OpenRaiser/Intern-Atlas huggingface dataset:
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with cold-start and data-sparse scenarios. Recent advancements in pre-trained large language models (LLMs) with rich semantic knowledge, offer promising solutions to these challenges. However, deploying LLMs at scale is hindered by their significant computational demands and latency. In this paper, we propose a novel and scalable LLM-RecSys framework, LLMInit, designed to integrate pretrained LLM embeddings into CF models through selective initialization strategies. Specifically, we identify the embedding collapse issue observed when CF models scale and match the large embedding sizes in LLMs and avoid the problem by introducing efficient sampling methods, including, random, uniform, and variance-based selections. Comprehensive experiments conducted on multiple real-world datasets demonstrate that LLMInit significantly improves recommendation performance while maintaining low computational costs, offering a practical and scalable solution for industrial applications. To facilitate industry adoption and promote future research, we provide open-source access to our implementation at https://github.com/DavidZWZ/LLMInit.
A Mapping Analysis of Requirements Between the CRA and the GDPR
Venue: arXiv cs.SE 2025
Year: 2025

A new Cyber Resilience Act (CRA) was recently agreed upon in the European Union (EU). The paper examines and elaborates what new requirements the CRA entails by contrasting it with the older General Data Protection Regulation (GDPR). According to the results, there are overlaps in terms confidentiality, integrity, and availability guarantees, data minimization, traceability, data erasure, and security testing. The CRA's seven new essential requirements originate from obligations to (1) ship products without known exploitable vulnerabilities and (2) with secure defaults, to (3) provide security patches typically for a minimum of five years, to (4) minimize attack surfaces, to (5) develop and enable exploitation mitigation techniques, to (6) establish a software bill of materials (SBOM), and to (7) improve vulnerability coordination, including a mandate to establish a coordinated vulnerability disclosure policy. With these results and an accompanying discussion, the paper contributes to requirements engineering research specialized into legal requirements, demonstrating how new laws may affect existing requirements.
A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

This study investigates the machine unlearning techniques within the context of large language models (LLMs), referred to as \textit{LLM unlearning}. LLM unlearning offers a principled approach to removing the influence of undesirable data (e.g., sensitive or illegal information) from LLMs, while preserving their overall utility without requiring full retraining. Despite growing research interest, there is no comprehensive survey that systematically organizes existing work and distills key insights; here, we aim to bridge this gap. We begin by introducing the definition and the paradigms of LLM unlearning, followed by a comprehensive taxonomy of existing unlearning studies. Next, we categorize current unlearning approaches, summarizing their strengths and limitations. Additionally, we review evaluation metrics and benchmarks, providing a structured overview of current assessment methodologies. Finally, we outline promising directions for future research, highlighting key challenges and opportunities in the field.
Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning
Venue: arXiv cs.CL 2025
Year: 2025

Advances in Large Language Models revolutionized medical education by enabling scalable and efficient learning solutions. This paper presents a pipeline employing Retrieval-Augmented Generation (RAG) system to prepare comments generation for Poland's State Specialization Examination (PES) based on verified resources. The system integrates these generated comments and source documents with a spaced repetition learning algorithm to enhance knowledge retention while minimizing cognitive overload. By employing a refined retrieval system, query rephraser, and an advanced reranker, our modified RAG solution promotes accuracy more than efficiency. Rigorous evaluation by medical annotators demonstrates improvements in key metrics such as document relevance, credibility, and logical coherence of generated content, proven by a series of experiments presented in the paper. This study highlights the potential of RAG systems to provide scalable, high-quality, and individualized educational resources, addressing non-English speaking users.
BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor
Venue: arXiv cs.CL 2025
Year: 2025

Thematic analysis of social media posts provides a major understanding of public discourse, yet traditional methods often struggle to capture the complexity and nuance of unstructured, large-scale text data. This study introduces a novel methodology for thematic analysis that integrates tweet embeddings from pre-trained language models, dimensionality reduction using and matrix factorization, and generative AI to identify and refine latent themes. Our approach clusters compressed tweet representations and employs generative AI to extract and articulate themes through an agentic Chain of Thought (CoT) prompting, with a secondary LLM for quality assurance. This methodology is applied to tweets from the autistic community, a group that increasingly uses social media to discuss their experiences and challenges. By automating the thematic extraction process, the aim is to uncover key insights while maintaining the richness of the original discourse. This autism case study demonstrates the utility of the proposed approach in improving thematic analysis of social media data, offering a scalable and adaptable framework that can be applied to diverse contexts. The results highlight the potential of combining machine learning and Generative AI to enhance the depth and accuracy of theme identification in online communities.
Datenschutzkonformer LLM-Einsatz: Eine Open-Source-Referenzarchitektur
Venue: arXiv cs.CR 2025
Year: 2025

The development of Large Language Models (LLMs) has led to significant advancements in natural language processing and enabled numerous applications across various industries. However, many LLM-based solutions operate as open systems relying on cloud services, which pose risks to data confidentiality and security. To address these challenges, organizations require closed LLM systems that comply with data protection regulations while maintaining high performance. In this paper, we present a reference architecture for developing closed, LLM-based systems using open-source technologies. The architecture provides a flexible and transparent solution that meets strict data privacy and security requirements. We analyze the key challenges in implementing such systems, including computing resources, data management, scalability, and security risks. Additionally, we introduce an evaluation pipeline that enables a systematic assessment of system performance and compliance.
Multi-models with averaging in feature domain for non-invasive blood glucose estimation
Venue: arXiv cs.LG 2025
Year: 2025

Diabetes is a serious chronic metabolic disease. In the recent years, more and more consumer technology enterprises focusing on human health are committed to implementing accurate and non-invasive blood glucose algorithm in their products. However, due to the interference from the external environment, these wearable non-invasive methods yield the low estimation accuracy. To address this issue, this paper employs different models based on different ranges of the blood glucose values for performing the blood glucose estimation. First the photoplethysmograms (PPGs) are acquired and they are denoised via the bit plane singular spectrum analysis (SSA) method. Second, the features are extracted. For the data in the training set, first the features are averaged across the measurements in the feature domain via the optimization approach. Second, the random forest is employed to sort the importance of each feature. Third, the training set is divided into three subsets according to the reference blood glucose values. Fourth, the feature vectors and the corresponding blood glucose values in the same group are employed to build an individual model. Fifth, for each feature, the average of the feature values for all the measurements in the same subset is computed. For the data in the test set, first, the sum of the weighted distances between the test feature values and the average values obtained in the above is computed for each model. Here, the weights are defined based on the importance sorted by the random forest obtained in the above. The model corresponding to the smallest sum is assigned. Finally, the blood glucose value is estimated based on the corresponding model. Compared to the state of arts methods, our proposed method can effectively improve the estimation accuracy.
NCL-UoR at SemEval-2025 Task 3: Detecting Multilingual Hallucination and Related Observable Overgeneration Text Spans with Modified RefChecker and Modified SeflCheckGPT
Venue: arXiv cs.CL 2025
Year: 2025

SemEval-2025 Task 3 (Mu-SHROOM) focuses on detecting hallucinations in content generated by various large language models (LLMs) across multiple languages. This task involves not only identifying the presence of hallucinations but also pinpointing their specific occurrences. To tackle this challenge, this study introduces two methods: modified RefChecker and modified SelfCheckGPT. The modified RefChecker integrates prompt-based factual verification into References, structuring them as claim-based tests rather than single external knowledge sources. The modified SelfCheckGPT incorporates external knowledge to overcome its reliance on internal knowledge. In addition, both methods' original prompt designs are enhanced to identify hallucinated words within LLM-generated texts. Experimental results demonstrate the effectiveness of the approach, achieving a high ranking on the test dataset in detecting hallucinations across various languages, with an average IoU of 0.5310 and an average COR of 0.5669.
Road Boundary Detection Using 4D mmWave Radar for Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Detecting road boundaries, the static physical edges of the available driving area, is important for safe navigation and effective path planning in autonomous driving and advanced driver-assistance systems (ADAS). Traditionally, road boundary detection in autonomous driving relies on cameras and LiDAR. However, they are vulnerable to poor lighting conditions, such as nighttime and direct sunlight glare, or prohibitively expensive for low-end vehicles. To this end, this paper introduces 4DRadarRBD, the first road boundary detection method based on 4D mmWave radar which is cost-effective and robust in complex driving scenarios. The main idea is that road boundaries (e.g., fences, bushes, roadblocks), reflect millimeter waves, thus generating point cloud data for the radar. To overcome the challenge that the 4D mmWave radar point clouds contain many noisy points, we initially reduce noisy points via physical constraints for road boundaries and then segment the road boundary points from the noisy points by incorporating a distance-based loss which penalizes for falsely detecting the points far away from the actual road boundaries. In addition, we capture the temporal dynamics of point cloud sequences by utilizing each point's deviation from the vehicle motion-compensated road boundary detection result obtained from the previous frame, along with the spatial distribution of the point cloud for point-wise road boundary segmentation. We evaluated 4DRadarRBD through real-world driving tests and achieved a road boundary point segmentation accuracy of 93$\%$, with a median distance error of up to 0.023 m and an error reduction of 92.6$\%$ compared to the baseline model.
Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI Perspective
Venue: arXiv cs.LG 2025
Year: 2025

Deploying large scale language models on edge devices faces inherent challenges such as high computational demands, energy consumption, and potential data privacy risks. This paper introduces the Shakti Small Language Models (SLMs) Shakti-100M, Shakti-250M, and Shakti-500M which target these constraints headon. By combining efficient architectures, quantization techniques, and responsible AI principles, the Shakti series enables on-device intelligence for smartphones, smart appliances, IoT systems, and beyond. We provide comprehensive insights into their design philosophy, training pipelines, and benchmark performance on both general tasks (e.g., MMLU, Hellaswag) and specialized domains (healthcare, finance, and legal). Our findings illustrate that compact models, when carefully engineered and fine-tuned, can meet and often exceed expectations in real-world edge-AI scenarios.
Protecting DeFi Platforms against Non-Price Flash Loan Attacks
Venue: arXiv cs.CR 2025
Year: 2025

Smart contracts in Decentralized Finance (DeFi) platforms are attractive targets for attacks as their vulnerabilities can lead to massive amounts of financial losses. Flash loan attacks, in particular, pose a major threat to DeFi protocols that hold a Total Value Locked (TVL) exceeding \$106 billion. These attacks use the atomicity property of blockchains to drain funds from smart contracts in a single transaction. While existing research primarily focuses on price manipulation attacks, such as oracle manipulation, mitigating non-price flash loan attacks that often exploit smart contracts' zero-day vulnerabilities remains largely unaddressed. These attacks are challenging to detect because of their unique patterns, time sensitivity, and complexity. In this paper, we present FlashGuard, a runtime detection and mitigation method for non-price flash loan attacks. Our approach targets smart contract function signatures to identify attacks in real-time and counterattack by disrupting the attack transaction atomicity by leveraging the short window when transactions are visible in the mempool but not yet confirmed. When FlashGuard detects an attack, it dispatches a stealthy dusting counterattack transaction to miners to change the victim contract's state which disrupts the attack's atomicity and forces the attack transaction to revert. We evaluate our approach using 20 historical attacks and several unseen attacks. FlashGuard achieves an average real-time detection latency of 150.31ms, a detection accuracy of over 99.93\%, and an average disruption time of 410.92ms. FlashGuard could have potentially rescued over \$405.71 million in losses if it were deployed prior to these attack instances. FlashGuard demonstrates significant potential as a DeFi security solution to mitigate and handle rising threats of non-price flash loan attacks.
Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions
Venue: arXiv cs.CL 2025
Year: 2025

Existing challenges in misinformation exposure and susceptibility vary across demographic groups, as some populations are more vulnerable to misinformation than others. Large language models (LLMs) introduce new dimensions to these challenges through their ability to generate persuasive content at scale and reinforcing existing biases. This study investigates the bidirectional persuasion dynamics between LLMs and humans when exposed to misinformative content. We analyze human-to-LLM influence using human-stance datasets and assess LLM-to-human influence by generating LLM-based persuasive arguments. Additionally, we use a multi-agent LLM framework to analyze the spread of misinformation under persuasion among demographic-oriented LLM agents. Our findings show that demographic factors influence susceptibility to misinformation in LLMs, closely reflecting the demographic-based patterns seen in human susceptibility. We also find that, similar to human demographic groups, multi-agent LLMs exhibit echo chamber behavior. This research explores the interplay between humans and LLMs, highlighting demographic differences in the context of misinformation and offering insights for future interventions.
USeR: A Web-based User Story eReviewer for Assisted Quality Optimizations
Venue: arXiv cs.SE 2025
Year: 2025

User stories are widely applied for conveying requirements within agile software development teams. Multiple user story quality guidelines exist, but authors like Product Owners in industry projects frequently fail to write high-quality user stories. This situation is exacerbated by the lack of tools for assessing user story quality. In this paper, we propose User Story eReviewer (USeR) a web-based tool that allows authors to determine and optimize user story quality. For developing USeR, we collected 77 potential quality metrics through literature review, practitioner sessions, and research group meetings and refined these to 34 applicable metrics through expert sessions. Finally, we derived algorithms for eight prioritized metrics using a literature review and research group meetings and implemented them with plain code and machine learning techniques. USeR offers a RESTful API and user interface for instant, consistent, and explainable user feedback supporting fast and easy quality optimizations. It has been empirically evaluated with an expert study using 100 user stories and four experts from two real-world agile software projects in the automotive and health sectors.
EPEE: Towards Efficient and Effective Foundation Models in Biomedicine
Venue: arXiv cs.AI 2025
Year: 2025

Foundation models, including language models, e.g., GPT, and vision models, e.g., CLIP, have significantly advanced numerous biomedical tasks. Despite these advancements, the high inference latency and the "overthinking" issues in model inference impair the efficiency and effectiveness of foundation models, thus limiting their application in real-time clinical settings. To address these challenges, we proposed EPEE (Entropy- and Patience-based Early Exiting), a novel hybrid strategy designed to improve the inference efficiency of foundation models. The core idea was to leverage the strengths of entropy-based and patience-based early exiting methods to overcome their respective weaknesses. To evaluate EPEE, we conducted experiments on three core biomedical tasks-classification, relation extraction, and event extraction-using four foundation models (BERT, ALBERT, GPT-2, and ViT) across twelve datasets, including clinical notes and medical images. The results showed that EPEE significantly reduced inference time while maintaining or improving accuracy, demonstrating its adaptability to diverse datasets and tasks. EPEE addressed critical barriers to deploying foundation models in healthcare by balancing efficiency and effectiveness. It potentially provided a practical solution for real-time clinical decision-making with foundation models, supporting reliable and efficient workflows.
Hebbian learning the local structure of language
Venue: arXiv cs.CL 2025
Year: 2025

Learning in the brain is local and unsupervised (Hebbian). We derive the foundations of an effective human language model inspired by these microscopic constraints. It has two parts: (1) a hierarchy of neurons which learns to tokenize words from text (whichiswhatyoudowhenyoureadthis); and (2) additional neurons which bind the learned symanticless patterns of the tokenizer into a symanticful token (an embedding). The model permits continuous parallel learning without forgetting; and is a powerful tokenizer which performs renormalization group. This allows it to exploit redundancy, such that it generates tokens which are always decomposable into a basis set (e.g an alphabet), and can mix features learned from multiple languages. We find that the structure of this model allows it to learn a natural language morphology WITHOUT data. The language data generated by this model predicts the correct distribution of word-forming patterns observed in real languages, and further demonstrates why microscopically human speech is broken up into words. This model provides the basis for understanding the microscopic origins of language and human creativity.
Deep Learning is Not So Mysterious or Different
Venue: arXiv cs.LG 2025
Year: 2025

Deep neural networks are often seen as different from other model classes by defying conventional notions of generalization. Popular examples of anomalous generalization behaviour include benign overfitting, double descent, and the success of overparametrization. We argue that these phenomena are not distinct to neural networks, or particularly mysterious. Moreover, this generalization behaviour can be intuitively understood, and rigorously characterized, using long-standing generalization frameworks such as PAC-Bayes and countable hypothesis bounds. We present soft inductive biases as a key unifying principle in explaining these phenomena: rather than restricting the hypothesis space to avoid overfitting, embrace a flexible hypothesis space, with a soft preference for simpler solutions that are consistent with the data. This principle can be encoded in many model classes, and thus deep learning is not as mysterious or different from other model classes as it might seem. However, we also highlight how deep learning is relatively distinct in other ways, such as its ability for representation learning, phenomena such as mode connectivity, and its relative universality.
DuSK: Faster Indirect Text Entry Supporting Out-Of-Vocabulary Words for Touchpads
Venue: arXiv cs.HC 2025
Year: 2025

Given the ubiquity of SmartTVs and head-mounted-display-based virtual environments, recent research has explored techniques to support eyes-free text entry using touchscreen devices. However, proposed techniques, leveraging lexicons, limit the user's ability to enter out-of-vocabulary words. In this paper, we investigate how to enter text while relying on unambiguous input to support out-of-vocabulary words. Through an iterative design approach, and after a careful investigation of actions that can be accurately and rapidly performed eyes-free, we devise DuSK, a {Du}al-handed, {S}troke-based, 1{K}eyboarding technique. In a controlled experiment, we show initial speeds of 10 WPM steadily increasing to 13~WPM with training. DuSK outperforms the common cursor-based text entry technique widely deployed in commercial SmartTVs (8 WPM) and is comparable to other eyes-free lexicon-based techniques, but with the added benefit of supporting out-of-vocabulary word input.
Enabling mixed-precision in spectral element codes
Venue: arXiv cs.MS 2025
Year: 2025

Mixed-precision computing has the potential to significantly reduce the cost of exascale computations, but determining when and how to implement it in programs can be challenging. In this article, we propose a methodology for enabling mixed-precision with the help of computer arithmetic tools, roofline model, and computer arithmetic techniques. As case studies, we consider Nekbone, a mini-application for the Computational Fluid Dynamics (CFD) solver Nek5000, and a modern Neko CFD application. With the help of the Verificarlo tool and computer arithmetic techniques, we introduce a strategy to address stagnation issues in the preconditioned Conjugate Gradient method in Nekbone and apply these insights to implement a mixed-precision version of Neko. We evaluate the derived mixed-precision versions of these codes by combining metrics in three dimensions: accuracy, time-to-solution, and energy-to-solution. Notably, mixed-precision in Nekbone reduces time-to-solution by roughly 1.62x and energy-to-solution by 2.43x on MareNostrum 5, while in the real-world Neko application, the gain is up to 1.3x in both time and energy, with the accuracy that matches double-precision results.