AgentVQA: A Unified Benchmark for Agentic Visual Understanding
AgentVQA is an offline benchmark for evaluating vision-language models on agentic visual understanding. It contains 1,000 human-verified examples drawn from 23 source datasets across five domains: web agents, robotics, egocentric videos, games, and spatial planning. The authors evaluated 22 VLMs and report substantial limitations in visual grounding, motion understanding, state tracking, and spatial reasoning. They state that model rankings on AgentVQA align with corresponding online evaluations.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
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AgentVQA is an offline benchmark for evaluating vision-language models on agentic visual understanding. It contains 1,000 human-verified examples drawn from 23 source datasets across five domains: web agents, robotics, egocentric videos, games, and spatial planning. The authors evaluated 22 VLMs and report substantial limitations in visual grounding, motion understanding, state tracking, and spatial reasoning. They state that model rankings on AgentVQA align with corresponding online evaluations.
Key findings from the stored brief
- AgentVQA contains 1,000 human-verified examples drawn from 23 source datasets.
- The benchmark spans five domains: web agents, robotics, egocentric videos, games, and spatial planning.
- The best of the 22 evaluated models reaches 58.5% accuracy.
- Errors are attributed mainly to grounding and state-tracking failures rather than task misinterpretation or downstream logical reasoning.
Source claims not independently checked; the ranking alignment with online evaluations is reported by the authors.
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Why this may matter
Online evaluation of interactive agents is costly and hard to scale. An offline benchmark whose model rankings track online results could offer a cheaper way to compare VLMs for agentic use.
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