AI看世界像翻书，不是像看画
人类看东西是闭环：眼睛不断移动，根据已有猜测调整视线。但现在的多模态大模型（MLLM）更像拍一张照片然后描述它。新基准ActiveVision专门测试AI的“主动观察”能力——需要反复看、调整视角才能完成的任务。结果：最强模型GPT-5.5只答对10.6%，17项任务中有11项得零分；而人类平均96.1%。即使让模型自己写代码处理图像，也因缺乏主动感知而失败。这不是你明天能用上的技术，但它揭示了一个关键前沿：AI的视觉系统缺少一个“眼睛转动的马达”。
📄 原文摘要(英文)
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-scoring model we evaluate, GPT-5.5 at the highest exposed reasoning-effort tier, solves only 10.6% of items and scores zero on 11 of the 17 tasks, and even Claude Fable 5, despite topping most reasoning and coding leaderboards, solves just 3.5%, far behind three human participants who average 96.1%. Furthermore, much of the gap persists even when models write and run their own vision code: such code is unreliable on realistic imagery, and catching its failures itself requires the active perception the models lack. Together, these results indicate that current MLLMs lack robust active visual observation, motivating architectures and training objectives that close the perception-reasoning loop.