Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?
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 ...
| Authors | Qianshan Wei, Yishan Yang, Siyi Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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