OpenRaiser/Intern-Atlas huggingface dataset:
The Impact of Artificial Intelligence on Traditional Art Forms: A Disruption or Enhancement
Venue: arXiv cs.CY 2025
Year: 2025

The introduction of Artificial Intelligence (AI) into the domains of traditional art (visual arts, performing arts, and crafts) has sparked a complicated discussion about whether this might be an agent of disruption or an enhancement of our traditional art forms. This paper looks at the duality of AI, exploring the ways that recent technologies like Generative Adversarial Networks and Diffusion Models, and text-to-image generators are changing the fields of painting, sculpture, calligraphy, dance, music, and the arts of craft. Using examples and data, we illustrate the ways that AI can democratize creative expression, improve productivity, and preserve cultural heritage, while also examining the negative aspects, including: the threats to authenticity within art, ethical concerns around data, and issues including socio-economic factors such as job losses. While we argue for the context-dependence of the impact of AI (the potential for creative homogenization and the devaluation of human agency in artmaking), we also illustrate the potential for hybrid practices featuring AI in cuisine, etc. We advocate for the development of ethical guidelines, collaborative approaches, and inclusive technology development. In sum, we are articulating a vision of AI in which it amplifies our innate creativity while resisting the displacement of the cultural, nuanced, and emotional aspects of traditional art. The future will be determined by human choices about how to govern AI so that it becomes a mechanism for artistic evolution and not a substitute for the artist's soul.
From Passive to Participatory: How Liberating Structures Can Revolutionize Our Conferences
Venue: arXiv cs.CY 2025
Year: 2025

Our conferences face a growing crisis: an overwhelming flood of submissions, increased reviewing burdens, and diminished opportunities for meaningful engagement. With AI making paper generation easier than ever, we must ask whether the current model fosters real innovation or simply incentivizes more publications. This article advocates for a shift from passive paper presentations to interactive, participatory formats. We propose Liberating Structures, facilitation techniques that promote collaboration and deeper intellectual exchange. By restructuring conferences into two tracks, one for generating new ideas and another for discussing established work, we can prioritize quality over quantity and reinvigorate academic gatherings. Embracing this change will ensure conferences remain spaces for real insight, creativity, and impactful collaboration in the AI era.
Enhancing Classification of Streaming Data with Image Distillation
Venue: arXiv cs.CV 2025
Year: 2025

This study tackles the challenge of efficiently classifying streaming data in envi-ronments with limited memory and computational resources. It delves into the application of data distillation as an innovative approach to improve the precision of streaming image data classification. By focusing on distilling essential features from data streams, our method aims to minimize computational demands while preserving crucial information for accurate classification. Our investigation com-pares this approach against traditional algorithms like Hoeffding Trees and Adap-tive Random Forest, adapted through embeddings for image data. The Distillation Based Classification (DBC) demonstrated superior performance, achieving a 73.1% accuracy rate, surpassing both traditional methods and Reservoir Sam-pling Based Classification (RBC) technique. This marks a significant advance-ment in streaming data classification, showcasing the effectiveness of our method in processing complex data streams and setting a new standard for accuracy and efficiency.
Automated Evaluation of Gender Bias Across 13 Large Multimodal Models
Venue: arXiv cs.CV 2025
Year: 2025

Large multimodal models (LMMs) have revolutionized text-to-image generation, but they risk perpetuating the harmful social biases in their training data. Prior work has identified gender bias in these models, but methodological limitations prevented large-scale, comparable, cross-model analysis. To address this gap, we introduce the Aymara Image Fairness Evaluation, a benchmark for assessing social bias in AI-generated images. We test 13 commercially available LMMs using 75 procedurally-generated, gender-neutral prompts to generate people in stereotypically-male, stereotypically-female, and non-stereotypical professions. We then use a validated LLM-as-a-judge system to score the 965 resulting images for gender representation. Our results reveal (p < .001 for all): 1) LMMs systematically not only reproduce but actually amplify occupational gender stereotypes relative to real-world labor data, generating men in 93.0% of images for male-stereotyped professions but only 22.5% for female-stereotyped professions; 2) Models exhibit a strong default-male bias, generating men in 68.3% of the time for non-stereotyped professions; and 3) The extent of bias varies dramatically across models, with overall male representation ranging from 46.7% to 73.3%. Notably, the top-performing model de-amplified gender stereotypes and approached gender parity, achieving the highest fairness scores. This variation suggests high bias is not an inevitable outcome but a consequence of design choices. Our work provides the most comprehensive cross-model benchmark of gender bias to date and underscores the necessity of standardized, automated evaluation tools for promoting accountability and fairness in AI development.
The Signalgate Case is Waiving a Red Flag to All Organizational and Behavioral Cybersecurity Leaders, Practitioners, and Researchers: Are We Receiving the Signal Amidst the Noise?
Venue: arXiv cs.CR 2025
Year: 2025

The Signalgate incident of March 2025, wherein senior US national security officials inadvertently disclosed sensitive military operational details via the encrypted messaging platform Signal, highlights critical vulnerabilities in organizational security arising from human error, governance gaps, and the misuse of technology. Although smaller in scale when compared to historical breaches involving billions of records, Signalgate illustrates critical systemic issues often overshadowed by a focus on external cyber threats. Employing a case-study approach and systematic review grounded in the NIST Cybersecurity Framework, we analyze the incident to identify patterns of human-centric vulnerabilities and governance challenges common to organizational security failures. Findings emphasize three critical points. (1) Organizational security depends heavily on human behavior, with internal actors often serving as the weakest link despite advanced technical defenses; (2) Leadership tone strongly influences organizational security culture and efficacy, and (3) widespread reliance on technical solutions without sufficient investments in human and organizational factors leads to ineffective practices and wasted resources. From these observations, we propose actionable recommendations for enhancing organizational and national security, including strong leadership engagement, comprehensive adoption of zero-trust architectures, clearer accountability structures, incentivized security behaviors, and rigorous oversight. Particularly during periods of organizational transition, such as mergers or large-scale personnel changes, additional measures become particularly important. Signalgate underscores the need for leaders and policymakers to reorient cybersecurity strategies toward addressing governance, cultural, and behavioral risks.
Statistical Methods in Generative AI
Venue: arXiv cs.AI 2025
Year: 2025

Generative Artificial Intelligence is emerging as an important technology, promising to be transformative in many areas. At the same time, generative AI techniques are based on sampling from probabilistic models, and by default, they come with no guarantees about correctness, safety, fairness, or other properties. Statistical methods offer a promising potential approach to improve the reliability of generative AI techniques. In addition, statistical methods are also promising for improving the quality and efficiency of AI evaluation, as well as for designing interventions and experiments in AI. In this paper, we review some of the existing work on these topics, explaining both the general statistical techniques used, as well as their applications to generative AI. We also discuss limitations and potential future directions.
SVGauge: Towards Human-Aligned Evaluation for SVG Generation
Venue: arXiv cs.GR 2025
Year: 2025

Generated Scalable Vector Graphics (SVG) images demand evaluation criteria tuned to their symbolic and vectorial nature: criteria that existing metrics such as FID, LPIPS, or CLIPScore fail to satisfy. In this paper, we introduce SVGauge, the first human-aligned, reference based metric for text-to-SVG generation. SVGauge jointly measures (i) visual fidelity, obtained by extracting SigLIP image embeddings and refining them with PCA and whitening for domain alignment, and (ii) semantic consistency, captured by comparing BLIP-2-generated captions of the SVGs against the original prompts in the combined space of SBERT and TF-IDF. Evaluation on the proposed SHE benchmark shows that SVGauge attains the highest correlation with human judgments and reproduces system-level rankings of eight zero-shot LLM-based generators more faithfully than existing metrics. Our results highlight the necessity of vector-specific evaluation and provide a practical tool for benchmarking future text-to-SVG generation models.
MedBench-IT: A Comprehensive Benchmark for Evaluating Large Language Models on Italian Medical Entrance Examinations
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) show increasing potential in education, yet benchmarks for non-English languages in specialized domains remain scarce. We introduce MedBench-IT, the first comprehensive benchmark for evaluating LLMs on Italian medical university entrance examinations. Sourced from Edizioni Simone, a leading preparatory materials publisher, MedBench-IT comprises 17,410 expert-written multiple-choice questions across six subjects (Biology, Chemistry, Logic, General Culture, Mathematics, Physics) and three difficulty levels. We evaluated diverse models including proprietary LLMs (GPT-4o, Claude series) and resource-efficient open-source alternatives (<30B parameters) focusing on practical deployability. Beyond accuracy, we conducted rigorous reproducibility tests (88.86% response consistency, varying by subject), ordering bias analysis (minimal impact), and reasoning prompt evaluation. We also examined correlations between question readability and model performance, finding a statistically significant but small inverse relationship. MedBench-IT provides a crucial resource for Italian NLP community, EdTech developers, and practitioners, offering insights into current capabilities and standardized evaluation methodology for this critical domain.
Crossword: Adaptive Consensus for Dynamic Data-Heavy Workloads
Venue: arXiv cs.DC 2025
Year: 2025

We present Crossword, a flexible consensus protocol for dynamic data-heavy workloads, a rising challenge in the cloud where replication payload sizes span a wide spectrum and introduce sporadic bandwidth stress. Crossword applies per-instance erasure coding and distributes coded shards intelligently to reduce critical-path data transfer significantly when desirable. Unlike previous approaches that statically assign shards to servers, Crossword enables an adaptive tradeoff between the assignment of shards and quorum size in reaction to dynamic workloads and network conditions, while always retaining the availability guarantee of classic protocols. Crossword handles leader failover gracefully by employing a lazy follower gossiping mechanism that incurs minimal impact on critical-path performance. We implement Crossword (along with relevant protocols) in Gazette, a distributed, replicated, and protocol-generic key-value store written in async Rust. We evaluate Crossword comprehensively to show that it matches the best performance among previous protocols (MultiPaxos, Raft, RSPaxos, and CRaft) in static scenarios, and outperforms them by up to 2.3x under dynamic workloads and network conditions. Our integration of Crossword with CockroachDB brings 1.32x higher aggregate throughput to TPC-C under 5-way replication. We will open-source Gazette upon publication.
Bodega: Serving Linearizable Reads Locally from Anywhere at Anytime via Roster Leases
Venue: arXiv cs.DC 2025
Year: 2025

We present Bodega, the first consensus protocol that serves linearizable reads locally from any desired node, regardless of interfering writes. Bodega achieves this via a novel roster leases algorithm that safeguards the roster, a new notion of cluster metadata. The roster is a generalization of leadership; it tracks arbitrary subsets of replicas as responder nodes for local reads. A consistent agreement on the roster is established through roster leases, an all-to-all leasing mechanism that generalizes existing all-to-one leasing approaches (Leader Leases, Quorum Leases), unlocking a new point in the protocol design space. Bodega further employs optimistic holding and early accept notifications to minimize interruption from interfering writes, and incorporates smart roster coverage and lightweight heartbeats to maximize practicality. Bodega is a non-intrusive extension to classic consensus; it imposes no special requirements on writes other than a responder-covering quorum. We implement Bodega and related works in Vineyard, a protocol-generic replicated key-value store written in async Rust. We compare it to previous protocols (Leader Leases, EPaxos, PQR, and Quorum Leases) and two production coordination services (etcd and ZooKeeper). Bodega speeds up average client read requests by 5.6x-13.1x on real WAN clusters versus previous approaches under moderate write interference, delivers comparable write performance, supports fast proactive roster changes as well as fault tolerance via leases, and closely matches the performance of sequentially-consistent etcd and ZooKeeper deployments across all YCSB workloads. We will open-source Vineyard upon publication.
Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement
Venue: arXiv cs.CV 2025
Year: 2025

Face enhancement techniques are widely used to enhance facial appearance. However, they can inadvertently distort biometric features, leading to significant decrease in the accuracy of deepfake detectors. This study hypothesizes that these techniques, while improving perceptual quality, can degrade the performance of deepfake detectors. To investigate this, we systematically evaluate whether commonly used face enhancement methods can serve an anti-forensic role by reducing detection accuracy. We use both traditional image processing methods and advanced GAN-based enhancements to evaluate the robustness of deepfake detectors. We provide a comprehensive analysis of the effectiveness of these enhancement techniques, focusing on their impact on Naïve, Spatial, and Frequency-based detection methods. Furthermore, we conduct adversarial training experiments to assess whether exposure to face enhancement transformations improves model robustness. Experiments conducted on the FaceForensics++, DeepFakeDetection, and CelebDF-v2 datasets indicate that even basic enhancement filters can significantly reduce detection accuracy achieving ASR up to 64.63\%. In contrast, GAN-based techniques further exploit these vulnerabilities, achieving ASR up to 75.12\%. Our results demonstrate that face enhancement methods can effectively function as anti-forensic tools, emphasizing the need for more resilient and adaptive forensic methods.
Dimensionally Reduced Open-World Clustering: DROWCULA
Venue: arXiv cs.CV 2025
Year: 2025

Working with annotated data is the cornerstone of supervised learning. Nevertheless, providing labels to instances is a task that requires significant human effort. Several critical real-world applications make things more complicated because no matter how many labels may have been identified in a task of interest, it could be the case that examples corresponding to novel classes may appear in the future. Not unsurprisingly, prior work in this, so-called, `open-world' context has focused a lot on semi-supervised approaches. Focusing on image classification, somehow paradoxically, we propose a fully unsupervised approach to the problem of determining the novel categories in a particular dataset. Our approach relies on estimating the number of clusters using Vision Transformers, which utilize attention mechanisms to generate vector embeddings. Furthermore, we incorporate manifold learning techniques to refine these embeddings by exploiting the intrinsic geometry of the data, thereby enhancing the overall image clustering performance. Overall, we establish new State-of-the-Art results on single-modal clustering and Novel Class Discovery on CIFAR-10, CIFAR-100, ImageNet-100, and Tiny ImageNet. We do so, both when the number of clusters is known or unknown ahead of time. The code is available at: https://github.com/DROWCULA/DROWCULA.
A multi-strategy improved gazelle optimization algorithm for solving numerical optimization and engineering applications
Venue: arXiv cs.NE 2025
Year: 2025

Aiming at the shortcomings of the gazelle optimization algorithm, such as the imbalance between exploration and exploitation and the insufficient information exchange within the population, this paper proposes a multi-strategy improved gazelle optimization algorithm (MSIGOA). To address these issues, MSIGOA proposes an iteration-based updating framework that switches between exploitation and exploration according to the optimization process, which effectively enhances the balance between local exploitation and global exploration in the optimization process and improves the convergence speed. Two adaptive parameter tuning strategies improve the applicability of the algorithm and promote a smoother optimization process. The dominant population-based restart strategy enhances the algorithms ability to escape from local optima and avoid its premature convergence. These enhancements significantly improve the exploration and exploitation capabilities of MSIGOA, bringing superior convergence and efficiency in dealing with complex problems. In this paper, the parameter sensitivity, strategy effectiveness, convergence and stability of the proposed method are evaluated on two benchmark test sets including CEC2017 and CEC2022. Test results and statistical tests show that MSIGOA outperforms basic GOA and other advanced algorithms. On the CEC2017 and CEC2022 test sets, the proportion of functions where MSIGOA is not worse than GOA is 92.2% and 83.3%, respectively, and the proportion of functions where MSIGOA is not worse than other algorithms is 88.57% and 87.5%, respectively. Finally, the extensibility of MSIGAO is further verified by several engineering design optimization problems.
GCond: Gradient Conflict Resolution via Accumulation-based Stabilization for Large-Scale Multi-Task Learning
Venue: arXiv cs.LG 2025
Year: 2025

In multi-task learning (MTL), gradient conflict poses a significant challenge. Effective methods for addressing this problem, including PCGrad, CAGrad, and GradNorm, in their original implementations are computationally demanding, which significantly limits their application in modern large models and transformers. We propose Gradient Conductor (GCond), a method that builds upon PCGrad principles by combining them with gradient accumulation and an adaptive arbitration mechanism. We evaluated GCond on self-supervised learning tasks using MobileNetV3-Small and ConvNeXt architectures on the ImageNet 1K dataset and a combined head and neck CT scan dataset, comparing the proposed method against baseline linear combinations and state-of-the-art gradient conflict resolution methods. The stochastic mode of GCond achieved a two-fold computational speedup while maintaining optimization quality, and demonstrated superior performance across all evaluated metrics, achieving lower L1 and SSIM losses compared to other methods on both datasets. GCond exhibited high scalability, being successfully applied to both compact models (MobileNetV3-Small) and large architectures (ConvNeXt-tiny and ConvNeXt-Base). It also showed compatibility with modern optimizers such as AdamW and Lion/LARS. Therefore, GCond offers a scalable and efficient solution to the problem of gradient conflicts in multi-task learning.
Benchmarking Information Retrieval Models on Complex Retrieval Tasks
Venue: arXiv cs.IR 2025
Year: 2025

Large language models (LLMs) are incredible and versatile tools for text-based tasks that have enabled countless, previously unimaginable, applications. Retrieval models, in contrast, have not yet seen such capable general-purpose models emerge. To achieve this goal, retrieval models must be able to perform complex retrieval tasks, where queries contain multiple parts, constraints, or requirements in natural language. These tasks represent a natural progression from the simple, single-aspect queries that are used in the vast majority of existing, commonly used evaluation sets. Complex queries naturally arise as people expect search systems to handle more specific and often ambitious information requests, as is demonstrated by how people use LLM-based information systems. Despite the growing desire for retrieval models to expand their capabilities in complex retrieval tasks, there exist limited resources to assess the ability of retrieval models on a comprehensive set of diverse complex tasks. The few resources that do exist feature a limited scope and often lack realistic settings making it hard to know the true capabilities of retrieval models on complex real-world retrieval tasks. To address this shortcoming and spur innovation in next-generation retrieval models, we construct a diverse and realistic set of complex retrieval tasks and benchmark a representative set of state-of-the-art retrieval models. Additionally, we explore the impact of LLM-based query expansion and rewriting on retrieval quality. Our results show that even the best models struggle to produce high-quality retrieval results with the highest average nDCG@10 of only 0.346 and R@100 of only 0.587 across all tasks. Although LLM augmentation can help weaker models, the strongest model has decreased performance across all metrics with all rewriting techniques.