---
id: 20260503-T0-03
title: "增强工具调用智能体：推理时引入实时反馈，及时纠正执行错误"
title_en: "Reinforced Agent Uses Real-Time Inference Feedback to Correct Tool-Calling Errors"
url: https://ai.daily.yangsir.net/daily/20260503-T0-03
issue_date: 2026-05-03
publish_date: 2026-05-02T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2604.27233
---

# 增强工具调用智能体：推理时引入实时反馈，及时纠正执行错误

现有的工具调用智能体通常在执行完整个轨迹后才进行错误评估，这种事后评估往往难以纠正已经发生的损失。arXiv发表的新研究（2604.27233）提出了一种在推理时引入反馈的强化智能体方案。该方法将评估机制直接接入执行循环中，能够在调用工具的过程中实时识别并纠正选错工具、参数错误和范围越界等问题，大幅提升了智能体在实际应用中的任务成功率。

## English Version

**Reinforced Agent Uses Real-Time Inference Feedback to Correct Tool-Calling Errors**

Current tool-calling agents rely on post-hoc trajectory assessments, making it hard to correct errors during execution. A new paper on arXiv (2604.27233) proposes a reinforced agent that introduces inference-time feedback directly into the execution loop. This real-time approach identifies and corrects tool selection and parameter errors on the fly, significantly improving task success rates in agentic applications.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2604.27233)

**详情页**：https://ai.daily.yangsir.net/daily/20260503-T0-03

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