OpenRaiser/Intern-Atlas huggingface dataset:
Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes
Venue: arXiv cs.CE 2026
Year: 2026

The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body flows with large wakes. Extending these methods to aerospace, particularly in the transonic regime, remains challenging due to the high level of non-linearity of compressible flows and 3D effects such as wingtip vortices. Existing aerospace datasets predominantly focus on 2D airfoils, neglecting these critical 3D phenomena. To address this gap, we present a new dataset of CFD simulations for 3D wings in the transonic regime. The dataset comprises volumetric and surface-level fields for around $30,000$ samples with unique geometry and inflow conditions. This allows computation of lift and drag coefficients, providing a foundation for data-driven aerodynamic optimization of the drag-lift Pareto front. We evaluate several state-of-the-art neural surrogates on our dataset, including Transolver and AB-UPT, focusing on their out-of-distribution (OOD) generalization over geometry and inflow variations. AB-UPT demonstrates strong performance for transonic flowfields and reproduces physically consistent drag-lift Pareto fronts even for unseen wing configurations. Our results demonstrate that AB-UPT can approximate drag-lift Pareto fronts for unseen geometries, highlighting its potential as an efficient and effective tool for rapid aerodynamic design exploration. To facilitate future research, we open-source our dataset at https://huggingface.co/datasets/EmmiAI/Emmi-Wing.
Self-Paced Learning for Images of Antinuclear Antibodies
Venue: arXiv cs.CV 2026
Year: 2026

Antinuclear antibody (ANA) testing is a crucial method for diagnosing autoimmune disorders, including lupus, Sjögren's syndrome, and scleroderma. Despite its importance, manual ANA detection is slow, labor-intensive, and demands years of training. ANA detection is complicated by over 100 coexisting antibody types, resulting in vast fluorescent pattern combinations. Although machine learning and deep learning have enabled automation, ANA detection in real-world clinical settings presents unique challenges as it involves multi-instance, multi-label (MIML) learning. In this paper, a novel framework for ANA detection is proposed that handles the complexities of MIML tasks using unaltered microscope images without manual preprocessing. Inspired by human labeling logic, it identifies consistent ANA sub-regions and assigns aggregated labels accordingly. These steps are implemented using three task-specific components: an instance sampler, a probabilistic pseudo-label dispatcher, and self-paced weight learning rate coefficients. The instance sampler suppresses low-confidence instances by modeling pattern confidence, while the dispatcher adaptively assigns labels based on instance distinguishability. Self-paced learning adjusts training according to empirical label observations. Our framework overcomes limitations of traditional MIML methods and supports end-to-end optimization. Extensive experiments on one ANA dataset and three public medical MIML benchmarks demonstrate the superiority of our framework. On the ANA dataset, our model achieves up to +7.0% F1-Macro and +12.6% mAP gains over the best prior method, setting new state-of-the-art results. It also ranks top-2 across all key metrics on public datasets, reducing Hamming loss and one-error by up to 18.2% and 26.9%, respectively. The source code can be accessed at https://github.com/fletcherjiang/ANA-SelfPacedLearning.
Data-Augmented Multimodal Feature Fusion for Multiclass Visual Recognition of Oral Cancer Lesions
Venue: arXiv cs.CV 2026
Year: 2026

Oral cancer is frequently diagnosed at later stages due to its similarity to other lesions. Existing research on computer aided diagnosis has made progress using deep learning; however, most approaches remain limited by small, imbalanced datasets and a dependence on single-modality features, which restricts model generalization in real-world clinical settings. To address these limitations, this study proposes a novel data-augmentation driven multimodal feature-fusion framework integrated within a (Vision Recognition)VR assisted oral cancer recognition system. Our method combines extensive data centric augmentation with fused clinical and image-based representations to enhance model robustness and reduce diagnostic ambiguity. Using a stratified training pipeline and an EfficientNetV2 B1 backbone, the system improves feature diversity, mitigates imbalance, and strengthens the learned multimodal embeddings. Experimental evaluation demonstrates that the proposed framework achieves an overall accuracy of 82.57 percent on 2 classes, 65.13 percent on 3 classes, and 54.97 percent on 4 classes, outperforming traditional single stream CNN models. These results highlight the effectiveness of multimodal feature fusion combined with strategic augmentation for reliable early oral cancer lesion recognition and serve as a foundation for immersive VR based clinical decision support tools.
The Need for Benchmarks to Advance AI-Enabled Player Risk Detection in Gambling
Venue: arXiv cs.CY 2026
Year: 2026

Artificial intelligence-based systems for player risk detection have become central to harm prevention efforts in the gambling industry. However, growing concerns around transparency and effectiveness have highlighted the absence of standardized methods for evaluating the quality and impact of these tools. This makes it impossible to gauge true progress; even as new systems are developed, their comparative effectiveness remains unknown. We argue the critical next innovation is developing a framework to measure these systems. This paper proposes a conceptual benchmarking framework to support the systematic evaluation of player risk detection systems. Benchmarking, in this context, refers to the structured and repeatable assessment of artificial intelligence models using standardized datasets, clearly defined tasks, and agreed-upon performance metrics. The goal is to enable objective, comparable, and longitudinal evaluation of player risk detection systems. We present a domain-specific framework for benchmarking that addresses the unique challenges of player risk detection in gambling and supports key stakeholders, including researchers, operators, vendors, and regulators. By enhancing transparency and improving system effectiveness, this framework aims to advance innovation and promote responsible artificial intelligence adoption in gambling harm prevention.
CrossCheck-Bench: Diagnosing Compositional Failures in Multimodal Conflict Resolution
Venue: arXiv cs.CL 2026
Year: 2026

Multimodal Large Language Models are primarily trained and evaluated on aligned image-text pairs, which leaves their ability to detect and resolve real-world inconsistencies largely unexplored. In open-domain applications visual and textual cues often conflict, requiring models to perform structured reasoning beyond surface-level alignment. We introduce CrossCheck-Bench, a diagnostic benchmark for evaluating contradiction detection in multimodal inputs. The benchmark adopts a hierarchical task framework covering three levels of reasoning complexity and defines seven atomic capabilities essential for resolving cross-modal inconsistencies. CrossCheck-Bench includes 15k question-answer pairs sourced from real-world artifacts with synthetically injected contradictions. The dataset is constructed through a multi-stage annotation pipeline involving more than 450 expert hours to ensure semantic validity and calibrated difficulty across perception, integration, and reasoning. We evaluate 13 state-of-the-art vision-language models and observe a consistent performance drop as tasks shift from perceptual matching to logical contradiction detection. Most models perform well on isolated entity recognition but fail when multiple clues must be synthesized for conflict reasoning. Capability-level analysis further reveals uneven skill acquisition, especially in tasks requiring multi-step inference or rule-based validation. Additional probing shows that conventional prompting strategies such as Chain-of-Thought and Set-of-Mark yield only marginal gains. By contrast, methods that interleave symbolic reasoning with grounded visual processing achieve more stable improvements. These results highlight a persistent bottleneck in multimodal reasoning and suggest new directions for building models capable of robust cross-modal verification.
German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies
Venue: arXiv cs.CL 2026
Year: 2026

The use of Large Language Models (LLMs) for simulating human perspectives via persona prompting is gaining traction in computational social science. However, well-curated, empirically grounded persona collections remain scarce, limiting the accuracy and representativeness of such simulations. Here, we introduce the German General Social Survey Personas (GGSS Personas) collection, a comprehensive and representative persona prompt collection built from the German General Social Survey (ALLBUS). The GGSS Personas and their persona prompts are designed to be easily plugged into prompts for all types of LLMs and tasks, steering models to generate responses aligned with the underlying German population. We evaluate GGSS Personas by prompting various LLMs to simulate survey response distributions across diverse topics, demonstrating that GGSS Personas-guided LLMs outperform state-of-the-art classifiers, particularly under data scarcity. Furthermore, we analyze how the representativity and attribute selection within persona prompts affect alignment with population responses. Our findings suggest that GGSS Personas provide a potentially valuable resource for research on LLM-based social simulations that enables more systematic explorations of population-aligned persona prompting in NLP and social science research.
Textual semantics and machine learning methods for data product pricing
Venue: arXiv cs.CE 2026
Year: 2026

Reasonable pricing of data products enables data trading platforms to maximize revenue and foster the growth of the data trading market. The textual semantics of data products are vital for pricing and contain significant value that remains largely underexplored. Therefore, to investigate how textual features influence data product pricing, we employ five prevalent text representation techniques to encode the descriptive text of data products. And then, we employ six machine learning methods to predict data product prices, including linear regression, neural networks, decision trees, support vector machines, random forests, and XGBoost. Our empirical design consists of two tasks: a regression task that predicts the continuous price of data products, and a classification task that discretizes price into ordered categories. Furthermore, we conduct feature importance analysis by the mRMR feature selection method and SHAP-based interpretability techniques. Based on empirical data from the AWA Data Exchange, we find that for predicting continuous prices, Word2Vec text representations capturing semantic similarity yield superior performance. In contrast, for price-tier classification tasks, simpler representations that do not rely on semantic similarity, such as Bag-of-Words and TF-IDF, perform better. SHAP analysis reveals that semantic features related to healthcare and demographics tend to increase prices, whereas those associated with weather and environmental topics are linked to lower prices. This analytical framework significantly enhances the interpretability of pricing models.
Wukong's 72 Transformations: High-fidelity Textured 3D Morphing via Flow Models
Venue: arXiv cs.CV 2026
Year: 2026

We present WUKONG, a novel training-free framework for high-fidelity textured 3D morphing that takes a pair of source and target prompts (image or text) as input. Unlike conventional methods -- which rely on manual correspondence matching and deformation trajectory estimation (limiting generalization and requiring costly preprocessing) -- WUKONG leverages the generative prior of flow-based transformers to produce high-fidelity 3D transitions with rich texture details. To ensure smooth shape transitions, we exploit the inherent continuity of flow-based generative processes and formulate morphing as an optimal transport barycenter problem. We further introduce a sequential initialization strategy to prevent abrupt geometric distortions and preserve identity coherence. For faithful texture preservation, we propose a similarity-guided semantic consistency mechanism that selectively retains high-frequency details and enables precise control over blending dynamics. This empowers WUKONG to support both global texture transitions and identity-preserving texture morphing, catering to diverse generation needs. Extensive quantitative and qualitative evaluations demonstrate that WUKONG significantly outperforms state-of-the-art methods, achieving superior results across diverse geometry and texture variations.
The Repeat Offenders: Characterizing and Predicting Extremely Bug-Prone Source Methods
Venue: arXiv cs.SE 2026
Year: 2026

Bug prediction has long been considered the "prince" of empirical software engineering research, and accordingly, a substantial body of work has focused on predicting bugs to enable early preventive actions. However, most existing studies operate at the class or file level, which practitioners have found to be of limited practical value. As a result, method-level bug prediction has gained increasing attention in recent years. Despite this shift, current method-level prediction models typically treat all buggy methods as equally fault-prone, regardless of whether a method has been associated with a bug once or repeatedly. We argue that methods involved in bugs multiple times-hereafter referred to as ExtremelyBuggy methods-are more harmful than methods that are buggy only once. In this study, we investigate the prevalence of ExtremelyBuggy methods, analyze their code quality metrics, and assess whether they can be predicted at the time of their introduction. In addition, we conduct a thematic analysis of 287 ExtremelyBuggy methods to gain deeper insights into their characteristics. Using a dataset of over 1.25 million methods extracted from 98 open-source Java projects, we find that only a small proportion of methods can be classified as ExtremelyBuggy, yet these methods account for a disproportionately large share of bugs within a project. Although we observe statistically significant differences between ExtremelyBuggy and other methods, ExtremelyBuggy methods remain difficult to predict at their inception. Nevertheless, our manual analysis reveals recurring characteristics among these methods. These findings can help practitioners avoid harmful patterns in practice and guide future research toward developing features and models that better capture the unique properties of such methods.
LC4-DViT: Land-cover Creation for Land-cover Classification with Deformable Vision Transformer
Venue: arXiv cs.CV 2026
Year: 2026

Land-cover underpins ecosystem services, hydrologic regulation, disaster-risk reduction, and evidence-based land planning; timely, accurate land-cover maps are therefore critical for environmental stewardship. Remote sensing-based land-cover classification offers a scalable route to such maps but is hindered by scarce and imbalanced annotations and by geometric distortions in high-resolution scenes. We propose LC4-DViT (Land-cover Creation for Land-cover Classification with Deformable Vision Transformer), a framework that combines generative data creation with a deformation-aware Vision Transformer. A text-guided diffusion pipeline uses GPT-4o-generated scene descriptions and super-resolved exemplars to synthesize class-balanced, high-fidelity training images, while DViT couples a DCNv4 deformable convolutional backbone with a Vision Transformer encoder to jointly capture fine-scale geometry and global context. On eight classes from the Aerial Image Dataset (AID)-Beach, Bridge, Desert, Forest, Mountain, Pond, Port, and River-DViT achieves 0.9572 overall accuracy, 0.9576 macro F1-score, and 0.9510 Cohen' s Kappa, improving over a vanilla ViT baseline (0.9274 OA, 0.9300 macro F1, 0.9169 Kappa) and outperforming ResNet50, MobileNetV2, and FlashInternImage. Cross-dataset experiments on a three-class SIRI-WHU subset (Harbor, Pond, River) yield 0.9333 overall accuracy, 0.9316 macro F1, and 0.8989 Kappa, indicating good transferability. An LLM-based judge using GPT-4o to score Grad-CAM heatmaps further shows that DViT' s attention aligns best with hydrologically meaningful structures. These results suggest that description-driven generative augmentation combined with deformation-aware transformers is a promising approach for high-resolution land-cover mapping.
Towards an algebraic approach to the reconfiguration CSP
Venue: arXiv cs.DS 2026
Year: 2026

This paper investigates the reconfiguration variant of the Constraint Satisfaction Problem (CSP), referred to as the Reconfiguration CSP (RCSP). Given a CSP instance and two of its solutions, RCSP asks whether one solution can be transformed into the other via a sequence of intermediate solutions, each differing by the assignment of a single variable. RCSP has attracted growing interest in theoretical computer science, and when the variable domain is Boolean, the computational complexity of RCSP exhibits a dichotomy depending on the allowed constraint types. A notable special case is the reconfiguration of graph homomorphisms -- also known as graph recoloring -- which has been studied using topological methods. We propose a novel algebraic approach to RCSP, inspired by techniques used in classical CSP complexity analysis. Unlike traditional methods based on total operations, our framework employs partial operations to capture a reduction involving equality constraints. This perspective facilitates the extension of complexity results from Boolean domains to more general settings, demonstrating the versatility of partial operations in identifying tractable RCSP instances.
MindPower: Enabling Theory-of-Mind Reasoning in VLM-based Embodied Agents
Venue: arXiv cs.AI 2026
Year: 2026

Theory of Mind (ToM) refers to the ability to infer others' mental states, such as beliefs, desires, and intentions. Current vision-language embodied agents lack ToM-based decision-making, and existing benchmarks focus solely on human mental states while ignoring the agent's own perspective, hindering coherent decision and action generation. To address this, we propose MindPower, a Robot-Centric framework integrating Perception, Mental Reasoning, Decision Making and Action. Given multimodal inputs, MindPower first perceives the environment and human states, then performs ToM Reasoning to model both self and others, and finally generates decisions and actions guided by inferred mental states. Furthermore, we introduce Mind-Reward, a novel optimization objective that encourages VLMs to produce consistent ToM Reasoning and behavior. Our model outperforms GPT-4o by 12.77% in decision making and 12.49% in action generation.
All for One and One for All: Program Logics for Exploiting Internal Determinism in Parallel Programs
Venue: arXiv cs.PL 2026
Year: 2026

Nondeterminism makes parallel programs challenging to write and reason about. To avoid these challenges, researchers have developed techniques for internally deterministic parallel programming, in which the steps of a parallel computation proceed in a deterministic way. Internal determinism is useful because it lets a programmer reason about a program as if it executed in a sequential order. However, no verification framework exists to exploit this property and simplify formal reasoning about internally deterministic programs. To capture the essence of why internally deterministic programs should be easier to reason about, this paper defines a property called schedule-independent safety. A program satisfies schedule-independent safety, if, to show that the program is safe across all orderings, it suffices to show that one terminating execution of the program is safe. We then present a separation logic called Musketeer for proving that a program satisfies schedule-independent safety. Once a parallel program has been shown to satisfy schedule-independent safety, we can verify it with a new logic called Angelic, which allows one to dynamically select and verify just one sequential ordering of the program. Using Musketeer, we prove the soundness of MiniDet, an affine type system for enforcing internal determinism. MiniDet supports several core algorithmic primitives for internally deterministic programming that have been identified in the research literature, including a deterministic version of a concurrent hash set. Because any syntactically well-typed MiniDet program satisfies schedule-independent safety, we can apply Angelic to verify such programs. All results in this paper have been verified in Rocq using the Iris separation logic framework.
Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon
Venue: arXiv cs.HC 2026
Year: 2026

Motivated by the Cybathlon 2024 competition, we developed a modular, online EEG-based brain-computer interface to address these challenges, increasing accessibility for individuals with severe mobility impairments. Our system uses three mental and motor imagery classes to control up to five control signals. The pipeline consists of four modules: data acquisition, preprocessing, classification, and the transfer function to map classification output to control dimensions. We use three diagonalized structured state-space sequence layers as a deep learning classifier. We developed a training game for our pilot where the mental tasks control the game during quick-time events. We implemented a mobile web application for live user feedback. The components were designed with a human-centred approach in collaboration with the tetraplegic user. We achieve up to 84% classification accuracy in offline analysis using an S4D-layer-based model. In a competition setting, our pilot successfully completed one task; we attribute the reduced performance in this context primarily to factors such as stress and the challenging competition environment. Following the Cybathlon, we further validated our pipeline with the original pilot and an additional participant, achieving a success rate of 73% in real-time gameplay. We also compare our model to the EEGEncoder, which is slower in training but has a higher performance. The S4D model outperforms the reference machine learning models. We provide insights into developing a framework for portable BCIs, bridging the gap between the laboratory and daily life. Specifically, our framework integrates modular design, real-time data processing, user-centred feedback, and low-cost hardware to deliver an accessible and adaptable BCI solution, addressing critical gaps in current BCI applications.