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arXiv:2601.17054v1 Announce Type: new Abstract: The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns. Using crime rate prediction with...
arXiv:2601.17055v1 Announce Type: new Abstract: This longitudinal pilot study tracked how generative AI reshapes problem-solving over six months across three waves in an academic setting....
arXiv:2601.17056v1 Announce Type: new Abstract: Egocentric video action recognition under domain shifts remains challenging due to large intra-class spatio-temporal variability, long-tailed feature distributions, and strong...
arXiv:2601.17057v1 Announce Type: new Abstract: In this paper, we revisited the role of data augmentation in contrastive learning for sequential recommendation, revealing its inherent bias...
arXiv:2601.17058v1 Announce Type: new Abstract: Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for...
arXiv:2601.17060v1 Announce Type: new Abstract: Artificially intelligent systems have become remarkably sophisticated. They hold conversations, write essays, and seem to understand context in ways that...
arXiv:2601.17062v1 Announce Type: new Abstract: Adjusting rifle sights, a process commonly called "zeroing," requires shooters to identify and differentiate bullet holes from multiple firing iterations....
arXiv:2601.17063v1 Announce Type: new Abstract: Recently, Mixture-of-Experts (MoE) models have gained attention for efficiently scaling large language models. Although these models are extremely large, their...
arXiv:2601.17064v1 Announce Type: new Abstract: This article examines the applicability of the Digital Services Act (DSA) to ChatGPT, arguing that it should be classified as...
arXiv:2601.17065v1 Announce Type: new Abstract: Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM-based...
arXiv:2601.17067v1 Announce Type: new Abstract: Large-scale video generation models have demonstrated emergent physical coherence, positioning them as potential world models. However, a gap remains between...
arXiv:2601.17069v1 Announce Type: new Abstract: Centralized training with decentralized execution (CTDE) has been the dominant paradigm in multi-agent reinforcement learning (MARL), but its reliance on...
arXiv:2601.17071v1 Announce Type: new Abstract: We present an efficient method for image segmentation in the presence of strong inhomogeneities. The approach can be interpreted as...
arXiv:2601.17072v1 Announce Type: new Abstract: Almost every industry today confronts the potential role of artificial intelligence and machine learning in its future. While many studies...
arXiv:2601.17073v1 Announce Type: new Abstract: Brain organization is increasingly characterized through multiple imaging modalities, most notably structural connectivity (SC) and functional connectivity (FC). Integrating these...
arXiv:2601.17074v1 Announce Type: new Abstract: The accurate estimation of Arctic snow depth ($h_s$) remains a critical time-varying inverse problem due to the extreme scarcity and...
arXiv:2601.17076v1 Announce Type: new Abstract: Multi-view multi-label classification (MvMLC) is indispensable for modern web applications aggregating information from diverse sources. However, real-world web-scale settings are...
arXiv:2601.17077v1 Announce Type: new Abstract: The problem of finding the general classification of geodetic graphs is still open. We believe that one of the obstacles...
arXiv:2601.17078v1 Announce Type: new Abstract: We give two natural definitions of polynomial-time computability for L2 functions; and we show them incomparable (unless complexity class FP_1...
arXiv:2601.17082v1 Announce Type: new Abstract: Despite substantial efforts toward improving the moral alignment of Vision-Language Models (VLMs), it remains unclear whether their ethical judgments are...
arXiv:2601.17086v1 Announce Type: new Abstract: Neural text-to-speech (TTS) systems systematically mispronounce low-resource proper nouns, particularly non-English names, brands, and geographic locations, due to their underrepresentation...
arXiv:2601.17087v1 Announce Type: new Abstract: Agentic benchmarks increasingly rely on LLM-simulated users to scalably evaluate agent performance, yet the robustness, validity, and fairness of this...
arXiv:2601.17088v1 Announce Type: new Abstract: Virtual try-on systems allow users to interactively try different products within VR scenarios. However, most existing VTON methods operate only...
arXiv:2601.17089v1 Announce Type: new Abstract: In recent years, Multimodal Large Language Models (MLLMs) have made significant progress in visual question answering tasks. However, directly applying...