OpenRaiser/Intern-Atlas huggingface dataset:
Exploring Multimodal Prompt for Visualization Authoring with Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

Recent advances in large language models (LLMs) have shown great potential in automating the process of visualization authoring through simple natural language utterances. However, instructing LLMs using natural language is limited in precision and expressiveness for conveying visualization intent, leading to misinterpretation and time-consuming iterations. To address these limitations, we conduct an empirical study to understand how LLMs interpret ambiguous or incomplete text prompts in the context of visualization authoring, and the conditions making LLMs misinterpret user intent. Informed by the findings, we introduce visual prompts as a complementary input modality to text prompts, which help clarify user intent and improve LLMs' interpretation abilities. To explore the potential of multimodal prompting in visualization authoring, we design VisPilot, which enables users to easily create visualizations using multimodal prompts, including text, sketches, and direct manipulations on existing visualizations. Through two case studies and a controlled user study, we demonstrate that VisPilot provides a more intuitive way to create visualizations without affecting the overall task efficiency compared to text-only prompting approaches. Furthermore, we analyze the impact of text and visual prompts in different visualization tasks. Our findings highlight the importance of multimodal prompting in improving the usability of LLMs for visualization authoring. We discuss design implications for future visualization systems and provide insights into how multimodal prompts can enhance human-AI collaboration in creative visualization tasks. All materials are available at https://OSF.IO/2QRAK.
A near-linear time exact algorithm for the $L_1$-geodesic Fréchet distance between two curves on the boundary of a simple polygon
Venue: arXiv cs.CG 2025
Year: 2025

Let $P$ be a polygon with $k$ vertices. Let $R$ and $B$ be two simple, interior disjoint curves on the boundary of $P$, with $n$ and $m$ vertices. We show how to compute the Fréchet distance between $R$ and $B$ using the geodesic $L_1$-distance in $P$ in $\mathcal{O}(k \log nm + (n+m) (\log^2 nm \log k + \log^4 nm))$ time.
Self-Mixing Laser Interferometry: In Search of an Ambient Noise-Resilient Alternative to Acoustic Sensing
Venue: arXiv cs.RO 2025
Year: 2025

Self-mixing interferometry (SMI) has been lauded for its sensitivity in detecting microvibrations, while requiring no physical contact with its target. Microvibrations, i.e., sounds, have recently been used as a salient indicator of extrinsic contact in robotic manipulation. In previous work, we presented a robotic fingertip using SMI for extrinsic contact sensing as an ambient-noise-resilient alternative to acoustic sensing. Here, we extend the validation experiments to the frequency domain. We find that for broadband ambient noise, SMI still outperforms acoustic sensing, but the difference is less pronounced than in time-domain analyses. For targeted noise disturbances, analogous to multiple robots simultaneously collecting data for the same task, SMI is still the clear winner. Lastly, we show how motor noise affects SMI sensing more so than acoustic sensing, and that a higher SMI readout frequency is important for future work. Design and data files are available at https://github.com/RemkoPr/icra2025-SMI-tactile-sensing.
MLEP: Multi-granularity Local Entropy Patterns for Universal AI-generated Image Detection
Venue: arXiv cs.CV 2025
Year: 2025

Advancements in image generation technologies have raised significant concerns about their potential misuse, such as producing misinformation and deepfakes. Therefore, there is an urgent need for effective methods to detect AI-generated images (AIGI). Despite progress in AIGI detection, achieving reliable performance across diverse generation models and scenes remains challenging due to the lack of source-invariant features and limited generalization capabilities in existing methods. In this work, we explore the potential of using image entropy as a cue for AIGI detection and propose Multi-granularity Local Entropy Patterns (MLEP), a set of entropy feature maps computed across shuffled small patches over multiple image scaled. MLEP comprehensively captures pixel relationships across dimensions and scales while significantly disrupting image semantics, reducing potential content bias. Leveraging MLEP, a robust CNN-based classifier for AIGI detection can be trained. Extensive experiments conducted in an open-world scenario, evaluating images synthesized by 32 distinct generative models, demonstrate significant improvements over state-of-the-art methods in both accuracy and generalization.
Fragile Watermarking for Image Certification Using Deep Steganographic Embedding
Venue: arXiv cs.CV 2025
Year: 2025

Modern identity verification systems increasingly rely on facial images embedded in biometric documents such as electronic passports. To ensure global interoperability and security, these images must comply with strict standards defined by the International Civil Aviation Organization (ICAO), which specify acquisition, quality, and format requirements. However, once issued, these images may undergo unintentional degradations (e.g., compression, resizing) or malicious manipulations (e.g., morphing) and deceive facial recognition systems. In this study, we explore fragile watermarking, based on deep steganographic embedding as a proactive mechanism to certify the authenticity of ICAO-compliant facial images. By embedding a hidden image within the official photo at the time of issuance, we establish an integrity marker that becomes sensitive to any post-issuance modification. We assess how a range of image manipulations affects the recovered hidden image and show that degradation artifacts can serve as robust forensic cues. Furthermore, we propose a classification framework that analyzes the revealed content to detect and categorize the type of manipulation applied. Our experiments demonstrate high detection accuracy, including cross-method scenarios with multiple deep steganography-based models. These findings support the viability of fragile watermarking via steganographic embedding as a valuable tool for biometric document integrity verification.
Decoding Vision Transformers: the Diffusion Steering Lens
Venue: arXiv cs.CV 2025
Year: 2025

Logit Lens is a widely adopted method for mechanistic interpretability of transformer-based language models, enabling the analysis of how internal representations evolve across layers by projecting them into the output vocabulary space. Although applying Logit Lens to Vision Transformers (ViTs) is technically straightforward, its direct use faces limitations in capturing the richness of visual representations. Building on the work of Toker et al. (2024)~\cite{Toker2024-ve}, who introduced Diffusion Lens to visualize intermediate representations in the text encoders of text-to-image diffusion models, we demonstrate that while Diffusion Lens can effectively visualize residual stream representations in image encoders, it fails to capture the direct contributions of individual submodules. To overcome this limitation, we propose \textbf{Diffusion Steering Lens} (DSL), a novel, training-free approach that steers submodule outputs and patches subsequent indirect contributions. We validate our method through interventional studies, showing that DSL provides an intuitive and reliable interpretation of the internal processing in ViTs.
Internal noise in hardware deep and recurrent neural networks helps with learning
Venue: arXiv cs.NE 2025
Year: 2025

Recently, the field of hardware neural networks has been actively developing, where neurons and their connections are not simulated on a computer but are implemented at the physical level, transforming the neural network into a tangible device. In this paper, we investigate how internal noise during the training of neural networks affects the final performance of recurrent and deep neural networks. We consider feedforward networks (FNN) and echo state networks (ESN) as examples. The types of noise examined originated from a real optical implementation of a neural network. However, these types were subsequently generalized to enhance the applicability of our findings on a broader scale. The noise types considered include additive and multiplicative noise, which depend on how noise influences each individual neuron, and correlated and uncorrelated noise, which pertains to the impact of noise on groups of neurons (such as the hidden layer of FNNs or the reservoir of ESNs). In this paper, we demonstrate that, in most cases, both deep and echo state networks benefit from internal noise during training, as it enhances their resilience to noise. Consequently, the testing performance at the same noise intensities is significantly higher for networks trained with noise than for those trained without it. Notably, only multiplicative correlated noise during training has minimal has almost no impact on both deep and recurrent networks.
Toward Portable GPU Performance: Julia Recursive Implementation of TRMM and TRSM
Venue: arXiv cs.MS 2025
Year: 2025

This paper presents a performant and portable recursive implementation of triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM) in Julia for GPUs, two kernels that underlie many linear-algebra algorithms. We restructure TRMM and TRSM so that most work is executed as general matrix-matrix multiplication (GEMM), improving use of the GPU memory hierarchy and reducing latency. Exploiting Julia's multiple dispatch and metaprogramming together with the GPUArrays and KernelAbstractions frameworks, we expose a single hardware-agnostic API that runs on NVIDIA, AMD, and Apple Silicon GPUs. For large matrices the recursive code reaches throughput comparable to vendor libraries such as cuBLAS and rocBLAS, while providing these routines on Apple Silicon for the first time. The entire implementation is only a few hundred lines of code, showing that unified Julia programs can deliver near-vendor performance across heterogeneous architectures.
Parameter-Efficient Continual Fine-Tuning: A Survey
Venue: arXiv cs.LG 2025
Year: 2025

The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented performance. However, these models inherit a fundamental limitation from traditional Machine Learning approaches: their strong dependence on the \textit{i.i.d.} assumption hinders their adaptability to dynamic learning scenarios. We believe the next breakthrough in AI lies in enabling efficient adaptation to evolving environments -- such as the real world -- where new data and tasks arrive sequentially. This challenge defines the field of Continual Learning (CL), a Machine Learning paradigm focused on developing lifelong learning neural models. One alternative to efficiently adapt these large-scale models is known Parameter-Efficient Fine-Tuning (PEFT). These methods tackle the issue of adapting the model to a particular data or scenario by performing small and efficient modifications, achieving similar performance to full fine-tuning. However, these techniques still lack the ability to adjust the model to multiple tasks continually, as they suffer from the issue of Catastrophic Forgetting. In this survey, we first provide an overview of CL algorithms and PEFT methods before reviewing the state-of-the-art on Parameter-Efficient Continual Fine-Tuning (PECFT). We examine various approaches, discuss evaluation metrics, and explore potential future research directions. Our goal is to highlight the synergy between CL and Parameter-Efficient Fine-Tuning, guide researchers in this field, and pave the way for novel future research directions.
Audit Cards: Contextualizing AI Evaluations
Venue: arXiv cs.CY 2025
Year: 2025

AI governance frameworks increasingly rely on audits, yet the results of their underlying evaluations require interpretation and context to be meaningfully informative. Even technically rigorous evaluations can offer little useful insight if reported selectively or obscurely. Current literature focuses primarily on technical best practices, but evaluations are an inherently sociotechnical process, and there is little guidance on reporting procedures and context. Through literature review, stakeholder interviews, and analysis of governance frameworks, we propose "audit cards" to make this context explicit. We identify six key types of contextual features to report and justify in audit cards: auditor identity, evaluation scope, methodology, resource access, process integrity, and review mechanisms. Through analysis of existing evaluation reports, we find significant variation in reporting practices, with most reports omitting crucial contextual information such as auditors' backgrounds, conflicts of interest, and the level and type of access to models. We also find that most existing regulations and frameworks lack guidance on rigorous reporting. In response to these shortcomings, we argue that audit cards can provide a structured format for reporting key claims alongside their justifications, enhancing transparency, facilitating proper interpretation, and establishing trust in reporting.
VoxLogicA UI: Supporting Declarative Medical Image Analysis
Venue: arXiv cs.HC 2025
Year: 2025

This Master's Thesis in Computer Science dives into the design and creation of a user-friendly interface for VoxLogicA, an image analysis tool using spatial model checking with a focus on neuroimaging. The research tackles the problem of existing tools being too complex, which makes them hard for medical professionals and researchers to use. By using spatial logic, the goal is to make these powerful analytical tools more practical and accessible in real-world clinical settings. The main objectives are to design a modern web interface that's easy to use, build it with the latest web technologies (e.g. Svelte and Niivue), and test its effectiveness through user studies and real-world case analyses.
A Pandemic for the Good of Digital Literacy? An Empirical Investigation of Newly Improved Digital Skills during COVID-19 Lockdowns
Venue: arXiv cs.HC 2025
Year: 2025

This research explores whether the rapid digital transformation due to COVID-19 managed to close or exacerbate the digital divide concerning users' digital skills. We conducted a pre-registered survey with N = 1143 German Internet users. Our findings suggest the latter: younger, male, and higher educated users were more likely to improve their digital skills than older, female, and less educated ones. According to their accounts, the pandemic helped Internet users improve their skills in communicating with others by using video conference software and reflecting critically upon information they found online. These improved digital skills exacerbated not only positive (e.g., feeling informed and safe) but also negative (e.g., feeling lonely) effects of digital media use during the pandemic. We discuss this research's theoretical and practical implications regarding the impact of challenges, such as technological disruption and health crises, on humans' digital skills, capabilities, and future potential, focusing on the second-level digital divide.
DoYouTrustAI: A Tool to Teach Students About AI Misinformation and Prompt Engineering
Venue: arXiv cs.HC 2025
Year: 2025

AI, especially Large Language Models (LLMs) like ChatGPT, have rapidly developed and gained widespread adoption in the past five years, shifting user preference from traditional search engines. However, the generative nature of LLMs raises concerns about presenting misinformation as fact. To address this, we developed a web-based application that helps K-12 students enhance critical thinking by identifying misleading information in LLM responses about major historical figures. In this paper, we describe the implementation and design details of the DoYouTrustAI tool, which can be used to provide an interactive lesson which teaches students about the dangers of misinformation and how believable generative AI can make it seem. The DoYouTrustAI tool utilizes prompt engineering to present the user with AI generated summaries about the life of a historical figure. These summaries can be either accurate accounts of that persons life, or an intentionally misleading alteration of their history. The user is tasked with determining the validity of the statement without external resources. Our research questions for this work were:(RQ1) How can we design a tool that teaches students about the dangers of misleading information and of how misinformation can present itself in LLM responses? (RQ2) Can we present prompt engineering as a topic that is easily understandable for students? Our findings highlight the need to correct misleading information before users retain it. Our tool lets users select familiar individuals for testing to reduce random guessing and presents misinformation alongside known facts to maintain believability. It also provides pre-configured prompt instructions to show how different prompts affect AI responses. Together, these features create a controlled environment where users learn the importance of verifying AI responses and understanding prompt engineering.
Utsarjan: A smartphone App for providing kidney care and real-time assistance to children with nephrotic syndrome
Venue: arXiv cs.HC 2025
Year: 2025

Background Telemedicine has the potential to provide secure and cost-effective healthcare at the touch of a button. Nephrotic syndrome is a chronic childhood illness involving frequent relapses and demands long/complex treatment. Hence, developing a remote means of doctor-patient interface will ensure the provision of quality healthcare to patients. Methods The Utsarjan mobile App framework was built with Flutter that enables cross-platform development (Android, iOS, Windows) with speed, smoothness, and open-source benefits. The frontend uses Dart for user interaction, while the backend employs Node.js, Express, and NGINX for APIs, load balancing and high performance. MongoDB ensures a flexible database, Bcrypt secures passwords, PM2 handles deployment, uptime and logs, while Firebase Cloud Messaging powers free push notifications. Results Utsarjan (means excretion) is a multi-functional smartphone application for giving nephrotic care and real-time assistance to all patients (especially those in rural regions and/or who do not have access to specialists). It helps patients and doctors by ensuring opportune visits, recording each clinical test/parameter and improving medication adherence. It gives a graphical visualization of relapses, medicine dosage as well as different anthropometric parameters (urine protein, BP, height and weight). This is the first nephrotic care App that enables prompt access to doctor's advice. Conclusions Utsarjan is a mobile App to provide kidney care and real-time assistance to children with nephrotic syndrome. It gives a graphical overview of changes in a patient's health over the long course of treatment. This will assist doctors in appropriately modifying the treatment regimen. Consequently, it will (hopefully) lead to the prevention of relapses and/or complications.
God's Innovation Project -- Empowering The Player With Generative AI
Venue: arXiv cs.HC 2025
Year: 2025

In this paper, we present God's Innovation Project (GIP), a god game where players collect words to dynamically terraform the landscape using generative AI. A god game is a genre where players take on the role of a deity, indirectly influencing Non-Player Characters (NPCs) to perform various tasks. These games typically grant players supernatural abilities, such as terrain manipulation or weather control. Traditional god games rely on predefined environments and mechanics, typically created by a human designer. In contrast, GIP allows players to shape the game world procedurally through text-based input. Using a lightweight generative AI model, we create a gamified pipeline which transforms the player's text prompts into playable game terrain in real time. To evaluate the impact of this AI-driven mechanic, we conduct a user study analyzing how players interacted with and experienced the system. Our findings provide insights into player engagement, the effectiveness of AI-generated terrain, and the role of generative AI as an interactive game mechanic.
A discrete physics-informed training for projection-based reduced order models with neural networks
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents a physics-informed training framework for projection-based Reduced Order Models (ROMs). We extend the PROM-ANN architecture by complementing snapshot-based training with a FEM-based, discrete physics-informed residual loss, bridging the gap between traditional projection-based ROMs and physics-informed neural networks (PINNs). Unlike conventional PINNs that rely on analytical PDEs, our approach leverages FEM residuals to guide the learning of the ROM approximation manifold. Key contributions include: (1) a parameter-agnostic, discrete residual loss applicable to non-linear problems, (2) an architectural modification to PROM-ANN improving accuracy for fast-decaying singular values, and (3) an empirical study on the proposed physics informed training process for ROMs. The method is demonstrated on a non-linear hyperelasticity problem, simulating a rubber cantilever under multi-axial loads. The main accomplishment in regards to the proposed residual-based loss is its applicability on non-linear problems by interfacing with FEM software while maintaining reasonable training times. The modified PROM-ANN outperforms POD by orders of magnitude in snapshot reconstruction accuracy, while the original formulation is not able to learn a proper mapping for this use-case. Finally, the application of physics informed training in ANN-PROM modestly narrows the gap between data reconstruction and ROM accuracy, however it highlights the untapped potential of the proposed residual-driven optimization for future ROM development. This work underscores the critical role of FEM residuals in ROM construction and calls for further exploration on architectures beyond PROM-ANN.
User Satisfaction -- UX Design Strategies for Seamless Virtual Experience
Venue: arXiv cs.HC 2025
Year: 2025

User Experience (UX) in virtual worlds is a fast-developing discipline that requires creative design concepts to overcome the divide between physical and virtual interaction. This research investigates primary principles and techniques to improve UX in virtual experiences based on usability, accessibility, user engagement, and technology advancements. It gives detailed insight into trends, issues, and prospects for UX design of virtual applications that guarantee an efficient, easy-to-use, and immersive experience.