Multimodal Large Language Models (MLLMs) are evolving from passive observers into active agents, solving problems through Visual Expansion (invoking visual tools) and Knowledge Expansion (open-web search). However, existing evaluations fall short: they lack flexible tool integration, test visual and search tools separately, and evaluate primarily by final answers. Consequently, they cannot verify if tools were actually invoked, applied correctly, or used efficiently. To address this, we introduce Agentic-MME, a process-verified benchmark for Multimodal Agentic Capabilities. It contains 418 real-world tasks across 6 domains and 3 difficulty levels to evaluate capability synergy, featuring over 2,000 stepwise checkpoints that average 10+ person-hours of manual annotation per task. Each task includes a unified evaluation framework supporting sandboxed code and APIs, alongside a human reference trajectory annotated with stepwise checkpoints along dual-axis: S-axis and V-axis. To enable true process-level verification, we audit fine-grained intermediate states rather than just final answers, and quantify efficiency via an overthinking metric relative to human trajectories. Experimental results show the best model, Gemini3-pro, achieves 56.3% overall accuracy, which falls significantly to 23.0% on Level-3 tasks, underscoring the difficulty of real-world multimodal agentic problem solving.
@article{arxiv.2604.03016,
title = {Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?},
author = {Qianshan Wei and Yishan Yang and Siyi Wang and Jinglin Chen and Binyu Wang and Jiaming Wang and Shuang Chen and Zechen Li and Yang Shi and Yuqi Tang and Weining Wang and Yi Yu and Chaoyou Fu and Qi Li and Yi-Fan Zhang},
journal= {arXiv preprint arXiv:2604.03016},
year = {2026}
}