arXiv:2603.14185v3 Announce Type: replace
Abstract: Generative multimodal models can exhibit safety failures that are inherently relational: two benign concepts can become unsafe when linked by a specific action or relation (e.g., child-drinking-wine)…
arXiv:2511.20924v2 Announce Type: replace
Abstract: Implicit Neural Representations (INRs) are widely used for modeling continuous 2D images, enabling high-fidelity reconstruction, super-resolution, and compression. Architectures such as SIREN, WIRE, …
arXiv:2603.24584v1 Announce Type: new
Abstract: Vision–Language–Action (VLA) policies have shown strong progress in mapping language instructions and visual observations to robotic actions, yet their reliability degrades in cluttered scenes with dis…
arXiv:2511.13649v4 Announce Type: replace
Abstract: Distribution Matching Distillation (DMD) facilitates efficient inference by distilling multi-step diffusion models into few-step variants. Concurrently, Reinforcement Learning (RL) has emerged as a v…
arXiv:2603.23845v1 Announce Type: new
Abstract: Deep learning and generative models are advancing rapidly, with synthetic data increasingly being integrated into training pipelines for downstream analysis tasks. However, in medical imaging, their adop…
arXiv:2512.18128v3 Announce Type: replace
Abstract: High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods using satellite imager…
arXiv:2603.23868v1 Announce Type: new
Abstract: In this paper, we address the challenging problem of single-scene, fully unsupervised video anomaly detection (VAD), where raw videos containing both normal and abnormal events are used directly for trai…
arXiv:2603.01601v2 Announce Type: replace
Abstract: Large 3D reconstruction models have revolutionized the 3D content generation field, enabling broad applications in virtual reality and gaming. Just like other large models, large 3D reconstruction mo…
arXiv:2603.24440v1 Announce Type: new
Abstract: Computer-use agents (CUAs) hold great promise for automating complex desktop workflows, yet progress toward general-purpose agents is bottlenecked by the scarcity of continuous, high-quality human demons…
arXiv:2603.24500v1 Announce Type: new
Abstract: Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer soft regularization, t…