OpenRaiser/Intern-Atlas huggingface dataset:
An Unsupervised Framework for Dynamic Health Indicator Construction and Its Application in Rolling Bearing Prognostics
Venue: arXiv cs.LG 2025
Year: 2025

Health indicator (HI) plays a key role in degradation assessment and prognostics of rolling bearings. Although various HI construction methods have been investigated, most of them rely on expert knowledge for feature extraction and overlook capturing dynamic information hidden in sequential degradation processes, which limits the ability of the constructed HI for degradation trend representation and prognostics. To address these concerns, a novel dynamic HI that considers HI-level temporal dependence is constructed through an unsupervised framework. Specifically, a degradation feature learning module composed of a skip-connection-based autoencoder first maps raw signals to a representative degradation feature space (DFS) to automatically extract essential degradation features without the need for expert knowledge. Subsequently, in this DFS, a new HI-generating module embedded with an inner HI-prediction block is proposed for dynamic HI construction, where the temporal dependence between past and current HI states is guaranteed and modeled explicitly. On this basis, the dynamic HI captures the inherent dynamic contents of the degradation process, ensuring its effectiveness for degradation tendency modeling and future degradation prognostics. The experiment results on two bearing lifecycle datasets demonstrate that the proposed HI construction method outperforms comparison methods, and the constructed dynamic HI is superior for prognostic tasks.
Sentinel: SOTA model to protect against prompt injections
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) are increasingly powerful but remain vulnerable to prompt injection attacks, where malicious inputs cause the model to deviate from its intended instructions. This paper introduces Sentinel, a novel detection model, qualifire/prompt-injection-sentinel, based on the \answerdotai/ModernBERT-large architecture. By leveraging ModernBERT's advanced features and fine-tuning on an extensive and diverse dataset comprising a few open-source and private collections, Sentinel achieves state-of-the-art performance. This dataset amalgamates varied attack types, from role-playing and instruction hijacking to attempts to generate biased content, alongside a broad spectrum of benign instructions, with private datasets specifically targeting nuanced error correction and real-world misclassifications. On a comprehensive, unseen internal test set, Sentinel demonstrates an average accuracy of 0.987 and an F1-score of 0.980. Furthermore, when evaluated on public benchmarks, it consistently outperforms strong baselines like protectai/deberta-v3-base-prompt-injection-v2. This work details Sentinel's architecture, its meticulous dataset curation, its training methodology, and a thorough evaluation, highlighting its superior detection capabilities.
A Neural Network Model of Spatial and Feature-Based Attention
Venue: arXiv cs.CV 2025
Year: 2025

Visual attention is a mechanism closely intertwined with vision and memory. Top-down information influences visual processing through attention. We designed a neural network model inspired by aspects of human visual attention. This model consists of two networks: one serves as a basic processor performing a simple task, while the other processes contextual information and guides the first network through attention to adapt to more complex tasks. After training the model and visualizing the learned attention response, we discovered that the model's emergent attention patterns corresponded to spatial and feature-based attention. This similarity between human visual attention and attention in computer vision suggests a promising direction for studying human cognition using neural network models.
Can LLMs Talk 'Sex'? Exploring How AI Models Handle Intimate Conversations
Venue: arXiv cs.CY 2025
Year: 2025

This study examines how four prominent large language models (Claude 3.7 Sonnet, GPT-4o, Gemini 2.5 Flash, and Deepseek-V3) handle sexually oriented requests through qualitative content analysis. By evaluating responses to prompts ranging from explicitly sexual to educational and neutral control scenarios, the research reveals distinct moderation paradigms reflecting fundamentally divergent ethical positions. Claude 3.7 Sonnet employs strict and consistent prohibitions, while GPT-4o navigates user interactions through nuanced contextual redirection. Gemini 2.5 Flash exhibits permissiveness with threshold-based limits, and Deepseek-V3 demonstrates troublingly inconsistent boundary enforcement and performative refusals. These varied approaches create a significant "ethical implementation gap," stressing a critical absence of unified ethical frameworks and standards across platforms. The findings underscore the urgent necessity for transparent, standardized guidelines and coordinated international governance to ensure consistent moderation, protect user welfare, and maintain trust as AI systems increasingly mediate intimate aspects of human life.
Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted
Venue: arXiv cs.AI 2025
Year: 2025

Contemporary businesses operate in dynamic environments requiring rapid adaptation to achieve goals and maintain competitiveness. Existing data platforms often fall short by emphasizing tools over alignment with business needs, resulting in inefficiencies and delays. To address this gap, I propose the Business Semantics Centric, AI Agents Assisted Data System (BSDS), a holistic system that integrates architecture, workflows, and team organization to ensure data systems are tailored to business priorities rather than dictated by technical constraints. BSDS redefines data systems as dynamic enablers of business success, transforming them from passive tools into active drivers of organizational growth. BSDS has a modular architecture that comprises curated data linked to business entities, a knowledge base for context-aware AI agents, and efficient data pipelines. AI agents play a pivotal role in assisting with data access and system management, reducing human effort, and improving scalability. Complementing this architecture, BSDS incorporates workflows optimized for both exploratory data analysis and production requirements, balancing speed of delivery with quality assurance. A key innovation of BSDS is its incorporation of the human factor. By aligning data team expertise with business semantics, BSDS bridges the gap between technical capabilities and business needs. Validated through real-world implementation, BSDS accelerates time-to-market for data-driven initiatives, enhances cross-functional collaboration, and provides a scalable blueprint for businesses of all sizes. Future research can build on BSDS to explore optimization strategies using complex systems and adaptive network theories, as well as developing autonomous data systems leveraging AI agents.
Understanding Community-Level Blocklists in Decentralized Social Media
Venue: arXiv cs.SI 2025
Year: 2025

Community-level blocklists are key to content moderation practices in decentralized social media. These blocklists enable moderators to prevent other communities, such as those acting in bad faith, from interacting with their own -- and, if shared publicly, warn others about communities worth blocking. Prior work has examined blocklists in centralized social media, noting their potential for collective moderation outcomes, but has focused on blocklists as individual-level tools. To understand how moderators perceive and utilize community-level blocklists and what additional support they may need, we examine social media communities running Mastodon, an open-source microblogging software built on the ActivityPub protocol. We conducted (1) content analysis of the community-level blocklist ecosystem, and (2) semi-structured interviews with twelve Mastodon moderators. Our content analysis revealed wide variation in blocklist goals, inclusion criteria, and transparency. Interviews showed moderators balance proactive safety, reactive practices, and caution around false positives when using blocklists for moderation. They noted challenges and limitations in current blocklist use, suggesting design improvements like comment receipts, category filters, and collaborative voting. We discuss implications for decentralized content moderation, highlighting trade-offs between openness, safety, and nuance; the complexity of moderator roles; and opportunities for future design.
Model Checking as Program Verification by Abstract Interpretation (Extended Version)
Venue: arXiv cs.LO 2025
Year: 2025

Abstract interpretation offers a powerful toolset for static analysis, tackling precision, complexity and state-explosion issues. In the literature, state partitioning abstractions based on (bi)simulation and property-preserving state relations have been successfully applied to abstract model checking. Here, we pursue a different track in which model checking is seen as an instance of program verification. To this purpose, we introduce a suitable language-called MOKA (for MOdel checking as abstract interpretation of Kleene Algebras)-which is used to encode temporal formulae as programs. In particular, we show that (universal fragments of) temporal logics, such as ACTL or, more generally, universal mu-calculus can be transformed into MOKA programs. Such programs return all and only the initial states which violate the formula. By applying abstract interpretation to MOKA programs, we pave the way for reusing more general abstractions than partitions as well as for tuning the precision of the abstraction to remove or avoid false alarms. We show how to perform model checking via a program logic that combines under-approximation and abstract interpretation analysis to avoid false alarms. The notion of locally complete abstraction is used to dynamically improve the analysis precision via counterexample-guided domain refinement.
SocialDF: Benchmark Dataset and Detection Model for Mitigating Harmful Deepfake Content on Social Media Platforms
Venue: arXiv cs.LG 2025
Year: 2025

The rapid advancement of deep generative models has significantly improved the realism of synthetic media, presenting both opportunities and security challenges. While deepfake technology has valuable applications in entertainment and accessibility, it has emerged as a potent vector for misinformation campaigns, particularly on social media. Existing detection frameworks struggle to distinguish between benign and adversarially generated deepfakes engineered to manipulate public perception. To address this challenge, we introduce SocialDF, a curated dataset reflecting real-world deepfake challenges on social media platforms. This dataset encompasses high-fidelity deepfakes sourced from various online ecosystems, ensuring broad coverage of manipulative techniques. We propose a novel LLM-based multi-factor detection approach that combines facial recognition, automated speech transcription, and a multi-agent LLM pipeline to cross-verify audio-visual cues. Our methodology emphasizes robust, multi-modal verification techniques that incorporate linguistic, behavioral, and contextual analysis to effectively discern synthetic media from authentic content.
Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
Venue: arXiv cs.MA 2025
Year: 2025

Collective risk social dilemmas (CRSD) highlight a trade-off between individual preferences and the need for all to contribute toward achieving a group objective. Problems such as climate change are in this category, and so it is critical to understand their social underpinnings. However, rigorous CRSD methodology often demands large-scale human experiments but it is difficult to guarantee sufficient power and heterogeneity over socio-demographic factors. Generative AI offers a potential complementary approach to address thisproblem. By replacing human participants with large language models (LLM), it allows for a scalable empirical framework. This paper focuses on the validity of this approach and whether it is feasible to represent a large-scale human-like experiment with sufficient diversity using LLM. In particular, where previous literature has focused on political surveys, virtual towns and classical game-theoretic examples, we focus on a complex CRSD used in the institutional economics and sustainability literature known as Port of Mars
ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation
Venue: arXiv cs.AR 2025
Year: 2025

Recent advances in large language models (LLMs) have enabled near-human performance on software coding benchmarks, but their effectiveness in RTL code generation remains limited due to the scarcity of high-quality training data. While prior efforts have fine-tuned LLMs for RTL tasks, they do not fundamentally overcome the data bottleneck and lack support for test-time scaling due to their non-reasoning nature. In this work, we introduce ScaleRTL, the first reasoning LLM for RTL coding that scales up both high-quality reasoning data and test-time compute. Specifically, we curate a diverse set of long chain-of-thought reasoning traces averaging 56K tokens each, resulting in a dataset of 3.5B tokens that captures rich RTL knowledge. Fine-tuning a general-purpose reasoning model on this corpus yields ScaleRTL that is capable of deep RTL reasoning. Subsequently, we further enhance the performance of ScaleRTL through a novel test-time scaling strategy that extends the reasoning process via iteratively reflecting on and self-correcting previous reasoning steps. Experimental results show that ScaleRTL achieves state-of-the-art performance on VerilogEval and RTLLM, outperforming 18 competitive baselines by up to 18.4% on VerilogEval and 12.7% on RTLLM.
Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society
Venue: arXiv cs.HC 2025
Year: 2025

Scenario building is an established method to anticipate the future of emerging technologies. Its primary goal is to use narratives to map future trajectories of technology development and sociotechnical adoption. Following this process, risks and benefits can be identified early on, and strategies can be developed that strive for desirable futures. In recent years, computer science has adopted this method and applied it to various technologies, including Artificial Intelligence (AI). Because computing technologies play such an important role in shaping modern societies, it is worth exploring how scenarios are being used as an anticipatory tool in the field -- and what possible traditional uses of scenarios are not yet covered but have the potential to enrich the field. We address this gap by conducting a systematic literature review on the use of scenario building methods in computer science over the last decade (n = 59). We guide the review along two main questions. First, we aim to uncover how scenarios are used in computing literature, focusing especially on the rationale for why scenarios are used. Second, in following the potential of scenario building to enhance inclusivity in research, we dive deeper into the participatory element of the existing scenario building literature in computer science.
Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks
Venue: arXiv cs.SE 2025
Year: 2025

A growing variety of prompt engineering techniques has been proposed for Large Language Models (LLMs), yet systematic evaluation of each technique on individual software engineering (SE) tasks remains underexplored. In this study, we present a systematic evaluation of 14 established prompt techniques across 10 SE tasks using four LLM models. As identified in the prior literature, the selected prompting techniques span six core dimensions (Zero-Shot, Few-Shot, Thought Generation, Ensembling, Self-Criticism, and Decomposition). They are evaluated on tasks such as code generation, bug fixing, and code-oriented question answering, to name a few. Our results show which prompting techniques are most effective for SE tasks requiring complex logic and intensive reasoning versus those that rely more on contextual understanding and example-driven scenarios. We also analyze correlations between the linguistic characteristics of prompts and the factors that contribute to the effectiveness of prompting techniques in enhancing performance on SE tasks. Additionally, we report the time and token consumption for each prompting technique when applied to a specific task and model, offering guidance for practitioners in selecting the optimal prompting technique for their use cases.
Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
Venue: arXiv cs.AI 2025
Year: 2025

We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within predefined workflows, we aim to automate workflow planning given high-level goals and scientist intuition. To this end, we propose Materials Agent unifying Planning, Physics, and Scientists, known as MAPPS. MAPPS consists of a Workflow Planner, a Tool Code Generator, and a Scientific Mediator. The Workflow Planner uses large language models (LLMs) to generate structured and multi-step workflows. The Tool Code Generator synthesizes executable Python code for various tasks, including invoking a force field foundation model that encodes physics. The Scientific Mediator coordinates communications, facilitates scientist feedback, and ensures robustness through error reflection and recovery. By unifying planning, physics, and scientists, MAPPS enables flexible and reliable materials discovery with greater autonomy, achieving a five-fold improvement in stability, uniqueness, and novelty rates compared with prior generative models when evaluated on the MP-20 data. We provide extensive experiments across diverse tasks to show that MAPPS is a promising framework for autonomous materials discovery.
FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning (FL) offers a decentralized framework for training and fine-tuning Large Language Models (LLMs) by leveraging computational resources across organizations while keeping sensitive data on local devices. It addresses privacy and security concerns while navigating challenges associated with the substantial computational demands of LLMs, which can be prohibitive for small and medium-sized organizations. FL supports the development of task-specific LLMs for cross-silo applications through fine-tuning but remains vulnerable to inference attacks, such as membership inference and gradient inversion, which threaten data privacy. Prior studies have utilized Differential Privacy (DP) in LLM fine-tuning, which, despite being effective at preserving privacy, can degrade model performance. To overcome these challenges, we propose a novel method, FedShield-LLM, that uses pruning with Fully Homomorphic Encryption (FHE) for Low-Rank Adaptation (LoRA) parameters, enabling secure computations on encrypted model updates while mitigating the attack surface by deactivating less important LoRA parameters. Furthermore, optimized federated algorithms for cross-silo environments enhance scalability and efficiency. Parameter-efficient fine-tuning techniques like LoRA substantially reduce computational and communication overhead, making FL feasible for resource-constrained clients. Experimental results show that the proposed method outperforms existing methods while maintaining robust privacy protection, enabling organizations to collaboratively train secure and efficient LLMs. The code and data are available at, https://github.com/solidlabnetwork/fedshield-llm
Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues
Venue: arXiv cs.CV 2025
Year: 2025

Three-dimensional digital urban reconstruction from multi-view aerial images is a critical application where deep multi-view stereo (MVS) methods outperform traditional techniques. However, existing methods commonly overlook the key differences between aerial and close-range settings, such as varying depth ranges along epipolar lines and insensitive feature-matching associated with low-detailed aerial images. To address these issues, we propose an Adaptive Depth Range MVS (ADR-MVS), which integrates monocular geometric cues to improve multi-view depth estimation accuracy. The key component of ADR-MVS is the depth range predictor, which generates adaptive range maps from depth and normal estimates using cross-attention discrepancy learning. In the first stage, the range map derived from monocular cues breaks through predefined depth boundaries, improving feature-matching discriminability and mitigating convergence to local optima. In later stages, the inferred range maps are progressively narrowed, ultimately aligning with the cascaded MVS framework for precise depth regression. Moreover, a normal-guided cost aggregation operation is specially devised for aerial stereo images to improve geometric awareness within the cost volume. Finally, we introduce a normal-guided depth refinement module that surpasses existing RGB-guided techniques. Experimental results demonstrate that ADR-MVS achieves state-of-the-art performance on the WHU, LuoJia-MVS, and München datasets, while exhibits superior computational complexity.
NGA: Non-autoregressive Generative Auction with Global Externalities for Advertising Systems
Venue: arXiv cs.IR 2025
Year: 2025

Online advertising auctions are fundamental to internet commerce, demanding solutions that not only maximize revenue but also ensure incentive compatibility, high-quality user experience, and real-time efficiency. While recent learning-based auction frameworks have improved context modeling by capturing intra-list dependencies among ads, they remain limited in addressing global externalities and often suffer from inefficiencies caused by sequential processing. In this work, we introduce the Non-autoregressive Generative Auction with global externalities (NGA), a novel end-to-end framework designed for industrial online advertising. NGA explicitly models global externalities by jointly capturing the relationships among ads as well as the effects of adjacent organic content. To further enhance efficiency, NGA utilizes a non-autoregressive, constraint-based decoding strategy and a parallel multi-tower evaluator for unified list-wise reward and payment computation. Extensive offline experiments and large-scale online A/B testing on commercial advertising platforms demonstrate that NGA consistently outperforms existing methods in both effectiveness and efficiency.
Pts3D-LLM: Studying the Impact of Token Structure for 3D Scene Understanding With Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Effectively representing 3D scenes for Multimodal Large Language Models (MLLMs) is crucial yet challenging. Existing approaches commonly only rely on 2D image features and use varied tokenization approaches. This work presents a rigorous study of 3D token structures, systematically comparing video-based and point-based representations while maintaining consistent model backbones and parameters. We propose a novel approach that enriches visual tokens by incorporating 3D point cloud features from a Sonata pretrained Point Transformer V3 encoder. Our experiments demonstrate that merging explicit 3D features significantly boosts performance. Furthermore, we show that point-based token structures can rival video-based ones when the points are cleverly sampled and ordered. Our best models from both structures achieve state-of-the-art results on multiple 3D understanding benchmarks. We emphasize our analysis of token structures as a key contribution, alongside transparent reporting of results averaged over multiple seeds, a practice we believe is vital for robust progress in the field.