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
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