Advait Gupta, Sachin Konan, Tom Yin, Gabriel Sarch, Zhuang Liu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31494 · open-access PDF
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 faster and more reliable comparisons across agentic domains, yet existing benchmarks remain limited and fragmented. To address this gap, we introduce AgentVQA, an offline benchmark for evaluating VLMs on realistic agentic
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