OpenRaiser/Intern-Atlas huggingface dataset:
Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition
Venue: arXiv cs.AI 2025
Year: 2025

In recent years, the rapid development of AI systems has brought about the benefits of intelligent services but also concerns about security and reliability. By fostering appropriate user reliance on an AI system, both complementary team performance and reduced human workload can be achieved. Previous empirical studies have extensively analyzed the impact of factors ranging from task, system, and human behavior on user trust and appropriate reliance in the context of one-step decision making. However, user reliance on AI systems in tasks with complex semantics that require multi-step workflows remains under-explored. Inspired by recent work on task decomposition with large language models, we propose to investigate the impact of a novel Multi-Step Transparent (MST) decision workflow on user reliance behaviors. We conducted an empirical study (N = 233) of AI-assisted decision making in composite fact-checking tasks (i.e., fact-checking tasks that entail multiple sub-fact verification steps). Our findings demonstrate that human-AI collaboration with an MST decision workflow can outperform one-step collaboration in specific contexts (e.g., when advice from an AI system is misleading). Further analysis of the appropriate reliance at fine-grained levels indicates that an MST decision workflow can be effective when users demonstrate a relatively high consideration of the intermediate steps. Our work highlights that there is no one-size-fits-all decision workflow that can help obtain optimal human-AI collaboration. Our insights help deepen the understanding of the role of decision workflows in facilitating appropriate reliance. We synthesize important implications for designing effective means to facilitate appropriate reliance on AI systems in composite tasks, positioning opportunities for the human-centered AI and broader HCI communities.
LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) hold promise for advancing legal practice by automating complex tasks and improving access to justice. However, their adoption is limited by concerns over client confidentiality, especially when lawyers include sensitive Personally Identifiable Information (PII) in prompts, risking unauthorized data exposure. To mitigate this, we introduce LegalGuardian, a lightweight, privacy-preserving framework tailored for lawyers using LLM-based tools. LegalGuardian employs Named Entity Recognition (NER) techniques and local LLMs to mask and unmask confidential PII within prompts, safeguarding sensitive data before any external interaction. We detail its development and assess its effectiveness using a synthetic prompt library in immigration law scenarios. Comparing traditional NER models with one-shot prompted local LLM, we find that LegalGuardian achieves a F1-score of 93% with GLiNER and 97% with Qwen2.5-14B in PII detection. Semantic similarity analysis confirms that the framework maintains high fidelity in outputs, ensuring robust utility of LLM-based tools. Our findings indicate that legal professionals can harness advanced AI technologies without compromising client confidentiality or the quality of legal documents.
MARIO: A Mixed Annotation Framework For Polyp Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Existing polyp segmentation models are limited by high labeling costs and the small size of datasets. Additionally, vast polyp datasets remain underutilized because these models typically rely on a single type of annotation. To address this dilemma, we introduce MARIO, a mixed supervision model designed to accommodate various annotation types, significantly expanding the range of usable data. MARIO learns from underutilized datasets by incorporating five forms of supervision: pixel-level, box-level, polygon-level, scribblelevel, and point-level. Each form of supervision is associated with a tailored loss that effectively leverages the supervision labels while minimizing the noise. This allows MARIO to move beyond the constraints of relying on a single annotation type. Furthermore, MARIO primarily utilizes dataset with weak and cheap annotations, reducing the dependence on large-scale, fully annotated ones. Experimental results across five benchmark datasets demonstrate that MARIO consistently outperforms existing methods, highlighting its efficacy in balancing trade-offs between different forms of supervision and maximizing polyp segmentation performance
AI Based Font Pair Suggestion Modelling For Graphic Design
Venue: arXiv cs.CV 2025
Year: 2025

One of the key challenges of AI generated designs in Microsoft Designer is selecting the most contextually relevant and novel fonts for the design suggestions. Previous efforts involved manually mapping design intent to fonts. Though this was high quality, this method does not scale for a large number of fonts (3000+) and numerous user intents for graphic design. In this work we create font visual embeddings, a font stroke width algorithm, a font category to font mapping dataset, an LLM-based category utilization description and a lightweight, low latency knowledge-distilled mini language model (Mini LM V2) to recommend multiple pairs of contextual heading and subheading fonts for beautiful and intuitive designs. We also utilize a weighted scoring mechanism, nearest neighbor approach and stratified sampling to rank the font pairs and bring novelty to the predictions.
SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

This work introduces SMARTe-VR, a platform for student monitoring in an immersive virtual reality environment designed for online education. SMARTe-VR aims to collect data for adaptive learning, focusing on facial biometrics and learning metadata. The platform allows instructors to create customized learning sessions with video lectures, featuring an interface with an AutoQA system to evaluate understanding, interaction tools (for example, textbook highlighting and lecture tagging), and real-time feedback. Furthermore, we released a dataset that contains 5 research challenges with data from 10 users in VR-based TOEIC sessions. This data set, which spans more than 25 hours, includes facial features, learning metadata, 450 responses, difficulty levels of the questions, concept tags, and understanding labels. Alongside the database, we present preliminary experiments using Item Response Theory models, adapted for understanding detection using facial features. Two architectures were explored: a Temporal Convolutional Network for local features and a Multilayer Perceptron for global features.
Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks
Venue: arXiv cs.CR 2025
Year: 2025

Adversarial attacks present significant challenges for malware detection systems. This research investigates the effectiveness of benign and malicious adversarial examples (AEs) in evasion and poisoning attacks on the Portable Executable file domain. A novel focus of this study is on benign AEs, which, although not directly harmful, can increase false positives and undermine trust in antivirus solutions. We propose modifying existing adversarial malware generators to produce benign AEs and show they are as successful as malware AEs in evasion attacks. Furthermore, our data show that benign AEs have a more decisive influence in poisoning attacks than standard malware AEs, demonstrating their superior ability to decrease the model's performance. Our findings introduce new opportunities for adversaries and further increase the attack surface that needs to be protected by security researchers.
pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup
Venue: arXiv cs.LG 2025
Year: 2025

Traditional Federated Learning (FL) methods encounter significant challenges when dealing with heterogeneous data and providing personalized solutions for non-IID scenarios. Personalized Federated Learning (PFL) approaches aim to address these issues by balancing generalization and personalization, often through parameter decoupling or partial models that freeze some neural network layers for personalization while aggregating other layers globally. However, existing methods still face challenges of global-local model discrepancy, client drift, and catastrophic forgetting, which degrade model accuracy. To overcome these limitations, we propose $\textit{pMixFed}$, a dynamic, layer-wise PFL approach that integrates $\textit{mixup}$ between shared global and personalized local models. Our method introduces an adaptive strategy for partitioning between personalized and shared layers, a gradual transition of personalization degree to enhance local client adaptation, improved generalization across clients, and a novel aggregation mechanism to mitigate catastrophic forgetting. Extensive experiments demonstrate that pMixFed outperforms state-of-the-art PFL methods, showing faster model training, increased robustness, and improved handling of data heterogeneity under different heterogeneous settings.
Building low-resource African language corpora: A case study of Kidawida, Kalenjin and Dholuo
Venue: arXiv cs.CL 2025
Year: 2025

Natural Language Processing is a crucial frontier in artificial intelligence, with broad applications in many areas, including public health, agriculture, education, and commerce. However, due to the lack of substantial linguistic resources, many African languages remain underrepresented in this digital transformation. This paper presents a case study on the development of linguistic corpora for three under-resourced Kenyan languages, Kidaw'ida, Kalenjin, and Dholuo, with the aim of advancing natural language processing and linguistic research in African communities. Our project, which lasted one year, employed a selective crowd-sourcing methodology to collect text and speech data from native speakers of these languages. Data collection involved (1) recording conversations and translation of the resulting text into Kiswahili, thereby creating parallel corpora, and (2) reading and recording written texts to generate speech corpora. We made these resources freely accessible via open-research platforms, namely Zenodo for the parallel text corpora and Mozilla Common Voice for the speech datasets, thus facilitating ongoing contributions and access for developers to train models and develop Natural Language Processing applications. The project demonstrates how grassroots efforts in corpus building can support the inclusion of African languages in artificial intelligence innovations. In addition to filling resource gaps, these corpora are vital in promoting linguistic diversity and empowering local communities by enabling Natural Language Processing applications tailored to their needs. As African countries like Kenya increasingly embrace digital transformation, developing indigenous language resources becomes essential for inclusive growth. We encourage continued collaboration from native speakers and developers to expand and utilize these corpora.
Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Tracking mouse body parts in video is often incomplete due to occlusions such that - e.g. - subsequent action and behavior analysis is impeded. In this conceptual work, videos from several perspectives are integrated via global exterior camera orientation; body part positions are estimated by 3D triangulation and bundle adjustment. Consistency of overall 3D track reconstruction is achieved by introduction of a 3D mouse model, deep-learned body part movements, and global motion-track smoothness constraint. The resulting 3D body and body part track estimates are substantially more complete than the original single-frame-based body part detection, therefore, allowing improved animal behavior analysis.
Bridging the Security Gap: Lessons from 5G and What 6G Should Do Better
Venue: arXiv cs.CR 2025
Year: 2025

The security requirements for future 6G mobile networks are anticipated to be significantly more complex and demanding than those of 5G. This increase stems from several factors: the proliferation of massive machine-type communications will dramatically increase the density of devices competing for network access; secure ultra-reliable low-latency communication will impose stringent requirements on security, latency, and reliability; and the widespread deployment of small cells and non-terrestrial networks, including satellite mega-constellations, will result in more frequent handovers. This paper provides a set of security recommendations for 6G networks, with a particular focus on access and handover procedures, which often lack encryption and integrity protection, making them more vulnerable to exploitation. Since 6G is expected to be a backward-compatible extension of 5G, and given that secure systems cannot be effectively designed without a clear understanding of their goals, it is imperative to first evaluate the limitations of the current generation. To this end, the paper begins by reviewing existing 5G access and authentication mechanisms, highlighting several critical vulnerabilities in these procedures. It then examines potential 6G challenges and concludes with actionable recommendations to enhance the security, resilience, and robustness of 6G access and handover mechanisms.
Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection
Venue: arXiv cs.CV 2025
Year: 2025

The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustness towards noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains under-explored. Existing noisy label learning methods designed for DML mainly discard suspicious noisy samples, resulting in a waste of the training data. To address this issue, we propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS), which constructs reliable positive pairs for noisy samples to enhance the sample utilization. Specifically, SGPS first effectively identifies clean and noisy samples by a probability-based clean sample selectionstrategy. To further utilize the remaining noisy samples, we discover their potential similar samples based on the subgroup information given by a subgroup generation module and then aggregate them into informative positive prototypes for each noisy sample via a positive prototype generation module. Afterward, a new contrastive loss is tailored for the noisy samples with their selected positive pairs. SGPS can be easily integrated into the training process of existing pair-wise DML tasks, like image retrieval and face recognition. Extensive experiments on multiple synthetic and real-world large-scale label noise datasets demonstrate the effectiveness of our proposed method. Without any bells and whistles, our SGPS framework outperforms the state-of-the-art noisy label DML methods. Code is available at \url{https://github.com/smuelpeng/SGPS-NoiseFreeDML}.
Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study
Venue: arXiv cs.CR 2025
Year: 2025

Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication.
Can LLM Generate Regression Tests for Software Commits?
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate the opportunities of LLMs for automatic regression test generation for programs that take highly structured, human-readable inputs, such as XML parsers or JavaScript interpreters. Concretely, we explore the following regression test generation scenarios for such programs that have so far been difficult to test automatically in the absence of corresponding input grammars: $\bullet$ Bug finding. Given a code change (e.g., a commit or pull request), our LLM-based approach generates a test case with the objective of revealing any bugs that might be introduced if that change is applied. $\bullet$ Patch testing. Given a patch, our LLM-based approach generates a test case that fails before but passes after the patch. This test can be added to the regression test suite to catch similar bugs in the future. We implement Cleverest, a feedback-directed, zero-shot LLM-based regression test generation technique, and evaluate its effectiveness on 22 commits to three subject programs: Mujs, Libxml2, and Poppler. For programs using more human-readable file formats, like XML or JavaScript, we found Cleverest performed very well. It generated easy-to-understand bug-revealing or bug-reproduction test cases for the majority of commits in just under three minutes -- even when only the code diff or commit message (unless it was too vague) was given. For programs with more compact file formats, like PDF, as expected, it struggled to generate effective test cases. However, the LLM-supplied test cases are not very far from becoming effective (e.g., when used as a seed by a greybox fuzzer or as a starting point by the developer).
Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework
Venue: arXiv cs.RO 2025
Year: 2025

Today's autonomous vehicles rely on a multitude of sensors to perceive their environment. To improve the perception or create redundancy, the sensor's alignment relative to each other must be known. With Multi-LiCa, we present a novel approach for the alignment, e.g. calibration. We present an automatic motion- and targetless approach for the extrinsic multi LiDAR-to-LiDAR calibration without the need for additional sensor modalities or an initial transformation input. We propose a two-step process with feature-based matching for the coarse alignment and a GICP-based fine registration in combination with a cost-based matching strategy. Our approach can be applied to any number of sensors and positions if there is a partial overlap between the field of view of single sensors. We show that our pipeline is better generalized to different sensor setups and scenarios and is on par or better in calibration accuracy than existing approaches. The presented framework is integrated in ROS 2 but can also be used as a standalone application. To build upon our work, our source code is available at: https://github.com/TUMFTM/Multi_LiCa.
Enhanced Suicidal Ideation Detection from Social Media Using a CNN-BiLSTM Hybrid Model
Venue: arXiv cs.CL 2025
Year: 2025

Suicidal ideation detection is crucial for preventing suicides, a leading cause of death worldwide. Many individuals express suicidal thoughts on social media, offering a vital opportunity for early detection through advanced machine learning techniques. The identification of suicidal ideation in social media text is improved by utilising a hybrid framework that integrates Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), enhanced with an attention mechanism. To enhance the interpretability of the model's predictions, Explainable AI (XAI) methods are applied, with a particular focus on SHapley Additive exPlanations (SHAP), are incorporated. At first, the model managed to reach an accuracy of 92.81%. By applying fine-tuning and early stopping techniques, the accuracy improved to 94.29%. The SHAP analysis revealed key features influencing the model's predictions, such as terms related to mental health struggles. This level of transparency boosts the model's credibility while helping mental health professionals understand and trust the predictions. This work highlights the potential for improving the accuracy and interpretability of detecting suicidal tendencies, making a valuable contribution to the progress of mental health monitoring systems. It emphasizes the significance of blending powerful machine learning methods with explainability to develop reliable and impactful mental health solutions.
Reproducibility review of "Why Not Other Classes": Towards Class-Contrastive Back-Propagation Explanations
Venue: arXiv cs.CV 2025
Year: 2025

"Why Not Other Classes?": Towards Class-Contrastive Back-Propagation Explanations (Wang & Wang, 2022) provides a method for contrastively explaining why a certain class in a neural network image classifier is chosen above others. This method consists of using back-propagation-based explanation methods from after the softmax layer rather than before. Our work consists of reproducing the work in the original paper. We also provide extensions to the paper by evaluating the method on XGradCAM, FullGrad, and Vision Transformers to evaluate its generalization capabilities. The reproductions show similar results as the original paper, with the only difference being the visualization of heatmaps which could not be reproduced to look similar. The generalization seems to be generally good, with implementations working for Vision Transformers and alternative back-propagation methods. We also show that the original paper suffers from issues such as a lack of detail in the method and an erroneous equation which makes reproducibility difficult. To remedy this we provide an open-source repository containing all code used for this project.