AgentVQA: A Unified Benchmark for Agentic Visual Understanding

Researchers introduced AgentVQA, a unified benchmark for measuring how vision-language models (VLMs) perform in agentic, interactive settings, arguing that existing online evaluations in simulators and real-world environments are costly. The benchmark targets the gap in understanding VLM performance in agentic contexts, where the source says it remains poorly understood.

Vision-language models VLMs can perform a broad range of tasks across diverse settings, yet their performance in agentic contexts remains poorly understood. Although online evaluations in simulators and real-world environments are essential for measuring interactive agent performance, they are cos