ActiveVision Benchmark Shows MLLMs Struggle with Active Visual Observation
A new preprint introduces ActiveVision, a benchmark designed to test whether multimodal large language models (MLLMs) can perform active visual observation—redirecting their 'gaze' based on intermediate reasoning, rather than relying on static images. Leading models such as GPT-5.5 and Claude Fable 5 scored only 10.6% and 3.5% respectively, compared to a human average of 96.1%. The findings suggest that current MLLMs lack robust active visual perception, even when allowed to write and execute their own vision code.
Why it matters: This work reveals a fundamental limitation in current MLLMs, highlighting the need for new architectures that integrate perception and reasoning in a closed loop.
Full story at: arXiv Computation and Language ↗