OpenRaiser/Intern-Atlas huggingface dataset:
A Wireless Foundation Model for Multi-Task Prediction
Venue: arXiv cs.AI 2025
Year: 2025

With the growing complexity and dynamics of the mobile communication networks, accurately predicting key system parameters, such as channel state information (CSI), user location, and network traffic, has become essential for a wide range of physical (PHY)-layer and medium access control (MAC)-layer tasks. Although traditional deep learning (DL)-based methods have been widely applied to such prediction tasks, they often struggle to generalize across different scenarios and tasks. In response, we propose a unified foundation model for multi-task prediction in wireless networks that supports diverse prediction intervals. The proposed model enforces univariate decomposition to unify heterogeneous tasks, encodes granularity for interval awareness, and uses a causal Transformer backbone for accurate predictions. Additionally, we introduce a patch masking strategy during training to support arbitrary input lengths. After trained on large-scale datasets, the proposed foundation model demonstrates strong generalization to unseen scenarios and achieves zero-shot performance on new tasks that surpass traditional full-shot baselines.
Robust Speech-Workload Estimation for Intelligent Human-Robot Systems
Venue: arXiv cs.RO 2025
Year: 2025

Demanding task environments (e.g., supervising a remotely piloted aircraft) require performing tasks quickly and accurately; however, periods of low and high operator workload can decrease task performance. Intelligent modulation of the system's demands and interaction modality in response to changes in operator workload state may increase performance by avoiding undesirable workload states. This system requires real-time estimation of each workload component (i.e., cognitive, physical, visual, speech, and auditory) to adapt the correct modality. Existing workload systems estimate multiple workload components post-hoc, but few estimate speech workload, or function in real-time. An algorithm to estimate speech workload and mitigate undesirable workload states in real-time is presented. An analysis of the algorithm's accuracy is presented, along with the results demonstrating the algorithm's generalizability across individuals and human-machine teaming paradigms. Real-time speech workload estimation is a crucial element towards developing adaptive human-machine systems.
DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations
Venue: arXiv cs.CL 2025
Year: 2025

Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic, LLM-based pipeline for synthetic data generation and in-context learning for document-level entity and relation extraction. In contrast to existing approaches that rely on manually annotated demonstrations or direct zero-shot inference, our method combines synthetic data generation with retrieval-based in-context learning, using a reasoning-optimized language model. This allows us to build a high-quality demonstration database without manual annotation and to dynamically retrieve relevant examples at inference time. Based on our approach we produce a synthetic dataset of over $5k$ Wikipedia abstracts with approximately $59k$ entities and $30k$ relation triples. Finally, we evaluate in-context learning performance on the DocIE shared task, extracting entities and relations from long documents in a zero-shot setting. We find that in-context joint entity and relation extraction at document-level remains a challenging task, even for state-of-the-art large language models.
Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature
Venue: arXiv cs.HC 2025
Year: 2025

As Artificial Intelligence (AI) increasingly becomes an active collaborator in co-creation, understanding the distribution and dynamic of agency is paramount. The Human-Computer Interaction (HCI) perspective is crucial for this analysis, as it uniquely reveals the interaction dynamics and specific control mechanisms that dictate how agency manifests in practice. Despite this importance, a systematic synthesis mapping agency configurations and control mechanisms within the HCI/CSCW literature is lacking. Addressing this gap, we reviewed 134 papers from top-tier HCI/CSCW venues (e.g., CHI, UIST, CSCW) over the past 20 years. This review yields four primary contributions: (1) an integrated theoretical framework structuring agency patterns, control mechanisms, and interaction contexts, (2) a comprehensive operational catalog of control mechanisms detailing how agency is implemented; (3) an actionable cross-context map linking agency configurations to diverse co-creative practices; and (4) grounded implications and guidance for future CSCW research and the design of co-creative systems, addressing aspects like trust and ethics.
Model Cards Revisited: Bridging the Gap Between Theory and Practice for Ethical AI Requirements
Venue: arXiv cs.SE 2025
Year: 2025

Model cards are the primary documentation framework for developers of artificial intelligence (AI) models to communicate critical information to their users. Those users are often developers themselves looking for relevant documentation to ensure that their AI systems comply with the ethical requirements of existing laws, guidelines, and standards. Recent studies indicate inadequate model documentation practices, suggesting a gap between AI requirements and current practices in model documentation. To understand this gap and provide actionable guidance to bridge it, we conducted a thematic analysis of 26 guidelines on ethics and AI, three AI documentation frameworks, three quantitative studies of model cards, and ten actual model cards. We identified a total of 43 ethical requirements relevant to model documentation and organized them into a taxonomy featuring four themes and twelve sub-themes representing ethical principles. Our findings indicate that model developers predominantly emphasize model capabilities and reliability in the documentation while overlooking other ethical aspects, such as explainability, user autonomy, and fairness. This underscores the need for enhanced support in documenting ethical AI considerations. Our taxonomy serves as a foundation for a revised model card framework that holistically addresses ethical AI requirements.
TextPixs: Glyph-Conditioned Diffusion with Character-Aware Attention and OCR-Guided Supervision
Venue: arXiv cs.CV 2025
Year: 2025

The modern text-to-image diffusion models boom has opened a new era in digital content production as it has proven the previously unseen ability to produce photorealistic and stylistically diverse imagery based on the semantics of natural-language descriptions. However, the consistent disadvantage of these models is that they cannot generate readable, meaningful, and correctly spelled text in generated images, which significantly limits the use of practical purposes like advertising, learning, and creative design. This paper introduces a new framework, namely Glyph-Conditioned Diffusion with Character-Aware Attention (GCDA), using which a typical diffusion backbone is extended by three well-designed modules. To begin with, the model has a dual-stream text encoder that encodes both semantic contextual information and explicit glyph representations, resulting in a character-aware representation of the input text that is rich in nature. Second, an attention mechanism that is aware of the character is proposed with a new attention segregation loss that aims to limit the attention distribution of each character independently in order to avoid distortion artifacts. Lastly, GCDA has an OCR-in-the-loop fine-tuning phase, where a full text perceptual loss, directly optimises models to be legible and accurately spell. Large scale experiments to benchmark datasets, such as MARIO-10M and T2I-CompBench, reveal that GCDA sets a new state-of-the-art on all metrics, with better character based metrics on text rendering (Character Error Rate: 0.08 vs 0.21 for the previous best; Word Error Rate: 0.15 vs 0.25), human perception, and comparable image synthesis quality on high-fidelity (FID: 14.3).
The most influential philosophers in Wikipedia: a multicultural analysis
Venue: arXiv cs.SI 2025
Year: 2025

We explore the influence and interconnectivity of philosophical thinkers within the Wikipedia knowledge network. Using a dataset of 237 articles dedicated to philosophers across nine different language editions (Arabic, Chinese, English, French, German, Japanese, Portuguese, Russian, and Spanish), we apply the PageRank and CheiRank algorithms to analyze their relative ranking and influence in each linguistic context. Furthermore, we compare our results with entries from the Stanford Encyclopedia of Philosophy and the Internet Encyclopedia of Philosophy, providing insight into the differences between general knowledge networks like Wikipedia and specialized philosophical databases. A key focus of our analysis is the sub-network of 21 presocratic philosophers, grouped into four traditional schools: Italic (Pythagorean + Eleatic), Ionian, Abderian (Atomist), and Sophist. Using the reduced Google matrix method, we uncover both direct and hidden links between these early thinkers, offering new perspectives on their intellectual relationships and influence within the Western philosophical tradition.
Enter, Exit, Page Fault, Leak: Testing Isolation Boundaries for Microarchitectural Leaks
Venue: arXiv cs.CR 2025
Year: 2025

CPUs provide isolation mechanisms like virtualization and privilege levels to protect software. Yet these focus on architectural isolation while typically overlooking microarchitectural side channels, exemplified by Meltdown and Foreshadow. Software must therefore supplement architectural defenses with ad-hoc microarchitectural patches, which are constantly evolving as new attacks emerge and defenses are proposed. Such reactive approach makes ensuring complete isolation a daunting task, and leaves room for errors and oversights. We address this problem by developing a tool that stress tests microarchitectural isolation between security domains such as virtual machines, kernel, and processes, with the goal of detecting flaws in the isolation boundaries. The tool extends model-based relational testing (MRT) methodology to enable detection of cross-domain information leakage. We design a new test case generator and execution sandbox to handle multi-domain execution, new leakage models to encode expected leaks, and new analysis techniques to manage nondeterminism. We use this tool to perform an in-depth testing campaign on six x86-64 CPUs for leakage across different isolation boundaries. The testing campaign exposed four new leaks and corroborated numerous known ones, with only two false positives throughout the entire campaign. These results show critical gaps in current isolation mechanisms as well as validate a robust methodology for detecting microarchitectural flaws. As such, this approach enables a shift from reactive patching to proactive security validation in processor design.
Entropy-Memorization Law: Evaluating Memorization Difficulty of Data in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are known to memorize portions of their training data, sometimes reproducing content verbatim when prompted appropriately. In this work, we investigate a fundamental yet under-explored question in the domain of memorization: How to characterize memorization difficulty of training data in LLMs? Through empirical experiments on OLMo, a family of open models, we present the Entropy-Memorization Law. It suggests that data entropy is linearly correlated with memorization score. Moreover, in a case study of memorizing highly randomized strings, or "gibberish", we observe that such sequences, despite their apparent randomness, exhibit unexpectedly low empirical entropy compared to the broader training corpus. Adopting the same strategy to discover Entropy-Memorization Law, we derive a simple yet effective approach to distinguish training and testing data, enabling Dataset Inference (DI).
FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Advancements in reasoning for large language models (LLMs) have lead to significant performance improvements for LLMs in various fields such as mathematics and programming. However, research applying these advances to the financial domain, where considerable domain-specific knowledge is necessary to complete tasks, remains limited. To address this gap, we introduce FEVO (Financial Evolution), a multi-stage enhancement framework developed to enhance LLM performance in the financial domain. FEVO systemically enhances LLM performance by using continued pre-training (CPT) to expand financial domain knowledge, supervised fine-tuning (SFT) to instill structured, elaborate reasoning patterns, and reinforcement learning (RL) to further integrate the expanded financial domain knowledge with the learned structured reasoning. To ensure effective and efficient training, we leverage frontier reasoning models and rule-based filtering to curate FEVO-Train, high-quality datasets specifically designed for the different post-training phases. Using our framework, we train the FEVO series of models - C32B, S32B, R32B - from Qwen2.5-32B and evaluate them on seven benchmarks to assess financial and general capabilities, with results showing that FEVO-R32B achieves state-of-the-art performance on five financial benchmarks against much larger models as well as specialist models. More significantly, FEVO-R32B demonstrates markedly better performance than FEVO-R32B-0 (trained from Qwen2.5-32B-Instruct using only RL), thus validating the effectiveness of financial domain knowledge expansion and structured, logical reasoning distillation
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Despite the success of deep learning across various domains, it remains vulnerable to adversarial attacks. Although many existing adversarial attack methods achieve high success rates, they typically rely on $\ell_{p}$-norm perturbation constraints, which do not align with human perceptual capabilities. Consequently, researchers have shifted their focus toward generating natural, unrestricted adversarial examples (UAEs). GAN-based approaches suffer from inherent limitations, such as poor image quality due to instability and mode collapse. Meanwhile, diffusion models have been employed for UAE generation, but they still rely on iterative PGD perturbation injection, without fully leveraging their central denoising capabilities. In this paper, we introduce a novel approach for generating UAEs based on diffusion models, named ScoreAdv. This method incorporates an interpretable adversarial guidance mechanism to gradually shift the sampling distribution towards the adversarial distribution, while using an interpretable saliency map to inject the visual information of a reference image into the generated samples. Notably, our method is capable of generating an unlimited number of natural adversarial examples and can attack not only classification models but also retrieval models. We conduct extensive experiments on ImageNet and CelebA datasets, validating the performance of ScoreAdv across ten target models in both black-box and white-box settings. Our results demonstrate that ScoreAdv achieves state-of-the-art attack success rates and image quality, while maintaining inference efficiency. Furthermore, the dynamic balance between denoising and adversarial perturbation enables ScoreAdv to remain robust even under defensive measures.
A Survey on Prompt Tuning
Venue: arXiv cs.CL 2025
Year: 2025

This survey reviews prompt tuning, a parameter-efficient approach for adapting language models by prepending trainable continuous vectors while keeping the model frozen. We classify existing approaches into two categories: direct prompt learning and transfer learning. Direct prompt learning methods include: general optimization approaches, encoder-based methods, decomposition strategies, and mixture-of-experts frameworks. Transfer learning methods consist of: general transfer approaches, encoder-based methods, and decomposition strategies. For each method, we analyze method designs, innovations, insights, advantages, and disadvantages, with illustrative visualizations comparing different frameworks. We identify challenges in computational efficiency and training stability, and discuss future directions in improving training robustness and broadening application scope.
QuHE: Optimizing Utility-Cost in Quantum Key Distribution and Homomorphic Encryption Enabled Secure Edge Computing Networks
Venue: arXiv cs.SI 2025
Year: 2025

Ensuring secure and efficient data processing in mobile edge computing (MEC) systems is a critical challenge. While quantum key distribution (QKD) offers unconditionally secure key exchange and homomorphic encryption (HE) enables privacy-preserving data processing, existing research fails to address the comprehensive trade-offs among QKD utility, HE security, and system costs. This paper proposes a novel framework integrating QKD, transciphering, and HE for secure and efficient MEC. QKD distributes symmetric keys, transciphering bridges symmetric encryption, and HE processes encrypted data at the server. We formulate an optimization problem balancing QKD utility, HE security, processing and wireless transmission costs. However, the formulated optimization is non-convex and NPhard. To solve it efficiently, we propose the Quantum-enhanced Homomorphic Encryption resource allocation (QuHE) algorithm. Theoretical analysis proves the proposed QuHE algorithm's convergence and optimality, and simulations demonstrate its effectiveness across multiple performance metrics.
Reflections Unlock: Geometry-Aware Reflection Disentanglement in 3D Gaussian Splatting for Photorealistic Scenes Rendering
Venue: arXiv cs.CV 2025
Year: 2025

Accurately rendering scenes with reflective surfaces remains a significant challenge in novel view synthesis, as existing methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) often misinterpret reflections as physical geometry, resulting in degraded reconstructions. Previous methods rely on incomplete and non-generalizable geometric constraints, leading to misalignment between the positions of Gaussian splats and the actual scene geometry. When dealing with real-world scenes containing complex geometry, the accumulation of Gaussians further exacerbates surface artifacts and results in blurred reconstructions. To address these limitations, in this work, we propose Ref-Unlock, a novel geometry-aware reflection modeling framework based on 3D Gaussian Splatting, which explicitly disentangles transmitted and reflected components to better capture complex reflections and enhance geometric consistency in real-world scenes. Our approach employs a dual-branch representation with high-order spherical harmonics to capture high-frequency reflective details, alongside a reflection removal module providing pseudo reflection-free supervision to guide clean decomposition. Additionally, we incorporate pseudo-depth maps and a geometry-aware bilateral smoothness constraint to enhance 3D geometric consistency and stability in decomposition. Extensive experiments demonstrate that Ref-Unlock significantly outperforms classical GS-based reflection methods and achieves competitive results with NeRF-based models, while enabling flexible vision foundation models (VFMs) driven reflection editing. Our method thus offers an efficient and generalizable solution for realistic rendering of reflective scenes. Our code is available at https://ref-unlock.github.io/.
A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems
Venue: arXiv cs.DC 2025
Year: 2025

Modern high-performance computing (HPC) systems generate massive volumes of heterogeneous telemetry data from millions of sensors monitoring compute, memory, power, cooling, and storage subsystems. As HPC infrastructures scale to support increasingly complex workloads-including generative AI-the need for efficient, reliable, and interoperable telemetry analysis becomes critical. Operational Data Analytics (ODA) has emerged to address these demands; however, the reliance on schema-less storage solutions limits data accessibility and semantic integration. Ontologies and knowledge graphs (KG) provide an effective way to enable efficient and expressive data querying by capturing domain semantics, but they face challenges such as significant storage overhead and the limited applicability of existing ontologies, which are often tailored to specific HPC systems only. In this paper, we present the first unified ontology for ODA in HPC systems, designed to enable semantic interoperability across heterogeneous data centers. Our ontology models telemetry data from the two largest publicly available ODA datasets-M100 (Cineca, Italy) and F-DATA (Fugaku, Japan)-within a single data model. The ontology is validated through 36 competency questions reflecting real-world stakeholder requirements, and we introduce modeling optimizations that reduce knowledge graph (KG) storage overhead by up to 38.84% compared to a previous approach, with an additional 26.82% reduction depending on the desired deployment configuration. This work paves the way for scalable ODA KGs and supports not only analysis within individual systems, but also cross-system analysis across heterogeneous HPC systems.
PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimization
Venue: arXiv cs.AR 2025
Year: 2025

Prefix adders are fundamental arithmetic circuits, but their design space grows exponentially with bit-width, posing significant optimization challenges. Previous works face limitations in performance, generalization, and scalability. To address these challenges, we propose PrefixAgent, a large language model (LLM)-powered framework that enables efficient prefix adder optimization. Specifically, PrefixAgent reformulates the problem into subtasks including backbone synthesis and structure refinement, which effectively reduces the search space. More importantly, this new design perspective enables us to efficiently collect enormous high-quality data and reasoning traces with E-graph, which further results in an effective fine-tuning of LLM. Experimental results show that PrefixAgent synthesizes prefix adders with consistently smaller areas compared to baseline methods, while maintaining scalability and generalization in commercial EDA flows.