An Exam for Active Observers
Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot. Decades of psychophysics and cognitive science have argued that this active observation is essential for a wide range of tasks. Whether today's multimodal large language models (MLLMs) exercise active observation is an empirical question that current vision-language benchmarks do not answer. We introduce ActiveVision, a benchmark that makes active observation measurable for MLLMs, comprising 17 tasks across 3 categories. Tasks are designed to force repeated visual perception rather than a single static description. Frontier MLLMs collapse on ActiveVision: the highest-sc...
| Authors | Jiarui Zhang, Muzi Tao, Shangshang Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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- Yezixiao/unified_vqa_v1Dataset · cited by
- activevisionai/ActiveVisionDataset · cited by
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- activevisionai/ActiveVisionDataset
- Yezixiao/unified_vqa_v1Dataset
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