OpenRaiser/Intern-Atlas huggingface dataset:
FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection
Venue: arXiv cs.CV 2025
Year: 2025

Multi-step or hybrid deepfakes, created by sequentially applying different deepfake creation methods such as Face-Swapping, GAN-based generation, and Diffusion methods, can pose an emerging and unforseen technical challenge for detection models trained on single-step forgeries. While prior studies have mainly focused on detecting isolated single manipulation, little is known about the detection model behavior under such compositional, hybrid, and complex manipulation pipelines. In this work, we introduce \textbf{FakeChain}, a large-scale benchmark comprising 1-, 2-, and 3-Step forgeries synthesized using five state-of-the-art representative generators. Using this approach, we analyze detection performance and spectral properties across hybrid manipulation at different step, along with varying generator combinations and quality settings. Surprisingly, our findings reveal that detection performance highly depends on the final manipulation type, with F1-score dropping by up to \textbf{58.83\%} when it differs from training distribution. This clearly demonstrates that detectors rely on last-stage artifacts rather than cumulative manipulation traces, limiting generalization. Such findings highlight the need for detection models to explicitly consider manipulation history and sequences. Our results highlight the importance of benchmarks such as FakeChain, reflecting growing synthesis complexity and diversity in real-world scenarios. Our sample code is available here\footnote{https://github.com/minjihh/FakeChain}.
Delving into Cryptanalytic Extraction of PReLU Neural Networks
Venue: arXiv cs.CR 2025
Year: 2025

The machine learning problem of model extraction was first introduced in 1991 and gained prominence as a cryptanalytic challenge starting with Crypto 2020. For over three decades, research in this field has primarily focused on ReLU-based neural networks. In this work, we take the first step towards the cryptanalytic extraction of PReLU neural networks, which employ more complex nonlinear activation functions than their ReLU counterparts. We propose a raw output-based parameter recovery attack for PReLU networks and extend it to more restrictive scenarios where only the top-m probability scores are accessible. Our attacks are rigorously evaluated through end-to-end experiments on diverse PReLU neural networks, including models trained on the MNIST dataset. To the best of our knowledge, this is the first practical demonstration of PReLU neural network extraction across three distinct attack scenarios.
The Role of Vocabularies in Learning Sparse Representations for Ranking
Venue: arXiv cs.IR 2025
Year: 2025

Learned Sparse Retrieval (LSR) such as SPLADE has growing interest for effective semantic 1st stage matching while enjoying the efficiency of inverted indices. A recent work on learning SPLADE models with expanded vocabularies (ESPLADE) was proposed to represent queries and documents into a sparse space of custom vocabulary which have different levels of vocabularic granularity. Within this effort, however, there have not been many studies on the role of vocabulary in SPLADE models and their relationship to retrieval efficiency and effectiveness. To study this, we construct BERT models with 100K-sized output vocabularies, one initialized with the ESPLADE pretraining method and one initialized randomly. After finetune on real-world search click logs, we applied logit score-based queries and documents pruning to max size for further balancing efficiency. The experimental result in our evaluation set shows that, when pruning is applied, the two models are effective compared to the 32K-sized normal SPLADE model in the computational budget under the BM25. And the ESPLADE models are more effective than the random vocab model, while having a similar retrieval cost. The result indicates that the size and pretrained weight of output vocabularies play the role of configuring the representational specification for queries, documents, and their interactions in the retrieval engine, beyond their original meaning and purposes in NLP. These findings can provide a new room for improvement for LSR by identifying the importance of representational specification from vocabulary configuration for efficient and effective retrieval.
MedCutMix: A Data-Centric Approach to Improve Radiology Vision-Language Pre-training with Disease Awareness
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Pre-training (VLP) is drawing increasing interest for its ability to minimize manual annotation requirements while enhancing semantic understanding in downstream tasks. However, its reliance on image-text datasets poses challenges due to privacy concerns and the high cost of obtaining paired annotations. Data augmentation emerges as a viable strategy to address this issue, yet existing methods often fall short of capturing the subtle and complex variations in medical data due to limited diversity. To this end, we propose MedCutMix, a novel multi-modal disease-centric data augmentation method. MedCutMix performs diagnostic sentence CutMix within medical reports and establishes the cross-attention between the diagnostic sentence and medical image to guide attentive manifold mix within the imaging modality. Our approach surpasses previous methods across four downstream radiology diagnosis datasets, highlighting its effectiveness in enhancing performance and generalizability in radiology VLP.
RelRepair: Enhancing Automated Program Repair by Retrieving Relevant Code
Venue: arXiv cs.SE 2025
Year: 2025

Automated Program Repair (APR) has emerged as a promising paradigm for reducing debugging time and improving the overall efficiency of software development. Recent advances in Large Language Models (LLMs) have demonstrated their potential for automated bug fixing and other software engineering tasks. Nevertheless, the general-purpose nature of LLM pre-training means these models often lack the capacity to perform project-specific repairs, which require understanding of domain-specific identifiers, code structures, and contextual relationships within a particular codebase. As a result, LLMs may struggle to generate correct patches when the repair depends on project-specific information. To address this limitation, we introduce RelRepair, a novel approach that retrieves relevant project-specific code to enhance automated program repair. RelRepair first identifies relevant function signatures by analyzing function names and code comments within the project. It then conducts deeper code analysis to retrieve code snippets relevant to the repair context. The retrieved relevant information is then incorporated into the LLM's input prompt, guiding the model to generate more accurate and informed patches. We evaluate RelRepair on two widely studied datasets, Defects4J V1.2 and ManySStuBs4J, and compare its performance against several state-of-the-art LLM-based APR approaches. RelRepair successfully repairs 101 bugs in Defects4J V1.2. Furthermore, RelRepair achieves a 17.1\% improvement in the ManySStuBs4J dataset, increasing the overall fix rate to 48.3\%. These results highlight the importance of providing relevant project-specific information to LLMs, shedding light on effective strategies for leveraging LLMs in APR tasks.
HypeMARL: Multi-Agent Reinforcement Learning For High-Dimensional, Parametric, and Distributed Systems
Venue: arXiv cs.LG 2025
Year: 2025

Deep reinforcement learning has recently emerged as a promising feedback control strategy for complex dynamical systems governed by partial differential equations (PDEs). When dealing with distributed, high-dimensional problems in state and control variables, multi-agent reinforcement learning (MARL) has been proposed as a scalable approach for breaking the curse of dimensionality. In particular, through decentralized training and execution, multiple agents cooperate to steer the system towards a target configuration, relying solely on local state and reward information. However, the principle of locality may become a limiting factor whenever a collective, nonlocal behavior of the agents is crucial to maximize the reward function, as typically happens in PDE-constrained optimal control problems. In this work, we propose HypeMARL: a decentralized MARL algorithm tailored to the control of high-dimensional, parametric, and distributed systems. HypeMARL employs hypernetworks to effectively parametrize the agents' policies and value functions with respect to the system parameters and the agents' relative positions, encoded by sinusoidal positional encoding. Through the application on challenging control problems, such as density and flow control, we show that HypeMARL (i) can effectively control systems through a collective behavior of the agents, outperforming state-of-the-art decentralized MARL, (ii) can efficiently deal with parametric dependencies, (iii) requires minimal hyperparameter tuning and (iv) can reduce the amount of expensive environment interactions by a factor of ~10 thanks to its model-based extension, MB-HypeMARL, which relies on computationally efficient deep learning-based surrogate models approximating the dynamics locally, with minimal deterioration of the policy performance.
Brain Connectivity Network Structure Learning For Brain Disorder Diagnosis
Venue: arXiv cs.GR 2025
Year: 2025

Recent studies in neuroscience highlight the significant potential of brain connectivity networks, which are commonly constructed from functional magnetic resonance imaging (fMRI) data for brain disorder diagnosis. Traditional brain connectivity networks are typically obtained using predefined methods that incorporate manually-set thresholds to estimate inter-regional relationships. However, such approaches often introduce redundant connections or overlook essential interactions, compromising the value of the constructed networks. Besides, the insufficiency of labeled data further increases the difficulty of learning generalized representations of intrinsic brain characteristics. To mitigate those issues, we propose a self-supervised framework to learn an optimal structure and representation for brain connectivity networks, focusing on individualized generation and optimization in an unsupervised manner. We firstly employ two existing whole-brain connectomes to adaptively construct their complementary brain network structure learner, and then introduce a multi-state graph-based encoder with a joint iterative learning strategy to simultaneously optimize both the generated network structure and its representation. By leveraging self-supervised pretraining on large-scale unlabeled brain connectivity data, our framework enables the brain connectivity network learner to generalize e ffectively to unseen disorders, while requiring only minimal finetuning of the encoder for adaptation to new diagnostic tasks. Extensive experiments on cross-dataset brain disorder diagnosis demonstrate that our method consistently outperforms state-of-the-art approaches, validating its effectiveness and generalizability. The code is publicly available at https://github.com/neochen1/BCNSL.
A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Accurately forecasting power outages is a complex task influenced by diverse factors such as weather conditions [1], vegetation, wildlife, and load fluctuations. These factors introduce substantial variability and noise into outage data, making reliable prediction challenging. Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), are particularly effective for modeling nonlinear and dynamic time-series data, with proven applications in stock price forecasting [2], energy demand prediction, demand response [3], and traffic flow management [4]. This paper introduces a hybrid deep learning framework, termed PCA-PR-Seq2Seq-Adam-LSTM, that integrates Principal Component Analysis (PCA), Poisson Regression (PR), a Sequence-to-Sequence (Seq2Seq) architecture, and an Adam-optimized LSTM. PCA is employed to reduce dimensionality and stabilize data variance, while Poisson Regression effectively models discrete outage events. The Seq2Seq-Adam-LSTM component enhances temporal feature learning through efficient gradient optimization and long-term dependency capture. The framework is evaluated using real-world outage records from Michigan, and results indicate that the proposed approach significantly improves forecasting accuracy and robustness compared to existing methods.
MMPart: Harnessing Multi-Modal Large Language Models for Part-Aware 3D Generation
Venue: arXiv cs.CV 2025
Year: 2025

Generative 3D modeling has advanced rapidly, driven by applications in VR/AR, metaverse, and robotics. However, most methods represent the target object as a closed mesh devoid of any structural information, limiting editing, animation, and semantic understanding. Part-aware 3D generation addresses this problem by decomposing objects into meaningful components, but existing pipelines face challenges: in existing methods, the user has no control over which objects are separated and how model imagine the occluded parts in isolation phase. In this paper, we introduce MMPart, an innovative framework for generating part-aware 3D models from a single image. We first use a VLM to generate a set of prompts based on the input image and user descriptions. In the next step, a generative model generates isolated images of each object based on the initial image and the previous step's prompts as supervisor (which control the pose and guide model how imagine previously occluded areas). Each of those images then enters the multi-view generation stage, where a number of consistent images from different views are generated. Finally, a reconstruction model converts each of these multi-view images into a 3D model.
Generative AI alone may not be enough: Evaluating AI Support for Learning Mathematical Proof
Venue: arXiv cs.HC 2025
Year: 2025

We evaluate the effectiveness of LLM-Tutor, a large language model (LLM)-powered tutoring system that combines an AI-based proof-review tutor for real-time feedback on proof-writing and a chatbot for mathematics-related queries. Our experiment, involving 148 students, demonstrated that the use of LLM-Tutor significantly improved homework performance compared to a control group without access to the system. However, its impact on exam performance and time spent on tasks was found to be insignificant. Mediation analysis revealed that students with lower self-efficacy tended to use the chatbot more frequently, which partially contributed to lower midterm scores. Furthermore, students with lower self-efficacy were more likely to engage frequently with the proof-review-AI-tutor, a usage pattern that positively contributed to higher final exam scores. Interviews with 19 students highlighted the accessibility of LLM-Tutor and its effectiveness in addressing learning needs, while also revealing limitations and concerns regarding potential over-reliance on the tool. Our results suggest that generative AI alone like chatbot may not suffice for comprehensive learning support, underscoring the need for iterative design improvements with learning sciences principles with generative AI educational tools like LLM-Tutor.
MoRoVoc: A Large Dataset for Geographical Variation Identification of the Spoken Romanian Language
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces MoRoVoc, the largest dataset for analyzing the regional variation of spoken Romanian. It has more than 93 hours of audio and 88,192 audio samples, balanced between the Romanian language spoken in Romania and the Republic of Moldova. We further propose a multi-target adversarial training framework for speech models that incorporates demographic attributes (i.e., age and gender of the speakers) as adversarial targets, making models discriminative for primary tasks while remaining invariant to secondary attributes. The adversarial coefficients are dynamically adjusted via meta-learning to optimize performance. Our approach yields notable gains: Wav2Vec2-Base achieves 78.21% accuracy for the variation identification of spoken Romanian using gender as an adversarial target, while Wav2Vec2-Large reaches 93.08% accuracy for gender classification when employing both dialect and age as adversarial objectives.
The Even Sheen of AI: Kitsch, LLMs, and Homogeneity
Venue: arXiv cs.CY 2025
Year: 2025

The exploding use and impact of Chatbots such as ChatGPT that are based on Large Language Models urgently call for a language which is fit to clearly describe functions and problems of the production process and qualities of the Chatbots' textual and image output. Recently, the discussion about appropriate and illuminating metaphors to describe LLMs has gained momentum. As an alternative to well-established metaphors such as "hallucinating" and "bullshit", we propose "kitsch" as a new metaphor. As an internationally widespread term from literary and cultural studies, we argue that "kitsch" is particularly suitable for analytically illuminating a previously neglected feature of LLM-based images and texts: their tendency to produce homogeneous and average content, which is becoming increasingly dominant as the proportion of AI-generated content on the internet grows. This is leading to the equalisation of language, style and argument. In view of the potential negative consequences of this averaging, including for human content producers on the internet, we advocate combining methods and insights from kitsch studies with AI research, philosophy, and communication studies in order to better understand the phenomenon and develop countermeasures.
Sublinear Time Quantum Sensitivity Sampling
Venue: arXiv cs.DS 2025
Year: 2025

We present a unified framework for quantum sensitivity sampling, extending the advantages of quantum computing to a broad class of classical approximation problems. Our unified framework provides a streamlined approach for constructing coresets and offers significant runtime improvements in applications such as clustering, regression, and low-rank approximation. Our contributions include: * $k$-median and $k$-means clustering: For $n$ points in $d$-dimensional Euclidean space, we give an algorithm that constructs an $ε$-coreset in time $\widetilde O(n^{0.5}dk^{2.5}~\mathrm{poly}(ε^{-1}))$ for $k$-median and $k$-means clustering. Our approach achieves a better dependence on $d$ and constructs smaller coresets that only consist of points in the dataset, compared to recent results of [Xue, Chen, Li and Jiang, ICML'23]. * $\ell_p$ regression: For $\ell_p$ regression problems, we construct an $ε$-coreset of size $\widetilde O_p(d^{\max\{1, p/2\}}ε^{-2})$ in time $\widetilde O_p(n^{0.5}d^{\max\{0.5, p/4\}+1}(ε^{-3}+d^{0.5}))$, improving upon the prior best quantum sampling approach of [Apers and Gribling, QIP'24] for all $p\in (0, 2)\cup (2, 22]$, including the widely studied least absolute deviation regression ($\ell_1$ regression). * Low-rank approximation with Frobenius norm error: We introduce the first quantum sublinear-time algorithm for low-rank approximation that does not rely on data-dependent parameters, and runs in $\widetilde O(nd^{0.5}k^{0.5}ε^{-1})$ time. Additionally, we present quantum sublinear algorithms for kernel low-rank approximation and tensor low-rank approximation, broadening the range of achievable sublinear time algorithms in randomized numerical linear algebra.
MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging
Venue: arXiv cs.CV 2025
Year: 2025

Multi-modal three-dimensional (3D) medical imaging data, derived from ultrasound, magnetic resonance imaging (MRI), and potentially computed tomography (CT), provide a widely adopted approach for non-invasive anatomical visualization. Accurate modeling, registration, and visualization in this setting depend on surface reconstruction and frame-to-frame interpolation. Traditional methods often face limitations due to image noise and incomplete information between frames. To address these challenges, we present MedGS, a semi-supervised neural implicit surface reconstruction framework that employs a Gaussian Splatting (GS)-based interpolation mechanism. In this framework, medical imaging data are represented as consecutive two-dimensional (2D) frames embedded in 3D space and modeled using Gaussian-based distributions. This representation enables robust frame interpolation and high-fidelity surface reconstruction across imaging modalities. As a result, MedGS offers more efficient training than traditional neural implicit methods. Its explicit GS-based representation enhances noise robustness, allows flexible editing, and supports precise modeling of complex anatomical structures with fewer artifacts. These features make MedGS highly suitable for scalable and practical applications in medical imaging.
Navigating Equity and Reflexive Practices in Gigwork Design: A Journey Mapping Experience
Venue: arXiv cs.HC 2025
Year: 2025

How do we create ethical and equitable experiences on global platforms? How might UX designers and developers incorporate reflexive practices--a continuous self-evaluation of one's assumptions and biases--to mitigate assumptions and workers' experience? This tutorial will explore ways to build equitable user experiences using gig work platforms as a target use case. With the rise of gig work platforms, the informal digital economy has altered how algorithmic systems manage occasional workers; its questionable assumptions have spread worldwide. Concerns over autonomy, gamification, and worker privacy and safety are amplified as these practices expand worldwide. We will practice reflexive techniques within this context by implementing an equity-focused journey-mapping experience. Journey mapping allows designers to map out the customer experience and identify potential pain points at each step that could hinder the user experience. Using a ride-sharing scenario, participants will be guided through a custom journey map highlighting equitable considerations that can facilitate responsible user experience innovation. NOTE: The tutorial was presented at Fairness, Accountability and Transparency Conference (FAccT '24) in Rio de Janeiro.
Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition-Informed Approach to Quantifying Identity Fusion from Text
Venue: arXiv cs.CL 2025
Year: 2025

Quantifying identity fusion -- the psychological merging of self with another entity or abstract target (e.g., a religious group, political party, ideology, value, brand, belief, etc.) -- is vital for understanding a wide range of group-based human behaviors. We introduce the Cognitive Linguistic Identity Fusion Score (CLIFS), a novel metric that integrates cognitive linguistics with large language models (LLMs), which builds on implicit metaphor detection. Unlike traditional pictorial and verbal scales, which require controlled surveys or direct field contact, CLIFS delivers fully automated, scalable assessments while maintaining strong alignment with the established verbal measure. In benchmarks, CLIFS outperforms both existing automated approaches and human annotation. As a proof of concept, we apply CLIFS to violence risk assessment to demonstrate that it can improve violence risk assessment by more than 240%. Building on our identification of a new NLP task and early success, we underscore the need to develop larger, more diverse datasets that encompass additional fusion-target domains and cultural backgrounds to enhance generalizability and further advance this emerging area. CLIFS models and code are public at https://github.com/DevinW-sudo/CLIFS.