AgentVQA benchmarks vision-language models on agentic visual tasks
Original title: AgentVQA: A Unified Benchmark for Agentic Visual Understanding
The best of 22 models scores 58.5% on 1,000 examples drawn from 23 datasets across five agent domains.
AgentVQA is an offline visual-question benchmark for vision-language models in realistic agent settings: web agents, robotics, egocentric video, games, and spatial planning. The authors manually selected 1,000 challenging examples from 23 source datasets and say model rankings track corresponding online evaluations. Across 22 models, the best reaches 58.5% accuracy. Errors are attributed mainly to failing to ground and track the visual state, not to task misreading or downstream logic.
- Domains are web agents, robotics, egocentric videos, games, and spatial planning.
- Failures cited include UI localization, motion, state change over time, and 3D spatial relations.
- The benchmark is positioned as a cheaper complement to online simulator and real-world tests.
From the original article
1 min read at huggingface.co
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 costly and challenging to scale.
By Advait Gupta, Sachin Konan, Tom Yin · huggingface.co
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