给LLM工具调用加验证机制，防重复执行，提升任务可靠性。
Verified Tool Calls Improve LLM Agent Reliability Under Non-Atomic Failures
- 用后置条件验证+重试前检查+幂等键，增强工具调用可靠性
- 模拟非原子故障下，重复操作减少70%以上，成功率基本不变
- 适合做多步任务的LLM代理开发者，无需改模型
大型语言模型（LLM）代理依赖外部工具完成多阶段任务。现有框架通常假设工具调用是原子的，并返回成功或失败的二元信号。然而真实系统中存在非原子行为，如调度后超时、结果延迟可见、部分状态更新等。这些不匹配导致重复操作、任务成功或不必要的工具调用等问题。本文提出一种轻量级、可验证的工具封装器，为工具调用增加后置条件验证、重试前验证逻辑和幂等键。在包含多类任务模板的受控仿真环境中注入非原子故障进行评估，结果表明该方法显著降低重复操作次数，同时保持与原有方法相当的任务成功率。研究说明，强化工具交互语义是提升LLM代理可靠性的有效路径，且无需修改底层语言模型。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents rely on external tools to perform multistage tasks. Existing agent frameworks typically assume that tool calls are atomic and return binary success or failure signals. However, real-world systems exhibit non-atomic behaviors such as timeouts after dispatch, delayed visibility, and partial state updates. These mismatches lead to reliability issues including duplicate actions, task success, and unnecessary tool executions. A lightweight, verification-aware tool wrapper is introduced that augments tool calls with postcondition verification, verify-before-retry logic, and idempotency keys. The approach is evaluated in a controlled simulated environment with injected non-atomic failures across multiple task templates. The results demonstrate that the proposed method significantly reduces duplicate actions, while maintaining comparable task success rates. Overall, the findings suggest that strengthening tool interaction semantics is a promising direction for improving LLM agent reliability without requiring modifications to the underlying language model.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。