OpenRaiser/Intern-Atlas huggingface dataset:
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
Venue: arXiv cs.AI 2025
Year: 2025

The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue goals across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.
One-step Diffusion Models with $f$-Divergence Distribution Matching
Venue: arXiv cs.LG 2025
Year: 2025

Sampling from diffusion models involves a slow iterative process that hinders their practical deployment, especially for interactive applications. To accelerate generation speed, recent approaches distill a multi-step diffusion model into a single-step student generator via variational score distillation, which matches the distribution of samples generated by the student to the teacher's distribution. However, these approaches use the reverse Kullback-Leibler (KL) divergence for distribution matching which is known to be mode seeking. In this paper, we generalize the distribution matching approach using a novel $f$-divergence minimization framework, termed $f$-distill, that covers different divergences with different trade-offs in terms of mode coverage and training variance. We derive the gradient of the $f$-divergence between the teacher and student distributions and show that it is expressed as the product of their score differences and a weighting function determined by their density ratio. This weighting function naturally emphasizes samples with higher density in the teacher distribution, when using a less mode-seeking divergence. We observe that the popular variational score distillation approach using the reverse-KL divergence is a special case within our framework. Empirically, we demonstrate that alternative $f$-divergences, such as forward-KL and Jensen-Shannon divergences, outperform the current best variational score distillation methods across image generation tasks. In particular, when using Jensen-Shannon divergence, $f$-distill achieves current state-of-the-art one-step generation performance on ImageNet64 and zero-shot text-to-image generation on MS-COCO. Project page: https://research.nvidia.com/labs/genair/f-distill
Integrating Domain Knowledge into Large Language Models for Enhanced Fashion Recommendations
Venue: arXiv cs.CL 2025
Year: 2025

Fashion, deeply rooted in sociocultural dynamics, evolves as individuals emulate styles popularized by influencers and iconic figures. In the quest to replicate such refined tastes using artificial intelligence, traditional fashion ensemble methods have primarily used supervised learning to imitate the decisions of style icons, which falter when faced with distribution shifts, leading to style replication discrepancies triggered by slight variations in input. Meanwhile, large language models (LLMs) have become prominent across various sectors, recognized for their user-friendly interfaces, strong conversational skills, and advanced reasoning capabilities. To address these challenges, we introduce the Fashion Large Language Model (FLLM), which employs auto-prompt generation training strategies to enhance its capacity for delivering personalized fashion advice while retaining essential domain knowledge. Additionally, by integrating a retrieval augmentation technique during inference, the model can better adjust to individual preferences. Our results show that this approach surpasses existing models in accuracy, interpretability, and few-shot learning capabilities.
Evaluating the Efficacy of Next.js: A Comparative Analysis with React.js on Performance, SEO, and Global Network Equity
Venue: arXiv cs.NI 2025
Year: 2025

This paper investigates the efficacy of Next.js as a framework addressing the challenges posed by React.js, particularly in performance, SEO, and equitable web accessibility. By constructing identical websites and web applications in both frameworks, we aim to evaluate the frameworks' behavior under diverse network conditions and capabilities. Beyond quantitative metrics like First Contentful Paint (FCP) and Time to Interactive (TTI), we incorporate qualitative user feedback to assess real-world usability. Our motivation stems from bridging the digital divide exacerbated by client-side rendering (CSR) frameworks and validating investments in modern technologies for businesses and institutions. Employing a novel LLM-assisted migration workflow, this paper also demonstrates the ease with which developers can transition from React.js to Next.js. Our results highlight Next.js's promise of better overall performance, without any degradation in user interaction experience, showcasing its potential to mitigate disparities in web accessibility and foster global network equity, thus highlighting Next.js as a compelling framework for the future of an inclusive web.
MAML: Towards a Faster Web in Developing Regions
Venue: arXiv cs.NI 2025
Year: 2025

The web experience in developing regions remains subpar, primarily due to the growing complexity of modern webpages and insufficient optimization by content providers. Users in these regions typically rely on low-end devices and limited bandwidth, which results in a poor user experience as they download and parse webpages bloated with excessive third-party CSS and JavaScript (JS). To address these challenges, we introduce the Mobile Application Markup Language (MAML), a flat layout-based web specification language that reduces computational and data transmission demands, while replacing the excessive bloat from JS with a new scripting language centered on essential (and popular) web functionalities. Last but not least, MAML is backward compatible as it can be transpiled to minimal HTML/JavaScript/CSS and thus work with legacy browsers. We benchmark MAML in terms of page load times and sizes, using a translator which can automatically port any webpage to MAML. When compared to the popular Google AMP, across 100 testing webpages, MAML offers webpage speedups by tens of seconds under challenging network conditions thanks to its significant size reductions. Next, we run a competition involving 25 university students porting 50 of the above webpages to MAML using a web-based editor we developed. This experiment verifies that, with little developer effort, MAML is quite effective in maintaining the visual and functional correctness of the originating webpages.
TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation
Venue: arXiv cs.IR 2025
Year: 2025

The integration of AI in education offers significant potential to enhance learning efficiency. Large Language Models (LLMs), such as ChatGPT, Gemini, and Llama, allow students to query a wide range of topics, providing unprecedented flexibility. However, LLMs face challenges, such as handling varying content relevance and lack of personalization. To address these challenges, we propose TutorLLM, a personalized learning recommender LLM system based on Knowledge Tracing (KT) and Retrieval-Augmented Generation (RAG). The novelty of TutorLLM lies in its unique combination of KT and RAG techniques with LLMs, which enables dynamic retrieval of context-specific knowledge and provides personalized learning recommendations based on the student's personal learning state. Specifically, this integration allows TutorLLM to tailor responses based on individual learning states predicted by the Multi-Features with Latent Relations BERT-based KT (MLFBK) model and to enhance response accuracy with a Scraper model. The evaluation includes user assessment questionnaires and performance metrics, demonstrating a 10% improvement in user satisfaction and a 5\% increase in quiz scores compared to using general LLMs alone.
The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition
Venue: arXiv cs.AI 2025
Year: 2025

This article investigates, within the field of neuropsychology of artificial intelligence, the process of categorical segmentation performed by language models. This process involves, across different neural layers, the creation of new functional categorical dimensions to analyze the input textual data and perform the required tasks. Each neuron in a multilayer perceptron (MLP) network is associated with a specific category, generated by three factors carried by the neural aggregation function: categorical priming, categorical attention, and categorical phasing. At each new layer, these factors govern the formation of new categories derived from the categories of precursor neurons. Through a process of categorical clipping, these new categories are created by selectively extracting specific subdimensions from the preceding categories, constructing a distinction between a form and a categorical background. We explore several cognitive characteristics of this synthetic clipping in an exploratory manner: categorical reduction, categorical selectivity, separation of initial embedding dimensions, and segmentation of categorical zones.
UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchain
Venue: arXiv cs.NI 2025
Year: 2025

This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for autonomous UAV coordination, and a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the decentralized coordination between them is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). The evaluation results demonstrate that the proposed selection and coordination mechanisms improve the stability of the selected relays and maximize the coverage and connectivity achieved by the UAVs.
Training AI to be Loyal
Venue: arXiv cs.CY 2025
Year: 2025

Loyal AI is loyal to the community that builds it. An AI is loyal to a community if the community has ownership, alignment, and control. Community owned models can only be used with the approval of the community and share the economic rewards communally. Community aligned models have values that are aligned with the consensus of the community. Community controlled models perform functions designed by the community. Since we would like permissionless access to the loyal AI's community, we need the AI to be open source. The key scientific question then is: how can we build models that are openly accessible (open source) and yet are owned and governed by the community. This seeming impossibility is the focus of this paper where we outline a concrete pathway to Open, Monetizable and Loyal models (OML), building on our earlier work on OML, arXiv:2411.03887(1) , and a representation via a cryptographic-ML library http://github.com/sentient-agi/oml-1.0-fingerprinting .
Detection of LLM-Generated Java Code Using Discretized Nested Bigrams
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) are currently used extensively to generate code by professionals and students, motivating the development of tools to detect LLM-generated code for applications such as academic integrity and cybersecurity. We address this authorship attribution problem as a binary classification task along with feature identification and extraction. We propose new Discretized Nested Bigram Frequency features on source code groups of various sizes. Compared to prior work, improvements are obtained by representing sparse information in dense membership bins. Experimental evaluation demonstrated that our approach significantly outperformed a commonly used GPT code-detection API and baseline features, with accuracy exceeding 96% compared to 72% and 79% respectively in detecting GPT-rewritten Java code fragments for 976 files with GPT 3.5 and GPT4 using 12 features. We also outperformed three prior works on code author identification in a 40-author dataset. Our approach scales well to larger data sets, and we achieved 99% accuracy and 0.999 AUC for 76,089 files and over 1,000 authors with GPT 4o using 227 features.
Text2Net: Transforming Plain-text To A Dynamic Interactive Network Simulation Environment
Venue: arXiv cs.NI 2025
Year: 2025

This paper introduces Text2Net, an innovative text-based network simulation engine that leverages natural language processing (NLP) and large language models (LLMs) to transform plain-text descriptions of network topologies into dynamic, interactive simulations. Text2Net simplifies the process of configuring network simulations, eliminating the need for users to master vendor-specific syntaxes or navigate complex graphical interfaces. Through qualitative and quantitative evaluations, we demonstrate Text2Net's ability to significantly reduce the time and effort required to deploy network scenarios compared to traditional simulators like EVE-NG. By automating repetitive tasks and enabling intuitive interaction, Text2Net enhances accessibility for students, educators, and professionals. The system facilitates hands-on learning experiences for students that bridge the gap between theoretical knowledge and practical application. The results showcase its scalability across various network complexities, marking a significant step toward revolutionizing network education and professional use cases, such as proof-of-concept testing.
Causal Covariate Shift Correction using Fisher information penalty
Venue: arXiv cs.LG 2025
Year: 2025

Evolving feature densities across batches of training data bias cross-validation, making model selection and assessment unreliable (\cite{sugiyama2012machine}). This work takes a distributed density estimation angle to the training setting where data are temporally distributed. \textit{Causal Covariate Shift Correction ($C^{3}$)}, accumulates knowledge about the data density of a training batch using Fisher Information, and using it to penalize the loss in all subsequent batches. The penalty improves accuracy by $12.9\%$ over the full-dataset baseline, by $20.3\%$ accuracy at maximum in batchwise and $5.9\%$ at minimum in foldwise benchmarks.
JExplore: Design Space Exploration Tool for Nvidia Jetson Boards
Venue: arXiv cs.AR 2025
Year: 2025

Nvidia Jetson boards are powerful systems for executing artificial intelligence workloads in edge and mobile environments due to their effective GPU hardware and widely supported software stack. In addition to these benefits, Nvidia Jetson boards provide large configurability by giving the user the choice to modify many hardware parameters. This large space of configurability creates the need of searching the optimal configurations based on the user's requirements. In this work, we propose JExplore, a multi-board software and hardware design space exploration tool. JExplore can be integrated with any search tool, hence creating a common benchmarking ground for the search algorithms. Moreover, it accelerates the exploration of user application and Nvidia Jetson configurations for researchers and engineers by encapsulating host-client communication, configuration management, and metric measurement.
Signal Collapse in One-Shot Pruning: When Sparse Models Fail to Distinguish Neural Representations
Venue: arXiv cs.LG 2025
Year: 2025

Neural network pruning is essential for reducing model complexity to enable deployment on resource constrained hardware. While performance loss of pruned networks is often attributed to the removal of critical parameters, we identify signal collapse a reduction in activation variance across layers as the root cause. Existing one shot pruning methods focus on weight selection strategies and rely on computationally expensive second order approximations. In contrast, we demonstrate that mitigating signal collapse, rather than optimizing weight selection, is key to improving accuracy of pruned networks. We propose REFLOW that addresses signal collapse without updating trainable weights, revealing high quality sparse sub networks within the original parameter space. REFLOW enables magnitude pruning to achieve state of the art performance, restoring ResNeXt101 accuracy from under 4.1% to 78.9% on ImageNet with only 20% of the weights retained, surpassing state of the art approaches.
OCCULT: Evaluating Large Language Models for Offensive Cyber Operation Capabilities
Venue: arXiv cs.CR 2025
Year: 2025

The prospect of artificial intelligence (AI) competing in the adversarial landscape of cyber security has long been considered one of the most impactful, challenging, and potentially dangerous applications of AI. Here, we demonstrate a new approach to assessing AI's progress towards enabling and scaling real-world offensive cyber operations (OCO) tactics in use by modern threat actors. We detail OCCULT, a lightweight operational evaluation framework that allows cyber security experts to contribute to rigorous and repeatable measurement of the plausible cyber security risks associated with any given large language model (LLM) or AI employed for OCO. We also prototype and evaluate three very different OCO benchmarks for LLMs that demonstrate our approach and serve as examples for building benchmarks under the OCCULT framework. Finally, we provide preliminary evaluation results to demonstrate how this framework allows us to move beyond traditional all-or-nothing tests, such as those crafted from educational exercises like capture-the-flag environments, to contextualize our indicators and warnings in true cyber threat scenarios that present risks to modern infrastructure. We find that there has been significant recent advancement in the risks of AI being used to scale realistic cyber threats. For the first time, we find a model (DeepSeek-R1) is capable of correctly answering over 90% of challenging offensive cyber knowledge tests in our Threat Actor Competency Test for LLMs (TACTL) multiple-choice benchmarks. We also show how Meta's Llama and Mistral's Mixtral model families show marked performance improvements over earlier models against our benchmarks where LLMs act as offensive agents in MITRE's high-fidelity offensive and defensive cyber operations simulation environment, CyberLayer.
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) are powerful tools for modern applications, but their computational demands limit accessibility. Quantization offers efficiency gains, yet its impact on safety and trustworthiness remains poorly understood. To address this, we introduce OpenMiniSafety, a human-curated safety dataset with 1.067 challenging questions to rigorously evaluate model behavior. We publicly release human safety evaluations for four LLMs (both quantized and full-precision), totaling 4.268 annotated question-answer pairs. By assessing 66 quantized variants of these models using four post-training quantization (PTQ) and two quantization-aware training (QAT) methods across four safety benchmarks including human-centric evaluations we uncover critical safety performance trade-offs. Our results show both PTQ and QAT can degrade safety alignment, with QAT techniques like QLORA or STE performing less safely. No single method consistently outperforms others across benchmarks, precision settings, or models, highlighting the need for safety-aware compression strategies. Furthermore, precision-specialized methods (e.g., QUIK and AWQ for 4-bit, AQLM and Q-PET for 2-bit) excel at their target precision, meaning that these methods are not better at compressing but rather different approaches.
Zero-Shot Commonsense Validation and Reasoning with Large Language Models: An Evaluation on SemEval-2020 Task 4 Dataset
Venue: arXiv cs.CL 2025
Year: 2025

This study evaluates the performance of Large Language Models (LLMs) on SemEval-2020 Task 4 dataset, focusing on commonsense validation and explanation. Our methodology involves evaluating multiple LLMs, including LLaMA3-70B, Gemma2-9B, and Mixtral-8x7B, using zero-shot prompting techniques. The models are tested on two tasks: Task A (Commonsense Validation), where models determine whether a statement aligns with commonsense knowledge, and Task B (Commonsense Explanation), where models identify the reasoning behind implausible statements. Performance is assessed based on accuracy, and results are compared to fine-tuned transformer-based models. The results indicate that larger models outperform previous models and perform closely to human evaluation for Task A, with LLaMA3-70B achieving the highest accuracy of 98.40% in Task A whereas, lagging behind previous models with 93.40% in Task B. However, while models effectively identify implausible statements, they face challenges in selecting the most relevant explanation, highlighting limitations in causal and inferential reasoning.