To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling 事件

To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling arXiv:2605.00737v2 Announce Type: replace Abstract: Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be redundant or even harmful. Effective tool use, therefore, hinges on a core LLM decision: whether to call or not call a tool when performing a task. This decision is particularly challenging for web search tools, where t