OpenRaiser/Intern-Atlas huggingface dataset:
RAG Security and Privacy: Formalizing the Threat Model and Attack Surface
Venue: arXiv cs.CR 2025
Year: 2025

Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external document retrieval to produce more accurate and grounded responses. While RAG has shown strong potential in reducing hallucinations and improving factual consistency, it also introduces new privacy and security challenges that differ from those faced by traditional LLMs. Existing research has demonstrated that LLMs can leak sensitive information through training data memorization or adversarial prompts, and RAG systems inherit many of these vulnerabilities. At the same time, reliance of RAG on an external knowledge base opens new attack surfaces, including the potential for leaking information about the presence or content of retrieved documents, or for injecting malicious content to manipulate model behavior. Despite these risks, there is currently no formal framework that defines the threat landscape for RAG systems. In this paper, we address a critical gap in the literature by proposing, to the best of our knowledge, the first formal threat model for retrieval-RAG systems. We introduce a structured taxonomy of adversary types based on their access to model components and data, and we formally define key threat vectors such as document-level membership inference and data poisoning, which pose serious privacy and integrity risks in real-world deployments. By establishing formal definitions and attack models, our work lays the foundation for a more rigorous and principled understanding of privacy and security in RAG systems.
Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
Venue: arXiv cs.LG 2025
Year: 2025

Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control. While deep learning models excel at capturing complex dynamics, currently, their deployment is limited due to physical inconsistency and robustness, hence constraining their reliability in regulated environments. We introduce process-informed forecasting (PIF) models for temperature in pharmaceutical lyophilization. We investigate a wide range of models, from classical ones such as Autoregressive Integrated Moving Average Model (ARIMA) and Exponential Smoothing Model (ETS), to modern deep learning architectures, including Kolmogorov-Arnold Networks (KANs). We compare three different loss function formulations that integrate a process-informed trajectory prior: a fixed-weight loss, a dynamic uncertainty-based loss, and a Residual-Based Attention (RBA) mechanism. We evaluate all models not only for accuracy and physical consistency but also for robustness to sensor noise. Furthermore, we test the practical generalizability of the best model in a transfer learning scenario on a new process. Our results show that PIF models outperform their data-driven counterparts in terms of accuracy, physical plausibility and noise resilience. This work provides a roadmap for developing reliable and generalizable forecasting solutions for critical applications in the pharmaceutical manufacturing landscape.
Interpreting Public Sentiment in Diplomacy Events: A Counterfactual Analysis Framework Using Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Diplomatic events consistently prompt widespread public discussion and debate. Public sentiment plays a critical role in diplomacy, as a good sentiment provides vital support for policy implementation, helps resolve international issues, and shapes a nation's international image. Traditional methods for gauging public sentiment, such as large-scale surveys or manual content analysis of media, are typically time-consuming, labor-intensive, and lack the capacity for forward-looking analysis. We propose a novel framework that identifies specific modifications for diplomatic event narratives to shift public sentiment from negative to neutral or positive. First, we train a language model to predict public reaction towards diplomatic events. To this end, we construct a dataset comprising descriptions of diplomatic events and their associated public discussions. Second, guided by communication theories and in collaboration with domain experts, we predetermined several textual features for modification, ensuring that any alterations changed the event's narrative framing while preserving its core facts.We develop a counterfactual generation algorithm that employs a large language model to systematically produce modified versions of an original text. The results show that this framework successfully shifted public sentiment to a more favorable state with a 70\% success rate. This framework can therefore serve as a practical tool for diplomats, policymakers, and communication specialists, offering data-driven insights on how to frame diplomatic initiatives or report on events to foster a more desirable public sentiment.
Speaker Style-Aware Phoneme Anchoring for Improved Cross-Lingual Speech Emotion Recognition
Venue: arXiv cs.CL 2025
Year: 2025

Cross-lingual speech emotion recognition (SER) remains a challenging task due to differences in phonetic variability and speaker-specific expressive styles across languages. Effectively capturing emotion under such diverse conditions requires a framework that can align the externalization of emotions across different speakers and languages. To address this problem, we propose a speaker-style aware phoneme anchoring framework that aligns emotional expression at the phonetic and speaker levels. Our method builds emotion-specific speaker communities via graph-based clustering to capture shared speaker traits. Using these groups, we apply dual-space anchoring in speaker and phonetic spaces to enable better emotion transfer across languages. Evaluations on the MSP-Podcast (English) and BIIC-Podcast (Taiwanese Mandarin) corpora demonstrate improved generalization over competitive baselines and provide valuable insights into the commonalities in cross-lingual emotion representation.
ConceptViz: A Visual Analytics Approach for Exploring Concepts in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks. Understanding how LLMs internally represent knowledge remains a significant challenge. Despite Sparse Autoencoders (SAEs) have emerged as a promising technique for extracting interpretable features from LLMs, SAE features do not inherently align with human-understandable concepts, making their interpretation cumbersome and labor-intensive. To bridge the gap between SAE features and human concepts, we present ConceptViz, a visual analytics system designed for exploring concepts in LLMs. ConceptViz implements a novel dentification => Interpretation => Validation pipeline, enabling users to query SAEs using concepts of interest, interactively explore concept-to-feature alignments, and validate the correspondences through model behavior verification. We demonstrate the effectiveness of ConceptViz through two usage scenarios and a user study. Our results show that ConceptViz enhances interpretability research by streamlining the discovery and validation of meaningful concept representations in LLMs, ultimately aiding researchers in building more accurate mental models of LLM features. Our code and user guide are publicly available at https://github.com/Happy-Hippo209/ConceptViz.
Beyond Global Emotion: Fine-Grained Emotional Speech Synthesis with Dynamic Word-Level Modulation
Venue: arXiv cs.CL 2025
Year: 2025

Emotional text-to-speech (E-TTS) is central to creating natural and trustworthy human-computer interaction. Existing systems typically rely on sentence-level control through predefined labels, reference audio, or natural language prompts. While effective for global emotion expression, these approaches fail to capture dynamic shifts within a sentence. To address this limitation, we introduce Emo-FiLM, a fine-grained emotion modeling framework for LLM-based TTS. Emo-FiLM aligns frame-level features from emotion2vec to words to obtain word-level emotion annotations, and maps them through a Feature-wise Linear Modulation (FiLM) layer, enabling word-level emotion control by directly modulating text embeddings. To support evaluation, we construct the Fine-grained Emotion Dynamics Dataset (FEDD) with detailed annotations of emotional transitions. Experiments show that Emo-FiLM outperforms existing approaches on both global and fine-grained tasks, demonstrating its effectiveness and generality for expressive speech synthesis.
Exploiting Page Faults for Covert Communication
Venue: arXiv cs.OS 2025
Year: 2025

We present a novel mechanism to construct a covert channel based on page faults. A page fault is an event that occurs when a process or a thread tries to access a page of memory that is not currently mapped to its address space. The kernel typically responds to this event by performing a context switch to allow another process or thread to execute while the page is being fetched from the disk. We exploit this behavior to allow a malicious process to leak secret data to another process, bypassing the isolation mechanisms enforced by the operating system. These attacks do not leverage timers and are hardwareagnostic. Experimental results demonstrate that this attack can achieve a bit error rate of under 4%
Defending against Stegomalware in Deep Neural Networks with Permutation Symmetry
Venue: arXiv cs.CR 2025
Year: 2025

Deep neural networks are being utilized in a growing number of applications, both in production systems and for personal use. Network checkpoints are as a consequence often shared and distributed on various platforms to ease the development process. This work considers the threat of neural network stegomalware, where malware is embedded in neural network checkpoints at a negligible cost to network accuracy. This constitutes a significant security concern, but is nevertheless largely neglected by the deep learning practitioners and security specialists alike. We propose the first effective countermeasure to these attacks. In particular, we show that state-of-the-art neural network stegomalware can be efficiently and effectively neutralized through shuffling the column order of the weight- and bias-matrices, or equivalently the channel-order of convolutional layers. We show that this effectively corrupts payloads that have been embedded by state-of-the-art methods in neural network steganography at no cost to network accuracy, outperforming competing methods by a significant margin. We then discuss possible means by which to bypass this defense, additional defense methods, and advocate for continued research into the security of machine learning systems.
Wartime Media Dynamics in Emerging Democracies: Case Study of Pakistani Media in May 2025 Indo-Pak Conflict
Venue: arXiv cs.CY 2025
Year: 2025

Democracies rely on opposition and dissent to function, but in emerging democracies, freedom of speech is often restricted. This effect intensifies during regional conflicts. This study examines how the India-Pakistan conflict of May 2025 influenced Pakistani media coverage. Analyzing approximately 2,600 news articles from three major newspapers using a large language model (LLM), the study found that war-related reporting significantly overshadowed coverage of political opposition and dissent. These findings highlight how conflict can marginalize democratic discourse, reinforcing the need to safeguard press freedom in volatile regions.
A Contrastive Learning Framework for Breast Cancer Detection
Venue: arXiv cs.CV 2025
Year: 2025

Breast cancer, the second leading cause of cancer-related deaths globally, accounts for a quarter of all cancer cases [1]. To lower this death rate, it is crucial to detect tumors early, as early-stage detection significantly improves treatment outcomes. Advances in non-invasive imaging techniques have made early detection possible through computer-aided detection (CAD) systems which rely on traditional image analysis to identify malignancies. However, there is a growing shift towards deep learning methods due to their superior effectiveness. Despite their potential, deep learning methods often struggle with accuracy due to the limited availability of large-labeled datasets for training. To address this issue, our study introduces a Contrastive Learning (CL) framework, which excels with smaller labeled datasets. In this regard, we train Resnet-50 in semi supervised CL approach using similarity index on a large amount of unlabeled mammogram data. In this regard, we use various augmentation and transformations which help improve the performance of our approach. Finally, we tune our model on a small set of labelled data that outperforms the existing state of the art. Specifically, we observed a 96.7% accuracy in detecting breast cancer on benchmark datasets INbreast and MIAS.
Enhancing Python Programming Education with an AI-Powered Code Helper: Design, Implementation, and Impact
Venue: arXiv cs.SE 2025
Year: 2025

This is the study that presents an AI-Python-based chatbot that helps students to learn programming by demonstrating solutions to such problems as debugging errors, solving syntax problems or converting abstract theoretical concepts to practical implementations. Traditional coding tools like Integrated Development Environments (IDEs) and static analyzers do not give robotic help while AI-driven code assistants such as GitHub Copilot focus on getting things done. To close this gap, our chatbot combines static code analysis, dynamic execution tracing, and large language models (LLMs) to provide the students with relevant and practical advice, hence promoting the learning process. The chatbots hybrid architecture employs CodeLlama for code embedding, GPT-4 for natural language interactions, and Docker-based sandboxing for secure execution. Evaluated through a mixed-methods approach involving 1,500 student submissions, the system demonstrated an 85% error resolution success rate, outperforming standalone tools like pylint (62%) and GPT-4 (73%). Quantitative results revealed a 59.3% reduction in debugging time among users, with pre- and post-test assessments showing a 34% improvement in coding proficiency, particularly in recursion and exception handling. Qualitative feedback from 120 students highlighted the chatbots clarity, accessibility, and confidence-building impact, though critiques included occasional latency and restrictive code sanitization. By balancing technical innovation with pedagogical empathy, this research provides a blueprint for AI tools that prioritize educational equity and long-term skill retention over mere code completion. The chatbot exemplifies how AI can augment human instruction, fostering deeper conceptual understanding in programming education.
Enhancing LLM-based Fault Localization with a Functionality-Aware Retrieval-Augmented Generation Framework
Venue: arXiv cs.SE 2025
Year: 2025

Fault localization (FL) is a critical but time-consuming task in software debugging, aiming to identify faulty code elements. While recent advances in large language models (LLMs) have shown promise for FL, they often struggle with complex systems due to the lack of project-specific knowledge and the difficulty of navigating large projects. To address these limitations, we propose FaR-Loc, a novel framework that enhances method-level FL by integrating LLMs with retrieval-augmented generation (RAG). FaR-Loc consists of three key components: LLM Functionality Extraction, Semantic Dense Retrieval, and LLM Re-ranking. First, given a failed test and its associated stack trace, the LLM Functionality Extraction module generates a concise natural language description that captures the failing behavior. Next, the Semantic Dense Retrieval component leverages a pre-trained code-understanding encoder to embed both the functionality description (natural language) and the covered methods (code) into a shared semantic space, enabling the retrieval of methods with similar functional behavior. Finally, the LLM Re-ranking module reorders the retrieved methods based on their contextual relevance. Our experiments on the widely used Defects4J benchmark show that FaR-Loc outperforms state-of-the-art LLM-based baselines SoapFL and AutoFL, by 14.6% and 9.1% in Top-1 accuracy, by 19.2% and 22.1% in Top-5 accuracy, respectively. It also surpasses all learning-based and spectrum-based baselines across all Top-N metrics without requiring re-training. Furthermore, we find that pre-trained code embedding models that incorporate code structure, such as UniXcoder, can significantly improve fault localization performance by up to 49.0% in Top-1 accuracy. Finally, we conduct a case study to illustrate the effectiveness of FaR-Loc and to provide insights for its practical application.
A Comparative Benchmark of Real-time Detectors for Blueberry Detection towards Precision Orchard Management
Venue: arXiv cs.CV 2025
Year: 2025

Blueberry detection in natural environments remains challenging due to variable lighting, occlusions, and motion blur due to environmental factors and imaging devices. Deep learning-based object detectors promise to address these challenges, but they demand a large-scale, diverse dataset that captures the real-world complexities. Moreover, deploying these models in practical scenarios often requires the right accuracy/speed/memory trade-off in model selection. This study presents a novel comparative benchmark analysis of advanced real-time object detectors, including YOLO (You Only Look Once) (v8-v12) and RT-DETR (Real-Time Detection Transformers) (v1-v2) families, consisting of 36 model variants, evaluated on a newly curated dataset for blueberry detection. This dataset comprises 661 canopy images collected with smartphones during the 2022-2023 seasons, consisting of 85,879 labelled instances (including 36,256 ripe and 49,623 unripe blueberries) across a wide range of lighting conditions, occlusions, and fruit maturity stages. Among the YOLO models, YOLOv12m achieved the best accuracy with a mAP@50 of 93.3%, while RT-DETRv2-X obtained a mAP@50 of 93.6%, the highest among all the RT-DETR variants. The inference time varied with the model scale and complexity, and the mid-sized models appeared to offer a good accuracy-speed balance. To further enhance detection performance, all the models were fine-tuned using Unbiased Mean Teacher-based semi-supervised learning (SSL) on a separate set of 1,035 unlabeled images acquired by a ground-based machine vision platform in 2024. This resulted in accuracy gains ranging from -1.4% to 2.9%, with RT-DETR-v2-X achieving the best mAP@50 of 94.8%. More in-depth research into SSL is needed to better leverage cross-domain unlabeled data. Both the dataset and software programs of this study are made publicly available to support further research.
Uncertainty-Aware Active Source Tracking of Marine Pollution using Unmanned Surface Vehicles
Venue: arXiv cs.RO 2025
Year: 2025

This paper proposes an uncertainty-aware marine pollution source tracking framework for unmanned surface vehicles (USVs). By integrating high-fidelity marine pollution dispersion simulation with informative path planning techniques, we demonstrate effective identification of pollution sources in marine environments. The proposed approach is implemented based on Robot Operating System (ROS), processing real-time sensor data to update probabilistic source location estimates. The system progressively refines the estimation of source location while quantifying uncertainty levels in its predictions. Experiments conducted in simulated environments with varying source locations, wave conditions, and starting positions demonstrate the framework's ability to localise pollution sources with high accuracy. Results show that the proposed approach achieves reliable source localisation efficiently and outperforms the existing baseline. This work contributes to the development of full autonomous environmental monitoring capabilities essential for rapid response to marine pollution incidents.
TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data
Venue: arXiv cs.LG 2025
Year: 2025

Quality of Experience (QoE) modeling is crucial for optimizing video streaming services to capture the complex relationships between different features and user experience. We propose a novel approach to QoE modeling in video streaming applications using interpretable Machine Learning (ML) techniques over raw time series data. Unlike traditional black-box approaches, our method combines Kolmogorov-Arnold Networks (KANs) as an interpretable readout on top of compact frequency-domain features, allowing us to capture temporal information while retaining a transparent and explainable model. We evaluate our method on popular datasets and demonstrate its enhanced accuracy in QoE prediction, while offering transparency and interpretability.
Function Spaces Without Kernels: Learning Compact Hilbert Space Representations
Venue: arXiv cs.LG 2025
Year: 2025

Function encoders are a recent technique that learn neural network basis functions to form compact, adaptive representations of Hilbert spaces of functions. We show that function encoders provide a principled connection to feature learning and kernel methods by defining a kernel through an inner product of the learned feature map. This kernel-theoretic perspective explains their ability to scale independently of dataset size while adapting to the intrinsic structure of data, and it enables kernel-style analysis of neural models. Building on this foundation, we develop two training algorithms that learn compact bases: a progressive training approach that constructively grows bases, and a train-then-prune approach that offers a computationally efficient alternative after training. Both approaches use principles from PCA to reveal the intrinsic dimension of the learned space. In parallel, we derive finite-sample generalization bounds using Rademacher complexity and PAC-Bayes techniques, providing inference time guarantees. We validate our approach on a polynomial benchmark with a known intrinsic dimension, and on nonlinear dynamical systems including a Van der Pol oscillator and a two-body orbital model, demonstrating that the same accuracy can be achieved with substantially fewer basis functions. This work suggests a path toward neural predictors with kernel-level guarantees, enabling adaptable models that are both efficient and principled at scale.
Reliability Analysis of Fully Homomorphic Encryption Systems Under Memory Faults
Venue: arXiv cs.CR 2025
Year: 2025

Fully Homomorphic Encryption (FHE) represents a paradigm shift in cryptography, enabling computation directly on encrypted data and unlocking privacy-critical computation. Despite being increasingly deployed in real platforms, the reliability aspects of FHE systems, especially how they respond to faults, have been mostly neglected. This paper aims to better understand of how FHE computation behaves in the presence of memory faults, both in terms of individual operations as well as at the level of applications, for different FHE schemes. Finally, we investigate how effective traditional and FHE-specific fault mitigation techniques are.