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Autonomous unmanned aerial vehicle (UAV) object search involves a closed loop of perception, decision-making, and action under partial observability. Urban environments pose several challenges: large search areas and narrow egocentric views limit coverage, dense 3D geometry constrains safe motion, and open-world instru...
Public emergencies often trigger large-scale panic emotions, which are further amplified by social media, potentially leading to serious consequences such as resource hoarding and social disorder. Therefore, timely identification and intervention of panic are of great significance. However, the development of panic emo...
Predicting the timing of individual and collective panic emotional arousal before manifestation is essential for timely emergency intervention. Existing methods incorporate cognitive elements but none of them model emotion in the generative direction of the arousal process, leaving arousal timing undetermined. We argue...
SBFNav is introduced, a closed-loop navigation framework centered on a language- conditioned Spatial Belief Field (SBF) that preserves multiple spatial hypotheses under par- tial evidence and further confirms the advantages of spatial-belief modeling over single-point prediction.
AGOS-Bench is introduced, the first dedicated benchmark for evaluating whether general-purpose Vision-Language Models (VLMs) can integrate aerial discoveries and ground-level verification through UAV-UGV cooperation, and AGOS-Agent is proposed, a training-free and tool-augmented approach to address the AGOS task.
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