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…
330

arXiv:2504.11900v3 Announce Type: replace
Abstract: Stories are a fundamental aspect of human experience. Engaging deeply with stories and spotting plot holes -- inconsistencies in a storyline that break the internal logic or rules of a story's world -- requires nuanced reasoning skills, including …
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…
321

arXiv:2512.16913v1 Announce Type: new
Abstract: In this work, we present a panoramic metric depth foundation model that generalizes across diverse scene distances. We explore a data-in-the-loop paradigm from the view of both data construction and framework design. We collect a large-scale dataset b…
330

arXiv:2512.15925v1 Announce Type: new
Abstract: Reading stories evokes rich interpretive, affective, and evaluative responses, such as inferences about narrative intent or judgments about characters. Yet, computational models of reader response are limited, preventing nuanced analyses. To address t…
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…
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 …
243

arXiv:2512.16233v1 Announce Type: cross
Abstract: We address network structure learning from zero-inflated count data by casting each node as a zero-inflated generalized linear model and optimizing a smooth, score-based objective under a directed acyclic graph constraint. Our Zero-Inflated Continuo…
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…
222

arXiv:2512.15738v1 Announce Type: new
Abstract: Financial market prediction is a challenging application of machine learning, where even small improvements in directional accuracy can yield substantial value. Most models struggle to exceed 55--57\% accuracy due to high noise, non-stationarity, and …
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…
120

arXiv:2503.08453v2 Announce Type: replace
Abstract: The new class of alternating-conjugate splitting methods is presented and analyzed. They are obtained by concatenating a given composition involving complex coefficients with the same composition but with the complex conjugate coefficients. We sho…
122

arXiv:2512.16247v1 Announce Type: new
Abstract: One of many impediments to applying graph neural networks (GNNs) to large-scale real-world graph data is the challenge of centralized training, which requires aggregating data from different organizations, raising privacy concerns. Federated graph lea…