arXiv cs.AI· Jiawei Guo, Donglei Yu, Yu Chen, Xiang Wang, Shuai Li, Xinpei Zhao, Huaxing Liu, Qinghao Wang, Minpeng Liao·· 4 小时前AI 评分52
arXiv 论文追踪多模态智能体能力增益，发现工具调用模式易学而工具贡献的能力有限
With Tools or Because of Tools? Tracing Capability Gains in Multimodal Agents
AI 导读
arXiv 论文研究 Thyme 和 DeepEyesV2 两个“用图像思考”的多模态智能体，发现工具调用带来的能力提升有限。93% 的 DeepEyesV2 和 96% 的 Thyme 工具解出的问题也能被至少一种非工具设定解出，工具调用并未稳定降低生成 token 成本，消融显示完整工具使用循环也不稳定优于单独的工具调用格式或执行结果，表明智能体更可靠地学到的是工具调用模式而非工具贡献的能力。
正文 · 原文
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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.02357 [cs.CV] |
| (or arXiv:2606.02357v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.02357 arXiv-issued DOI via DataCite |
Submission history
From: Jiawei Guo [view email]
[v1]
Mon, 1 Jun 2026 15:04:25 UTC (1,002 KB)
[v2]
Thu, 27 Aug 2026 10:57:33 UTC (1,002 KB)
[v3]
Tue, 10 Nov 2026 05:14:31 UTC (1,002 KB)
来源：arXiv cs.AI · arxiv.org