---
id: 20260703-T0-06
title: "Making Failure Safe：一个可约束、可验证的智能体框架用于网页数据采集"
title_en: "Making Failure Safe: A Constrained, Verifiable Agent Framework for Web Data Collection"
url: https://ai.daily.yangsir.net/daily/20260703-T0-06
issue_date: 2026-07-03
publish_date: 2026-07-02T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2607.00035
---

# Making Failure Safe：一个可约束、可验证的智能体框架用于网页数据采集

arXiv 新论文提出一种可约束、可验证的智能体框架，用于从自然语言需求生成可靠的数据采集器。现有LLM生成网页爬虫时，常因依赖错误、选择器失效、模式不匹配和页面结构异构而不可靠。该框架通过引入约束和验证机制，在生成阶段预防常见错误，确保采集器在开放网页环境下的鲁棒性。对需要自动化数据采集的分析师和开发者有实际帮助。

## English Version

**Making Failure Safe: A Constrained, Verifiable Agent Framework for Web Data Collection**

A new arXiv paper proposes a constrained, verifiable agent framework for generating reliable web scrapers from natural-language requirements. Direct generation with LLMs remains unreliable due to dependency errors, broken selectors, schema mismatches, and heterogeneous page structures. The framework introduces constraints and verification to prevent common errors, ensuring robustness in open-web environments. It practically assists analysts and developers needing automated data collection.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2607.00035)

**详情页**：https://ai.daily.yangsir.net/daily/20260703-T0-06

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