arXiv:2606.03054v1 Announce Type: new 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
The proliferation of advanced vision-language models (VLMs) and the increasing complexity of their tool integrations necessitate more efficient resource management.
Improving the efficiency and cost-effectiveness of tool-augmented AI agents directly impacts their scalability, deployment, and practical utility in real-world applications.
This research introduces a method for pre-call control, allowing agents to intelligently decide whether to execute a proposed tool call, potentially reducing unnecessary computations and costs.
- · AI developers
- · Cloud computing providers (reduced cost for users)
- · Enterprises deploying VLM agents
- · Inefficient AI models
- · Tool providers (if usage drops due to selectivity, though overall adoption may i
AI agents become more cost-effective and faster in complex decision-making scenarios.
This efficiency gain accelerates the deployment of sophisticated AI agents across various industries, expanding their operational scope.
Increased efficiency contributes to the broader adoption of AI agents, potentially leading to more advanced multi-agent systems and new autonomous workflows.
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Read at arXiv cs.AI