# AsyncTool：异步工具调用能力评测基准

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-27 08:00
- AIHOT 分数：61
- AIHOT 链接：https://aihot.virxact.com/items/cmpqb0qot03duslnoigmx7viu
- 原文链接：https://arxiv.org/abs/2605.27995

## AI 摘要

当前对大语言模型智能体的评估忽略了工具使用的时序维度，尤其未考虑工具响应延迟的影响，且多局限于单任务场景。为此，研究团队提出了AsyncTool，这是一个评估智能体在具有延迟工具反馈的交互式多任务环境中表现的基准。它同时呈现多个异构任务，模拟真实的响应延迟，并在步骤、子任务和任务三个级别进行评估，引入了效率导向的指标。实验表明，延迟的工具反馈对现有智能体构成重大挑战并导致性能下降，而能更好协调任务切换与状态维护的模型表现更优。

## 正文

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. Using a hybrid data evolution strategy, we construct a diverse asynchronous multitasking dataset that covers multiple scenarios and tool-use patterns. We evaluate models at the step, sub-task, and task levels, and introduce efficiency-oriented metrics to measure task coordination and completion efficiency. Extensive experiments show that delayed tool feedback poses substantial challenges to current agents and leads to clear performance degradation. Models that better coordinate task switching, dependency tracking, and state maintenance achieve stronger performance on AsyncTool. Our analysis identifies key failure modes of current tool-using agents and provides practical insights for designing future systems with stronger temporal reasoning and coordination capabilities.