---
id: 20260905-T0-07
title: "A-CEGIS框架：用反例反馈提升AI智能体自我纠错能力"
title_en: "A-CEGIS Uses Counterexamples to Boost Agent Self-Correction"
url: https://ai.daily.yangsir.net/daily/20260905-T0-07
issue_date: 2026-09-05
publish_date: 2026-09-04T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2609.02892
---

# A-CEGIS框架：用反例反馈提升AI智能体自我纠错能力

论文提出A-CEGIS，一个轻量级框架，可在代码生成后用反例作为反馈帮助智能体自我纠错。单轮代码评估未覆盖实际部署中的修复能力，A-CEGIS通过具体反例引导智能体调整，提升多轮交互下的修复准确率。

## English Version

**A-CEGIS Uses Counterexamples to Boost Agent Self-Correction**

A-CEGIS is a lightweight framework using counterexamples as feedback for agent self-correction after code generation. Single-turn metrics miss repair ability in real deployment. A-CEGIS guides adjustments via concrete examples, improving multi-turn fix accuracy.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2609.02892)

**详情页**：https://ai.daily.yangsir.net/daily/20260905-T0-07

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