arXiv:2509.07523v4 Announce Type: replace
Abstract: Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. In many cases, this problem naturally decomposes int…
arXiv:2604.26368v1 Announce Type: new
Abstract: In several applications it is desired to have 3D models not only from the outdoor spaces but also from inside the building. In the context of First Responder enhancement in large scale natural and man-ma…
arXiv:2603.01999v2 Announce Type: replace-cross
Abstract: Reliable obstacle avoidance in industrial settings demands 3D scene understanding, but widely used 2D LiDAR sensors perceive only a single horizontal slice of the environment, missing critical …
arXiv:2604.26379v1 Announce Type: new
Abstract: Reliable seizure detection in mouse models is essential for preclinical epilepsy research, yet manual review of synchronized video-EEG recordings is labor-intensive and single-modality systems fail for c…
arXiv:2603.19199v2 Announce Type: replace-cross
Abstract: Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world. Existing asynchronous inference methods primarily optimize trajectory smoothness, but neg…
arXiv:2604.26031v1 Announce Type: new
Abstract: This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-t…
arXiv:2604.26133v1 Announce Type: new
Abstract: Informal settlements face disproportionate exposure to climate-related health hazards. However, existing methodologies lack systematic approaches to link diverse settlement characteristics with environme…
arXiv:2604.25936v1 Announce Type: cross
Abstract: Implicit neural representations are powerful for geometric modeling, but their practical use is often limited by the high computational cost of network evaluations. We observe that implicit representat…
arXiv:2604.26181v1 Announce Type: new
Abstract: Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input complexity, and available platform resources. Current n…
arXiv:2604.26091v1 Announce Type: new
Abstract: We study reliability in autonomous language-model agents that translate user mandates into validated tool actions under real capital. The setting is DX Terminal Pro, a 21-day deployment in which 3,505 us…