arXiv:2605.09252v2 Announce Type: replace Abstract: Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing benchmark systematically studies when a tool call is actually needed. We propose When2Tool, a benchmark of 18 environments (15 single-hop, 3 multi-hop) spanning three categories of tool necessity -- computational scale, knowledge boundaries, and execution reliability -- each with controlled difficulty levels that create a clear decision boundary between tool-necessary and to
The proliferation of advanced LLM agents necessitates more efficient and cost-effective tool utilization, as current models often make unnecessary API calls.
This development improves the efficiency and reduces the operational cost of AI agents, making them more practical for real-world applications.
LLM agents can now more intelligently decide when external tools are genuinely required, leading to optimized performance and resource consumption.
- · AI Agent developers
- · Cloud API providers (who charge for usage)
- · Enterprises deploying LLM agents
- · Inefficient LLM agent architectures
Reduced API costs and latency for tool-augmented LLM agents.
Accelerated deployment and adoption of sophisticated AI agent systems across various industries.
Enhanced overall economic viability and scalability of agentic AI solutions, potentially leading to faster automation of complex tasks.
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Read at arXiv cs.CL