ToolGate: Token-Efficient Pre-Call Control for Tool-Augmented Vision-Language Agents
📰 ArXiv cs.AI
Learn to optimize Tool-Augmented Vision-Language Agents with ToolGate, a token-efficient pre-call control method, to improve performance and reduce unnecessary tool calls
Action Steps
- Implement ToolGate in your vision-language agent to filter out unnecessary tool calls
- Evaluate the proposed tool calls using a token-efficient metric
- Configure the pre-call control mechanism to balance accuracy and efficiency
- Test the optimized agent on benchmarks to measure performance improvements
- Compare the results with baseline agents to demonstrate the effectiveness of ToolGate
Who Needs to Know This
AI researchers and engineers working on vision-language models can benefit from this technique to optimize their models' performance and efficiency
Key Insight
💡 Token-efficient pre-call control can significantly improve the performance of tool-augmented vision-language agents
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🤖 Optimize your vision-language agents with ToolGate! 🚀 Reduce unnecessary tool calls and improve performance 📈
Key Takeaways
Learn to optimize Tool-Augmented Vision-Language Agents with ToolGate, a token-efficient pre-call control method, to improve performance and reduce unnecessary tool calls
Full Article
Title: ToolGate: Token-Efficient Pre-Call Control for Tool-Augmented Vision-Language Agents
Abstract:
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 exhibi
Abstract:
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 exhibi
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