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arXiv · 2606.03054

ToolGate: Token-Efficient Pre-Call Control for Tool-Augmented Vision-Language Agents

Anjie Liu·Yan Song·Zhixun Chen·Ziqin Gong·Zhongwei Yu·Jun Wang
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Abstract

Tool-augmented vision-language agents can acquire external perceptual evidence through OCR, detection, segmentation, and other tools, but executing every proposed tool call is costly and sometimes unnecessary. We study the pre-call control problem: after a ReAct-style VLM agent proposes a perceptual tool call, should the call be executed, or skipped before its output enters the context? Across five benchmarks, we find that the baseline agent exhibits poor local selectivity: helpful and harmful calls occur at similar rates (11.8% vs. 9.9%), while most calls do not change the immediate forced-answer prediction. We introduce ToolGate, a lightweight external controller that predicts execute/skip decisions from trajectory text and simple structural features. Across two Qwen3-VL backbones, ToolGate reduces token cost to 64-69% of the unrestricted ReAct baseline while preserving average accuracy in cross-domain settings. With matched-domain trajectory training on Qwen3-VL-30B, it further improves average accuracy by 1.65 points. These results show that tool-augmented VLM agents benefit not only from better perceptual tools, but also from explicit control over when tool outputs are worth paying for.

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Anjie Liu, Yan Song, Zhixun Chen, Ziqin Gong, Zhongwei Yu, Jun Wang. 2026-06-02. ToolGate: Token-Efficient Pre-Call Control for Tool-Augmented Vision-Language Agents. https://arxiv.org/abs/2606.03054

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