelling. This transdisciplinary approach supports learners in developing curiosity, critical thinking, and collaborative problem-solving. Focusing on the classroom context, we apply CLASS to two cases, one commercial narrative simulation and one educational prototype, offering a comparative analysis and practical recommendations for future design and implementation. By combining narrative, systems mapping, and participatory design, this paper highlights how IDNs can become powerful tools for transformative, systems-oriented learning in an increasingly complex world.
Human-in-the-Loop Systems for Adaptive Learning Using Generative AI
Venue: arXiv cs.HC 2025
Year: 2025

A Human-in-the-Loop (HITL) approach leverages generative AI to enhance personalized learning by directly integrating student feedback into AI-generated solutions. Students critique and modify AI responses using predefined feedback tags, fostering deeper engagement and understanding. This empowers students to actively shape their learning, with AI serving as an adaptive partner. The system uses a tagging technique and prompt engineering to personalize content, informing a Retrieval-Augmented Generation (RAG) system to retrieve relevant educational material and adjust explanations in real time. This builds on existing research in adaptive learning, demonstrating how student-driven feedback loops can modify AI-generated responses for improved student retention and engagement, particularly in STEM education. Preliminary findings from a study with STEM students indicate improved learning outcomes and confidence compared to traditional AI tools. This work highlights AI's potential to create dynamic, feedback-driven, and personalized learning environments through iterative refinement.
Abundance-Aware Set Transformer for Microbiome Sample Embedding
Venue: arXiv cs.LG 2025
Year: 2025

Microbiome sample representation to input into LLMs is essential for downstream tasks such as phenotype prediction and environmental classification. While prior studies have explored embedding-based representations of each microbiome sample, most rely on simple averaging over sequence embeddings, often overlooking the biological importance of taxa abundance. In this work, we propose an abundance-aware variant of the Set Transformer to construct fixed-size sample-level embeddings by weighting sequence embeddings according to their relative abundance. Without modifying the model architecture, we replicate embedding vectors proportional to their abundance and apply self-attention-based aggregation. Our method outperforms average pooling and unweighted Set Transformers on real-world microbiome classification tasks, achieving perfect performance in some cases. These results demonstrate the utility of abundance-aware aggregation for robust and biologically informed microbiome representation. To the best of our knowledge, this is one of the first approaches to integrate sequence-level abundance into Transformer-based sample embeddings.