TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents
The article introduces MM-ToolBench, a benchmark designed for evaluating task-oriented omni-modal tool-using agents. It aims to bridge the gap between isolated evaluations of tool use and real-world applications by incorporating closed-loop multimodal verification. Experiments reveal that even advanced models struggle to meet human performance benchmarks, highlighting the challenge of the tasks presented.
- ▪MM-ToolBench includes 100 executable tasks across two macro task families: Customer Service and Intelligent Creation.
- ▪The benchmark is supported by 27 MCP servers and 324 tools, emphasizing its comprehensive design.
- ▪Experiments show that the best-performing coding-agent model achieves only 32.0% task success, significantly lower than the 94.0% success rate of humans.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16909 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.16909 (cs) [Submitted on 16 May 2026] Title:TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents Authors:Zhiqiang Liu, Wenhui Dong, Yilang Tan, Yuwen Qu, Haochen Yin, Chenyang Si View a PDF of the paper titled TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents, by Zhiqiang Liu and 5 other authors View PDF HTML (experimental) Abstract:Tool-using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate external tools, inspect intermediate artifacts, and revise their actions before producing a final result.
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