OpenRaiser/Intern-Atlas huggingface dataset:
LaWa: Using Latent Space for In-Generation Image Watermarking
Venue: arXiv cs.CV 2025
Year: 2025

With generative models producing high quality images that are indistinguishable from real ones, there is growing concern regarding the malicious usage of AI-generated images. Imperceptible image watermarking is one viable solution towards such concerns. Prior watermarking methods map the image to a latent space for adding the watermark. Moreover, Latent Diffusion Models (LDM) generate the image in the latent space of a pre-trained autoencoder. We argue that this latent space can be used to integrate watermarking into the generation process. To this end, we present LaWa, an in-generation image watermarking method designed for LDMs. By using coarse-to-fine watermark embedding modules, LaWa modifies the latent space of pre-trained autoencoders and achieves high robustness against a wide range of image transformations while preserving perceptual quality of the image. We show that LaWa can also be used as a general image watermarking method. Through extensive experiments, we demonstrate that LaWa outperforms previous works in perceptual quality, robustness against attacks, and computational complexity, while having very low false positive rate. Code is available here.
Online-Score-Aided Federated Learning: Taming the Resource Constraints in Wireless Networks
Venue: arXiv cs.LG 2025
Year: 2025

While federated learning (FL) is a widely popular distributed machine learning (ML) strategy that protects data privacy, time-varying wireless network parameters and heterogeneous configurations of the wireless devices pose significant challenges. Although the limited radio and computational resources of the network and the clients, respectively, are widely acknowledged, two critical yet often ignored aspects are (a) wireless devices can only dedicate a small chunk of their limited storage for the FL task and (b) new training samples may arrive in an online manner in many practical wireless applications. Therefore, we propose a new FL algorithm called online-score-aided federated learning (OSAFL), specifically designed to learn tasks relevant to wireless applications under these practical considerations. Since clients' local training steps differ under resource constraints, which may lead to client drift under statistically heterogeneous data distributions, we leverage normalized gradient similarities and exploit weighting clients' updates based on optimized scores that facilitate the convergence rate of the proposed OSAFL algorithm without incurring any communication overheads to the clients or requiring any statistical data information from them. We theoretically show how the new factors, i.e., online score and local data distribution shifts, affect the convergence bound and derive the necessary conditions for a sublinear convergence rate. Our extensive simulation results on two different tasks with multiple popular ML models validate the effectiveness of the proposed OSAFL algorithm compared to modified state-of-the-art FL baselines.
Urban Region Pre-training and Prompting: A Graph-based Approach
Venue: arXiv cs.AI 2025
Year: 2025

Urban region representation is crucial for various urban downstream tasks. However, despite the proliferation of methods and their success, acquiring general urban region knowledge and adapting to different tasks remains challenging. Existing work pays limited attention to the fine-grained functional layout semantics in urban regions, limiting their ability to capture transferable knowledge across regions. Further, inadequate handling of the unique features and relationships required for different downstream tasks may also hinder effective task adaptation. In this paper, we propose a $\textbf{G}$raph-based $\textbf{U}$rban $\textbf{R}$egion $\textbf{P}$re-training and $\textbf{P}$rompting framework ($\textbf{GURPP}$) for region representation learning. Specifically, we first construct an urban region graph and develop a subgraph-centric urban region pre-training model to capture the heterogeneous and transferable patterns of entity interactions. This model pre-trains knowledge-rich region embeddings using contrastive learning and multi-view learning methods. To further refine these representations, we design two graph-based prompting methods: a manually-defined prompt to incorporate explicit task knowledge and a task-learnable prompt to discover hidden knowledge, which enhances the adaptability of these embeddings to different tasks. Extensive experiments on various urban region prediction tasks and different cities demonstrate the superior performance of our framework.
Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributes
Venue: arXiv cs.LG 2025
Year: 2025

Discrimination mitigation within machine learning (ML) models could be complicated because multiple factors may be interwoven hierarchically and historically. Yet few existing fairness measures can capture the discrimination level within ML models in the face of multiple sensitive attributes (SAs). To bridge this gap, we propose a fairness measure based on distances between sets from a manifold perspective, named as 'Harmonic Fairness measure via Manifolds (HFM)' with two optional versions, which can deal with a fine-grained discrimination evaluation for several SAs of multiple values. Because directly computing HFM may be costly, to accelerate its subprocedure -- the computation of distances of sets, we further propose two approximation algorithms named 'Approximation of distance between sets for one sensitive attribute with multiple values (ApproxDist)' and 'Approximation of extended distance between sets for several sensitive attributes with multiple values (ExtendDist)' to respectively resolve bias evaluation of one single SA with multiple values and that of several SAs with multiple values. Moreover, we provide an algorithmic effectiveness analysis for ApproxDist under certain assumptions to explain how well it could work. The empirical results demonstrate that our proposed fairness measure HFM is valid and approximation algorithms (i.e. ApproxDist and ExtendDist) are effective and efficient.
A Multi-Year Grey Literature Review on AI-assisted Test Automation
Venue: arXiv cs.SE 2025
Year: 2025

Context: Test Automation (TA) techniques are crucial for quality assurance in software engineering but face limitations such as high test suite maintenance costs and the need for extensive programming skills. Artificial Intelligence (AI) offers new opportunities to address these issues through automation and improved practices. Objectives: Given the prevalent usage of AI in industry, sources of truth are held in grey literature as well as the minds of professionals, stakeholders, developers, and end-users. This study surveys grey literature to explore how AI is adopted in TA, focusing on the problems it solves, its solutions, and the available tools. Additionally, the study gathers expert insights to understand AI's current and future role in TA. Methods: We reviewed over 3,600 grey literature sources over five years, including blogs, white papers, and user manuals, and finally filtered 342 documents to develop taxonomies of TA problems and AI solutions. We also cataloged 100 AI-driven TA tools and interviewed five expert software testers to gain insights into AI's current and future role in TA. Results: The study found that manual test code development and maintenance are the main challenges in TA. In contrast, automated test generation and self-healing test scripts are the most common AI solutions. We identified 100 AI-based TA tools, with Applitools, Testim, Functionize, AccelQ, and Mabl being the most adopted in practice. Conclusion: This paper offers a detailed overview of AI's impact on TA through grey literature analysis and expert interviews. It presents new taxonomies of TA problems and AI solutions, provides a catalog of AI-driven tools, and relates solutions to problems and tools to solutions. Interview insights further revealed the state and future potential of AI in TA. Our findings support practitioners in selecting TA tools and guide future research directions.
Targeted Deep Learning System Boundary Testing
Venue: arXiv cs.SE 2025
Year: 2025

Evaluating the behavioral boundaries of deep learning (DL) systems is crucial for understanding their reliability across diverse, unseen inputs. Existing solutions fall short as they rely on untargeted random, model- or latent-based perturbations, due to difficulties in generating controlled input variations. In this work, we introduce Mimicry, a novel black-box test generator for fine-grained, targeted exploration of DL system boundaries. Mimicry performs boundary testing by leveraging the probabilistic nature of DL outputs to identify promising directions for exploration. It uses style-based GANs to disentangle input representations into content and style components, enabling controlled feature mixing to approximate the decision boundary. We evaluated Mimicry's effectiveness in generating boundary inputs for five widely used DL image classification systems of increasing complexity, comparing it to two baseline approaches. Our results show that Mimicry consistently identifies inputs closer to the decision boundary. It generates semantically meaningful boundary test cases that reveal new functional (mis)behaviors, while the baselines produce mainly corrupted or invalid inputs. Thanks to its enhanced control over latent space manipulations, Mimicry remains effective as dataset complexity increases, maintaining competitive diversity and higher validity rates, confirmed by human assessors.
Comparing Vibrotactile and Skin-Stretch Haptic Feedback for Conveying Spatial Information of Virtual Objects to Blind VR Users
Venue: arXiv cs.HC 2025
Year: 2025

Perceiving spatial information of a virtual object (e.g, direction, distance) is critical yet challenging for blind users seeking an immersive virtual reality (VR) experience. To facilitate VR accessibility for blind users, in this paper, we investigate the effectiveness of two types of haptic cues - vibrotactile and skin-stretch cues - in conveying the spatial information of a virtual object when applied to the dorsal side of a blind user's hand. We conducted a user study with 10 blind users to investigate how they perceive static and moving objects in VR with a custom-made haptic apparatus. Our results reveal that blind users can more accurately understand an object's location and movement when receiving skin-stretch cues, as opposed to vibrotactile cues. We discuss the pros and cons of both types of haptic cues and conclude with design recommendations for future haptic solutions for VR accessibility.
IFShip: Interpretable Fine-grained Ship Classification with Domain Knowledge-Enhanced Vision-Language Models
Venue: arXiv cs.CL 2025
Year: 2025

End-to-end interpretation currently dominates the remote sensing fine-grained ship classification (RS-FGSC) task. However, the inference process remains uninterpretable, leading to criticisms of these models as "black box" systems. To address this issue, we propose a domain knowledge-enhanced Chain-of-Thought (CoT) prompt generation mechanism, which is used to semi-automatically construct a task-specific instruction-following dataset, TITANIC-FGS. By training on TITANIC-FGS, we adapt general-domain vision-language models (VLMs) to the FGSC task, resulting in a model named IFShip. Building upon IFShip, we develop an FGSC visual chatbot that redefines the FGSC problem as a step-by-step reasoning task and conveys the reasoning process in natural language. Experimental results show that IFShip outperforms state-of-the-art FGSC algorithms in both interpretability and classification accuracy. Furthermore, compared to VLMs such as LLaVA and MiniGPT-4, IFShip demonstrates superior performance on the FGSC task. It provides an accurate chain of reasoning when fine-grained ship types are recognizable to the human eye and offers interpretable explanations when they are not. Our dataset is publicly available at: https://github.com/lostwolves/IFShip.
Hierarchical Structured Neural Network: Efficient Retrieval Scaling for Large Scale Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Retrieval, the initial stage of a recommendation system, is tasked with down-selecting items from a pool of tens of millions of candidates to a few thousands. Embedding Based Retrieval (EBR) has been a typical choice for this problem, addressing the computational demands of deep neural networks across vast item corpora. EBR utilizes Two Tower or Siamese Networks to learn representations for users and items, and employ Approximate Nearest Neighbor (ANN) search to efficiently retrieve relevant items. Despite its popularity in industry, EBR faces limitations. The Two Tower architecture, relying on a single dot product interaction, struggles to capture complex data distributions due to limited capability in learning expressive interactions between users and items. Additionally, ANN index building and representation learning for user and item are often separate, leading to inconsistencies exacerbated by representation (e.g. continuous online training) and item drift (e.g. items expired and new items added). In this paper, we introduce the Hierarchical Structured Neural Network (HSNN), an efficient deep neural network model to learn intricate user and item interactions beyond the commonly used dot product in retrieval tasks, achieving sublinear computational costs relative to corpus size. A Modular Neural Network (MoNN) is designed to maintain high expressiveness for interaction learning while ensuring efficiency. A mixture of MoNNs operate on a hierarchical item index to achieve extensive computation sharing, enabling it to scale up to large corpus size. MoNN and the hierarchical index are jointly learnt to continuously adapt to distribution shifts in both user interests and item distributions. HSNN achieves substantial improvement in offline evaluation compared to prevailing methods.
TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation
Venue: arXiv cs.LG 2025
Year: 2025

Time series generation is widely used in real-world applications such as simulation, data augmentation, and hypothesis testing. Recently, diffusion models have emerged as the de facto approach to time series generation, enabling diverse synthesis scenarios. However, the fixed standard-Gaussian diffusion prior may be ill-suited for time series data, which exhibit properties such as temporal order and fixed time points. In this paper, we propose TimeBridge, a framework that flexibly synthesizes time series data by using diffusion bridges to learn paths between a chosen prior and the data distribution. We then explore several prior designs tailored to time series synthesis. Our framework covers (i) data- and time-dependent priors for unconditional generation and (ii) scale-preserving priors for conditional generation. Experiments show that our framework with data-driven priors outperforms standard diffusion models on time series generation.
Polynomial 2D Green Coordinates for High-order Cages
Venue: arXiv cs.CG 2025
Year: 2025

We propose conformal polynomial coordinates for 2D closed high-order cages, which consist of polynomial curves of any order. The coordinates enable the transformation of the input polynomial curves into polynomial curves of any order. We extend the classical 2D Green coordinates to define our coordinates, thereby leading to cage-aware conformal harmonic deformations. We extensively test our method on various 2D deformations, allowing users to manipulate the \Bezier control points to easily generate the desired deformation.
The advantages of context specific language models: the case of the Erasmian Language Model
Venue: arXiv cs.CL 2025
Year: 2025

The current trend to improve language model performance seems to be based on scaling up with the number of parameters (e.g. the state of the art GPT4 model has approximately 1.7 trillion parameters) or the amount of training data fed into the model. However this comes at significant costs in terms of computational resources and energy costs that compromise the sustainability of AI solutions, as well as risk relating to privacy and misuse. In this paper we present the Erasmian Language Model (ELM) a small context specific, 900 million parameter model, pre-trained and fine-tuned by and for Erasmus University Rotterdam. We show how the model performs adequately in a classroom context for essay writing, and how it achieves superior performance in subjects that are part of its context. This has implications for a wide range of institutions and organizations, showing that context specific language models may be a viable alternative for resource constrained, privacy sensitive use cases.
Neural Speech and Audio Coding: Modern AI Technology Meets Traditional Codecs
Venue: arXiv cs.SD 2025
Year: 2025

This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the subjective evaluation processes of speech and audio codecs and discusses the limitations of purely data-driven approaches, which often require inefficiently large architectures to match the performance of model-based methods. The study presents hybrid systems as a viable solution, offering significant improvements to the performance of conventional codecs through meticulously chosen design enhancements. Specifically, it introduces a neural network-based signal enhancer designed to post-process existing codecs' output, along with the autoencoder-based end-to-end models and LPCNet--hybrid systems that combine linear predictive coding (LPC) with neural networks. Furthermore, the paper delves into predictive models operating within custom feature spaces (TF-Codec) or predefined transform domains (MDCTNet) and examines the use of psychoacoustically calibrated loss functions to train end-to-end neural audio codecs. Through these investigations, the paper demonstrates the potential of hybrid systems to advance the field of speech and audio coding by bridging the gap between traditional model-based approaches and modern data-driven techniques.
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
Venue: arXiv cs.LG 2025
Year: 2025

The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particular domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field.
V3rified: Revelation vs Non-Revelation Mechanisms for Decentralized Verifiable Computation
Venue: arXiv cs.GT 2025
Year: 2025

In the era of Web3, decentralized technologies have emerged as the cornerstone of a new digital paradigm. Backed by a decentralized blockchain architecture, the Web3 space aims to democratize all aspects of the web. From data-sharing to learning models, outsourcing computation is an established, prevalent practice. Verifiable computation makes this practice trustworthy as clients/users can now efficiently validate the integrity of a computation. As verifiable computation gets considered for applications in the Web3 space, decentralization is crucial for system reliability, ensuring that no single entity can suppress clients. At the same time, however, decentralization needs to be balanced with efficiency: clients want their computations done as quickly as possible. Motivated by these issues, we study the trade-off between decentralization and efficiency when outsourcing computational tasks to strategic, rational solution providers. Specifically, we examine this trade-off when the client employs (1) revelation mechanisms, i.e. auctions, where solution providers bid their desired reward for completing the task by a specific deadline and then the client selects which of them will do the task and how much they will be rewarded, and (2) simple, non-revelation mechanisms, where the client commits to the set of rules she will use to map solutions at specific times to rewards and then solution providers decide whether they want to do the task or not. We completely characterize the power and limitations of revelation and non-revelation mechanisms in our model.
A Review of Pseudo-Labeling for Computer Vision
Venue: arXiv cs.CV 2025
Year: 2025

Deep neural models have achieved state of the art performance on a wide range of problems in computer science, especially in computer vision. However, deep neural networks often require large datasets of labeled samples to generalize effectively, and an important area of active research is semi-supervised learning, which attempts to instead utilize large quantities of (easily acquired) unlabeled samples. One family of methods in this space is pseudo-labeling, a class of algorithms that use model outputs to assign labels to unlabeled samples which are then used as labeled samples during training. Such assigned labels, called pseudo-labels, are most commonly associated with the field of semi-supervised learning. In this work we explore a broader interpretation of pseudo-labels within both self-supervised and unsupervised methods. By drawing the connection between these areas we identify new directions when advancements in one area would likely benefit others, such as curriculum learning and self-supervised regularization.