OpenRaiser/Intern-Atlas huggingface dataset:
Rescue Operators' Perspectives on KIRETT Wearable Technology: A Qualitative Study
Venue: arXiv cs.HC 2025
Year: 2025

In emergencies, treatment needs to be fast, accu-rate and patient-specific. For instance, in emergency scenarios, obstacles like treatment environments and medical difficulties can lead to bad outcomes for patients. Additionally, a drastic change of health vitals can force paramedics to shift to a different treatment in the ongoing treatment of the patient in order to save a patient's life. The KIRETT (engl.: 'Artificial intelligence in rescue operations') demonstrator is developed to provide a rescue operator with a wrist-worn device, enabling treatment recommendation (with the help of knowledge graph) with situation detection models to improve the emergency treatment of a patient. This paper aims to provide a qualitative evaluation of the 2-days testing in the KIRETT project with the focus of knowledge graphs, knowledge fusion, and user-experience-design (UX-design).
SemShareKV: Efficient KVCache Sharing for Semantically Similar Prompts via Token-Level LSH Matching
Venue: arXiv cs.CL 2025
Year: 2025

As large language models (LLMs) continue to scale, the memory footprint of key-value (KV) caches during inference has become a significant bottleneck. Existing approaches primarily focus on compressing KV caches within a single prompt or reusing shared prefixes or frequently ocurred text segments across prompts. However, such strategies are limited in scenarios where prompts are semantically similar but lexically different, which frequently occurs in tasks such as multi-document summarization and conversational agents. We propose \textit{SemShareKV}, a KV cache sharing and compression framework that accelerates LLM inference by reusing KVCache in semantically similar prompts. Instead of relying on exact token matches, SemShareKV applies fuzzy token matching using locality-sensitive hashing (LSH) on token embeddings and incorporates Rotary Position Embedding (RoPE) to better preserve positional information. By selectively reusing relevant key-value pairs from a reference prompt's cache, SemShareKV reduces redundant computation while maintaining output quality. Experiments on diverse summarization datasets show up to 6.25$\times$ speedup and 42\% lower GPU memory usage with 5k tokens input, with negligible quality degradation. These results highlight the potential of semantic-aware cache sharing for efficient LLM inference.
A Hierarchical Error Framework for Reliable Automated Coding in Communication Research: Applications to Health and Political Communication
Venue: arXiv cs.CL 2025
Year: 2025

Automated content analysis increasingly supports communication research, yet scaling manual coding into computational pipelines raises concerns about measurement reliability and validity. We introduce a Hierarchical Error Correction (HEC) framework that treats model failures as layered measurement errors (knowledge gaps, reasoning limitations, and complexity constraints) and targets the layers that most affect inference. The framework implements a three-phase methodology: systematic error profiling across hierarchical layers, targeted intervention design matched to dominant error sources, and rigorous validation with statistical testing. Evaluating HEC across health communication (medical specialty classification) and political communication (bias detection), and legal tasks, we validate the approach with five diverse large language models. Results show average accuracy gains of 11.2 percentage points (p < .001, McNemar's test) and stable conclusions via reduced systematic misclassification. Cross-model validation demonstrates consistent improvements (range: +6.8 to +14.6pp), with effectiveness concentrated in moderate-to-high baseline tasks (50-85% accuracy). A boundary study reveals diminished returns in very high-baseline (>85%) or precision-matching tasks, establishing applicability limits. We map layered errors to threats to construct and criterion validity and provide a transparent, measurement-first blueprint for diagnosing error profiles, selecting targeted interventions, and reporting reliability/validity evidence alongside accuracy. This applies to automated coding across communication research and the broader social sciences.
Blockchain-Driven Federation for Distributed Edge Systems: Design and Experimental Validation
Venue: arXiv cs.NI 2025
Year: 2025

Edge computing brings computation near end users, enabling the provisioning of novel use cases. To satisfy end-user requirements, the concept of edge federation has recently emerged as a key mechanism for dynamic resources and services sharing across edge systems managed by different administrative domains. However, existing federation solutions often rely on pre-established agreements and face significant limitations, including operational complexity, delays caused by manual operations, high overhead costs, and dependence on trusted third parties. In this context, blockchain can create dynamic federation agreements that enable service providers to securely interact and share services without prior trust. This article first describes the problem of edge federation, using the standardized ETSI multi-access edge computing framework as a reference architecture, and how it is being addressed. Then, it proposes a novel solution using blockchain and smart contracts to enable distributed MEC systems to dynamically negotiate and execute federation in a secure, automated, and scalable manner. We validate our framework's feasibility through a performance evaluation using a private Ethereum blockchain, built on the open-source Hyperledger Besu platform. The testbed includes a large number of MEC systems and compares two blockchain consensus algorithms. Experimental results demonstrate that our solution automates the entire federation lifecycle-from negotiation to deployment-with a quantifiable overhead, achieving federation in approximately 18 seconds in a baseline scenario. The framework scales efficiently in concurrent request scenarios, where multiple MEC systems initiate federation requests simultaneously. This approach provides a promising direction for addressing the complexities of dynamic, multi-domain federations across the edge-to-cloud continuum.
Enhanced Automatic Drum Transcription via Drum Stem Source Separation
Venue: arXiv cs.SD 2025
Year: 2025

Automatic Drum Transcription (ADT) remains a challenging task in MIR but recent advances allow accurate transcription of drum kits with up 5 classes - kick, snare, hi-hats, toms and cymbals - via the ADTOF package. In addition, several drum kit \emph{stem} separation models in the open source community support separation for more than 6 stem classes, including distinct crash and ride cymbals. In this work we explore the benefits of combining these tools to improve the realism of drum transcriptions. We describe a simple post-processing step which expands the transcription output from five to seven classes and furthermore, we are able to estimate MIDI velocity values based on the separated stems. Our solution achieves strong performance when assessed against a baseline of 8-class drum transcription and produces realistic MIDI transcriptions suitable for MIR or music production tasks.
Beyond the Hook: Predicting Billboard Hot 100 Chart Inclusion with Machine Learning from Streaming, Audio Signals, and Perceptual Features
Venue: arXiv cs.LG 2025
Year: 2025

The advent of digital streaming platforms have recently revolutionized the landscape of music industry, with the ensuing digitalization providing structured data collections that open new research avenues for investigating popularity dynamics and mainstream success. The present work explored which determinants hold the strongest predictive influence for a track's inclusion in the Billboard Hot 100 charts, including streaming popularity, measurable audio signal attributes, and probabilistic indicators of human listening. The analysis revealed that popularity was by far the most decisive predictor of Billboard Hot 100 inclusion, with considerable contribution from instrumentalness, valence, duration and speechiness. Logistic Regression achieved 90.0% accuracy, with very high recall for charting singles (0.986) but lower recall for non-charting ones (0.813), yielding balanced F1-scores around 0.90. Random Forest slightly improved performance to 90.4% accuracy, maintaining near-perfect precision for non-charting singles (0.990) and high recall for charting ones (0.992), with F1-scores up to 0.91. Gradient Boosting (XGBoost) reached 90.3% accuracy, delivering a more balanced trade-off by improving recall for non-charting singles (0.837) while sustaining high recall for charting ones (0.969), resulting in F1-scores comparable to the other models.
Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation
Venue: arXiv cs.LG 2025
Year: 2025

Uncertainty quantification is essential in human-machine collaboration, as human agents tend to adjust their decisions based on the confidence of the machine counterpart. Reliably calibrated model uncertainties, hence, enable more effective collaboration, targeted expert intervention and more responsible usage of Machine Learning (ML) systems. Conformal prediction has become a well established model-agnostic framework for uncertainty calibration of ML models, offering statistically valid confidence estimates for both regression and classification tasks. In this work, we apply conformal prediction to $\textit{SoilNet}$, a multimodal multitask model for describing soil profiles. We design a simulated human-in-the-loop (HIL) annotation pipeline, where a limited budget for obtaining ground truth annotations from domain experts is available when model uncertainty is high. Our experiments show that conformalizing SoilNet leads to more efficient annotation in regression tasks and comparable performance scores in classification tasks under the same annotation budget when tested against its non-conformal counterpart. All code and experiments can be found in our repository: https://github.com/calgo-lab/BGR
Environment-Aware Satellite Image Generation with Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion-based foundation models have recently garnered much attention in the field of generative modeling due to their ability to generate images of high quality and fidelity. Although not straightforward, their recent application to the field of remote sensing signaled the first successful trials towards harnessing the large volume of publicly available datasets containing multimodal information. Despite their success, existing methods face considerable limitations: they rely on limited environmental context, struggle with missing or corrupted data, and often fail to reliably reflect user intentions in generated outputs. In this work, we propose a novel diffusion model conditioned on environmental context, that is able to generate satellite images by conditioning from any combination of three different control signals: a) text, b) metadata, and c) visual data. In contrast to previous works, the proposed method is i) to our knowledge, the first of its kind to condition satellite image generation on dynamic environmental conditions as part of its control signals, and ii) incorporating a metadata fusion strategy that models attribute embedding interactions to account for partially corrupt and/or missing observations. Our method outperforms previous methods both qualitatively (robustness to missing metadata, higher responsiveness to control inputs) and quantitatively (higher fidelity, accuracy, and quality of generations measured using 6 different metrics) in the trials of single-image and temporal generation. The reported results support our hypothesis that conditioning on environmental context can improve the performance of foundation models for satellite imagery, and render our model a promising candidate for usage in downstream tasks. The collected 3-modal dataset is to our knowledge, the first publicly-available dataset to combine data from these three different mediums.
Vehicle Classification under Extreme Imbalance: A Comparative Study of Ensemble Learning and CNNs
Venue: arXiv cs.CV 2025
Year: 2025

Accurate vehicle type recognition underpins intelligent transportation and logistics, but severe class imbalance in public datasets suppresses performance on rare categories. We curate a 16-class corpus (~47k images) by merging Kaggle, ImageNet, and web-crawled data, and create six balanced variants via SMOTE oversampling and targeted undersampling. Lightweight ensembles, such as Random Forest, AdaBoost, and a soft-voting combiner built on MobileNet-V2 features are benchmarked against a configurable ResNet-style CNN trained with strong augmentation and label smoothing. The best ensemble (SMOTE-combined) attains 74.8% test accuracy, while the CNN achieves 79.19% on the full test set and 81.25% on an unseen inference batch, confirming the advantage of deep models. Nonetheless, the most under-represented class (Barge) remains a failure mode, highlighting the limits of rebalancing alone. Results suggest prioritizing additional minority-class collection and cost-sensitive objectives (e.g., focal loss) and exploring hybrid ensemble or CNN pipelines to combine interpretability with representational power.
Meta-Learning Theory-Informed Inductive Biases using Deep Kernel Gaussian Processes
Venue: arXiv cs.AI 2025
Year: 2025

Normative and task-driven theories offer powerful top-down explanations for biological systems, yet the goals of quantitatively arbitrating between competing theories, and utilizing them as inductive biases to improve data-driven fits of real biological datasets are prohibitively laborious, and often impossible. To this end, we introduce a Bayesian meta-learning framework designed to automatically convert raw functional predictions from normative theories into tractable probabilistic models. We employ adaptive deep kernel Gaussian processes, meta-learning a kernel on synthetic data generated from a normative theory. This Theory-Informed Kernel specifies a probabilistic model representing the theory predictions -- usable for both fitting data and rigorously validating the theory. As a demonstration, we apply our framework to the early visual system, using efficient coding as our normative theory. We show improved response prediction accuracy in ex vivo recordings of mouse retinal ganglion cells stimulated by natural scenes compared to conventional data-driven baselines, while providing well-calibrated uncertainty estimates and interpretable representations. Using exact Bayesian model selection, we also show that our informed kernel can accurately infer the degree of theory-match from data, confirming faithful encapsulation of theory structure. This work provides a more general, scalable, and automated approach for integrating theoretical knowledge into data-driven scientific inquiry in neuroscience and beyond.
KIRETT -- A wearable device to support rescue operations using artificial intelligence to improve first aid
Venue: arXiv cs.AI 2025
Year: 2025

This short paper presents first steps in the scientific part of the KIRETT project, which aims to improve first aid during rescue operations using a wearable device. The wearable is used for computer-aided situation recognition by means of artificial intelligence. It provides contextual recommendations for actions and operations to rescue personnel and is intended to minimize damage to patients due to incorrect treatment, as well as increase the probability of survival. The paper describes a first overview of research approaches within the project.
Experimental Study of Magnetic Near-Field Microstrip Electronic Probe for PCB EMC Emission Measurement
Venue: arXiv cs.NI 2025
Year: 2025

An experimental study on magnetic near-field (NF) scanning of printed circuit board (PCB) emission radiation is developed in this paper. The design and installation of the electromagnetic (EM) NF scanner is introduced. The test bed of magnetic NF emission in the microwave frequency range is described. The methodology of the microstrip magnetic NF probe is discussed. The probe calibration process was performed following the IEC 61967-1 NF scanning standard. The NF scanner functioning is tested with passive microstrip circuit square loop probe and device under test (DUT) PCB radiation in the test plan positioned at 1-mm above the ground plane. Based on the standard test with I-shape 50-$Ω$ transmission line (TL), the calibration process of radiated magnetic field was validated by comparison between HFSS__ simulation and experimentation in very wideband frequency from 0.1-GHz to 3-GHz. Then, a nonstandard TL based DUT was experimented. Accordingly, the cartographies of scanned magnetic NF at two different test frequencies, 2 GHz and 3 GHz, are discussed. The NF scanner is under development for targeting the EMC radiated emission of PCB dedicated to operate in 6G wireless communication.
MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Generative robot policies such as Flow Matching offer flexible, multi-modal policy learning but are sample-inefficient. Although object-centric policies improve sample efficiency, it does not resolve this limitation. In this work, we propose Multi-Stream Generative Policy (MSG), an inference-time composition framework that trains multiple object-centric policies and combines them at inference to improve generalization and sample efficiency. MSG is model-agnostic and inference-only, hence widely applicable to various generative policies and training paradigms. We perform extensive experiments both in simulation and on a real robot, demonstrating that our approach learns high-quality generative policies from as few as five demonstrations, resulting in a 95% reduction in demonstrations, and improves policy performance by 89 percent compared to single-stream approaches. Furthermore, we present comprehensive ablation studies on various composition strategies and provide practical recommendations for deployment. Finally, MSG enables zero-shot object instance transfer. We make our code publicly available at https://msg.cs.uni-freiburg.de.
SemanticShield: LLM-Powered Audits Expose Shilling Attacks in Recommender Systems
Venue: arXiv cs.CL 2025
Year: 2025

Recommender systems (RS) are widely used in e-commerce for personalized suggestions, yet their openness makes them susceptible to shilling attacks, where adversaries inject fake behaviors to manipulate recommendations. Most existing defenses emphasize user-side behaviors while overlooking item-side features such as titles and descriptions that can expose malicious intent. To address this gap, we propose a two-stage detection framework that integrates item-side semantics via large language models (LLMs). The first stage pre-screens suspicious users using low-cost behavioral criteria, and the second stage employs LLM-based auditing to evaluate semantic consistency. Furthermore, we enhance the auditing model through reinforcement fine-tuning on a lightweight LLM with carefully designed reward functions, yielding a specialized detector called SemanticShield. Experiments on six representative attack strategies demonstrate the effectiveness of SemanticShield against shilling attacks, and further evaluation on previously unseen attack methods shows its strong generalization capability. Code is available at https://github.com/FrankenstLee/SemanticShield.
PanoWorld-X: Generating Explorable Panoramic Worlds via Sphere-Aware Video Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Generating a complete and explorable 360-degree visual world enables a wide range of downstream applications. While prior works have advanced the field, they remain constrained by either narrow field-of-view limitations, which hinder the synthesis of continuous and holistic scenes, or insufficient camera controllability that restricts free exploration by users or autonomous agents. To address this, we propose PanoWorld-X, a novel framework for high-fidelity and controllable panoramic video generation with diverse camera trajectories. Specifically, we first construct a large-scale dataset of panoramic video-exploration route pairs by simulating camera trajectories in virtual 3D environments via Unreal Engine. As the spherical geometry of panoramic data misaligns with the inductive priors from conventional video diffusion, we then introduce a Sphere-Aware Diffusion Transformer architecture that reprojects equirectangular features onto the spherical surface to model geometric adjacency in latent space, significantly enhancing visual fidelity and spatiotemporal continuity. Extensive experiments demonstrate that our PanoWorld-X achieves superior performance in various aspects, including motion range, control precision, and visual quality, underscoring its potential for real-world applications.
Color, Gender, and Bias: Examining the Role of Stereotyped Colors in Visualization-Driven Pay Decisions
Venue: arXiv cs.HC 2025
Year: 2025

We investigate the impact of stereotyped gender-color associations in a visualization-driven decision-making task. In the context of gender data visualization, the well-known "pink for girls and blue for boys" color assignment is associated with stereotypes that could bias readers and decision-makers. Understanding the effects of using stereotyped colors in visualizations for decision-making can help designers better choose colors in stereotype-prone contexts. We therefore explore the potential impact of stereotyped colors on compensation decision-making through two crowdsourced experiments. In these experiments, we evaluate how the association of color with gender (stereotyped vs non-stereotyped) affects the user's allocation decisions in the context of salary adjustments. Our results indicate that explicit expression of the color-gender associations, in the form of a legend on the data visualization, leads to in-group favoritism. However, in the absence of a legend, this in-group favoritism disappears, and a small effect of non-stereotyped colors is observed. A free copy of this paper with all supplemental materials is available at https://osf.io/d4q3v/?view_only=22b636d6f7bb4a7991d9576933b3aaad
MARCOS: Deep Thinking by Markov Chain of Continuous Thoughts
Venue: arXiv cs.LG 2025
Year: 2025

The current paradigm for reasoning in large language models (LLMs) involves models "thinking out loud" via a sequence of tokens, known as chain-of-thought (CoT). This approach, while effective, has several significant drawbacks. Firstly, inference requires autoregressive generation of often thousands of CoT tokens, which is slow and computationally expensive. Secondly, it constrains reasoning to the discrete space of tokens, creating an information bottleneck across reasoning steps. Thirdly, it fundamentally entangles reasoning with token generation, forcing LLMs to "think while speaking," which causes potentially short-sighted reasoning. In light of these limitations, we re-imagine reasoning in LLMs and present a new paradigm: MARCOS. In our approach, rather than autoregressively generating tokens, we model reasoning as a hidden Markov chain of continuous, high-dimensional "thoughts". Each reasoning step involves a transition of the internal thoughts, where explicit reasoning steps (which may consist of hundreds of tokens) serve as observable variables, which are windows to peek into the implicit thoughts. Since this latent process is incompatible with the standard supervised learning, we further propose a two-phase variational training scheme. Our experiments on three benchmarks demonstrate that MARCOS outperforms existing continuous reasoning methods and, for the first time, achieves performance comparable to token-based CoT, even surpassing it by 4.7% on GSM8K with up to 15.7x speedup in inference. Beyond this, MARCOS offers additional advantages, such as step-level instead of token-level control over randomness, opening significant opportunities for reinforcement learning and reasoning in LLMs.