标题： 工具链中的蝴蝶效应：对LLM工具代理系统中失败参数填充的全面分析 显示英文标题

标题： Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems

摘要： 工具代理范式的出现拓宽了大型语言模型（LLM）的能力边界，使其能够完成更复杂的任务。 然而，由于执行过程中出现的参数故障问题，该范式的有效性受到限制。 为了探索这一现象并提出相应的建议，我们在本文中首先构建了一个参数故障分类法。 我们从主流工具代理的调用链中推导出五种故障类别。 然后，我们通过应用15种输入扰动方法到输入中，探讨了三种不同的输入源与故障类别之间的相关性。 实验结果表明，参数名称幻觉故障主要源于LLM的固有局限性，而输入源的问题主要导致其他故障模式。 为了提高工具代理交互的可靠性和有效性，我们提出了相应的改进建议，包括标准化工具返回格式、改进错误反馈机制以及确保参数一致性。 显示英文摘要

摘要： The emergence of the tool agent paradigm has broadened the capability boundaries of the Large Language Model (LLM), enabling it to complete more complex tasks. However, the effectiveness of this paradigm is limited due to the issue of parameter failure during its execution. To explore this phenomenon and propose corresponding suggestions, we first construct a parameter failure taxonomy in this paper. We derive five failure categories from the invocation chain of a mainstream tool agent. Then, we explore the correlation between three different input sources and failure categories by applying 15 input perturbation methods to the input. Experimental results show that parameter name hallucination failure primarily stems from inherent LLM limitations, while issues with input sources mainly cause other failure patterns. To improve the reliability and effectiveness of tool-agent interactions, we propose corresponding improvement suggestions, including standardizing tool return formats, improving error feedback mechanisms, and ensuring parameter consistency.