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arXiv:2511.21448v4 Announce Type: replace Abstract: Phishing and spam emails remain a major cybersecurity threat, with attackers increasingly leveraging Large Language Models (LLMs) to craft highly...
arXiv:2511.22463v3 Announce Type: replace Abstract: Multimodal Emotion Recognition in Conversation (MERC) significantly enhances emotion recognition performance by integrating complementary emotional cues from text, audio, and...
arXiv:2511.22645v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have undergone rapid development in advancing geospatial scene understanding. Recent studies have sought to enhance...
arXiv:2512.00181v2 Announce Type: replace Abstract: Tabular data drive most real-world machine learning applications, yet building general-purpose models for them remains difficult. Mixed numeric and categorical...
arXiv:2512.00891v2 Announce Type: replace Abstract: Streaming Video Large Language Models (VideoLLMs) have demonstrated impressive performance across various video understanding tasks, but they face significant challenges...
arXiv:2512.01702v3 Announce Type: replace Abstract: Learning neural operators on heterogeneous and irregular geometries remains a fundamental challenge, as existing approaches typically rely on structured discretisations...
arXiv:2512.02371v2 Announce Type: replace Abstract: Tensor accelerators now represent a growing share of compute resources in modern CPUs and GPUs. However, they are hard to...
arXiv:2512.02571v2 Announce Type: replace Abstract: This paper presents an algorithmic study of a class of covering mixed-integer linear programming problems which encompasses classic cover problems,...
arXiv:2512.03048v2 Announce Type: replace Abstract: I argue that content-based AI value alignment--any approach that treats alignment as optimizing toward a formal value-object (reward function, utility...
arXiv:2512.04890v5 Announce Type: replace Abstract: We present E(3)-Pose, a novel fast pose estimation method that jointly and explicitly models rotation equivariance and object symmetry. Our...
arXiv:2512.08104v2 Announce Type: replace Abstract: As AI agents increasingly operate in complex environments, ensuring reliable, context-aware privacy is critical for regulatory compliance. Traditional access controls...
arXiv:2512.08640v3 Announce Type: replace Abstract: We show how interval-based temporal separation on the extension of Moszkowski's discrete time interval temporal logic (Moszkowski, 1986) by the...
arXiv:2512.08892v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs) by grounding outputs in retrieved evidence, but faithfulness failures,...
arXiv:2512.09446v2 Announce Type: replace Abstract: Recent vision language models (VLMs) like CLIP have demonstrated impressive anomaly detection performance under significant distribution shift by utilizing high-level...
arXiv:2512.11228v2 Announce Type: replace Abstract: Payload swing during rapid slewing of mobile cranes poses a safety risk, as it generates overturning moments that can lead...
arXiv:2512.12768v2 Announce Type: replace Abstract: Recent advances in large multimodal models suggest that explicit reasoning mechanisms play a critical role in improving model reliability, interpretability,...
arXiv:2512.13956v2 Announce Type: replace Abstract: The proliferation of cloud-native architectures, characterized by microservices and dynamic orchestration, has rendered modern IT infrastructures exceedingly complex and volatile....
arXiv:2512.15933v3 Announce Type: replace Abstract: Leveraging multimodal large language models (MLLMs) to develop embodied agents offers significant promise for addressing complex real-world tasks. However, current...
arXiv:2512.18542v2 Announce Type: replace Abstract: AI coding assistants produce vulnerable code in 45\% of security-relevant scenarios~\cite{veracode2025}, yet no public training dataset teaches both traditional web...
arXiv:2512.20677v2 Announce Type: replace Abstract: The increasing deployment of large language models (LLMs) in safety-critical applications raises fundamental challenges in systematically evaluating robustness against adversarial...
arXiv:2512.20963v2 Announce Type: replace Abstract: Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective....
arXiv:2512.22240v4 Announce Type: replace Abstract: Machine learning models are primarily judged by predictive performance, especially in applied settings. Once a model reaches high accuracy, its...
arXiv:2512.23171v2 Announce Type: replace Abstract: Federated unlearning has become an attractive approach to address privacy concerns in collaborative machine learning, for situations when sensitive data...
arXiv:2601.00725v2 Announce Type: replace Abstract: Deep neural networks show great potential for automating various visual quality inspection tasks in manufacturing. However, their applicability is limited...