321

arXiv:2512.16294v1 Announce Type: new
Abstract: Semantic overlap among land-cover categories, highly imbalanced label distributions, and complex inter-class co-occurrence patterns constitute significant challenges for multi-label remote-sensing image retrieval. In this article, Multi-Label Adaptive…
319

arXiv:2512.12793v2 Announce Type: replace
Abstract: This paper presents Vision-Language Global Localization (VLG-Loc), a novel global localization method that uses human-readable labeled footprint maps containing only names and areas of distinctive visual landmarks in an environment. While humans n…
321

arXiv:2512.16755v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) have made significant progress in explicit instruction-based navigation; however, their ability to interpret implicit human needs (e.g., "I am thirsty") in dynamic urban environments remains underexplored. This paper intr…
242

TheCUBE spent 2025 talking directly with top tech executives as they worked through how computing is being rebuilt inside their organizations, driven by real-world constraints rather than abstract roadmaps. Across those conversations, a consistent shift emerged away from theory and toward execution.…
230

arXiv:2507.21503v3 Announce Type: replace
Abstract: Recently Multimodal Large Language Models (MLLMs) have achieved considerable advancements in vision-language tasks, yet produce potentially harmful or untrustworthy content. Despite substantial work investigating the trustworthiness of language mo…
209

arXiv:2512.15764v1 Announce Type: new
Abstract: Large Language Models (LLMs) can perform many NLP tasks well, but fully fine-tuning them is expensive and requires a lot of memory. Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA reduce this cost by adding small low-rank updates to frozen…
211

arXiv:2410.19931v3 Announce Type: replace
Abstract: Despite their empirical success, the internal mechanism by which transformer models align tokens during language processing remains poorly understood. This paper provides a mechanistic and theoretical explanation of token alignment in LLMs. We fir…
219

arXiv:2512.08854v2 Announce Type: replace
Abstract: It has been hypothesized that human-level visual perception requires a generative approach in which internal representations result from inverting a decoder. Yet today's most successful vision models are non-generative, relying on an encoder that …
210

arXiv:2512.16428v1 Announce Type: new
Abstract: Since Generative AI came out it has quickly embedded itself in our social fabric, triggering lots of discussions, predictions, and efforts from research, industry, government and capital market to experiment and embrace the technology. The question fo…
211

arXiv:2512.16381v1 Announce Type: new
Abstract: Agentic systems, powered by Large Language Models (LLMs), assist network engineers with network configuration synthesis and network troubleshooting tasks. For network troubleshooting, progress is hindered by the absence of standardized and accessible …
210

arXiv:2512.16841v1 Announce Type: new
Abstract: Automatic radiology report generation is a promising application of multimodal deep learning, aiming to reduce reporting workload and improve consistency. However, current state-of-the-art (SOTA) systems - such as Multimodal AI for Radiology Applicati…
122

arXiv:2512.16037v1 Announce Type: new
Abstract: Big Data has become central to modern applications in finance, insurance, and cybersecurity, enabling machine learning systems to perform large-scale risk assessments and fraud detection. However, the increasing dependence on automated analytics intro…
120

arXiv:2512.15791v1 Announce Type: new
Abstract: In Artificial Intelligence (AI), language models have gained significant importance due to the widespread adoption of systems capable of simulating realistic conversations with humans through text generation. Because of their impact on society, develo…
109

arXiv:2512.16123v1 Announce Type: new
Abstract: Deep learning-based object detection models play a critical role in real-world applications such as autonomous driving and security surveillance systems, yet they remain vulnerable to adversarial examples. In this work, we propose an autoencoder-based…
111

arXiv:2512.15818v1 Announce Type: new
Abstract: Identity-preserving models have led to notable progress in generating personalized content. Unfortunately, such models also exacerbate risks when misused, for instance, by generating threatening content targeting specific individuals. This paper intro…