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arXiv · 2606.02357

Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains

Jiawei Guo·Donglei Yu·Yu Chen·Xiang Wang·Shuai Li·Xinpei Zhao·Huaxing Liu·Qinghao Wang·Minpeng Liao
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Abstract

Tool-augmented multimodal agents show strong benchmark gains, often taken as evidence that agents have learned to use tools. We argue that this interpretation can be premature: a tool-call trace alone does not show whether the tool supplied answer-critical information. We study two representative ``thinking with images'' agents, Thyme and DeepEyesV2, across real-world understanding, OCR, chart understanding, and mathematical reasoning. Each agent is compared with its Tool-Free counterpart and with a Pure-Text Reasoner trained from the same source pool without tool-calling trajectories. Tool access yields little consistent aggregate improvement, does not reliably reduce generated-token cost, and leaves only a small tool-only solved set: 93% of DeepEyesV2's tool-solved problems and 96% of Thyme's are also solved by at least one non-tool setting. Mechanism ablations further show that the full tool-use loop does not consistently outperform either the tool-call format or the returned execution result alone. In the settings we study, the analyzed agents appear to learn tool-calling patterns more reliably than tool-contributed capabilities, suggesting that evaluation should distinguish tool availability from whether tools actually expand what agents can solve.

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Jiawei Guo, Donglei Yu, Yu Chen, Xiang Wang, Shuai Li, Xinpei Zhao, Huaxing Liu, Qinghao Wang, Minpeng Liao. 2026-08-27. Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains. https://arxiv.org/abs/2606.02357

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