AsyncTool: Evaluating the Asynchronous Function Calling Capability under Multi-Task Scenarios 文章

ArXiv CS.AI2026-05-28NEWSen作者: Kou Shi (University of Science and Technology of China), Ziao Zhang (University of Science and Technology of China), Shiting Huang (University of Science and Technology of China), Avery Nie (University of Toronto), Zhen Fang (University of Science and Technology of China), Qiuchen Wang (University of Science and Technology of China), Lin Chen (University of Science and Technology of China), Huaian Chen (University of Science and Technology of China), Zehui Chen (University of Science and Technology of China), Feng Zhao (University of Science and Technology of China)

摘要

arXiv:2605.27995v1 Announce Type: new Abstract: Large language model (LLM)-based agents have shown strong capabilities in using external tools to solve complex tasks. However, existing evaluations often overlook the temporal dimension of tool use, especially the impact of tool response latency, and are usually limited to single-task settings. In real-world applications, multiple tasks often need to be executed concurrently, and overall efficiency depends on whether an agent can use idle time while waiting for tool responses. We refer to this capability as asynchronous tool calling. To evaluate it, we propose AsyncTool, a benchmark for assessing LLM-based agents in interactive multi-task tool-use environments with delayed tool feedback. AsyncTool presents multiple heterogeneous tasks simultaneously and simulates realistic tool response latency during execution.