CrossView: Can Vision-Language Models Reason Across Cameras?

Video understanding benchmarks have long centered on sin-gle-camera settings, where modern multi-modal language models achievestrong performance across image and video tasks. Yet, the real world runson multi-camera networks: autonomous vehicles, security systems, androbots all gather data across many simultaneous views. We argue thatthis is not simply “more” of the single-camera problem; it is fundamen-tally different. Multi-camera reasoning requires handling context thatscales with the number of views, resolving occlusions visible from only asubset of cameras, judging which views matter, and integrating evidenceacross perspectives that may overlap or diverge. Current models strugglewith exactly these challenges, yet no benchmark systematically targetsthem. We introduce CrossView, a multi-camera video question-answeringbenchmark spanning autonomous driving, security surveillance, egocen-tric/exocentric video, and robotics. Evaluation of proprietary models,such as GPT-5.2, and open-source models, like Qwen3-VL, reveals con-sistently low accuracy, with open-source models trailing by a wide mar-gin. Performance scales strongly with a model’s ability to jointly pro-cess multiple viewpoints, positioning CrossView as a rigorous bench-mark for multi-camera video. We open-source our code and dataset athttps://utaustin-swarmlab.github.io/CrossView.