OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series
Venue: arXiv cs.LG 2025
Year: 2025

The manifold hypothesis suggests that high-dimensional neural time series lie on a low-dimensional manifold shaped by simpler underlying dynamics. To uncover this structure, latent dynamical variable models such as state-space models, recurrent neural networks, neural ordinary differential equations, and Gaussian Process Latent Variable Models are widely used. We propose a novel hierarchical stochastic differential equation (SDE) model that balances computational efficiency and interpretability, addressing key limitations of existing methods. Our model assumes the trajectory of a manifold can be reconstructed from a sparse set of samples from the manifold trajectory. The latent space is modeled using Brownian bridge SDEs, with points - specified in both time and value - sampled from a multivariate marked point process. These Brownian bridges define the drift of a second set of SDEs, which are then mapped to the observed data. This yields a continuous, differentiable latent process capable of modeling arbitrarily complex time series as the number of manifold points increases. We derive training and inference procedures and show that the computational cost of inference scales linearly with the length of the observation data. We then validate our model on both synthetic data and neural recordings to demonstrate that it accurately recovers the underlying manifold structure and scales effectively with data dimensionality.
RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors
Venue: arXiv cs.CV 2025
Year: 2025

Online high-definition (HD) map construction is crucial for scaling autonomous driving systems. While Transformer-based methods have become prevalent in online HD map construction, most existing approaches overlook the inherent spatial dependencies and semantic relationships among map elements, which constrains their accuracy and generalization capabilities. To address this, we propose RelMap, an end-to-end framework that explicitly models both spatial relations and semantic priors to enhance online HD map construction. Specifically, we introduce a Class-aware Spatial Relation Prior, which explicitly encodes relative positional dependencies between map elements using a learnable class-aware relation encoder. Additionally, we design a Mixture-of-Experts-based Semantic Prior, which routes features to class-specific experts based on predicted class probabilities, refining instance feature decoding. RelMap is compatible with both single-frame and temporal perception backbones, achieving state-of-the-art performance on both the nuScenes and Argoverse 2 datasets.
Ethical Classification of Non-Coding Contributions in Open-Source Projects via Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

The development of Open-Source Software (OSS) is not only a technical challenge, but also a social one due to the diverse mixture of contributors. To this aim, social-coding platforms, such as GitHub, provide the infrastructure needed to host and develop the code, but also the support for enabling the community's collaboration, which is driven by non-coding contributions, such as issues (i.e., change proposals or bug reports) or comments to existing contributions. As with any other social endeavor, this development process faces ethical challenges, which may put at risk the project's sustainability. To foster a productive and positive environment, OSS projects are increasingly deploying codes of conduct, which define rules to ensure a respectful and inclusive participatory environment, with the Contributor Covenant being the main model to follow. However, monitoring and enforcing these codes of conduct is a challenging task, due to the limitations of current approaches. In this paper, we propose an approach to classify the ethical quality of non-coding contributions in OSS projects by relying on Large Language Models (LLM), a promising technology for text classification tasks. We defined a set of ethical metrics based on the Contributor Covenant and developed a classification approach to assess ethical behavior in OSS non-coding contributions, using prompt engineering to guide the model's output.
Exploring the Link Between Bayesian Inference and Embodied Intelligence: Toward Open Physical-World Embodied AI Systems
Venue: arXiv cs.AI 2025
Year: 2025

Embodied intelligence posits that cognitive capabilities fundamentally emerge from - and are shaped by - an agent's real-time sensorimotor interactions with its environment. Such adaptive behavior inherently requires continuous inference under uncertainty. Bayesian statistics offers a principled probabilistic framework to address this challenge by representing knowledge as probability distributions and updating beliefs in response to new evidence. The core computational processes underlying embodied intelligence - including perception, action selection, learning, and even higher-level cognition - can be effectively understood and modeled as forms of Bayesian inference. Despite the deep conceptual connection between Bayesian statistics and embodied intelligence, Bayesian principles have not been widely or explicitly applied in today's embodied intelligence systems. In this work, we examine both Bayesian and contemporary embodied intelligence approaches through two fundamental lenses: search and learning - the two central themes in modern AI, as highlighted in Rich Sutton's influential essay "The Bitter Lesson". This analysis sheds light on why Bayesian inference has not played a central role in the development of modern embodied intelligence. At the same time, it reveals that current embodied intelligence systems remain largely confined to closed-physical-world environments, and highlights the potential for Bayesian methods to play a key role in extending these systems toward truly open physical-world embodied intelligence.
GuidPaint: Class-Guided Image Inpainting with Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, diffusion models have been widely adopted for image inpainting tasks due to their powerful generative capabilities, achieving impressive results. Existing multimodal inpainting methods based on diffusion models often require architectural modifications and retraining, resulting in high computational cost. In contrast, context-aware diffusion inpainting methods leverage the model's inherent priors to adjust intermediate denoising steps, enabling high-quality inpainting without additional training and significantly reducing computation. However, these methods lack fine-grained control over the masked regions, often leading to semantically inconsistent or visually implausible content. To address this issue, we propose GuidPaint, a training-free, class-guided image inpainting framework. By incorporating classifier guidance into the denoising process, GuidPaint enables precise control over intermediate generations within the masked areas, ensuring both semantic consistency and visual realism. Furthermore, it integrates stochastic and deterministic sampling, allowing users to select preferred intermediate results and deterministically refine them. Experimental results demonstrate that GuidPaint achieves clear improvements over existing context-aware inpainting methods in both qualitative and quantitative evaluations.
Automated Detection of Antarctic Benthic Organisms in High-Resolution In Situ Imagery to Aid Biodiversity Monitoring
Venue: arXiv cs.CV 2025
Year: 2025

Monitoring benthic biodiversity in Antarctica is vital for understanding ecological change in response to climate-driven pressures. This work is typically performed using high-resolution imagery captured in situ, though manual annotation of such data remains laborious and specialised, impeding large-scale analysis. We present a tailored object detection framework for identifying and classifying Antarctic benthic organisms in high-resolution towed camera imagery, alongside the first public computer vision dataset for benthic biodiversity monitoring in the Weddell Sea. Our approach addresses key challenges associated with marine ecological imagery, including limited annotated data, variable object sizes, and complex seafloor structure. The proposed framework combines resolution-preserving patching, spatial data augmentation, fine-tuning, and postprocessing via Slicing Aided Hyper Inference. We benchmark multiple object detection architectures and demonstrate strong performance in detecting medium and large organisms across 25 fine-grained morphotypes, significantly more than other works in this area. Detection of small and rare taxa remains a challenge, reflecting limitations in current detection architectures. Our framework provides a scalable foundation for future machine-assisted in situ benthic biodiversity monitoring research.
Probabilistic Consistency in Machine Learning and Its Connection to Uncertainty Quantification
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning (ML) is often viewed as a powerful data analysis tool that is easy to learn because of its black-box nature. Yet this very nature also makes it difficult to quantify confidence in predictions extracted from ML models, and more fundamentally, to understand how such models are mathematical abstractions of training data. The goal of this paper is to unravel these issues and their connections to uncertainty quantification (UQ) by pursuing a line of reasoning motivated by diagnostics. In such settings, prevalence - i.e. the fraction of elements in class - is often of inherent interest. Here we analyze the many interpretations of prevalence to derive a level-set theory of classification, which shows that certain types of self-consistent ML models are equivalent to class-conditional probability distributions. We begin by studying the properties of binary Bayes optimal classifiers, recognizing that their boundary sets can be reinterpreted as level-sets of pairwise density ratios. By parameterizing Bayes classifiers in terms of the prevalence, we then show that they satisfy important monotonicity and class-switching properties that can be used to deduce the density ratios without direct access to the boundary sets. Moreover, this information is sufficient for tasks such as constructing the multiclass Bayes-optimal classifier and estimating inherent uncertainty in the class assignments. In the multiclass case, we use these results to deduce normalization and self-consistency conditions, the latter being equivalent to the law of total probability for classifiers. We also show that these are necessary conditions for arbitrary ML models to have valid probabilistic interpretations. Throughout we demonstrate how this analysis informs the broader task of UQ for ML via an uncertainty propagation framework.
APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing
Venue: arXiv cs.CV 2025
Year: 2025

Latent Diffusion Models (LDMs) are generally trained at fixed resolutions, limiting their capability when scaling up to high-resolution images. While training-based approaches address this limitation by training on high-resolution datasets, they require large amounts of data and considerable computational resources, making them less practical. Consequently, training-free methods, particularly patch-based approaches, have become a popular alternative. These methods divide an image into patches and fuse the denoising paths of each patch, showing strong performance on high-resolution generation. However, we observe two critical issues for patch-based approaches, which we call ``patch-level distribution shift" and ``increased patch monotonicity." To address these issues, we propose Adaptive Path Tracing (APT), a framework that combines Statistical Matching to ensure patch distributions remain consistent in upsampled latents and Scale-aware Scheduling to deal with the patch monotonicity. As a result, APT produces clearer and more refined details in high-resolution images. In addition, APT enables a shortcut denoising process, resulting in faster sampling with minimal quality degradation. Our experimental results confirm that APT produces more detailed outputs with improved inference speed, providing a practical approach to high-resolution image generation.
Few-Shot Vision-Language Reasoning for Satellite Imagery via Verifiable Rewards
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in large language and vision-language models have enabled strong reasoning capabilities, yet they remain impractical for specialized domains like remote sensing, where annotated data is scarce and expensive. We present the first few-shot reinforcement learning with verifiable reward (RLVR) framework for satellite imagery that eliminates the need for caption supervision--relying solely on lightweight, rule-based binary or IoU-based rewards. Adapting the "1-shot RLVR" paradigm from language models to vision-language models, we employ policy-gradient optimization with as few as one curated example to align model outputs for satellite reasoning tasks. Comprehensive experiments across multiple remote sensing benchmarks--including classification, visual question answering, and grounding--show that even a single example yields substantial improvements over the base model. Scaling to 128 examples matches or exceeds models trained on thousands of annotated samples. While the extreme one-shot setting can induce mild, task-specific overfitting, our approach consistently demonstrates robust generalization and efficiency across diverse tasks. Further, we find that prompt design and loss weighting significantly influence training stability and final accuracy. Our method enables cost-effective and data-efficient development of domain-specialist vision-language reasoning models, offering a pragmatic recipe for data-scarce fields: start from a compact VLM, curate a handful of reward-checkable cases, and train via RLVR.
SAT-Based Bounded Fitting for the Description Logic ALC
Venue: arXiv cs.AI 2025
Year: 2025

Bounded fitting is a general paradigm for learning logical formulas from positive and negative data examples, that has received considerable interest recently. We investigate bounded fitting for the description logic ALC and its syntactic fragments. We show that the underlying size-restricted fitting problem is NP-complete for all studied fragments, even in the special case of a single positive and a single negative example. By design, bounded fitting comes with probabilistic guarantees in Valiant's PAC learning framework. In contrast, we show that other classes of algorithms for learning ALC concepts do not provide such guarantees. Finally, we present an implementation of bounded fitting in ALC and its fragments based on a SAT solver. We discuss optimizations and compare our implementation to other concept learning tools.
The Performance of Low-Synchronization Variants of Reorthogonalized Block Classical Gram--Schmidt
Venue: arXiv cs.DC 2025
Year: 2025

Numerous applications, such as Krylov subspace solvers, make extensive use of the block classical Gram-Schmidt (BCGS) algorithm and its reorthogonalized variants for orthogonalizing a set of vectors. For large-scale problems in distributed memory settings, the communication cost, particularly the global synchronization cost, is a major performance bottleneck. In recent years, many low-synchronization BCGS variants have been proposed in an effort to reduce the number of synchronization points. The work [E. Carson, Y. Ma, arXiv preprint 2411.07077] recently proposed stable one-synchronization and two-synchronization variants of BCGS, i.e., BCGSI+P-1S and BCGSI+P-2S. In this work, we evaluate the performance of BCGSI+P-1S and BCGSI+P-2S on a distributed memory system compared to other well-known low-synchronization BCGS variants. In comparison to the classical reorthogonalized BCGS algorithm (BCGSI+), numerical experiments demonstrate that BCGSI+P-1S and BCGSI+P-2S can achieve up to 4 times and 2 times speedups, respectively, and perform similarly to other (less stable) one-synchronization and two-synchronization variants. BCGSI+P-1S and BCGSI+P-2S are therefore recommended as the best choice in practice for computing an economic QR factorization on distributed memory systems due to their superior stability when compared to other variants with the same synchronization cost.
Helping or Homogenizing? GenAI as a Design Partner to Pre-Service SLPs for Just-in-Time Programming of AAC
Venue: arXiv cs.HC 2025
Year: 2025

Augmentative and alternative communication (AAC) devices are used by many people around the world who experience difficulties in communicating verbally. One AAC device which is especially useful for minimally verbal autistic children in developing language and communication skills are visual scene displays (VSD). VSDs use images with interactive hotspots embedded in them to directly connect language to real-world contexts which are meaningful to the AAC user. While VSDs can effectively support emergent communicators, their widespread adoption is impacted by how difficult these devices are to configure. We developed a prototype that uses generative AI to automatically suggest initial hotspots on an image to help non-experts efficiently create VSDs. We conducted a within-subjects user study to understand how effective our prototype is in supporting non-expert users, specifically pre-service speech-language pathologists (SLP) who are not familiar with VSDs as an AAC intervention. Pre-service SLPs are actively studying to become clinically certified SLPs and have domain-specific knowledge about language and communication skill development. We evaluated the effectiveness of our prototype based on creation time, quality, and user confidence. We also analyzed the relevance and developmental appropriateness of the automatically generated hotspots and how often users interacted with the generated hotspots. Our results were mixed with SLPs becoming more efficient and confident. However, there were multiple negative impacts as well, including over-reliance and homogenization of communication options. The implications of these findings reach beyond the domain of AAC, especially as generative AI becomes more prevalent across domains, including assistive technology. Future work is needed to further identify and address these risks associated with integrating generative AI into assistive technology.
Prompt template for a fictitious LLM agent in a content-flagging experiment
Venue: arXiv cs.CY 2025
Year: 2025

Digital regulations such as the European Union's Digital Services Act (DSA) represent major efforts to shape human-centered and human rights-based frameworks for society. Yet, as these laws are translated into practice, challenges emerge at the intersection of technology, law, and design. This paper presents a qualitative case study examining how designers act as mediators between abstract legal requirements and real-world digital experiences for users, focusing on the design of content reporting mechanisms under Article 16 of the DSA. Through an expert workshop with professional designers from diverse fields (N=9), we explore how legal obligations are interpreted by designers and reflected in discussions and design solutions. Our findings resonate with previous research on the design of reporting mechanisms and dark patterns, highlighting how UX design choices can mislead or hinder users' decision-making and therefore also highlighting the crucial role of design decisions. We show how participatory design methods can bridge disciplinary divides, making legal obligations accessible in compliance fostering design solutions. By using legal design as a lens, we argue that the co-creation of digital regulations and user experience is a core site for digital humanism; where designers, engineers, and legal scholars must collaborate to ensure that systems uphold legal standards to address the challenge the regulation poses to these disciplines.
Aether Weaver: Multimodal Affective Narrative Co-Generation with Dynamic Scene Graphs
Venue: arXiv cs.CV 2025
Year: 2025

We introduce Aether Weaver, a novel, integrated framework for multimodal narrative co-generation that overcomes limitations of sequential text-to-visual pipelines. Our system concurrently synthesizes textual narratives, dynamic scene graph representations, visual scenes, and affective soundscapes, driven by a tightly integrated, co-generation mechanism. At its core, the Narrator, a large language model, generates narrative text and multimodal prompts, while the Director acts as a dynamic scene graph manager, and analyzes the text to build and maintain a structured representation of the story's world, ensuring spatio-temporal and relational consistency for visual rendering and subsequent narrative generation. Additionally, a Narrative Arc Controller guides the high-level story structure, influencing multimodal affective consistency, further complemented by an Affective Tone Mapper that ensures congruent emotional expression across all modalities. Through qualitative evaluations on a diverse set of narrative prompts encompassing various genres, we demonstrate that Aether Weaver significantly enhances narrative depth, visual fidelity, and emotional resonance compared to cascaded baseline approaches. This integrated framework provides a robust platform for rapid creative prototyping and immersive storytelling experiences.
Cardiovascular Disease Prediction using Machine Learning: A Comparative Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Cardiovascular diseases (CVDs) are a main cause of mortality globally, accounting for 31% of all deaths. This study involves a cardiovascular disease (CVD) dataset comprising 68,119 records to explore the influence of numerical (age, height, weight, blood pressure, BMI) and categorical gender, cholesterol, glucose, smoking, alcohol, activity) factors on CVD occurrence. We have performed statistical analyses, including t-tests, Chi-square tests, and ANOVA, to identify strong associations between CVD and elderly people, hypertension, higher weight, and abnormal cholesterol levels, while physical activity (a protective factor). A logistic regression model highlights age, blood pressure, and cholesterol as primary risk factors, with unexpected negative associations for smoking and alcohol, suggesting potential data issues. Model performance comparisons reveal CatBoost as the top performer with an accuracy of 0.734 and an ECE of 0.0064 and excels in probabilistic prediction (Brier score = 0.1824). Data challenges, including outliers and skewed distributions, indicate a need for improved preprocessing to enhance predictive reliability.
Leveraging LLMs for Persona-Based Visualization of Election Data
Venue: arXiv cs.HC 2025
Year: 2025

Visualizations are essential tools for disseminating information regarding elections and their outcomes, potentially influencing public perceptions. Personas, delineating distinctive segments within the populace, furnish a valuable framework for comprehending the nuanced perspectives, requisites, and behaviors of diverse voter demographics. In this work, we propose making visualizations tailored to these personas to make election information easier to understand and more relevant. Using data from UK parliamentary elections and new developments in Large Language Models (LLMs), we create personas that encompass the diverse demographics, technological preferences, voting tendencies, and information consumption patterns observed among voters.Subsequently, we elucidate how these personas can inform the design of visualizations through specific design criteria. We then provide illustrative examples of visualization prototypes based on these criteria and evaluate these prototypes using these personas and LLMs. We finally propose some actionable insights based upon the framework and the different design artifacts.