AgentVQA: A Unified Benchmark for Agentic Visual Understanding
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. Complementary offline VQA benchmarks can enable fa
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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. Complementary offline VQA benchmarks can enable fa
This benchmark provides a standardized way to evaluate Vision-Language Models (VLMs) in agentic contexts, which is crucial for developing more capable and autonomous AI systems. For ML engineers, understanding agentic evaluation methodologies is key to building and deploying VLMs in complex, interactive environments.